A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> D. Reported findings (RQ.5) <s> The objectives of this paper are to discuss how vehicle automation technology can be used to benefit car drivers and also to propose a concept of an autonomous highway vehicle which improves highway driving safety. <s> BIB001 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> D. Reported findings (RQ.5) <s> The navigation of mobile robots or unmanned autonomous vehicles (UAVs) in an environment full of obstacles has a significant impact on its safety. If the robot maneuvers too close to an obstacle, it increases the probability of an accident. Preventing this is crucial in dynamic environments, where the obstacles, such as other UAVs, are moving. This kind of safe navigation is needed in any autonomous movement application but it is of a vital importance in applications such as automated transportation of nuclear or chemical waste. This paper presents the Maximum Margin Search using a Convex Hull construction (MMS-CH), an algorithm for a fast construction of a maximum margin between sets of obstacles and its maintenance as the input data are dynamically altered. This calculation of the safest path is inspired by the Support Vector Machines (SVM). It utilizes the convex hull construction to preprocess the input data and uses the boundaries of the hulls to search for the optimal margin. The MMS-CH algorithm takes advantage of the elementary geometrical properties of the 2-dimensional Euclidean space resulting in 1) significant reduction of the problem complexity by eliminating irrelevant data; 2) computationally less expensive approach to maximum margin calculation than standard SVM-based techniques; and 3) inexpensive recomputation of the solution suitable for real time dynamic applications. <s> BIB002 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> D. Reported findings (RQ.5) <s> This study aims at developing a method that predicts the CPU usage patterns of software tasks running on a self-driving car. To ensure safety of such dynamic systems, the worst-case-based CPU utilization analysis has been used; however, the nature of dynamically changing driving contexts requires more flexible approach for an efficient computing resource management. To better understand the dynamic CPU usage patterns, this paper presents an effort of designing a feature vector to represent the information of driving environments and of predicting, using regression methods, the selected tasks' CPU usage patterns given specific driving contexts. Experiments with real-world vehicle data show a promising result and validate the usefulness of the proposed method. <s> BIB003 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> D. Reported findings (RQ.5) <s> Forecasts of automation in driving suggest that wide spread market penetration of fully autonomous vehicles will be decades away and that before such vehicles will gain acceptance by all stake holders, there will be a need for driving assistance in key driving tasks, supplemented by automated active safety capability. This paper advances a Bayesian Artificial Intelligence model for the design of 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. Systems can be designed to feature collision warnings as well as automated active safety capabilities. In addition to the high level architecture of the Bayesian transition model, example scenarios illustrate the function of the real-time transition model. <s> BIB004 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> D. Reported findings (RQ.5) <s> Automation in transportation (rail, air, road, etc.) is becoming increasingly complex and interconnected. Ensuring that these sophisticated non-deterministic software systems can be trusted and remain resilient is a community concern. As technology evolves, systems are moving increasingly towards autonomy where the “machine” is intelligent: perceiving, deciding, learning, etc. often without human engagement. Our current mechanisms and policies for oversight and certification of these systems to ensure they operate robustly in safety-critical situations are not keeping pace with technology advancements. How is an autonomous system different than an advanced automatic system? How is trust different for an autonomous system? What are different perspectives on trust? Is it appropriate to apply the techniques used to establish trust in a today’s transportation systems to establishing trust in an autonomous system? This paper will examine these questions and propose a framework for discussing autonomy assurance and trust in transportation applications. We will explore further with two examples: 1) the notion of a self-driving taxi-cab; and 2) the evolution of a two-pilot flight deck, to single-pilot operations. <s> BIB005 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> D. Reported findings (RQ.5) <s> Today research is going on within different essential functions need to bring automatic vehicles to the roads. However, there will be manual driven vehicles for many years before it is fully automated vehicles on roads. In complex situations, automated vehicles will need human assistance for long time. So, for road safety driver monitoring is even more important in the context of autonomous vehicle to keep the driver alert and awake. But, limited effort has been done in total integration between automatic vehicle and human driver. Therefore, human drivers need to be monitored and able to take over control within short notice. This papers provides an overview on autonomous vehicles and un-obstructive driver monitoring approaches that can be implemented in future autonomous vehicles to monitor driver e.g., to diagnose and predict stress, fatigue etc. in semi-automated vehicles. <s> BIB006 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> D. Reported findings (RQ.5) <s> Traffic engineers, vehicle manufacturers, technology groups, and government agencies are anticipating and preparing for the emergence of fully automated vehicles into the American transportation system. This new technology has the potential to revolutionize many aspects of transportation, particularly safety. However, fully automated vehicles may not create the truly crash-free environment predicted. One particular problem is crash assignment, especially between automated vehicles and nonautomated vehicles. Although some researchers have indicated that automated vehicles will need to be programmed with some sort of ethical system in order to make decisions on how to crash, few, if any, studies have been conducted on how particular ethical theories will actually make crash decisions and how these ethical paradigms will affect automated vehicle programming. The integration of three ethical theories—utilitarianism, respect for persons, and virtue ethics—with vehicle automation is examined, and a simple progr... <s> BIB007 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> D. Reported findings (RQ.5) <s> Automatic detection of traffic lights has great importance to road safety. This paper presents a novel approach that combines computer vision and machine learning techniques for accurate detection and classification of different types of traffic lights, including green and red lights both in circular and arrow forms. Initially, color extraction and blob detection are employed to locate the candidates. Subsequently, a pretrained PCA network is used as a multiclass classifier to obtain frame-by-frame results. Furthermore, an online multiobject tracking technique is applied to overcome occasional misses and a forecasting method is used to filter out false positives. Several additional optimization techniques are employed to improve the detector performance and handle the traffic light transitions. When evaluated using the test video sequences, the proposed system can successfully detect the traffic lights on the scene with high accuracy and stable results. Considering hardware acceleration, the proposed technique is ready to be integrated into advanced driver assistance systems or self-driving vehicles. We build our own data set of traffic lights from recorded driving videos, including circular lights and arrow lights in different directions. Our experimental data set is available at http://computing.wpi.edu/Dataset.html. <s> BIB008 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> D. Reported findings (RQ.5) <s> PreCrash problem of Intelligent Control of autonomous vehicles robot is a very complex problem, especially vehicle pre-crash scenariws and at points of intersections in real-time environmenta. This Paper presents a novel architecture of Intelligent adaptive control for autonomous vehicle agent that depends on Artificial Intelligence Techniques that applies case-based reasoning techniques, where Parallel CBR Engines are implemented for different scenarios' of PreCrash problem and sub-problems of intersection safety and collision avoidance, in the higher level of the controller and A∗ path planner for path planning and at lower-levels it also uses some features of autonomous vehicle dynamics. Moreover, the planner is enhanced by combination of Case-Based Planner. All modules are presented and discussed. Experimental results are conducted in the framework of Webots autonomous vehicle tool and overall results are good for the CBR Engine for Adaptive control and also for the hybrid Case-Based Planner, A∗ and D∗ motion planner along with conclusion and future work. <s> BIB009 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> D. Reported findings (RQ.5) <s> Abstract Advanced driver assistance systems (ADASs) enhance transportation safety and mobility, and reduce impacts on the environment and economical costs, through decreasing driver errors. One of the main features of ADASs is cruise control system that maintains the driver's desired speed without intervention from the driver. Adaptive cruise control (ACC) systems adjust the vehicle's speed to maintain a safe following distance to the vehicle in front. Adding vehicle-to-vehicle and vehicle-to-infrastructure communications (V2X) to ACC systems, result in cooperative adaptive cruise control (CACC) systems, where each vehicle has trajectory data of all other vehicles in the same lane. Although CACC systems offer advantages over ACC systems in increasing throughput and average speed, they are more vulnerable to cyber-security attacks. This is due to V2X communications that increase the attack surface from one vehicle to multiple vehicles. In this paper, we inject common types of attack on the application layer of connected vehicles to show their vulnerability in comparison to autonomous vehicles. We also proposed a decision support system that eliminates risk of inaccurate information. The microscopic work simulates a CACC system with a bi-objective PID controller and a fuzzy detector. A case study is illustrated in detail to verify the system functionality. <s> BIB010 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> D. Reported findings (RQ.5) <s> This work applies artificial intelligence (AI) to secure wireless communications of Connected Vehicles. Vehicular Ad-hoc Network (VANET) facilitates exchange of safety messages for collision avoidance, leading to self-driving cars. An AI system continuously learns to augment its ability in discerning and recognizing its surroundings. Such ability plays a vital role in evaluating the authenticity and integrity of safety messages for cars driven by computers. Falsification of meter readings, disablement of brake function, and other unauthorized controls by spoofed messages injected into VANET emerge as security threats. Countermeasures must be considered at design stage, as opposed to afterthought patches, effectively against cyber-attacks. However, current standards oversubscribe security measures by validating every message circulating among Connected Vehicles, making VANET subject to denial-of-service (DoS) Attacks. This interdisciplinary research shows promising results by searching the pivot point to balance between message authentication and DoS prevention, making security measures practical for the real-world deployment of Connected Vehicles. Message authentication adopts Context-Adaptive Signature Verification strategy, applying AI filters to reduce both communication and computation overhead. Combining OMNET++, a data network simulator, and SUMO, a road traffic simulator, with Veins, an open source framework for VANET simulation, the study evaluates AI filters comparatively under various attacking scenarios. The results lead to an effective design choice of securing wireless communications for Connected Vehicles. <s> BIB011 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> D. Reported findings (RQ.5) <s> In the field of self-driving technology, the stability and comfort of the intelligent vehicle are the focus of attention. The paper applies cognitive psychology theory to the research of driving behavior and analyzes the behavior mechanism about the driver’s operation. Through applying the theory of hierarchical analysis, we take the safety and comfort of intelligent vehicle as the breakthrough point. And then we took the data of human drivers’ perception behavior as the training set and did regression analysis using the method of regression analysis of machine learning according to the charts of the vehicle speed and the visual field, the vehicle speed and the gaze point as well as the vehicle speed and the dynamic vision. At last we established linear and nonlinear regression models (including the logarithmic model) for the training set. The change in thinking is the first novelty of this paper. Last but not least important, we verified the accuracy of the model through the comparison of different regression analysis. Eventually, it turned out that using logarithmic relationship to express the relationship between the vehicle speed and the visual field, the vehicle speed and the gaze point as well as the vehicle speed and the dynamic vision is better than other models. In the aspect of application, we adopted the technology of multi-sensor fusion and transformed the acquired data from radar, navigation and image to log-polar coordinates, which makes us greatly simplify information when dealing with massive data problems from different sensors. This approach can not only reduce the complexity of the server’s processing but also drives the development of intelligent vehicle in information computing. We also make this model applied in the intelligent driver’s cognitive interactive debugging program, which can better explain and understand the intelligent driving behavior and improved the safety of intelligent vehicle to some extent. <s> BIB012 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> D. Reported findings (RQ.5) <s> Researchers and automotive industry experts are in favour of accelerating the development of “cognitive vehicle” features, which will integrate intelligent technology and human factors for providing non-distracting interface for safety, efficiency and environmental sustainability in driving. In addition, infotainment capability is a desirable feature of the vehicle of the future, provided that it does not add to driver distraction. Further, these features are expected to be a stepping-stone to fully autonomous driving. This paper describes advances in driver-vehicle interface and presents highlights of research in design. Specifically, the design features of the cognitive vehicle are presented and measures needed to address major issues are noted. The application of Bayesian Artificial Intelligence is described in the design of driving assistance system, and design considerations are advanced in order to overcome issues in in-vehicle telematics systems. Finally, conclusions are advanced based on coverage of research material in the paper. <s> BIB013 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> D. Reported findings (RQ.5) <s> Mobility is a basic necessity of contemporary society and it is a key factor in global economic development. The basic requirements for the transport of people and goods are: safety and duration of travel, but also a number of additional criteria are very important: energy saving, pollution, passenger comfort. Due to advances in hardware and software, automation has penetrated massively in transport systems both on infrastructure and on vehicles, but man is still the key element in vehicle driving. However, the classic concept of 'human-in-the-loop' in terms of 'hands on' in driving the cars is competing aside from the self-driving startups working towards so-called 'Level 4 autonomy', which is defined as "a self-driving system that does not requires human intervention in most scenarios". In this paper, a conceptual synthesis of the autonomous vehicle issue is made in connection with the artificial intelligence paradigm. It presents a 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. The issue of autonomous vehicle management is addressed on three levels: navigation, movement in traffic, respectively effective maneuver and vehicle dynamics control. Each level is then described in terms of specific tasks, such as: route selection, planning and reconfiguration, recognition of traffic signs and reaction to signaling and traffic events, as well as control of effective speed, distance and direction. The approach will lead to a better understanding of the way technology is moving when talking about autonomous cars, smart/intelligent cars or intelligent transport systems. Keywords: self-driving vehicle, artificial intelligence, deep learning, intelligent transport systems. <s> BIB014 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> D. Reported findings (RQ.5) <s> Developers of autonomous systems face distinct challenges in conforming to established methods of validating safety. It is well known that testing alone is insufficient to assure safety, because testing long enough to establish ultra-dependability is generally impractical. Thatfis why software safety standards emphasize high quality development processes. Testing then validates process execution rather than directly validating dependability. Two significant challenges arise in applying traditional safety processes to autonomous vehicles. First, simply gathering a complete set of system requirements is difficult because of the sheer number of combinations of possible scenarios and faults. Second, autonomy systems commonly use machine learning (ML) in a way that makes the requirements and design of the system opaque. After training, usually we know what an ML component will do for an input it has seen, but generally not what it will do for at least some other inputs until we try them. Both of these issues make it difficult to trace requirements and designs to testing as is required for executing a safety validation process. In other words, we are building systems that can not be validated due to incomplete or even unknown requirements and designs. Adaptation makes the problem even worse by making the system that must be validated a moving target. In the general case, it is impractical to validate all the possible adaptation states of an autonomy system using traditional safety design processes. An approach that can help with the requirements, design, and adaptation problems is basing a safety argument not on correctness of the autonomy functionality itself, but rather on conformance to a set of safety envelopes. Each safety envelope describes a boundary within the operational state space of the autonomy system. A system operating within a "safe" envelope knows that it is safe and can operate with full autonomy. A system operating within an "unsafe" envelope knows that it is unsafe, and must invoke a failsafe action. Multiple partial specifications can be used as an envelope set, with the intersection of safe envelopes permitting full autonomy, and the union of unsafe envelopes provoking validated, and potentially complex, failsafe responses. Envelope mechanisms can be implemented using traditional software engineering techniques, reducing the problems with requirements, design, and adaptation that would otherwise impede safety validation. Rather than attempting to prove that autonomy will always work correctly (which is still a valuable goal to improve availability), the envelope approach measures the behavior of one or more autonomous components to determine if the result is safe. While this is not necessarily an easy thing to do, there is reason to believe that checking autonomy behaviors for safety is easier than implementing perfect, optimized autonomy actions. This envelope approach might be used to detect faults during development and to trigger failsafes in fleet vehicles. Inevitably there will be tension between simplicity of the envelope definitions and permissiveness, with more permissive envelope definitions likely being more complex. Operating in the gap areas between "safe" and "unsafe" requires human supervision, because the autonomy system can not be sure it is safe. One way to look at the progression from partial to full autonomy is that, over time, systems can increase permissiveness by defining and growing "safe" envelopes, shrinking "unsafe" envelopes, and eliminating any gap areas. <s> BIB015 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> D. Reported findings (RQ.5) <s> Autonomous vehicles pose new challenges to their testing, which is required for safety certification. While Autonomous vehicles will use training sets as specification for machine learning algorithms, traditional validation depends on the system’s requirements and design.The presented approach uses training sets which are observations of traffic situations as system specification. It aims at deriving test-cases which incorporate the continuous behavior of other traffic participants. Hence, relevant scenarios are mined by analyzing and categorizing behaviors. By using abstract descriptions of the behaviors we discuss how test-cases can be compared to each other, so that similar test-cases are avoided in the test-suite. We demonstrate our approach using a combination of an overtake assistant and an adaptive cruise control. <s> BIB016 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> D. Reported findings (RQ.5) <s> Adrian Weller acknowledges support by the Alan Turing Institute under the EPSRC grant EP/N510129/1, and by the Leverhulme Trust via the CFI. <s> BIB017
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  , for example. Likewise, some papers reported SVM has been applied successfully to detect road  , traffic light BIB008 , and pedestrian  . 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 BIB009 , while SVM was used to support a safest path planning in a dynamic environment to avoid maneuvers too close to an obstacle BIB002 . 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 BIB003 , and another one proposed an approach but did not report results  . All the others papers, related to the detection of cyberattack, presented promising positive results for the application of ANNs BIB009 , Estimation Filters BIB011 , and Fuzzy-Logic BIB010 , for example. Also, preliminary positive results have been reported on the use of a regression-based model to predict the CPU patterns BIB003 . 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 BIB012 , 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 BIB004 . The other 2 papers did not present specific findings, due to their theoretical nature related to the design considerations for the driving assistance system BIB013 and human drivers monitoring to enhance the integration between AVs and human drivers BIB006 . 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 BIB014 [62] BIB001 . 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 BIB015 , certification  [74], autonomy assurance and trust when non-deterministic and adaptive algorithms are used BIB005 -crash assignment facing distinct ethical theories BIB007 , 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. BIB007 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. BIB016 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 BIB017 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.