A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <s> The overall objective of the Strategic Computing (SC) Program of the Defense Advanced Research Projects Agency (DARPA) is to develop and demonstrate a new generation of machine intelligence technology which can form the basis for more capable military systems in the future and also maintain a position of world leadership for the US in computer technology. Begun in 1983, SC represents a focused research strategy for accelerating the evolution of new technology and its rapid prototyping in realistic military contexts. Among the very ambitious demonstration prototypes being developed within the SC Program are: 1) the Pilot's Associate which will aid the pilot in route planning, aerial target prioritization, evasion of missile threats, and aircraft emergency safety procedures during flight; 2) two battle management projects one for the for the Army, which is just getting started, called the AirLand Battle Management program (ALBM) which will use knowledge-based systems technology to assist in the generation and evaluation of tactical options and plans at the Corps level; 3) the other more established program for the Navy is the Fleet Command Center Battle Management Program (FCCBIVIP) at Pearl Harbor. The FCCBMP is employing knowledge-based systems and natural language technology in a evolutionary testbed situated in an operational command center to demonstrate and evaluate intelligent decision-aids which can assist in the evaluation of fleet readiness and explore alternatives during contingencies; and 4) the Autonomous Land Vehicle (ALV) which integrates in a major robotic testbed the technologies for dynamic image understanding, knowledge-based route planning with replanning during execution, hosted on new advanced parallel architectures. The goal of the Strategic Computing computer vision technology base (SCVision) is to develop generic technology that will enable the construction of complete, robust, high performance image understanding systems to support a wide range of DoD applications. Possible applications include autonomous vehicle navigation, photointerpretation, smart weapons, and robotic manipulation. This paper provides an overview of the technical and program management plans being used in evolving this critical national technology. <s> BIB001 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <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> BIB002 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <s> The final purpose of Automated Vehicle Guidance Systems (AVGSs) is to obtain fully automatic driven vehicles to optimize transport systems, minimizing delays, increasing safety and comfort. In order to achieve these goals, lots of Artificial Intelligence techniques must be improved and merged. In this article we focus on learning and simulating the Human-Level decisions involved in driving a racing car. To achieve this, we have studied the convenience of using Neuroevolution of Augmenting Topologies (NEAT). To experiment and obtain comparative results we have also developed an online videogame prototype called Screaming Racers, which is used as test-bed environment. <s> BIB003 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <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> BIB004 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <s> Increasing demands on safety and energy efficiency will require higher levels of automation in transportation systems. This involves dealing with safety-critical distributed coordination. In this paper we demonstrate how a Satisfiability Modulo Theories (SMT) solver can be used to prove correctness of a vehicular coordination problem. We formalise a recent distributed coordination protocol and validate our approach using an intersection collision avoidance (ICA) case study. The system model captures continuous time and space, and an unbounded number of vehicles and messages. The safety of the case study is automatically verified using the Z3 theorem prover. <s> BIB005 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <s> Recently several artificial intelligence labs have suggested the use of fully equipped vehicles with the capability of sensing the surrounding environment to enhance roadway safety. As a result, it is anticipated in the future that many vehicles will be autonomous and thus there is a need to optimize the movement of these vehicles. This paper presents a new tool for optimizing the movements of autonomous vehicles through intersections: iCACC. The main concept of the proposed tool is to control vehicle trajectories using Cooperative Adaptive Cruise Control (CACC) systems to avoid collisions and minimize intersection delay. Simulations were executed to compare conventional signal control with iCACC considering two measures of effectiveness - delay and fuel consumption. Savings in delay and fuel consumption in the range of 91 and 82 percent relative to conventional signal control were demonstrated, respectively. <s> BIB006 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <s> Abstract The intelligent control of autonomous vehicles is one of the most important challenges that intelligent transport systems face today. The application of artificial intelligence techniques to the automatic management of vehicle actuators enables the different Advanced Driver Assistance Systems (ADAS) or even autonomous driving systems, to perform a low level management in a very similar way to that of human drivers by improving safety and comfort. In this paper, we present a control schema to manage these low level vehicle actuators (steering throttle and brake) based on fuzzy logic, an artificial intelligence technique that is able to mimic human procedural behavior, in this case, when performing the driving task. This 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. <s> BIB007 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <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> BIB008 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <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> BIB009 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <s> Highly automated driving on highways requires a complex artificial intelligence that makes optimal decisions based on ongoing measurements. Notably no attempt has been performed to evaluate the impacts of such a sophisticated system on traffic. Another important point is the impact of continuous increase in the number of highly automated vehicles on future traffic safety and traffic flow. This work introduces a novel framework to evaluate these impacts in a developed microscopic traffic simulation environment. This framework is used on the one hand to ensure the functionality of the driving strategy in the early development stage. On the other hand, the impacts of increasing automation rates, up to hundred percent, on traffic safety and traffic flow is evaluated. <s> BIB010 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <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> BIB011 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <s> In Intelligent Transportation System, Advance Driver Assistance Systems (ADAS) plays a vital role. In ADAS, many research works are done in the area of traffic sign recognition, Forward Collision Warning, Automotive navigation system, Lane departure warning system but an another important area to look through is speed bumps detection. The recognition of speed bump is a safety to a human and a vehicle. Early research in speed bump detection is done with the help of sensors, accelerometer and GPS. In this paper, a novel method is presented to achieve speed bump detection and recognition either to alert or to interact directly with the vehicle. Detection of speed bump is recognized with a help of image processing concepts. This methodology is effortless and simple to implement without the investment of special sensors, hardware, Smartphone and GPS. This procedure suits very well for the roads constructed with proper marking, and can be used in self-driving car. <s> BIB012 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <s> To reduce roadway congestion and improve traffic safety, accurate traffic metrics, such as number of vehicles travelling through lane-ways, are required. Unfortunately most existing infrastructure, such as loop-detectors and many video detectors, do not feasibly provide accurate vehicle counts. Consequently, a novel method is proposed which models vehicle motion using hidden Markov models (HMM). The proposed method represents a specified small region of the roadway as 'empty', 'vehicle entering', 'vehicle inside', and 'vehicle exiting', and then applies a modified Viterbi algorithm to the HMM sequential estimation framework to initialize and track vehicles. Vehicle observations are obtained using an Adaboost trained Haar-like feature detector. When tested on 88 hours of video, from three distinct locations, the proposed method proved to be robust to changes in lighting conditions, moving shadows, and camera motion, and consistently out-performed Multiple Target Tracking (MTT) and Virtual Detection Line(VDL) implementations. The median vehicle count error of the proposed method is lower than MTT and VDL by 28%, and 70% respectively. As future work, this algorithm will be implemented to provide the traffic industry with improved automated vehicle counting, with the intent to eventually provide real-time counts. <s> BIB013 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <s> The constant growth of the number of vehicles in today’s world demands improvements in the safety and efficiency of roads and road use. This can be in part satisfied by the implementation of autonomous driving systems because of their greater precision than human drivers in controlling a vehicle. As result, the capacity of the roads would be increased by reducing the spacing between vehicles. Moreover, greener driving modes could be applied so that the fuel consumption, and therefore carbon emissions, would be reduced. This paper presents the results obtained by the AUTOPIA program during a public demonstration performed in June 2012. This driverless experiment consisted of a 100-kilometre route around Madrid (Spain), including both urban and motorway environments. A first vehicle – acting as leader and manually driven – transmitted its relevant information – i.e., position and speed – through an 802.11p communication link to a second vehicle, which tracked the leader’s trajectory and speed while maintaining a safe distance. The results were encouraging, and showed the viability of the AUTOPIA approach. ::: First published online: 28 Jan 2015 <s> BIB014 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <s> Greater production and improved safety in the mining industry can be enhanced by the use of automated vehicles. This paper presents results in applying Learning from Demonstration (LfD) to a laboratory semi-automated mine inspection robot following a path through a simulated mine. Three methods, Gaussian Mixture Model (GMM), Continuous Hidden Markov Model (CHMM), and Discrete Hidden Markov Model (DHMM) were used to implement the LfD and a comparison of the implementation results is presented. The results from the different models were then used to implement a novel, optimised path decomposition technique that may be suitable for possible robot use within an underground mine. <s> BIB015 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <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> BIB016 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <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> BIB017 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <s> Detecting changing traffic conditions is of primal importance for the safety of autonomous cars navigating in urban environments. Among the traffic situations that require more attention and careful planning, road junctions are the most significant. This work presents an empirical study of the application of well known machine learning techniques to create a robust method for road junction detection. Features are extracted from 3D pointclouds corresponding to single frames of data collected by a laser rangefinder. Three well known classifiers-support vector machines, adaptive boosting and artificial neural networks-are used to classify them into “junctions” or “roads”. The best performing classifier is used in the next stage, where structured classifiers-hidden Markov models and conditional random fields-are used to incorporate contextual information, in an attempt to improve the performance of the method. We tested and compared these approaches on datasets from two different 3D laser scanners, and in two different countries, Germany and Brazil. <s> BIB018 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <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> BIB019 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <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> BIB020 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <s> Autonomous Vehicle (AV) or self-driving vehicle technology promises to provide many economical and societal benefits and impacts. Safety is on the top of these benefits. Trajectory or path planning is one of the essential and critical tasks in operating the autonomous vehicle. In this paper we are tackling the problem of trajectory planning for fully-autonomous vehicles. Our use cases are designed for autonomous vehicles in a cloud based connected vehicle environment. This paper presents a method for selecting safe-optimal trajectory in autonomous vehicles. Selecting the safe trajectory in our work mainly based on using Big Data mining and analysis of real-life accidents data and real-time connected vehicles' data. The decision of selecting this trajectory is done automatically without any human intervention. The human touches in this scenario could be only at defining and prioritizing the driving preferences and concerns at the beginning of the planned trip. Safety always overrides the ranked user preferences listed in this work. The output of this work is a safe trajectory that represented by the position, ETA, distance, and the estimated fuel consumption for the entire trip. <s> BIB021 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <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> BIB022 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <s> Abstract: Growing research progress in Internet of Things (IoT), automated/connected cars, Artificial Intelligence and person's data acquisition (Quantified Self) will help to reduce behavioral uncertainties in transport and unequivocally influence future transport landscapes. This vision paper argues that by capitalizing advances in data collection and methodologies from emerging research disciplines, we could make the driver amenable to a knowable and monitorable entity, which will improve road safety. We present an interdisciplinary framework, inspired by the Safe system, to extract knowledge from the large amount of available data during driving. The limitation of our approach is discussed. <s> BIB023 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <s> RobIL program is a collaborative effort of several leading Israeli academic institutions and industries in the field of robotics. The current objective of the program is to develop an Autonomous Off-Road Unmanned Ground Vehicle (UGV). The intention is to deal through this project with some of the most urgent challenges in the field of autonomous vehicles. One of these challenges, is the lack of efficient Safety Performance Verification technique, as the existing tools for hardware and software reliability and safety engineering do not provide a comprehensive solution regarding algorithms that are based on Artificial Intelligent (AI) and Machine Learning. In order to deal with this gap a novel methodology that is based on statistical testing in simulated environment, is presented. In this work the RobIL UGV project with special emphasis on the performance and safety verification methodology is presented. <s> BIB024 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <s> The future will see autonomous machines acting in the same environment as humans, in areas as diverse as driving, assistive technology, and health care. Think of self-driving cars, companion robots, and medical diagnosis support systems. We also believe that humans and machines will often need to work together and agree on common decisions. Thus hybrid collective decision making systems will be in great need. ::: ::: In this scenario, both machines and collective decision making systems should follow some form of moral values and ethical principles (appropriate to where they will act but always aligned to humans'), as well as safety constraints. In fact, humans would accept and trust more machines that behave as ethically as other humans in the same environment. Also, these principles would make it easier for machines to determine their actions and explain their behavior in terms understandable by humans. Moreover, often machines and humans will need to make decisions together, either through consensus or by reaching a compromise. This would be facilitated by shared moral values and ethical principles. <s> BIB025 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <s> Today, In the content of road vehicles, intelligent systems and autonomous vehicles, one of the important problem that should be solved is Road Terrain Classification that improves driving safety and comfort passengers. There are many studies in this area that improved the accuracy of classification. An improved classification method using color feature extraction is proposed in this paper. Color Feature of images is used to classify The Road Terrain Type and then a Neural Network (NN) is used to classify the Color Features extracted from images. Proposed idea is to identify road by processing digital images taken from the roads with a camera installed on a car. Asphalt, Grass, Dirt and Rocky are four types of terrain that identified in this study. <s> BIB026 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <s> Traffic safety is an important problem for autonomous vehicles. The development of Traffic Sign Recognition (TSR) dedicated to reducing the number of fatalities and the severity of road accidents is an important and an active research area. Recently, most TSR approaches of machine learning and image processing have achieved advanced performance in traditional natural scenes. However, there exists a limitation on the accuracy in road sign recognition and on time consuming. This paper proposes a real-time algorithm for shape classification of traffic signs and their recognition to provide a driver alert system. The proposed algorithm is mainly composed of two phases: shape classification and content classification. This algorithm takes as input a list of Bounding Boxes generated in a previous work, and will classify them. The traffic sign's shape is classified by an artificial neural network (ANN). Traffic signs are classified according to their shape characteristics, as triangular, squared and circular shapes. Combining color and shape information, traffic signs are classified into one of the following classes: danger, information, obligation or prohibition. The classified circular and triangular shapes are passed on to the second ANN in the third phase. These identify the pictogram of the road sign. The output of the second artificial neural network allows the full classification of the road sign. The algorithm proposed is evaluated on a dataset of road signs of a Tunisian database sign. <s> BIB027 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <s> Understanding intent and relevance of surrounding agents from video is an essential task for many applications in robotics and computer vision. The modeling and evaluation of contextual, spatio-temporal situation awareness is particularly important in the domain of intelligent vehicles, where a robot is required to smoothly navigate in a complex environment while also interacting with humans. In this paper, we address these issues by studying the task of on-road object importance ranking from video. First, human-centric object importance annotations are employed in order to analyze the relevance of a variety of multi-modal cues for the importance prediction task. A deep convolutional neural network model is used for capturing video-based contextual spatial and temporal cues of scene type, driving task, and object properties related to intent. Second, the proposed importance annotations are used for producing novel analysis of error types in image-based object detectors. Specifically, we demonstrate how cost-sensitive training, informed by the object importance annotations, results in improved detection performance on objects of higher importance. This insight is essential for an application where navigation mistakes are safety-critical, and the quality of automation and human-robot interaction is key. HighlightsWe study a notion of object relevance, as measured in a spatio-temporal context of driving a vehicle.Various spatio-temporal object and scene cues are analyzed for the task of object importance classification.Human-centric metrics are employed for evaluating object detection and studying data bias.Importance-guided training of object detectors is proposed, showing significant improvement over an importance-agnostic baseline. <s> BIB028 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <s> Ever increasing autonomy of machines and the need to interact with them creates challenges to ensure safe operation. Recent technical and commercial interest in increasing autonomy of vehicles has led to the integration of more sensors and actuators inside the vehicle, making them more like robots. For interaction with semi-autonomous cars, the use of these sensors could help to create new safety mechanisms. This work explores the concept of using motion tracking (i.e. skeletal tracking) data gathered from the driver whilst driving to learn to classify the manoeuvre being performed. A kernel-based classifier is trained with empirically selected features based on data gathered from a Kinect V2 sensor in a controlled environment. This method shows that skeletal tracking data can be used in a driving scenario to classify manoeuvres and sets a background for further work. <s> BIB029 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <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> BIB030 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <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> BIB031 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <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> BIB032 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <s> The sole idea of the autonomous driving is to reduce the number of accidents which will be caused by human errors. There will be a phase where autonomous vehicles will have to take part with the human driven vehicles. In this phase autonomous vehicle and vehicle drivers must be careful in driving maneuvers, since it is impossible to just have autonomous vehicles on the road while the transition to fully autonomous driving takes place. To make the system more reliable and robust, authors feel that it must be substantially improved and to achieve this 3 methodologies have been proposed. The use cases are derived from the camera placed in the suitable positions of every vehicle. This creates several use cases with different driving behaviors, which truly reflects the conditions which the autonomous car needs to undergo in series. Authors propose several architectures to address the safety related issues. Since, the number of use cases are so random that it becomes practically impossible to manage data, so we propose to have a secured cloud storage. The cloud provides a secure and reliable data management system to the machine learning algorithm computation. The data management till now is effectively done via trained unique use cases. This make a big data which is encapsulation of different unique use cases under different driving conditions. So we conclude that the usage of ML and cloud based data management is the way forward for handling autonomous vehicle reliably. This may not be the complete solution, but nevertheless we are one step closer towards 2025. This concept can be implemented only in co-operation of the OEMs and Public authorities. <s> BIB033 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <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> BIB034 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <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> BIB035 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <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> BIB036 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> B. Main topics of the studies (RQ.2) <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> BIB037
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 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 BIB001 , Doppler sensing   , road junction detection BIB018 , road terrain detection BIB026 , traffic signal detection BIB027 [18], situation awareness BIB028 , speed bump detection [25] BIB012 , traffic light detection BIB019 , vehicle detection BIB013 and virtual worlds for training detection  . 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  ; adaptive pre-crash control BIB020 ; safe trajectory selection BIB021 ; AV following another car driven by a human pilot (Trailing) BIB014 ; safe navigation BIB004 ; heuristic optimization algorithm for unsigned intersection crossing  ; vehicle coordination BIB005 ; maneuver classification BIB029 ; learning to navigate from demonstration BIB015 ; AV movements optimization in intersection BIB006 ; learning and simulation of the Human Level decisions involved in driving a racing car BIB003 ; path tracking  ; and fuzzy-logic control approach to manage low level vehicle actuators (steering throttle and brake) BIB007 . Six papers with research related to Fault Prevention were found. These studies encompass researches related to the preventing the occurrence or introduction of faults  , such as AI for security of wireless communication to ensure safety BIB030 ; remote diagnosis, maintenance and prognosis Framework  ; prediction of computational workload BIB008 ; vehicle security against cyber-attack [39] BIB022 ; and diagnosis of sensor faults  . 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 BIB031 ; design of real time transition from assisted driving to automated driving under conditions of high probability of a collision BIB009 ; diagnosing and predicting stress and fatigue of driver in semi-automated vehicles BIB016 ; and advances in driver-vehicle interface BIB032 . 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 BIB033 ; AV conceptual model BIB034 ; an interdisciplinary framework to extract knowledge from the large amount of available data during driving to reduce driver's behavioral uncertainties BIB023 ; and a proposition of an AV highway concept to improve highway driving safety BIB002 . 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 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  , such as: aspects (and limitations) related to safety validation BIB035 ; performance and safety verification methodology BIB024 ; test suites for AV BIB036 ; end-to-end safety for AV design  ; 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 BIB010 . 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 BIB017 . The other studies discuss the scope of AI on AV with ethical aspects  , ethics in AV design  , and moral values and ethical principles for autonomous machines BIB025 . 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  . According to  , trust can be defined as accepted dependability. The studies found are thus related to: safety issues BIB037 and current mechanisms to ensure robust operation in safety-critical situations facing the introduction of non-deterministic software BIB011 .