Assignement
1
Abstract—Autonomous Vehicles (AV) are expected to bring
considerable benefits to society, such as traffic optimization and
accidents reduction. They rely heavily on advances in many
Artificial Intelligence (AI) approaches and techniques. However,
while some researchers in this field believe AI is the core element
to enhance safety, others believe AI imposes new challenges to
assure the safety of these new AI-based systems and applications.
In this non-convergent context, this paper presents a systematic
literature review to paint a clear picture of the state of the art of
the literature in AI on AV safety. Based on an initial sample of
4870 retrieved papers, 59 studies were selected as the result of the
selection criteria detailed in the paper. The shortlisted studies were
then mapped into six categories to answer the proposed research
questions. An AV system model was proposed and applied to
orient the discussions about the SLR findings. As a main result, we
have reinforced our preliminary observation about the necessity
of considering a serious safety agenda for the future studies on AI-
based AV systems.
Keywords: Autonomous vehicles, safety, artificial intelligence,
machine intelligence, machine learning, SLR.
I. INTRODUCTION
Advances in Artificial Intelligence (AI) are one of the key
enablers of the Autonomous Vehicles (AVs) development. In
fact, AVs rely on AI to interpret the environment, understand
its conditions, and make driving-related decisions. Thus, it
basically replicates the human driver actions when driving a
vehicle. In this context, AI applied to AV has become an
important research topic.
AV is a safety-critical system. When operating in an
undesirable way, AV can jeopardize human lives or the
environment in which it operates. It has the potential to threaten
the lives of its own passengers, pedestrians and people in other
vehicles, and damage other transportation system elements (e.g.
other vehicles and transportation infrastructure). Therefore, it is
mandatory to assure AV is safe, mainly when operating on
public roads in which resources will be shared with other
systems (and people).
Although safety is a mandatory characteristic to AV, and
although the researchers seem to agree on the importance of AI
This work was supported in part by the Research, Development and
innovation Center, Ericsson Telecommunication S.A., Brazil. 1Safety Analysis Group from Polytechnic School of University of São Paulo,
São Paulo, Brazil (e-mail: alexandremoreiranascimento@gmail.com).
applied to autonomous vehicles, they seem to disagree on the
AIs impact on AV safety. Many researchers, in special those
related to the AI community and AV manufacturers, advocate
AI as one of the core elements to enhance AV safety. Their
hypothesis is the automation of the driving tasks will lead to a
significant reduction of the car accidents. However, other
researchers, mainly in the system safety community, argue that
AI can potentially jeopardize AVs safety.
This study is the first, as far as we are aware, to map and to
organize the related literature and to provide a complete view
of the aspects related to both visions, and to subsidize future
studies. A preliminary study on the concerns about the
differences between AI and system safety mindsets impacting
AV safety was published in [1]. In this non-convergent context,
this paper presents a systematic literature review (SLR) aiming
to present a clear picture of the state of the art of the literature
in AI on AVs safety.
This paper is structured into 5 sections. Section II presents
details about the research methodology used. Section III
presents the data analysis results from the SLR based on the
proposed methodology. Section IV proposes an AV system
model that is used to orient the discussions about SLR findings.
Finally, Section V presents the final remarks.
II. RESEARCH METHODOLOGY
This study was performed using the systematic literature
review (SLR) method. The reasons supporting the SLR use are:
(1) its established tradition as a tool to understand state-of-the-
art research in technology-related fields [2]; (2) it helps to
understand existing studies and supports readers in identifying
new directions in the research field [3]; and (3) it helps to create
a foundation for advancing knowledge [4].
The protocol used (Figure 1) was based on the tasks
suggested by [5][6] for defining the research questions,
identification of search string, source selection, study selection
criteria, and data mapping. Also, the protocol followed the
recommendations of [7], [8], [4] and [9] for extracting,
analyzing, interpreting and reporting the literature-based
findings.
2Ericsson Research, Ericsson AB, Stockholm, Sweden (e-mail:
rafia.inam@ericsson.com). 3Ericsson Telecommunication S.A., Indaiatuba, SP, Brazil (e-mail:
maria.marquezini@ericsson.com).
A Systematic Literature Review about the
impact of Artificial Intelligence on
Autonomous Vehicle Safety
A. M. Nascimento1, L. F. Vismari1, C. B. S. T. Molina1, P.S. Cugnasca1, J.B. Camargo Jr.1, J.R. de
Almeida Jr.1, R. Inam2, E. Fersman2, M. V. Marquezini3, and A. Y. Hata3
2
Figure 1. Protocol used to support systematic literature review.
A. Definition of Research Questions
The first step was to define the research questions (RQ). In
order to support the research goal of presenting a clear picture
of the state of the art in the literature about AI on AV safety, the
following research questions were posed:
RQ1. How do AI-based systems impact system safety?
RQ2. Which are the topics (context domain) of the studies
identified?
RQ3. Which AI-related techniques are used on the
studies?
RQ4. Which problems do the techniques seek to address?
RQ5. Which findings are reported by the study’s authors?
RQ6. Which future studies are suggested in these studies?
B. Identification of Search String and Source Selection
The search strategy was structured through the selection of
source databases and the appropriate search terms. No date
range was used, to ensure that relevant studies were covered,
regardless of their publication date. A broad selection of online
databases indexing scientific literature was considered: ACM,
Engineering Village, ScienceDirect, Scopus, SpringerLink,
Wiley and Web of Science (WoS). Please note that IEEExplore
is already covered by the selected databases for this SLR study.
The search string was designed based on the synonyms of the
3 main concepts related to the investigated topics: Safety,
Artificial Intelligence and Autonomous Vehicle. Many
synonyms are present in the literature for the terms "artificial
intelligence" and "autonomous vehicle". Therefore, an
exploratory study of their most representative synonyms was
performed. Then, a careful selection of synonyms was made to
ensure the search process would have an appropriate coverage.
As a result, the following string with Boolean operators was
selected: (“safety” AND (“artificial intelligence” OR
“machine intelligence” OR “machine learning”) AND
(“autonomous vehicle” OR “autonomous car” OR “automated
vehicle” OR “automated car” OR “self-driven vehicle” OR
“self-driving” OR “driverless”)). Note that the synonyms for
each one of the topics are already presented in the Boolean
string previously displayed.
Different instances of the search string were created to adapt
it to the distinct database search syntax rules, but the same
logical value was kept. In each database, the appropriate options
were selected to limit the search process to the Title-Abstract-
Keyword (TAK) field set. This is an important measure to
reduce the number of non-related or duplicated studies
retrieved. However, it was observed that not all databases
support a search limited on TAK field set, leading to an inflated
number of papers found (e.g. SpringerLink). Table I shows the
initial number of papers found per database.
TABLE I
NUMBER OF PAPERS PER DATABASE
Database #Entries
ACM 36
Engineering Village 191
ScienceDirect 81
Scopus 182
SpringerLink 3999
Wiley 329
WoS 52
Total 4870
C. Study Selection Criteria and Papers Review
The study selection process is shown in Figure 2. Each step
indicates the number of papers remaining as a sample after the
corresponding step was executed. The first selection criterion
applied was to ensure that only the studies with the TAK fields
returning positive to the Boolean search expression would be
selected. The information (metadata) available for each paper
found, in the first step of the selection process, was collected by
exporting the results to a spreadsheet. A spreadsheet macro was
developed to analyze the TAK fields and to properly select the
papers. After this check, only 230 papers remained as a sample.
Using the spreadsheet Remove Duplicate tools, the duplicated
entries were removed. The 97 remaining papers composed the
selected sample.
Figure 2. Study selection process.
As a reasonable number of papers (97) was found [10], book
chapters, editorials, notes or reports were excluded - level 5
exclusion [10] - and 86 papers remained. The abstracts, titles
and keywords of the remaining 86 peer reviewed papers were
scrutinized to check their fitness with the goals of this research.
After a careful examination (sometimes a full-paper skimming
was necessary), 27 papers were considered not related to this
Complete Review (59)
Level 5 Exclusion
(86)
Duplicated Removal (97)
TAK Filtering (230)
Papers Retrieved (4870)
3
research and were excluded from the sample of the literature
mapping. Finally, a sample of 59 papers was considered for this
study.
There was a considerable drop in the number of studies, from
the initial 4870 to the final 59 papers selected. It occurred for
different reasons, such as: misuse of the terminology; correct
use of the terminology in the context of an example within a
paper that did not actually focus on the topic; or lack of
restricted search in TAK fields in some databases (in our study,
the SpringerLink).
D. Data Mapping
The data mapping from the selected papers were executed
after they had been completely reviewed and scrutinized. It was
performed categorizing the 59 sample papers into 6 categories
(CT.1-6) to answer the 6 research questions (RQ.1-6),
respectively. The categories defined were based on the
corresponding research question: (CT.1) Impact, (CT.2)
Topics, (CT.3) Techniques, (CT.4) Problem, (CT.5) Findings
and (CT.6) Future Studies. The categorization process was
based on the agreement of researchers working in this study.
Different strategies were used to create the codes for each of the
categories. For (CT.1) Impact, the code increase was used when
the paper described AI as a factor of increasing the safety risk
(negative impact on safety) and the code decrease was used
when the paper presented AI as a factor of decreasing the safety
risk (positive impact on safety). For (CT.2) Topics, (CT.5)
Findings and (CT.6) Future Studies, the codifications were
derived by the context domain of the study according to what
was reported by their authors, as suggested by [11]. Lastly, for
(CT.3) Techniques and (CT.4) Problem, similarly to other
categories, the codes were based on what was reported by their
authors [11] and, due to the wide range of techniques, subfields
and misuses of terms, the terminologies were adapted and
normalized according to field references [12][13]
[14][15][16][17].
III. DATA ANALYSIS
The distribution of the studies over the years can provide an
overview of the size and evolution of the field (Figure 3). The
left chart in Figure 3 shows the distribution from 1987 until
2018 (April). The oldest study found dates back to 1987. No
work was found for over a decade – from 1991 to 2002 –
considering the adopted search criteria.
This period can be labeled as the "first winter" in this research
topic as an analogy to the Artificial Intelligence "winters"1.
Only one paper a year was found over the following 3 years –
from 2003 to 2005. A second short winter was found from 2006
to 2008. Only 1 paper was found in 2009 and another in 2011,
while no paper was found in 2010. Finally, the combination of
AI, safety and autonomous vehicles started to get more
attention from the scientific community in 2012 when 5 papers
were found, although no paper was found in 2013. In fact, 86%
(51) of the papers found were published from 2012 to 2018.
The right chart in Figure 3 shows the distribution of the
studies over the last decade. The year 2018 was excluded from
the plot to avoid misinterpretation. Considering the results
presented in Figure 3, the field is gaining momentum based on
the continuous growth in the number of published studies since
2014. The trend line built in the last decade data shows a higher
angular coefficient, indicating the momentum in recent years.
Most of the papers found are from conference proceedings.
In fact, 45 papers (76%) are from conferences. Only 14 papers
(24%) were published by journals. Therefore, it is reasonable to
expect a growth in the number of publications about this topic
in journals. Besides evaluating the time distribution of papers,
another important aspect is the consistency-check of the
selected keywords in the papers considered. This was
performed by checking the most representative keywords
among all the synonyms of each of the 3 sets (previously
presented) in the search string. All the keywords from the
search string found on each paper TAK were accounted. As a
result, the total number of hits per keyword was computed.
Table II shows the number of studies with each keyword
present (hits per keyword) and the percentage of the 59 sample
papers with the keyword. Note the sum of the number of hits
does not totalize 59. Also, the sum of the percentages for all the
keywords for each distinct concept does not totalize 100%. This
is because many papers have more than 1 synonym present,
which makes it be accounted more than once.
Thus, it is possible to note the most representative keyword
for each concept: safety, artificial intelligence and autonomous
vehicle. In fact, a search string using only those keywords
would result in 36 papers, which corresponds to 61% of the
sample size of the present study. However, many other
keywords used could not be ignored, since they have a
considerable representativeness, such as: machine learning,
automated vehicle, self-driving and autonomous car.
Conversely the keyword autonomous truck surprisingly had
only one hit.
The following sub-sections present the results for each
research question (RQ.1-6).
A. AI-based systems impact on safety (RQ.1)
The RQ.1 was answered with the categorization of the
sample studies into CT.1 (Impact). Most studies consider AI a
technology that increases the system safety (positive impacts on
safety). So, 81% (48) of the papers were actually coded as
decrease, because they argue that AI decreases the safety risks.
Only 19% (11) of the studies consider AI a potential threat to
the system safety.
B. Main topics of the studies (RQ.2)
In order to answer RQ.2, the sample papers were classified
into the category CT.2 (Topics). Studies were grouped based on
their CT.1 coding into two distinct sets: Increase Safety
1 The Artificial Intelligence field had periods of warm enthusiasms and some
periods of very low enthusiasm, with a much lower number of publications
and contributions. The literature named those low enthusiasm periods as AI
winters.
4
Figure 3. Studies distribution over the years: a) depicting all studies till 2018; b) depicting studies in the last ten years.
TABLE II
KEYWORDS HITS
Concept Keyword #Hits %Papers
Safety Safety 59 100
Artificial
Intelligence
Artificial intelligence 36 61
Machine learning 27 46
Machine intelligence 1 2
Autonomous
Vehicle
Autonomous vehicle 37 63
Automated vehicle 12 20
Self-driving 11 19
Autonomous car 6 10
Driverless 2 3
Autonomous truck 1 2
Risks and Decrease Safety Risks. Then studies were grouped
by their similarities and each group was coded with a label that
could encompass all its members. Table III shows the results of
this coding process. As observed, the papers positioning AI as
a factor that decreases safety risks (48 papers, 81%), they
studied the subjects related to five main topics: Sensors and
Perception (21 papers, 44%), Navigation and Control (13
papers, 27%), Fault Prevention (6 papers, 13%), Conceptual
Model and Framework (4 papers, 8%) and Human Factor (4
papers, 8%). In turn, the papers positioning AI as a risk to
system safety (11 papers, 19%) studied subjects related to three
main topics: Fault Forecasting (5 papers, 45%), Ethics and
Policies (4 papers, 36%) and Dependability and Trust (2 papers,
18%). The complete list of references for each code in this
category can also be found in Table III.
The main topics for each group of papers differ reasonably
from each other. While the papers in the category decrease
focus on important aspects to support or to enhance the vehicle
autonomy, the papers in the category increase (endanger safety)
focus on topics related to safety assurance.
Sensors and Perception is the topic with the largest number
of studies (21). They are mostly related to computer vision and
detection techniques necessary for adding the necessary
capabilities to detect different aspects of the navigation
environment and supporting the autonomy of the AVs, such as:
general computer vision [19], Doppler sensing [20], lane
detection [21], daylight detection and evaluation [22], obstacles
detection [23][24][25], pedestrian detection [26][27],
pedestrian trajectory prediction [28], road detection
[24][29][75], road junction detection [30], road terrain
detection [31], traffic signal detection [32][18], situation
awareness [33], speed bump detection [25][34], traffic light
detection [35], vehicle detection [36] and virtual worlds for
training detection [37].
The second largest number of papers (13) found
encompasses studies related to Navigation and Control. They
are mostly related to techniques necessary to ensure the proper
autonomous navigation and control capabilities required by
AVs, such as: remote-controlled semi-AV based on IoT [38];
adaptive pre-crash control [39]; safe trajectory selection [76];
AV following another car driven by a human pilot (Trailing)
[40]; safe navigation [41]; heuristic optimization algorithm for
unsigned intersection crossing [42]; vehicle coordination [43];
maneuver classification [44]; learning to navigate from
demonstration [45]; AV movements optimization in
intersection [46]; learning and simulation of the Human Level
decisions involved in driving a racing car [47]; path tracking
[48]; and fuzzy-logic control approach to manage low level
vehicle actuators (steering throttle and brake) [49].
Six papers with research related to Fault Prevention were
found. These studies encompass researches related to the
preventing the occurrence or introduction of faults [50], such as
AI for security of wireless communication to ensure safety [51];
remote diagnosis, maintenance and prognosis Framework [52];
prediction of computational workload [53]; vehicle security
against cyber-attack [39][54]; and diagnosis of sensor faults
[55].
Four studies were found for each of the topics Human Factor
and Conceptual Model and Framework. The studies on human
factor cover important aspects to be considered in the
autonomous cars engineering due to the human-in-the-loop
factor, such as: safety, comfort, and stability based on the
human driver perception behavior [56]; design of real time
transition from assisted driving to automated driving under
conditions of high probability of a collision [57]; diagnosing
and predicting stress and fatigue of driver in semi-automated
vehicles [58]; and advances in driver-vehicle interface [59].
Considering the studies (4) proposing conceptual models and
frameworks, they have a considerable diversity of focus, such
as: ML and cloud-based framework proposed to address safety
and reliability-related issues [60]; AV conceptual model [61];
an interdisciplinary framework to extract knowledge from the
large amount of available data during driving to reduce driver’s
behavioral uncertainties [62]; and a proposition of an AV
highway concept to improve highway driving safety [63].
Considering the group of papers positioning AI as a potential
factor of decreasing the safety, the highest number of studies
was related to Fault Forecasting. In other words, those
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TABLE III
IMPACT OF AI-BASED SYSTEMS ON SAFETY AND ITS MAIN TOPICS AND REFERENCES
Category Codes #Hits %Papers References
CT.1 - Impact Decrease Safety Risks (Positive Impact on Safety) 48 81
CT.2 - Topics
Sensors and Perception 21 44
[18][75][35][36][21][25][27][29]
[23][22][34][20][44][32][31][33]
[26][28][30][24][19]
Navigation and Control 13 27 [38][39][76][40][41][47][42][43]
[48][44][45][46][49]
Fault Prevention 6 13 [39][51][52][53][54][55]
Conceptual Model and Framework 4 8 [60][61][62][63]
Human Factor 4 8 [56][57][58][59]
CT.1 - Impact Increase Safety Risks (Negative Impact on Safety) 11 19
CT.2 - Topics
Fault Forecasting 5 45 [64][65][66][67][68]
Ethics and Policies 4 36 [69][70][71][72]
Dependability and Trust 2 18 [73][74]
papers dealt with the limitations to estimate the present number
and future incidence of faults in AI-based systems, by executing
activities related to evaluation, testing, verification and
validation [50], such as: aspects (and limitations) related to
safety validation [64]; performance and safety verification
methodology [65]; test suites for AV [66]; end-to-end safety for
AV design [67]; and a framework to evaluate the impacts of
such a sophisticated system on traffic and the impact of
continuous increase in the number of highly automated vehicles
on future traffic safety and traffic flow [68].
There were four studies related to discussions about Ethics
and Policies. One of the studies discussed and performed
experiments on how distinct ethical frameworks adopted to
make decisions about AV crashes can affect the number of lives
endangered [69]. The other studies discuss the scope of AI on
AV with ethical aspects [70], ethics in AV design [71], and
moral values and ethical principles for autonomous machines
[72]. As can be seen, those studies are quite recent since the
oldest one was published in 2015.
Finally, 2 papers were found related to Dependability and
Trust. Dependability is an important concept in critical systems,
because it comprises attributes such as safety, security,
availability, reliability and maintainability, as well as how (the
mechanisms) to keep these systems attributes [50]. According
to [50], trust can be defined as accepted dependability. The
studies found are thus related to: safety issues [73] and current
mechanisms to ensure robust operation in safety-critical
situations facing the introduction of non-deterministic software
[74].
C. Techniques used (RQ.3) and problems they seek to address (RQ.4)
Aiming to answer RQ.3 and RQ.4, the sample papers were
classified into categories CT.3 (techniques) and CT.4
(problems) based on how their authors described the AI
technique used in the study. Then, some terminologies used to
define the codification for the categories CT.3 and CT.4 were
adapted based on the field literature [12][13][14][15][16][17],
when necessary.
Most reviewed papers reported the specific AI related
techniques used in the research. Some reported the use of more
than one technique, whereas others reported only the approach
used. Some papers (14 papers, 24%) were related to general
aspects of AI or ML techniques, without mentioning specific
techniques used or researched [68][71][72][61][64]
[67][69][74][70][65][63][58][62][19].
All the techniques found in the reviewed papers were mapped
considering the problem (CT.4) that they were solving. As a
result, Table VIII - placed in the Appendix - lists the techniques
found, the number of papers in which they were used, the main
problems they were seeking to address, and the references.
As can be seen, there is a considerable number of studies
(22%,13) that used techniques related to artificial neural
networks. Also, there is a reasonable number of studies
reporting the use of SVM (17%, 10). Some studies used Fuzzy
Logic (8%, 5), Bayesian Artificial Intelligence (e.g. Bayesian
Deep Learning, Naive Bayes Classifier-NBC, etc) (7%, 4),
Hidden Markov Based Models (e.g. Continuous Hidden
Markov Model-CHMM and Discrete Hidden Markov Model
DHMM) (7%, 4), Estimation Filters (e.g. Kalman Filter and
Particle Filters) (7%, 4), Nearest-Neighbour-Based Algorithm
(e.g. k-Nearest Neighbours - kNN) (7%, 4), Adaptive Boosting
(AdaBoost) (5%, 3), Ramer-Douglas-Peucker or Rameri
Douglas algorithm (5%, 3), Haar-like feature detector (5%, 3),
Histogram of Oriented Gradient (HOG) (5%, 3), Hough
Transformation (5%, 3), Optimization Heuristics (5%, 3),
Regression-Based Models (5%, 3) and Principal Components
Analysis (PCA) (3%, 2).
Analyzing Table VIII, it shows that each of the following
techniques were reported, in all the reviewed papers, only once:
Canny Edge Detection Algorithm, Case-based reasoning
(CBR), Channel Features, Clustering Algorithm k-mean,
Complex Decision Trees (CDT), Conditional Random Fields
(CRFs), Distributed Random Forest (DRF), Gaussian Mixture
Model (GMM), Linear Temporal Logic (LTL), Local Binary
Patterns (LBP), Neuroevolution of Augmenting Topologies
(NEAT), Novel Image Recognition Technique, Path Planning
Algorithms (A* and D*), Satisfiability Modulo Theories (SMT)
6
Solver, and Viterbi Algorithm. Thus, there is room for new
studies using techniques not yet used or under-represented by
the set of papers considered.
D. Reported findings (RQ.5)
Question RQ.5 is answered by CT.5 (findings), based on the
information about the findings reported on the sample papers.
Some papers did not report specific main findings in a
straightforward way because the propose frameworks or
approaches had not yet been tested or the results were still
incipient. Other papers described very specific findings that
would require a background section to support a proper
discussion. In those cases, only a higher level of abstraction of
the results is presented. Finally, because of space limitation,
only some specific examples are described here, while most of
the results are presented grouped around the main topic of
research. A complete list, oriented by the discussion presented
at Section IV, can be found on the Table IX presented in
Appendix.
The papers about topics related to Sensors and Perception
presented positive and promising results with the techniques
employed to address their research problems. In fact, this topic
already achieved significant results with the recent
developments in AI and sensor technologies. While AI had the
image and pattern recognition boosted by advancements such
as the new architectures of ANNs and new machine learning
techniques, sensor technologies have been boosted in the last
decades by the advancements in the robotics and mobile phone
industries. As a result, the papers demonstrated applications of
enhancements in the techniques or combination of techniques
and sensors in order to recognize and to detect important
elements and signals the human drivers need to handle to ensure
the proper operation of a vehicle. In this context, the findings
are positive for the application of ANNs to recognize turn signal
[18], road environment and signals [27][32][31][30], and
pedestrian [26][28], for example. Likewise, some papers
reported SVM has been applied successfully to detect road [75],
traffic light [35], and pedestrian [27].
The papers related to Navigation and Control also reported
positive and promising results. As presented previously, they
used diverse AI techniques to seek to address a broad range of
problems. For example, a hybrid AI architecture encompassing
ANN, CBR, and a hybrid Case-Based Planner (A* and D*
motion planner) was successfully tested to tackle the precrash
problem of intelligent control of autonomous vehicles [39],
while SVM was used to support a safest path planning in a
dynamic environment to avoid maneuvers too close to an
obstacle [41].
This SLR found 6 papers for the topic Fault Prevention. Each
of these papers used a distinct AI technique for the research
problems. One paper presented a preliminary result [53], and
another one proposed an approach but did not report results
[55]. All the others papers, related to the detection of cyber-
attack, presented promising positive results for the application
of ANNs [39], Estimation Filters [51], and Fuzzy-Logic [54],
for example. Also, preliminary positive results have been
reported on the use of a regression-based model to predict the
CPU patterns [53].
Two from the four remaining papers related to the topic
Human Factor, have presented preliminary positive results. One
presented promising results from using a regression-based
model to deal with selective attention mechanism [56], while
the other presented some examples of scenarios where the use
of Bayesian AI could avoid the collision when no action is taken
by the human driver [57]. The other 2 papers did not present
specific findings, due to their theoretical nature related to the
design considerations for the driving assistance system [59] and
human drivers monitoring to enhance the integration between
AVs and human drivers [58].
The papers proposing conceptual models and frameworks
did not present findings related to experimental results. Most of
them relied on general AI/ML instead of a specific technique
[61][62][63]. Also, besides the proposed approaches
themselves, they focused the discussions around the issues they
aimed to address, the theoretical background and future
potential problems to be addressed in the field.
The last three topics (Fault Forecasting, Ethics and Policies,
and Dependability and Trust) have papers more oriented to
theoretical discussions and propositions around the challenges
AVs are facing or will face related to safety topics, such as test
and validation [64], certification [67][74], autonomy assurance
and trust when non-deterministic and adaptive algorithms are
used [74] - crash assignment facing distinct ethical theories
[69], for example. In this context, most of them do not present
specific findings using experimental setups; instead, they
envision potential future solutions for the discussed challenges.
In other words, those papers try to shed an alert light on the
important topics that seem to be neglected by the AV
enthusiasts, trying to push the research agenda towards safety
engineering mindset.
As exceptions, 3 papers presented practical applications and
results. [69] presented some interesting findings using a simple
experimental simulated environment to test specific crash
scenarios under three ethical theories. They found that
understanding rational ethics is crucial for developing safe
automated vehicles. The results of their experiment indicate that
in specific crash scenarios, utilitarian ethics may reduce the
total number of fatalities that result from automated vehicle
crashes. [66] proposed an approach to describe test-cases for
validating autonomous vehicles using recordings of traffic
situations for creating a minimal test-suit that could help in the
certification process. Considering the example presented, they
show how minimalism is achieved by manually comparing the
test-cases. Although it is an interesting and promising approach,
there are no evidences that it could address a safety certification
processes requirement when considering non-deterministic
algorithms. Hence, the research was still preliminary. Finally,
although [73] presents an end-to-end Bayesian Deep Learning
architecture to reduce the risks of hard classifications by
adopting probabilistic predictions accounting for each model,
no findings from real experiments were presented.
E. Reported future studies (RQ.6)
Question RQ.6 is answered by CT.6 (future studies), based
7
on the collected information about future studies reported on
the sample papers. Some papers did not suggest future studies.
Other papers described intended future studies or works under
development. Those are frequently small incremental changes,
such as change of parameter or new test scenarios. Therefore,
they are not reported here since their specificities would require
a considerable background on the papers contents. That is out
of the scope of the systematic literature review.
The studies related to Sensors and Perception propose many
future studies, but mostly around improvements that would be
made in the future to address some of the limitations of the
presented research. Due to the space limitations, only some
examples are described here. [18] suggests additional research
on image recognition of low contrast images and vehicle images
with brake lamps. [35] suggests future work on traffic lights
detection under severe weather or night conditions. [34]
suggests more research on detecting speed bump during night
time. They also suggest research on speed bumper detection
when they have no pattern or marking. In addition to that, [34]
suggests research to improve the recognition capabilities to
distinguish zebra crossing from speed bump. [75] proposed
future research about road detection using road lane markers
that could be detected by LIDAR, while [21] proposed more
research focused on optimizing the lane detection and vehicle
recognition algorithms to reduce their computational costs.
Also considering the high computational costs, [27] proposed
using parallel computing to increase the speed of the image
recognition algorithms. Finally, according to [37], additional
research is needed on using the virtual environments for testing
because the authors believe their usage for training and testing
intelligent systems are becoming more relevant.
Most of the studies related to Navigation and Control suggest
future studies. The majority suggests extensions to the work
they presented. Here, few examples are presented. The study
proposing hybrid control architecture [39] suggests an
extension to consider the full kinematics and dynamic
limitations of the vehicle, while constantly acting to avoid
collisions and unsafe driving. The paper proposing an approach
using SVM to avoid maneuvers too close to an obstacle by
adding a safety margin [41] proposes future re-search to extend
it using a combination with the kinetic convex hulls2 to enable
the possibility of computing the solution ahead in time.
According to the authors, this would help to predict the position
and the width of the optimal margin. As a result, it would
improve the approach by adding the ability of reduce the
collision risk by preventing the AV from driving into a
dangerous situation. The study using Fuzzy Logic as the main
approach to control a semi-autonomous car 100-km experiment
[40] proposes future research using new sensors and filtering
methods for data fusion to reduce the risk on scenarios where
the GPS signal is lost. Finally, the study on AVs intersection
crossing [46] describes future work in which more types of
vehicles and more adjacent intersections would be included in
the simulations.
Most of the studies (4 of 6) related to Fault Prevention
2 Check [76] for more information about kinetic convex hulls.
suggest future studies. Half of the studies are related to security
aspects, while the other half is related to
diagnosis/prognosis/prediction. The study proposing a cyber-
attack detection system based on ANNs [39] suggests a future
study to apply the proposed approach to a real vehicle in
addition to the application of LSTM to detect online sensor
attack. The study proposing the use of Particle Filter and
Kalman Filter to secure connected vehicles against DoS attack
[51] proposes future work to assess the proposed security
scheme under many distinct scenarios, and also to execute tests
in real world set-ups. The study about predicting ADAS
remaining useful life for the prognosis of its safety critical
components using ANNs and other techniques, such as SVM
[52], proposes a considerably wide range of future studies, such
as using Least Square Support Vector Machine (LSs SVM);
using big data techniques to analyze the server data; studying
connected vehicle prognosis; using driver, vehicle and region
profile data to understand the impact on the environment and
driving style impact on the system lifespan; and more studies
on prognostics-enabled decision Making (PDM). Finally, the
work presenting the use of regression-based methods to predict
the CPU usage patterns of software tasks running on an AV [53]
suggest future work on the use of some regularization methods
for automatic feature selection, but also to particularly
investigate the effects of underestimating CPU utilization, and
how to handle underestimation of CPU utilization when it
happens, aiming to better understand how safe over (or under)
estimation of CPU utilization is in terms of reliable autonomous
driving.
The studies about Ethics and Policies on AVs basically
suggest more research on those topics. In the same way, most
of the studies tackling human-factor-related topics do not
propose future studies. As an exception, the paper proposing the
application of regression-based model for the selective attention
mechanism subject [56] proposed a future study to help to
reveal the mechanism of rear end collision accident to some
extent.
Half (2 of 4) of the studies related to Conceptual Model and
Framework do not suggest any future studies. However,
implicitly, the next steps would be the deployment of those
suggested approaches on experimental set-ups to collect real
results. The study proposing a framework to reduce the
uncertainty of a driver behavior prediction model [62] suggests
more studies focusing on the resilience and sustainability of the
system when deployed on a large scale in a complex system.
The papers about Fault Forecasting suggest some future
research. Among them, [64] suggests more research on safety
envelope mechanisms to describe a boundary within the state
space of the AVs rather than trying to prove that it will always
work correctly. Koopman, in another paper [67], suggest that
the accepted practices must be updated to create an end-to-end
design and validation process to address all the safety concerns
considering cost, risk, and ethical considerations. [66] proposes
more work on creating automated test-cases. [68] proposes
more studies based on the framework they proposed to evaluate
8
the impacts of AVs on traffic safety, specially using stochastic
simulations with random number seeds to achieve a broader
representative and a variety of traffic situations, as well as using
the proper statistical analysis techniques to ensure the statistical
validity of the results.
Finally, the 2 studies about Dependability and Trust also
present some suggestions of future studies. [73] asks for more
research on new concrete safety evaluation metrics. [74]
suggests more research on understanding the dependence of the
system components on AVs is needed to establish trust. They
also suggest that could be achieved by investigating the many
ways in which people, the system, and the environment
interrelate.
IV. SLR FINDINGS ORIENTED BY AN AV SYSTEM MODEL
In the previous section, the state of the art in the literature
about AI on AV safety was identified and investigated by means
of a SLR. Six research questions oriented the literature
identification, in which studies that include keywords related to
safety, AI and AV were considered. The resulting studies were
investigated and mapped into 6 categories: Impact (increase or
decrease safety risks), Topics (sensors and perception;
navigation and control; fault prevention; conceptual model and
framework; human factor; etc.), Techniques (general AI/ML;
ANN; SVM; etc.), Problem (AV validation; road detection;
collision avoidance; etc.), Findings, and Future Studies.
These results considered the AV as a system, but its specific
components and functions in an architectural point-of-view
were not considered. For deepening the understanding about the
state of the art of AI on AV safety it is necessary to show how
the presented works are applied/fitted on AV in an architectural
point-of-view. In other words, which of AV
modules/components and functions are already being
developed and which one could be more explored. In order to
achieve this goal, it is going to be considered the AV
architecture proposed in [78].
An automotive manufacturer consortium (CAMP-AVR) [78]
proposed a high-level architecture considering the main system
components demanded for the vehicle movement control, to be
used in the deployment of future Dynamic Driving Tasks
(DDT). Figure 4 (left) illustrates the model considering a
traditional vehicle (i.e. human operation with no automation
deployed), and Figure 4 (right) illustrates the introduction of
some level of machine automation (hybrid) in Sensors,
Controller and Actuators elements. While the diagram
considering the human operation can solely be mapped to the
SAE Automation Level 0 (no automation), the hybrid one
encompassing machine automation with human-in-the-loop can
be mapped to the SAE Automation Levels 1 to 4 (semi-
autonomous) [78].
In this context, a modified version of the semi-autonomous
model is proposed here (Figure 5) including the system
boundary. Also, the human related components were grouped
as one single component (human-in-the-loop), which interacts
with Machine Actuators, Machine Control, Machine Perception
and Environment. A single component represents a more
realistic approach facing the complexity added by the human in
the system and allows the examination of the user actions and
interactions as suggested by [79]. Also, it supports a necessary
human-centered and holistic view [80] to better support the
complexity of the human behavior and its interaction to the
system. It avoids the misconceptions of the too logical designs
from some engineering designs and helps to consider and accept
human behavior the way it is, not the way engineers would wish
it to be [81]. In fact, this is a necessary upgrade considering the
original model is derived by the classical view from the
automation engineering for industrial applications, where the
environment was under control of the system designer, the
human interactions had a considerable narrower scope, and its
potential impact to the whole system were much lower, when
compared to its application to the semi-autonomous vehicles.
As a result, the proposed DDT version (Figure 5) can be used
to map the selected scientific literature. Therefore, it can
provide a concise perspective on how the field literature covers
those main components and which the uncovered areas are.
Also, it can provide a good overview on the predominance of
the papers valence (increase or decrease) on safety.
Table IV shows how CT.1 (Impact) and CT.2 (Topic) codes
are mapped to the components of the modified semi-
autonomous system model, as well as the relationship between
CT.1 and CT.2. Most of the papers are related to machine
perception, followed by papers related to a broad system view.
Then, the next largest group of papers is related to the machine
control component. The remaining papers are related to the
human-in-the-loop aspects. An interesting aspect is that only
the studies with a broad system aspect were found to have both
CT.1 codes (increase and decrease system safety). Basically,
the studies focused on distinct components solely understand
AI can increase the safety risk. Therefore, there is a lack of
studies with a critical mindset that explore the potential
negative impacts of AI on the individual components. Finally,
no papers were found related to the vehicle, machine actuators
or environment.
9
Figure 4. Dynamic Driving Tasks Models: No Automation (left) x Semi-Automation (right) – Source: [78]
Figure 5. An adapted version of Dynamic Driving Task (DDT) Model
Table V shows how the wide range of AI techniques (CT.3
code) is mapped to the components of the modified semi-
autonomous system model. The AI techniques are grouped
around their scope: system-oriented (32%, 19) and component-
oriented (68%, 40). When a paper uses a combination of
techniques, for example, ANN and SVM, it results into a unit
added to the total number of papers using ANN and a unit added
to the total number of papers using SVM. In this context, most
of the studies (63%, 12) related to system-wide scope referred
to general AI/MI. Most of the studies (20%, 11) related to
machine perception used ANNs. In fact, ANN, SVM and HMM
(Hidden Markov Model) account for 48% of the studies related
to machine perception. Fuzzy logic (18%, 3) is the most widely
used technique in the machine control-related papers. Fuzzy
Logic, SVM, Optimization Heuristics and Ramer-Douglas-
Peucker or Ramert Douglas algorithm account for 53% of the
studies related to machine control. Finally, Bayesian Artificial
Intelligence techniques are used in most of the studies (29%)
related to human-in-the-loop.
Table VI shows the total count of each AI technique
occurrence over the sample papers. The sample papers have
different heterogeneity in the applied AI approaches. Besides
24% of the papers using generic AI/ML concepts, 49% of the
papers applied only one type of AI technique. Therefore, they
are homogeneous in terms of the applied AI technique. In those
studies, the most widely used techniques were Artificial Neural
Networks (28%, 8), Fuzzy Logic (14%, 4) and Support Vector
Machine (SVM) (10%, 3). The remaining 27% employed a
hybrid approach by combining multiple types of AI techniques.
Among those papers, the combination of Artificial Neural
Networks to other techniques (44%, 7), Support Vector
Machine to other techniques (SVM) (25%, 4) and Hidden
Markov-Based Models (e.g. Continuous Hidden Markov
Model-CHMM and Discrete Hidden Markov Model-DHMM)
to other techniques (13%, 2) were the most frequent hybrid
approaches found in the papers selected.
TABLE IV MODIFIED SEMI-AUTONOMOUS DDT SYSTEM MODEL X CT3 CODES
CT.3 - Topic Component of the Modified
DDT System Model (#Hits) %
CT.2 – Impact on Safety: Increase Safety (+)
Sensors and
Perception Machine Perception (21) 36
Navigation and
Control Machine Control (13) 22
Human Factor Human-in-the-loop (6) 10
Fault Prevention
System (+) (8)
32
Conceptual Model
and Framework
CT.2 – Impact on Safety: Decrease Safety (-)
Fault Forecasting
System (-) (11) Ethics and Policies
Dependence and
Trust
Many different combinations of ANNs with other techniques
were found (7 papers). As shown in Table VII most of those
papers are related to Sensors and Perception (3 papers) as well
as Navigation and Control (2 papers). Also, papers related to
Conceptual Model and Framework and Fault Prevention
employed hybrid approach (2 papers). The papers that used a
10
combination of models associated to Hidden Markov Based
Models were related to Navigation and Control as well as
Sensors and Perception. The paper that used Hough
Transformation combined to other models is related to
Navigation and Control. The paper that employed a
combination of techniques to propose a Novel Image
Recognition Technique is related to Sensors and Perception.
The paper using Regression- Based Models combined to other
techniques is related to AV Navigation and Control. Finally, all
the papers employing SVM combined to other techniques were
related to the topic Sensors and Perception. The same grouping
strategy applied to Table V (system-oriented and component-
oriented) can be applied here to evaluate the problems (CT.4).
System-level problems include 16 papers: AV Validation [64],
Machine-learning-based systems validation to the ultra-
dependable levels required for AV [67], Human and Machine
Driver Co-existence [60], Coexistence Human Machine
Controller [70], Driving Car Tasks Classification [61], Lack of
efficient Safety Performance Verification technique when
AI/ML is used [65], Crash assignment, especially between
automated vehicles and non-automated vehicles [69], Reduce
the uncertainty of a driver behavior prediction model [62],
Investigate three underexplored themes for AV research: safety,
interpretability, and compliance [73], How vehicle autonomy
technology can be used to benefit car drivers and also to propose
a concept of an autonomous highway vehicle which improves
highway driving safety [63], AV decisions in complex
dilemmas as a social agent [71], Hybrid (humans and machines)
collective decision-making systems [72], Autonomy assurance
and trust in Automated Transportation Systems [74], AV Test
[66] and, Evaluate the impacts of the number of highly
automated vehicles on future traffic safety and traffic flow [68].
TABLE V TECHNIQUES X DDT SYSTEM MODEL COMPONENT
DDT System Model Technique #Hits %Paper Accum.%
System-oriented
(System)
General AI/ML 12 63% 63%
Hough Transformation related approaches 2 11% 74%
Artificial Neural Networks 2 11% 84%
Optimization Heuristics 1 5% 89%
Estimation Filters (e.g. Kalman Filter and Particle Filters) 1 5% 95%
Linear Temporal Logic (LTL) 1 5% 100%
Component-
oriented
Machine
Perception
Artificial Neural Networks 11 20% 20%
Support Vector Machine (SVM) 8 15% 35%
Hidden Markov Based Models (e.g. Continuous Hidden
Markov Model-CHMM and Discrete Hidden Markov
Model-DHMM)
4 7% 43%
Estimation Filters (e.g. Kalman Filter and Particle Filters) 3 6% 48%
Histogram of Oriented Gradient (HOG) 3 6% 54%
Nearest-Neighbor Based Algorithm (e.g. k-Nearest
Neighbours - kNN) 3 6% 59%
Adaptive Boosting (AdaBoost) 3 6% 65%
Principal Components Analysis (PCA) 2 4% 69%
Haar-like feature detector 2 4% 72%
Fuzzy Logic 2 4% 76%
Viterbi algorithm 1 2% 78%
Bayesian Artificial Intelligence (e.g. Bayesian Deep
Learning, Naive Bayes Classifier-NBC, etc) 1 2% 80%
Regression Based Models 1 2% 81%
Hough Transformation related approaches 1 2% 83%
Ramer-Douglas-Peucker or Ramer-Douglas algorithm 1 2% 85%
Novel Image Recognition Technique 1 2% 87%
Gaussian Mixture Model (GMM) 1 2% 89%
General AI/ML 1 2% 91%
Complex Decision Trees (CDT) 1 2% 93%
Channel Features 1 2% 94%
Local Binary Patterns (LBP) 1 2% 96%
Clustering algorithm k-mean 1 2% 98%
Conditional Random Fields (CRFs) 1 2% 100%
Machine
Control
Fuzzy Logic 3 18% 18%
Support Vector Machine (SVM) 2 12% 29%
Optimization Heuristics 2 12% 41%
Ramer-Douglas-Peucker or Ramer-Douglas algorithm 2 12% 53%
Case-based reasoning (CBR) 1 6% 59%
11
Nearest-Neighbor Based Algorithm (e.g. k-Nearest
Neighbours - kNN) 1 6% 65%
Basic AI Path Planning algorithms such as A* and D* 1 6% 71%
Artificial Neural Networks 1 6% 76%
Regression Based Models 1 6% 82%
Distributed Random Forest (DRF) 1 6% 88%
Neuroevolution of Augmenting Topologies (NEAT) -
ANN + GA 1 6% 94%
Satisfiability Modulo Theories (SMT) Solver 1 6% 100%
Human-in-
the-loop
Bayesian Artificial Intelligence (e.g. Bayesian Deep
Learning, Naive Bayes Classifier-NBC, etc) 2 29% 29%
Regression Based Models 1 14% 43%
Haar-like feature detector 1 14% 57%
Canny Edge Detection Algorithm 1 14% 71%
Hough Transformation related approaches 1 14% 86%
General AI/ML 1 14% 100%
Considering the component-level problems, 21 papers
(36%) are related to dealing with algorithms and techniques
to deal with Machine Perception issues, such as: Vehicle
Cyber Attack [39], Turn Signal Recognition [18], Securing
connected vehicles against Denial of Service (DoS) attack [51],
Road Detection [75], Traffic Light Detection [35], Prediction of
advanced driver assistance systems (ADAS) remaining useful
life (RUL) for the prognosis of ADAS safety critical
components [52], Vehicle Detection and Counting [36],
predicts the CPU usage patterns of software tasks running on a
self-driving car [53], a safety warning and driver-assistance
system and an automatic pilot for rural and urban traffic
environments [21], reliable and robust obstacles detection
continues to be largely investigated and still remains an open
challenge, especially for difficult scenarios and, in general
cases, with loosened constraints and multiple simultaneous use-
cases [25], Pedestrian Detection [27], Road environmental
recognition and various object detection in real driving
conditions [29], Obstacle clustering and tracking [23]. For an
autonomous behavior, each truck must be able to follow the
vehicle ahead. Due to that, each vehicle must be able to
recognize the leading vehicle [22], Speed bump detection [34],
providing road safety to connected drivers and connected
autonomous vehicles [20], how to ”automate” manual
annotation for images to train visual perception for AVs [44],
Road Sign Classification in Real-time [32], Road Terrain
detection [31], Spatio-temporal situation awareness [33],
Pedestrian detection and movement direction recognition [26],
Pedestrian Trajectory Prediction [28], Road junction detection
[30], Cyber Attack in V2X [54], Learn from Demonstration
[45], Early detection of faults or malfunction [55], Road and
Obstacle Detection [24] and Enhance Image Understanding
[19].
The problems related to Machine Control were found in 17
papers (22%). Those problems include: Pre-Crash problem of
Intelligent Control of autonomous vehicles robot [39], Safe-
optimal trajectory selection for autonomous vehicle [76],
Driverless car 100 km experiment [40], Robot maneuvers too
close to an obstacle, which increases the probability of an
accident. Preventing this is crucial in dynamic environments,
where the obstacles, such as other UAVs, are moving [41],
Learning and simulation of the Human-Level decisions
involved in driving a racing car [47], Control intersection
crossing (all way stop) and optimizing it [42], How to prove the
correctness of an algorithm for Vehicle Coordination [43], Path
tracking [48], Drivers maneuver classification [44], AVs
intersections crossing optimization [46] and Manage low level
vehicle actuators (steering throttle and brake) [49].
Finally, the problems related to Human-in-the-loop new
DDT component (Figure 5) are present in 7 papers (10%).
Those problems include: Selective Attention Mechanism [56],
Developing remote controlled car with some automation to deal
with traffic light detection, obstacle avoidance system and lane
detection system to be driven from anywhere over a secured
internet connection [38], Collision avoidance when no action is
taken by driver to avoid the collision [57], Human drivers
monitoring system to ensure they will be able to take over
control within short notice [58] and, Design of driving
assistance system [59]. This seems to be an attention-point; this
problem category can be considered one serious challenge to
semi-autonomous vehicles (SAE Level 1 to Level4). Therefore,
more research is needed into this topic because only 6 papers
were found.
TABLE VI
HETEROGENEITY OF THE USED AI APPROACHES
Heterogeneity % Main Technique #Hits %Papers
Generic 24% General AI/ML 14 100%
Homogenous 49%
Artificial Neural Networks 8 28%
Fuzzy Logic 4 14%
Support Vector Machine (SVM) 3 10%
12
Regression Based Models 2 7%
Estimation Filters (e.g. Kalman Filter and Particle Filters) 2 7%
Bayesian Artificial Intelligence 2 7%
Optimization Heuristics 2 7%
Ramer-Douglas-Peucker or Ramer-Douglas algorithm 2 7%
Hough Transformation 1 3%
Satisfiability Modulo Theories (SMT) Solver 1 3%
Adaptive Boosting (AdaBoost) 1 3%
Linear Temporal Logic (LTL) 1 3%
Hybrid 27%
Artificial Neural Network combined to other techniques 7 44%
Support Vector Machine (SVM) combined to other techniques 4 25%
Hidden Markov Based Models (e.g. Continuous Hidden Markov Model-CHMM
and Discrete Hidden Markov Model-DHMM) combined to other techniques 2 13%
Hough Transformation related approaches combined to other techniques 1 6%
Regression Based Models combined to other techniques 1 6%
Novel Image Recognition Technique 1 6%
V. FINAL REMARKS
Machine Perception has more studies with practical results.
Considering the other components, few studies with practical
results from real deployments were found. Most of the papers
presented preliminary results. In fact, some papers start with a
promise and finish with more promises. Considering only 24%
of the total papers considered in this study were published by
journals, it is possible to conclude the field is not mature yet.
Some similar issues were studied in more than one paper
about Machine Perception, and distinct techniques were applied
to address them (for example, ANN and SVM applied to similar
issues as well issues as well distinct techniques applied to the
topic cyber-attack). Considering some of those techniques have
different working mechanisms, that fact can be an important
finding for the safety of autonomous cars as regards the need of
redundant components. Similar issues being addressed by
different techniques were not identified for Machine Control.
The papers related to Navigation and Control also reported
positive and promising results, although the level of maturity of
the achievements are clearly much lower than the sensors and
perception as well as far from what would be expected for an
autonomous vehicle considering the potential hazardous
situations it may face. In fact, most of the results presented are
preliminary.
Only few of the studies related to system described practical
results from real deployments. The papers proposing
conceptual models and frameworks bring important
contributions, but they are mostly not tested in real set-ups.
There is thus a lack of reported results from models and
frameworks that could build the foundation of AVs safety.
Also, few human-in-the-loop studies had practical results from
real deployments. However, they seem to be one of the most
important topics seeing that there will be more semi-
autonomous cars than fully autonomous ones for a while, and
they will co-exist. The human factor will thus be an important
variable in the system to be considered not only as the impacted
side of the safety, but as one of the sources of interactions
influencing the safety levels. The topic requires
multidisciplinary studies involving fields beyond engineering
and computer science, such as neurosciences. This shows the
field is not mature yet.
The amplitude and range of the reported future researches in
the papers reviewed suggest that there is an empty space for
new research into this field. For example, only few studies were
found about the three topics positioning AI as a potential source
of negative impact on safety - Fault Forecasting, Ethics and
Policies, and Dependability and Trust. When combined to the
other findings reported by the present study, it confirms the
impressions formed during an exploratory research of the
literature [1]. It reinforces the perception that the field of AI and
AV is not heavily influenced by the safety engineering culture
yet. In fact, the studies published about this current topic seem
to be more driven by computation-related domains, with no
tradition regarding safety culture, than other fields that are
much more connected to safety in critical systems [1].
Additional research is necessary for most of the studies
reviewed. They need to be extended to be tested in simulated or
real set-ups, new and broader scenarios, with new and more
data, and consider experimental designs whereby the results
from the proposed approach are compared to benchmarks and
alternative techniques. Many AI techniques have achieved
impressive results; however, it is still arguable whether the error
rates are suitable for real deployments in AVs under the light of
a (missing) hazard analysis. Therefore, additional studies with
improvements in those techniques are required. Finally, a
stronger influence of safety engineering on most of the studies
would benefit the field.
A research agenda must consider a serious safety agenda for
future studies, at system-level, component-level and AI
technique-level. In fact, there are some topics related to safety
concerns over AVs, which are critical-path to the development
of the field. Some of the suggested topics are related to the
challenges with validating machine-learning- based systems to
the ultra-dependable levels required for AVs; wider and deeper
studies about human-machine collaboration in the context of
AVs; autonomy assurance and trust in AVs; ethical and moral
decisions in the context of AVs; among other topics, from
13
Validating machine-learning-based systems to the ultra-
dependable levels required for AVs; and autonomy assurance
and trust in AVs seem to be the holy grail towards a fully
autonomous AV - SAE level 5 . They are also key topics for the
Safety Certification of non-deterministic control systems. In
those contexts, there are many gaps to be filled by future
researches, such as AVs software testing, Fault Injection
Testing for AI on AVs, Failure Modes and Effects Analysis
(FMEA) for AI on AVs, AI safeguards for AVs, AI safety
envelopes for AVs, AI redundancy for AVs (many possible
approaches, such as a hybrid connectionist and symbolic
architecture using causal inference), explainable AI for AVs, AI
fault forecasting. Finally, studies on V2X communication can
help autonomy assurance by providing channels for hardware
and software redundancy. Human-machine collaboration in the
context of AVs is another key topic with special impact on the
semi-autonomous vehicles (SAE levels 1 to 4). Investigations
on the best way humans and AVs can interact during normal
operations and facing hazardous situations are needed to meet
the adequate safety requirements the semi-autonomous vehicles
must have. Those studies must consider hybrid collective
decision-making systems to enable humans and machines to
work together and to agree on common decisions, as well as
how to deal with the lack of agreement in some situations.
Also, there is another important discussion arising in the
context of human-machine collaboration that must be
investigated. On the one hand, there are reports about advanced
driver assistant technologies that failed (such as Tesla
Autopilot) and the driver was not able to react in time to avoid
the accident. They ended-up in life losses and property
damages. On the other hand, there are reports about situations
in which the advanced driver assistant technologies saved the
drivers’ life by automatically taking the driver suffering a heart
attack to the hospital; fully controlling the car with a drunk
driver sleeping; and using a defensive lane change
TABLE VII HYBRID AI APPROACHES X TOPIC
Main AI Technique Topic AI Techniques Reference
Artificial Neural
Network
Conceptual Model
and Framework
HoughTransforms, HoughLines, LocalMaximaFinder,
Kalman filters and Convolutional Neural Network (CNN) [60]
Fault Prevention KNN, SVM Regression (SMO), ANN [52]
Navigation and
Control
CBR, ANN, fuzzy logic, Nearest-Neighbor Retrieval
Algorithm, Basic AI Path Planning algorithms such as A* and
D*
[39]
ANN combined to Genetic Algorithm - Neuroevolution of
Augmenting Topologies (NEAT) [47]
Sensors and
Perception
ANNs, AdaBoost, SVM, Hidden Markov Models (HMMs),
CRFs [30]
Clustering algorithm k- mean, ANN [31]
HOG, SVM, PCA, ANN [29]
Hidden Markov Based
Models
Navigation and
Control
GMM, Continuous Hidden Markov Model (CHMM),
Discrete Hidden Markov Model (DHMM) [45]
Sensors and
Perception
HMM, Viterbi algorithm, Adaboost trained Haar-like feature
detector [36]
Hough Transformation Navigation and
Control
Haar Feature Based Cascade Classifier, Canny edge detection
and Hough line transformation [38]
Novel Image
Recognition Technique
Sensors and
Perception Combination of mathematical techniques [34]
Regression Based
Models
Navigation and
Control (DRF) and Linear Regression (LR) [76]
Support Vector
Machine (SVM)
Sensors and
Perception
Haar, HOG, LBP, Chanel features, SVM [37]
k-Nearest Neighbours (kNN), Naïve Bayes classifier (NBC),
SVM [27]
Principal component analysis network (PCANet), SVM [35]
SVM, HOG [75]
maneuver to avoid being hit by a truck changing its lane. Some
players in the industry are pushing the automation evolution
steps towards full automation by requiring the human driver to
be a backup to the automated driver. Other players in the
industry believe the automated driver must be a backup to the
human driver. It looks like the second approach can be a
smoother and safer path towards a SAE level-5 automation.
Immersive environments for training and testing AVs
represent another research trend. As the underlying
technologies supporting AVs development evolve, higher
automation-levels become possible. Considering the potential
hazards until the AVs are well trained and fine-tuned, the
14
immersive technologies are becoming an important tool to
support the development, training and tests of fully autonomous
machines. Another broad topic requiring further research is
related to ethical and moral decisions in AVs. Some studies
only mention issues related to moral dilemmas while others
provide some simple experiments involving simulated
environments and/or human interviews. However, they
misinterpret important concepts and bring the discussions
around the decisions AVs must make when life losses are
involved, besides the moral and ethical perceptions from the
human perspective. All of them miss important points such as
statistical considerations and the societal result. In other words,
the discussions are not deep enough as regards situations such
as whether an AV should hit an old man or a child, while a true
safe machine control should consider all the probabilities
involved and select the one that minimizes the chances of life
losses instead of just picking an option. For example, the system
must consider small signals, such as which of the potential
victims is paying attention to the approaching AV and what
would their potential reaction be and effectiveness of it based
on the age and other metrics, as well, considering the multiple
scenarios, and the configuration of each, such as speed, region
of the car hitting which region of each victim, the potential
damages and the severity of the damages considering the
estimated weight and overall physical condition, to decide
based on the minimization of chances of life losses. This
approach will result into higher safety levels for society.
Finally, only 1 paper about autonomous truck was found.
Considering some specificities of autonomous truck and its
risks, at least a few more studies about the topic could be
expected.
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APPENDIX
TABLE VIII
CT.3 X CT.4
Technique
(CT.3) Hits Papers Addressed Problem (CT.4) References
General AI/ML 14 24%
AV Validation; Challenge with validating machine-learning based
systems to the ultra-dependable levels required for autonomous
vehicle; Coexistence Human Machine Controller; Driving Car Tasks
Classification; Lack of efficient Safety Performance Verification
technique when AI/ML is used; Crash assignment, especially between
automated vehicles and non-automated vehicles; Reducing the
uncertainty of a driver behavior prediction model; Integration between
automatic vehicle and human driver; How t h e vehicle autonomy
technology can be used to benefit car drivers and t o improve highway
driving safety by a concept of an autonomous highway vehicle; AV
decisions in complex dilemmas as a social agent; Hybrid (humans and
machines) collective decision making systems (work together and
agree on common decisions); Autonomy assurance and trust
(CERTIFICATION PROA CESS) in Automated Transportation
Systems; Evaluating the impacts of the number of highly automated
vehicles on future traffic safety and traffic flow; Enhancing Image
Understanding.
[64],[67],
[70],[61],
[65],[69],
[62],[58],
[63],[71],
[72],[74],
[68],[19]
Artificial
Neural Networks
(ANN)
13 22%
Vehicle Cyber Attack; Turn Signal Recognition; Pre-crash issues of
Intelligent Control of autonomous vehicles robot; Real-time Road
Sign Classification; Road Terrain detection; Spatio-temporal situation
awareness; Pedestrian detection and movement direction recognition;
Pedestrian Trajectory Prediction; Road junction detection; Early faults
or malfunction detection; Prediction of advanced driver assistance
systems (ADAS) remaining useful life (RUL) for the prognosis of
ADAS safety critical components; Road environmental recognition and
various objects detection in real driving conditions; Human and
Machine Driver Co-existence;
[39],[18],
[32],[31],
[33],[26],
[28],[30],
[55],[52],
[29],[60]
Support
Vector Machine
(SVM)
10 17%
Road Detection; Robot maneuvers too close to an obstaR cle; Road
environmental recognition and various object detection in real driving
conditions; Drivers maneuver classification; Traffic Light Detection;
[75],[41],
[29],[20],
[44],[35],
17
Prediction of adc vanced driver assistance systems (ADAS) remaining
useful life (RUL) for the prognosis of ADAS safety critical components
Pedestrian Detection; How to ”automate” manual annotation for
images to train visual perception for AVs Road junction detection;
[52],[27],
[37],[30]
Bayesian
Artificial
Intelligence
4 7%
Collision avoidance when no action is taken by driver; Safety,
interpretability, and compliance; Pedestrian Detection; Design of
driving assistance system;
[57],[73],
[27],[59]
Fuzzy Logic 5 8%
PreCrash problem of Intelligent Control of autonomous vehicles robot;
Driverless car 100 km experiment Cyber Attack in V2X; Manage
low level vehicle actuators (steering throttle and brake); Road and
Obstacle Detection;
[39],[40],
[54],[49],
[23]
Hidden
Markov
Based Models
4 7% Vehicle Detection and Counting; Road junction detection; Learn from
Demonstration;
[36],[29],
[45]
Estimation Filters 4 7%
Human and Machine Driver Co-existence; Securing connected vehicles
against Denial of Service (DoS) attack; Reliable and robust obstacles
detection;
[60],[51],
[24]
Nearest
Neighbour-Based
Algorithm
4 7%
Pre-crash problem of Intelligent Control of autonomous vehicles
robot; Pedestrian Detection; Providing road safety to connected
drivers and connected autonomous vehicles;
[39],[26],
[19]
Adaptive
Boosting
(AdaBoost)
3 5% Vehicle Detection and Counting; Leading vehicle recogV nition in
platooning; Road junction detection;
[36],[21],
[29]
Ramer-Douglas
Peucker
or Ramer-
Douglas
algorithm
3 5% Obstacle clustering and tracking; Path tracking; [22],[48]
Haar-like
feature
detector
3 5%
Developing remotecontrolled car with some automation to deal with
traffic light detection, obstacle avoidance system and lane detection
system to be driven from anywhere over a secured internet
connection; Vehicle Detection and Counting; How to ”automate”
manual annotation for images to train visual perception for AVs;
[38],[36],
[37]
Histogram
of Oriented
Gradient
(HOG)
3 5%
Road Detection; Road environmental recognition and various objects
detection in real driving conditions; How to ”automate” manual
annotation for images to train visual perception for AVs;
[75],[28],
[37]
Hough
Transfor-
mation
3 5%
Road Detection; Road environmental recognition and various object
detection in real driving conditions; How to ”automate” manual
annotation for images to train visual perception for AVs;
[60],[38],
[20]
Optimiza-tion
Heuristics 3 5%
Control intersection crossing (all way stop) and op- timization;
Autonomous vehicles intersections crossing optimization; Human and
Machine Driver Co-existence;
[46],[42],
[60]
Regression-
Based
Models
3 5%
Selective Attention Mechanism; Safe-optimal trajectory selection for
autonomous vehicles; Predicts the CPU usage patterns of software tasks
running on a self-driving car;
[56],[79],
[53]
Principal
Componen-ts
Analysis (PCA)
2 3% Traffic Light Detection; Road environmental recognition and various
object detection in real driving conditions; [35],[28]
Canny Edge
De- tection
Algorithm
1 2%
Developing remote- controlled car with some automation to deal
with traffic light detection, obstacle avoidance system and lane
detection system to be driven from anywhere over a secured internet
connection;
[38]
Case-based
reasoning
(CBR)
1 2% Pre-crash problem of Intelligent Control of autonomous vehicles robot; [39]
Channel
Features 1 2%
How to ”automate” manual annotation for images to train visual
perception for AVs; [37]
18
Clustering
Algo- rithm k-
mean
1 2% Road Terrain detection; [30]
Complex
Decision Trees
(CDT)
1 2% Providing road safety to connected drivers and connected autonomous
vehicles; [19]
Conditional
Random
Fields (CRFs)
1 2% Road junction detection; [29]
Distributed
Random Forest
(DRF)
1 2% Safe-optimal trajectory selection for autonomous vehicle; [79]
Gaussian
Mixture Model
(GMM)
1 2% Learn from Demonstration; [45]
Linear
Temporal
Logic (LTL)
1 2% AV Test; [66]
Local Binary
Patterns (LBP) 1 2%
How to ”automate” manual annotation for images to train visual
perception for AVs; [37]
Neuroevo-lution
of Augmen-
ting
Topologies
(NEAT)
1 2% Learning and simulation of the Human-Level decisions involved in
driving a racing car; [47]
Novel
Image
Recogni-tion
Technique
1 2% Speed bump detection; [34]
Path Planning
Al- gorithms
(A* and D*)
1 2% PreCrash problem of Intelligent Control of autonomous vehicles robot; [39]
Satisfiability
Modulo
Theories
(SMT) Solver
1 2% How to prove the correctness of an algorithm for Vehicle Coordination; [43]
Viterbi
Algorithm 1 2% Vehicle Detection and Counting; [36]
TABLE IX
FINDINGS ON PAPERS ORIENTED BY THE DISCUSSION
Issue Suggested
Approach AI
Technique Findings Reference
DDT System Model Component - System
Human and Machine
Driver Co-existence
Continuously monitor
the driving behavior of
the neighboring vehicles,
sensor behavior and
processor behavior of the
ego vehicle regardless of
the vehicle being
autonomous or not
ANN (Hybrid)
The authors have foreseen and proposed
the solutions for future problems, which
would occur while the autonomous vehicles
are a part of driving. All the three described
architectures have addressed the safety
related problems. The architectures are
based on the availability of the resources for
vehicles. The first two architectures address
the safety failure due to the human
ignorance and autonomous vehicle
behavior. Third architecture addresses the
way of securing the failed vehicle due to
system failure. All the architectures rely
highly on the efficient connectivity and
[60]
19
computer vision algorithms.
Investigate three
under-explored
themes for AV
research: safety,
interpretability, and
compliance
End-to-end Bayesian
deep learning
architecture to propagate
uncertainty throughout
the AV framework. In
this case, standard deep
learning makes hard
predictions, whereas
Bayesian deep learning
outputs probabilistic
predictions accounting
for each model’s
ignorance about the
world.
ANN (Hybrid)
3 critical themes for a smooth adoption of
AV systems by society were highlighted.
Hard decisions are dangerous. Soft
(uncertain) classifications should be
propagated through to the decision layer.
This enables the AV to act more cautiously
in the event of greater uncertainty. We also
discussed the themes. Authors argument
interpretability and compliance as ways to
build trust and mitigate fears which
passengers might otherwise reasonably
have about unfamiliar black-box AV
systems. Also, they discussed about the
importance of clear metrics to evaluate each
component’s probabilistic output based on
their ultimate effect on the vehicle’s
performance.
[73]
AV Validation Safety Envelopes General
AI/ML No findings, only suggested approaches. [64]
Challenge with
validating machine-
learning based
systems to the ultra-
dependable levels
required for
autonomous vehicle
Safety certification
strategy addressing the
cross-disciplinary
concerns of safety
engineering, hardware
reliability, software
validation, robotics,
security, testing, human-
computer interaction,
social acceptance, and a
viable legal framework.
General
AI/ML
The paper only points out the challenge
with validating machine-learning based
systems to the ultra-dependable levels
required for autonomous vehicle fleets, and
how that challenge relates to a number of
other areas. It does not provide any
particular finding.
[67]
Coexistence Human
Machine Controller
In-car Virtual
Assistants
General
AI/ML
It is too early to assess whether
carmakers' optimistic vision of in-car virtual
assistants will match users' experience or
follow a similar destiny of "Clippy the
Paperclip" from Microsoft Word.
Proprietary systems are predominant and
there is no common framework addressing
ethical principles, liability, data protection,
privacy and security on many of the
technologies associated to AVs. Due to
these shortcomings, allowing a fully
autonomous approach could generate more
drawbacks than benefits. At least in a first
phase, it seems more appropriate to apply
AI-based virtual assistants to autonomously
execute tasks and take decisions (i.e. replace
humans) on safety-related functions only, in
line with the requirements defined by
international standards.
[70]
Driving Car Tasks
Classification
Classification of the
tasks that take place
during the driving of the
vehicle and its modeling
from the perspective of
traditional control
engineering and artificial
intelligence
General
AI/ML
The major issues realted to safety and and
the efforts to make sure the technologies
involved are robust are discussed: test the
safety of the ADAS, standardization, the
development of models and algorithms, the
appropriate constructing solutions for
implementation, and ethical issues. No
specific findings are presented.
[61]
Lack of efficient
Safety Performance
Methodology to
generate an estimation of
General
AI/ML
Detailed methodology was proposed to
deal with the issue by means of statistical [65]
20
Verification
technique when
AI/ML is used
probability of fatal
mishap of an
autonomous UGV
navigation algorithm
based on Statistical
Testing in a MonteCarlo
manner in a Simulated
Environment
testing via simulation. Demonstration was
still a work in process.
Crash assignment,
especially between
automated vehicles
and nonautomated
vehicles
The integration of
three ethical theories—
utilitarianism, respect for
persons, and virtue
ethics—with vehicle
automation is used in a
simple crash scenario
where an automated
vehicle must choose
between three crash
types on the basis of a
randomly assigned
ethical theory to
understand the outcomes
of distinct ethical
frameworks
General
AI/ML
The results of the experiment indicated
that in specific crash scenarios, utilitarian
ethics may reduce the total number of
fatalities that result from automated vehicle
crashes, although other ethical systems may
be useful for developing rules used in
machine learning. The experiment
demonstrates that understanding rational
ethics is crucial for developing safe
automated vehicles.
[69]
Reduce the
uncertainty of a
driver behaviour
prediction model
Proposes a Data
Analysis Framework to
exploit AI, quantified
self, internet of things
and automated driving to
build a computational
driver behavioural
model aming to reduce
the uncertainty of a
driver behaviour
prediction model and be
used monitor, predict
and control a
transportation system.
General
AI/ML
It is very hard to predict due to the fluidity
and interactions of the driving factors
determining the driver performance.
[62]
How vehicle
autonomy
technology can be
used to benefit car
drivers and also to
propose a concept of
an autonomous
highway vehicle
which improves
highway driving
safety
Conceptual discussion General
AI/ML
No specific findings were presented by
the conceptual discussion [63]
AV decisions in
complex dilemmas
as a social agent
Proposition of a
framework for an ethics
policy for the artificial
intelligence of an AV
General
AI/ML
The ethics of automated vehicles is
becoming a major issue from legal, social,
and vehicle control perspectives. AV will
have to make decisions
that might eventually harm an agent and that
these decisions should not contradict the
interests of the end users or the principal
stakeholders. An ethics policy for
automated vehicles is needed and the
proposed framework (AVEthics) is only the
beginning of a long path.
[71]
21
Humans and
machines will often
need to work
together and agree on
common decisions.
Shared moral values
and ethical principles
General
AI/ML
Hybrid collective decision-making
systems will be in great need [72]
Autonomy assurance
and trust
(certification
process) in
Automated
Transportation
Systems
Framework
Proposition for the
discussion around the
topic
General
AI/ML
Authors explored some of the unique
challenges that autonomous transportation
systems present with regard to traditional
certification approaches such being non-
deterministic and employing adaptive
algorithms. Authors discussed the concept
of multiparty trust and how it can be
extended to a framework illustrating the
relationships between disparate roles. Two
thought experiments showed that building
and
maintaining trust in the perception and
judgment of increasingly autonomous
systems will be a challenge for the
transportation community.
[74]
Evaluate the impacts
of the number of
highly automated
vehicles on future
traffic safety and
traffic flow
Framework to
evaluate the impacts of
AV on traffic and the
impact of continuous
increase in the number of
highly automated
vehicles on future traffic
safety and traffic flow
General
AI/ML
The results of impact evaluation in this
study show that the increase in the
penetration rate of the highly automated
vehicle together with proper adjustment of
model parameter may result in considerate
improvements of safety in traffic in terms of
defined indicators. The developed
methodology allows to compare traffic
efficiency and safety measures with
different penetration rates in various
scenarios by means of microscopic traffic
simulation.
[68]
AV Test
Creation of Minimal
Test-Suites with Test-
cases for the validation
of AVs using recordings
of traffic situations
LTL
The process of test-case derivation can be
applied was demonstrated. According to
the authors, the derivation of test-cases
categorizes the recordings automatically
and allows test engineers to specify test
inputs. The test-case descriptions use the
Linear Temporal Logic (LTL) and allow the
execution of continuous behaviors, which
may also react to the behavior of the tested
vehicle. According to the authors, as the
traffic recordings can also be used for
machine learning algorithms, the
contributes to the discussion of their safety
certification. They also state the approach
is flexible as it can be extended if new traffic
situations are supposed to be covered by
testing.
[66]
DDT System Model Component – Human-in-the-loop
Collision avoidance
when no action is
taken by driver to
avoid the collision
Real time transition
from assisted driving to
automated driving under
conditions of high
probability of a collision
if no action is taken to
avoid the collision
Bayesian
Artificial
Intelligence
Systems can be designed to feature
collision warnings as well as automated
active safety capabilities. The high-level
architecture of the Bayesian transition
model seems promising. Example scenarios
illustrate the function of the real-time
transition model.
[57]
Design of driving
assistance system
Discussion about
Design considerations
are advanced in order to
Bayesian
Artificial
Intelligence
No specific findings, only discussions
about the important considerations to be
taken into account when designing AVs
[59]
22
overcome issues in in-
vehicle telematics
systems
Integration between
AV and human
driver
un-obstructive human
driver monitoring
approaches to ensure
they will be able to take
over control within short
notice
General
AI/ML
One of the most essential parts of the
autonomous driving system is to monitor
driver’s physical and mental state to avoid
unexpected traffic accidents. There are
some solutions for non-contact
measurement of vital signs, such as HR, RR
include laser Doppler, microwave Doppler
radar, and thermal imaging. Several AI
approaches that have been applied in
classifying non-contact physiological
sensor signals in different other domains
could be possible to investigate in
classifying driver’s signals.
This paper shows that the assessment of
non-contact physiological parameters
presents a greater challenge and few
attempts have been made to adopt it for the
driving situation.
[58]
Develop remote
controled car with
some automation to
deal with traffic light
detection, obstace
avoidance system
and lane detection
system to be driven
from anywhere over
a secured internet
connection
Traffic Light
Detection: Haar Feature
Based Cascade
Classifier; Lane
Detection: Canny Edge
detection and Hough line
transformation was used
Hough
Transformation
related
approaches
combined to
other
techniques
Low-cost remote-controlled car
prototype with basic automated functions
and using basic off-the-shelf components
with promising results. It proved cheap and
useful prototypes can be built for research.
Results with the proposed road detection
techniques proved to be highly efficient for
a road with clearly visible lane market.
Canny Edge detection proved to have low
error rate.
[38]
Selective Attention
Mechanism Weber–Fechner law
Regression
Based Models
Besides, the model is consistent with the
famous Weber–Fechner law. The Weber–
Fechner law says that all people’s feeling,
including visual feeling, auditory feeling
and so on all comply with the fact that the
feeling is not proportional to the strength of
the corresponding physical quantity but
proportional to the logarithm of the
corresponding physical quantity.
[56]
DDT System Model Component – Machine Control
Pre-crash problem of
AV Intelligent
Control
AV Intelligent
Adaptive Control
architecture using an
hybrid AI architecture:
CBR Engine for
Adaptive control (high
level) + hybrid Case-
Based Planner (A* and
D* motion planner)
Artificial
Neural
Network
combined to
other
techniques
Its is flexible to be integrated to lower
levels of vehicles controller and path
planners as (A* & D*). It is an ongoing
reasearch. Some limited and embrionary
experimental are mentioned and the authors
claim present research ideas for different
pre-crash scenarios and cases such as
intersection safety and some general cases.
The paper also discusses approaches to
integrate basic kinematics of AVs features
and presents future prospective on the
possibility of integration of high-level
intelligent controller with lower-levels
mechanical and kinematics features of
vehicles or robotics in general concepts.
[39]
Learning and
simulationf the
human-level
Use Neuroevolution
of Augmenting
Topologies (NEAT) and
Artificial
Neural
Network
Pilots' learning curve is irregular, due to
the characteristics of the problem, but
presents a good positive tendency which
[47]
23
decisions involved in
driving a racing car
a online videogame
prototype as a test-bed
environment. NEAT is a
combination of ANN
and Genetic Algorithm
(GA)
combined to
other
techniques
leads them to acquire fruitful abilities in just
50 generations with a population of 120
individuals. Pilots easily learn how to turn
following soft cruves, but still have big
poblems ientifying and steering hard ones,
which even make them crash into track
limits sometimes. The paper presents
individuals habing only 2 output neurons:
one for turning and the other one for
throttling/braking. Therefore, for this
reatively young ANNs, is very dificult to
acquire the high-level behaviours that have
to completely change the sign of the output
of the second neuron, whenevver a sharp
curve is near.
AV experiment (100
km)
AV following a
manually driven car
(trailing)
Fuzzy Logic
The authors state this paper is one of the
first communications fully describing the
control system and techniques required to
perform an experiment with autonomous
vehicles on open roads. It introduced a
different control approach for controlling
autonomous vehicles on urban and
motorway environments. A method for
online adjustment of the CACC fuzzy
controller is described and implemented,
coping with the most relevant disturbances
and uncertain parameters, such as road
slopes, passenger weight, or gear ratio. The
experiment successfully proves the
capability of the developed system to drive
more than one hundred kilometres
autonomously. A public demonstration of
the described system was conducted in June
2012, comprising a 100-km route through
urban and motorway environments. It was
able to cope with such gaps as motorway
overpasses, traffic signals, etc.The authors
stated the tracking results obtained with the
CACC system were very precise, with the
distance error being kept to less than 1
metre. Likewise, the lateral control was able
to maintain the vehicle on the path of the
leader with acceptable errors for both
scenarios. However, the localization system
needs to be improved to allow longer GPS
gaps. Also, the presence of a 900-metre-
long tunnel forced the deactivation of the
autonomous system while the vehicle
passed through.
[40]
Manage and control
low level vehicle
actuators (steering
throttle and brake)
Control schema to
manage low level
vehicle actuators
(steering throttle and
brake) based on fuzzy
logic
Fuzzy Logic
The proposed automatic low-level
control system has been defined,
implemented and tested in a Citroen C3
testbed vehicle, whose actuators have been
automated and can receive control signals
from an onboard computer where the soft
computing-based control system is running.
The preliminary results are confirming the
potential of the proposed technique.
[49]
24
Control intersection
crossing (all way
stops) and optimize it
Use a simulation to
model and simulate the
scenario. Developed a
heuristic optimization
algorithm for driverless
vehicles at unsignalized
intersections using a
multi-agent system.
Optimization
Heuristics
Although, the proposed research was still
in its initial stages, it presented some
significant time savings compared to an
AWSC intersection control. It showed that
by applying the proposed algorithm on only
four crossing vehicles, the total delay was
reduced by approximately 35 seconds,
which is equivalent to a 65 percent
reduction in the total intersection delay.
[42]
Autonomous
vehicles
intersections
crossing
optimization
Proposition of a new
tool for optimizing the
AVs movements through
intersections -
Cooperative Adaptive
Cruise Control (CACC)
Optimization
Heuristics
A simulation with one vehicle type and a
single intersection was performed. Also, all
vehicles in the simulation were assumed to
have CACC system to send/receive
information and follow speed advices as
directed. The preliminary results are
promissing and encourage future studies
where the authors plan to use simulations
with more types of vehicles and a greater
number of adjacent intersections.
According to the author, the results from
this research also warrant studies with
regard to incorporating non-CACC vehicles
into the system and studies pertaining to
tackling unexpected system changes,
pedestrian movements etc.
[46]
Path tracking cutting
corners using
traditional geometric
algorithms
A curve safety sub-
system for path tracking
based on the Pure Pursuit
algorithm and a dynamic
look-ahead distance
definition (based on
vehicle current speed
and lateral error). A sub-
system for path tracking
where an algorithm that
analyzes GPS
information offline
classifies high curvature
segments and estimates
the ideal speed for each
one.
Ramer-
Douglas-
Peucker or
Ramer-
Douglas
algorithm
Experimental results showed
improvements in comfort and safety due to
the extracted geometry information and
speed control, stabilizing the vehicle and
minimizing the lateral error
[48]
Safe-optimal
trajectory selection
for autonomous
vehicle
Use Big Data Mining
approach for crash
prediction and ETA.
Regression
Based Models
combined to
other
techniques
A Big Data based method and algorithm
has been presented to find the safest-optimal
trajectory for fully autonomous vehicles.
The method proposed relies strongly on the
results obtained from Big Data prediction
system which predicts accidents, ETA, and
clearance time. The algorithms for checking
and calculating the optimal trajectory are
very lightweight and straightforward. The
simulations using the available data are
promising.
[76]
How to prove the
correctness of an
algorithm for
Vehicle
Coordination
Use a distributed
coordination protocol
and an intersection
collision avoidance
(ICA) case study + Z3
Theorem prover +
Satisfiability Modulo
Theories (SMT) solver
Satisfiability
Modulo
Theories
(SMT) Solver
Paper presented a formalisation of the
distributed coordination problem
encountered by intelligent vehicles while
contending for the same physical resource.
It formalised a coordination protocol and an
intersection collision avoidance case study
in the SMT-lib language and proved system
safety using the Z3 theorem prover. The two
[43]
25
to prove correctness and
safety of a vehicular
coordination problem
main conclusions are: (1) The responsibility
approach to distributed coordination is a
suitable abstraction for formal reasoning on
system safety. The core of this approach is
that every entity is responsible for making
sure that it does not enter an unsafe state
with respect to any other entity. This can be
contrasted with the other approaches where
consensus is required between all nodes,
decisions are made by a central manager, or
where each pair of nodes negotiates
independently, all of which seem
problematic from a scalability point of
view; (2) The automatic verification of
collaborative vehicular applications with
the help of SMT solvers is at least plausible.
Some cases were found where the model
could not be verified and increasing the
detail and scale of the model would
certainly enlarge this problem. However,
there are certainly domain-specific
approximations that can be made to
alleviate some of these problems.
Robot maneuvers too
close to an obstacle
increases the
probability of an
accident. Preventing
this is crucial in
dynamic
environments, where
the obstacles, such as
other UAVs, are
moving
SVM-Inspired
Dynamic Safe
Navigation Using
Convex Hull
Construction an
algorithm for a fast
construction of a
maximum margin
between sets of obstacles
and its maintenance as
the input data are
dynamically altered
Support Vector
Machine
(SVM)
MMS-CH algorithm for calculating the
safest path in dynamic environment was
presented. It used the construction of
convex hulls over the input data to eliminate
data points irrelevant for the solution and to
use the boundary of the hulls to search for
the optimal separation margin between sets
of obstacles. The tests showed the algorithm
performs well in dynamic scenarios where
the input data might be altered by insertion
or deletion of data points. The
preprocessing phase of the MMS-CH
algorithm can recognize whether the change
in the data set does or does not require any
recalculation of the previous solution and
thus prevents unnecessary computations.
[41]
Drivers’ manoeuver
classification
Motion tracking (i.e
skeletal tracking) data
gathered from the driver
whilst driving to learn to
classify the manoeuvre
being performed
(Kinnect)
Support Vector
Machine
(SVM)
Preliminary results show that skeletal
tracking data can be used in a driving
scenario to classify maneuvers.
[44]
DDT System Model Component – Machine Perception
Recognize leading
vehicle in a convoy
Object detection using
Thermal infrared
classifiers and visible
light classifiers
Adaptive
Boosting
(AdaBoost)
Thermal infrared classifiers and visible
light classifiers were compared and
evaluated. Both approaches perform very
well. However, the accuracy of the visible
light classifiers cannot be reached by
thermal infra-red classifiers. But because of
the good performance of the thermal infra-
red classifier under all weather conditions,
the performance of the thermal infra-red
classifier is acceptable.
[22]
Prediction of
advanced driver
assistance systems
ML Classification
techniques
Artificial
Neural
Network
SVM shows best ML classification
performance (low errors and correlation
coefficient near 1) in prognosis of the
[55]
26
(ADAS) remaining
useful life (RUL) for
the prognosis of
ADAS’ safety
critical components
combined to
other
techniques
ADAS systems under the given
experimental assumption and Neural
Networks have the worst classification
performance. This work just proposes a
framework for a new area of research in
prognostics for automotive domain.
Road environmental
recognition and
various object
detection in real
driving conditions
Single monocular
camera for autonomous
vehicle in real driving
conditions
Artificial
Neural
Network
combined to
other
techniques
Pedestrian detection algorithm with GPU
were 6 times faster than CPU. Traffic sign
and traffc light recognition are 2 to 3 times
faster than pedestrian detection. However,
when the days are dark or there is backlight,
it was hard to separate the objects from
background.
[27]
Road Terrain
detection
Color feature
extraction + Clustering
algorithm k- mean +
ANN
Artificial
Neural
Network
combined to
other
techniques
Color Feature Extraction was used to
classify the Road Terrain with a Neural
Network (NN). 7666 images were used for
classification and results were promising.
[31]
Road junction
detection
3D point clouds
approach
Artificial
Neural
Network
combined to
other
techniques
The performance of ANNs, SVMs and
AdaBoost were compared for the second
step of the method, and of HMMs and CRFs
for the last. AdaBoost was considered the
best classifier, as it managed to learn the
training set without overfitting, generalizing
well to the test set. On a frame-by-frame
analysis, subsequent use of CRF and HMM
do not seem to improve from the results
obtained by AdaBoost itself. However, both
methods removed a lot of the classification
noise, generating an output that allows to
more clearly detect the start and end of a
road junction.
[30]
Vehicle Cyber
Attack Detection System
Artificial
Neural
Networks
Paper aimed to address the problem of
attack detection and identification when the
majority of multiple sensors was attacked in
an automotive CPS. LSTM and GRU
detected and identified attacks by
considering sequential information with real
data. It was demonstrated that the accuracy
of LSTM is the highest among data-based
methods (i.e., Neural Network, SVM,
simple RNN, GRU and LSTM). The
accuracy of LSTM followed the accuracy of
GRU. Especially, LSTM and GRU had
superior ability to detect coinstantaneous
attacks. LSTM and GRU showed high
performance in identification of Class 2, 3,
4, 5 and 6. Although calculation time of
GRU is faster than that of LSTM, it is no
matter to detect the attacks of the sensors on
a general computer to use a CPU.
[39]
Turn Signal
Recognition
Image Recognition
and Timming (95% and
82.2% accuracy)
Artificial
Neural
Networks
This paper proposed the flushing light
detection for preceding vehicles. The results
show that the obtained classifier detects turn
signals with an accuracy of 95(%).
Moreover, the proposed method is capable
of recognizing an appropriate frequency of
flushing light with an accuracy of 82.2(%)
for sequential driving data.
[18]
27
Road Sign
Classification in
Real-time
Use ANN (2 steps:
MLP + SLP)
Artificial
Neural
Networks
A novel approach based on two modules
was presented. The first module consists of
classifying the road sign's shape using MLP.
The shapes are classified in four classes:
triangular, inverted triangular, circular and
octagonal shapes. The accuracy of the MLP,
however, is improved when using only six
features values for increasing the speed of
the algorithm and minimizing the memory.
The second module is reserved to the
identification of the contents of recognized
shapes: the circular and triangular signs via
a simple SLP. As for the octagonal sign and
upside down triangular, they have a unique
indication which are the obligation to stop
and give way. A Performance Factor was
introduced in order to make a subjective
comparison between our proposed approach
and the other methods available in the
literature, which revealed that our proposed
system outperforms most of the other
approaches. Regarding running time, the
current software implementation takes
relatively a real time.
[32]
Spatio-temporal
situation awareness Deep Learning
Artificial
Neural
Networks
Given a driving video, the research aimed
to model which of the surrounding vehicles
are most important to the immediate driving
task. Employing human-centric annotations
allowed for gaining insights as to how
drivers perceive different on-road objects.
Although perception of surrounding agents
is influenced by previous experience and
driving style, we demonstrated a consistent
human-centric framework for importance
ranking. Extensive experiments showed a
wide range of spatio-temporal cues to be
essential when modeling object-level
importance. Furthermore, the importance
annotations proved useful when evaluating
vision algorithms designed for on-road
applications and autonomous driving.
[33]
Pedestrian detection
and movement
direction recognition
Deep Learning
Artificial
Neural
Networks
Paper presented a method to differentiate
the motion of pedestrians in real life
environments. Using a novel input-filtered
image based on the post-processing of static
recorded video frames, it could successfully
distinguish three different pedestrian
movement directions. Additionally, it has
been proved how CNNs can impressively
perform in such a task by training them with
a specialized dataset. Moreover, it has been
demonstrated how the results can be
enhanced even further by searching for the
best hyper-parameters once the CNN has
been fine-tuned for the specific problem, in
this case tuning the momentum and weight
decay CNN parameters. Paper also
presented an evaluation of the current state-
of-the-art CNNs, with ResNet being the
best-performing CNN for the image
[26]
28
recognition problem used (94% accuracy in
the validation set and 79% in the test set).
Pedestrian
Trajectory Prediction
Self-learning
Trajectory Prediction
Artificial
Neural
Networks
Results show that the LSTM prediction
model is superior to a constant velocity
Kalman Filter for pedestrian prediction
even on small datasets. Also, it was showed
that the prediction model can adapt to
changes in the pedestrian walking path
using only a small part of the new data. By
that, the size of the dataset can be kept rather
small although depicting the pedestrian’s
movement patterns
[28]
Early detection of
faults or malfunction
On-chip sensor
diagnosis
Artificial
Neural
Networks
The paper discusses the suitability and
feasibility of enhancing the reliability of
microsensor by adding an on chip self-
diagnosis capability. The approach used AI
techniques and sensors with no accessible
internal signals are taken as an example.
Some common acceleration sensor faults
are considered, and an indication is given of
the manner in which these faults can be
detected and isolated, either on an
individual sensor basis or based on
cooperative work within a sensor network.
The design requirements for such self-
diagnosable measument systems are set and
further practical implementation issues are
raised.
[55]
Securing connected
vehicles against
Denial of Service
(DoS) attack
Augment message
authentication with
Particle Filter (PF) and it
to Kalman Filter (KF).
Estimation
Filters (e.g.
Kalman Filter
and Particle
Filters)
Particle filter significantly reduces
communication overhead while keeping the
same detection level of spoofed messages
when compared to Kalman filter in VANET
applications. Stimulating different scenarios
with Context adaptive beacon verification
along with Kalman and particle filter on
University of Massachusetts Dartmouth and
State Road (Dartmouth) proved that it can
detect and prevent spoofed attacks and help
reducing the computational overhead. But,
the Current method of securing the
connected vehicle with filters leave the
burden of privacy protection on VANET.
The practice makes the autonomous cars the
target of attack because of the number of
spoofed messages missed by context
adaptive beacon verification is around 11%
(41 out if 46 were detected) which leaves
the undetected rate too high to be replaced
by conventional verification method. KF is
good when road was linear and lags when
the path is non-linear. PF is good for both.
KF saves upto 86.5% while Particle Filter
can save 85.94% computational overhead
for the same scenario. Detect around 76%
(24% missed) spoofed beacons with
Kalman Filter and 89% (11% missed)
spoofed beacons with Particle Filter.
[51]
Reliable and robust
obstacles detection
An innovative multi-
dimensional structure
based on association
Estimation
Filters (e.g.
Kalman Filter
The presented system was able to track
and fuse obstacles coming from a laser and
a stereo camera. The approach has been
[25]
29
costs originating from a
classifier provides an
optimal solution to the
association problem with
respect to the total
association cost.
and Particle
Filters)
compared with other state of the art
algorithms, showing better results in all the
considered metrics. Moreover, the system
uses less computational resources and thus,
fixed the platform, may work at higher
frame rate compared to other solutions,
making it more appropriate for automotive
applications. The system has demonstrated
a correct reconstruction of the dynamic
world surrounding the vehicle, proving to
be able to help the driver in the assessment
of critical situations. In particular, the
developed algorithm provides a stable,
robust and reliable detection, classification
and tracking of the multiple targets coming
from different sources. Moreover, the
proposed approaches were seen to
outperform the state-of-the-art approaches
on a public dataset.
A fault tolerant and reliable system requires
sensors redundancy and complementarity.
Common approaches rely on object level
fusion. It has been introduced a medium-
level fusion which take advantage from both
the approaches. The fusion is performed at
object level but preserving the low-level
information; in this way it is guaranteed a
real-time processing exploiting all available
information.
Cyber Attack in V2X Fuzzy Detector Fuzzy Logic
To address security issues of a system of
connected vehicles (CVs), a fuzzy detector
was also introduced that detects possibility
of a cyber threat and takes proper actions in
response to the specific attack. Results show
the designed system can detect any
adversary access to the system and can
prevent subsequent crashes by adjusting the
safe following distance.
[54]
Road and Obstacle
Detection Sensor Fusion Fuzzy Logic
A high-level data-fusion strategy has
been devised, which is based on the
identification and representation of the
descriptive and procedural knowledge
required. Such a strategy yields better
recognition results by merging the various
hypotheses generated by the separate
channels and solving possible conflicts
through a fuzzy representation of
knowledge when compared to a benchmark.
In addition, the data-fusion system has
performed a more accurate segmentation
process by using goal-driven low-level
procedures, according to which the image
regions have been assigned relative fuzzy
memberships to the object to be recognized.
[24]
Enhance Image
Understanding
Develop generic
technology that will
enable the construction
of complete, robust, high
performance image
understanding systems
General
AI/ML
This paper provided an overview of the
technical and program management plans
being used in evolving the proposed
technology, but no results were presented.
[19]
30
to support a wide range
of DoD applications
Learn from
Demonstration
Use a Semi-automated
mine robot
Hidden
Markov Based
Models (e.g.
Continuous
Hidden
Markov
Model-CHMM
and Discrete
Hidden
Markov
Model-
DHMM)
combined to
other
techniques
In this paper, three methods were
compared based on three trajectories in the
low noise and noisy environments. The
GMM based method had the best
performance in a low noise environment. In
practice, there’s always unexpected noise
around a robot, implying the GMM based
method was not practical for real
environments. The CHMM based method
was suitable for turning trajectories, while
The DHMM based method was more robust
for straight trajectories.
[45]
Vehicle Detection
and Counting
Hidden Markov
Model + Viterbi
algorithm + Adaboost
trained Haar-like feature
detector Approach
Hidden
Markov Based
Models (e.g.
Continuous
Hidden
Markov
Model-CHMM
and Discrete
Hidden
Markov
Model-
DHMM)
combined to
other
techniques
The proposed method has been shown to
give significantly better vehicle volume
counts than both multiple targets moving
object tracking and VDL on a dataset of
over 88 hours of video. On this testing set,
the proposed method achieved a median 5-
minute-bin error of 0.0686 for this counting
task while the multiple target motion
tracking and VDL implementations had
median errors of 0.0957 and 0.2290
respectively. The proposed method was also
more reliable having fewer and less severe
occurrences of 5-minute-bin errors
throughout the testing set.
[36]
Safety-warning and
driver-assistance
system and an
automatic pilot for
rural and urban
traffic environments
Adaptive randomized
HT (RHT) for robust and
accurate detection of
lane markings without
manual initialization or a
priori information under
different road
environments
Hough
Transformation
In this paper, a prototype was
demonstrated, and tasks of lane detection
detailed. Preliminary experimental results
in different road scene and a comparison
with other methods have proven the validity
of the proposed method
[21]
Speed bump
detection
Use Camera and
image recognition
Novel Image
Recognition
Technique
The average performance of the system
considering only speed bump with proper
marking is 85%.
[34]
Obstacle clusteering
and tracking
Lidar + split-and-
merge algorithm
Ramer-
Douglas-
Peucker or
Ramer-
Douglas
algorithm
Paper presents a robust platform for
implementing a perception system for
ground vehicles using a LIDAR sensor and
two cameras has been designed. Tests were
performed using this platform and different
implementations, and the results were
checked with the real-world scenes,
demonstrating the technique validity.
[23]
Predicts the CPU
usage patterns of
software tasks
running on a self-
driving car
Methods for learning
the patterns of tasks’
CPU utilizations in given
driving contexts
Regression
Based Models
A feature vector was designed to
represent the internal and external states of
a self-driving car and five regression
methods were used to predict the CPU
usage patterns of four software tasks
running on a self-driving car. Through
testing with the actual driving data, the
results showed a regression method could
be used to predict a software task’s dynamic
[53]
31
CPU utilization.
Providing road safety
to connected drivers
and connected
autonomous vehicles
Observing the
Doppler profile
Support Vector
Machine
(SVM)
The paper presented a collision and
driving scenario classification system based
on the Doppler profile which could
potentially decouple the safety benefits of
V2V communications from relying on SM
content. The Doppler profile in V2V
networks showed rich data about the
vehicles and their environments and could
be exploited to potentially provide a reliable
collision avoidance service directly from
the radio front-end. No experimental results
were presented.
[20]
Road Detection
Road area detection
method using a support
vector machine (SVM)
and histogram of
oriented gradient (HOG)
features and 3D lidar
Support Vector
Machine
(SVM)
combined to
other
techniques
Classifier to differentiate road areas from
other areas using a 3D Lidar with machine
larning techniques. Range data of lidar
changes in the layer direction but not in the
rotational direction. HOG features of the
reaos concentrate in the same bin. In
contract the features of the non-road areas
are distributed among several bins
representing various directions. Found
differences between the histograms for the
roas planes and the other areas. In real world
data, same tendences of HO features. Error
rate of 8.51% using SVM. Area up to 10m
ahead of the vehicle can be identified
correctly.
[75]
Traffic Light
Detection
Two-stage
preprocessing using
Principal Component
Analysis (PCA)
followed by SVM
Support Vector
Machine
(SVM)
combined to
other
techniques
Paper presents a system that can detect
multiple types of green and red traffic lights
accurately and reliably. Color extraction
and blob detection were applied to locate the
candidates with proper optimization. A
classification and validation method using
PCANet was then used for frame-by-frame
detection. Multiobject tracking method and
forecasting technique were succesfully
employed to improve accuracy and
produced stable results.
[35]
Pedestrian Detection
Use High-Definition
3D Range Data (from a
LIDAR)
Support Vector
Machine
(SVM)
combined to
other
techniques
An exhaustive analysis of the
performance of three different machine
learning algorithms have been carried out:
k-Nearest Neighbours (kNN), Naïve Bayes
classifier (NBC), and Support Vector
Machine (SVM). Each algorithm was
trained with a training set comprising tool
277 pedestrians and 1654 no pedestrian
samples and different kernel functions:
kNN with Euclidean and Mahalanobis
distances, NBC with Gauss and KSF
functions and SVM with linear and
quadratic functions. LOOCV and ROC
analysis were used to detect the best
algorithm to be used for pedestrian
detection. The proposed algorithm has been
tested in real traffic scenarios with 16
samples of pedestrians and 469 samples of
non-pedestrians. The results obtained were
used to validate theoretical results. An
overfitting problem in the SVM with
[27]
32
quadratic kernel was found. Finally, SVM
with linear function was selected since it
offered the best results. A comparison of the
proposed method with five other works that
also use High Definition 3D LIDAR to
carry-out the pedestrian detection,
comparing the AUC and Fscore metrics.
Conclusions are the proposed method
obtains better performance results in every
case. Pedestrian detection has traditionally
been performed using machine vision and
cameras, but these techniques are affected
by changing lighting conditions. 3D LIDAR
technology provides more accurate data
(more than 1 million points per revolution),
which can be successfully used to detect
pedestrians in any kind of lighting
conditions
How to "automate"
manual annotation
for images to train
visual perception for
AVs
Training visual
models using photo-
realistic computer
graphics
Support Vector
Machine
(SVM)
combined to
other
techniques
The experiments showed that virtual-
world data is effective for training vision-
based pedestrian detectors which can be
adapted to operate in real scenarios. The
different adaptation procedures have shown
to provide adapted detectors that improve
those trained only on virtual data, as well as
those trained using only the real-world data
available for the adaptation (which
constrained to save a ∼90% annotation effort along the experiments).
[37]