A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> E. Reported future studies (RQ.6) <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> BIB001 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> E. Reported future studies (RQ.6) <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> BIB002 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> E. Reported future studies (RQ.6) <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> E. Reported future studies (RQ.6) <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> BIB004 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> E. Reported future studies (RQ.6) <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> E. Reported future studies (RQ.6) <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> BIB006 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> E. Reported future studies (RQ.6) <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> BIB007 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> E. Reported future studies (RQ.6) <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> E. Reported future studies (RQ.6) <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> E. Reported future studies (RQ.6) <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> BIB010 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> E. Reported future studies (RQ.6) <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> BIB011 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> E. Reported future studies (RQ.6) <s> This Automated driving is an emerging technology in which a car performs recognition, decision making, and control. Recognizing surrounding vehicles is a key technology in order to generate a trajectory of ego vehicle. This paper is focused on detecting a turn signal information as one of the driver's intention for surrounding vehicles. Such information helps to predict their behavior in advance especially about lane change and turn left-or-right on intersection. Using their intension, the automated vehicle is able to generate the safety trajectory before they begin to change their behavior. The proposed method recognizes the turn signal for target vehicle based on mono-camera. It detects lighting state using Convolutional Neural Network, and then calculates a flashing frequency using Fast Fourier Transform. <s> BIB012 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> E. Reported future studies (RQ.6) <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> BIB013 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> E. Reported future studies (RQ.6) <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> BIB014 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> E. Reported future studies (RQ.6) <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> E. Reported future studies (RQ.6) <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> E. Reported future studies (RQ.6) <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.6 is answered by CT.6 (future studies), based 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. BIB012 suggests additional research on image recognition of low contrast images and vehicle images with brake lamps. BIB008 suggests future work on traffic lights detection under severe weather or night conditions. BIB006 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, BIB006 suggests research to improve the recognition capabilities to distinguish zebra crossing from speed bump.  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,  proposed using parallel computing to increase the speed of the image recognition algorithms. Finally, according to  , 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 BIB009 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 BIB001 proposes future re-search to extend it using a combination with the kinetic convex hulls 2 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 BIB007 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 BIB002 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 BIB010 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 cyberattack detection system based on ANNs BIB009 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 BIB013 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  , 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 BIB003 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 BIB014 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 BIB011 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, BIB015 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  , 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. BIB016 proposes more work on creating automated test-cases. BIB004 proposes more studies based on the framework they proposed to evaluate 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. BIB017 asks for more research on new concrete safety evaluation metrics. BIB005 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.