A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> Cyber Attack in V2X <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> BIB001 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> Cyber Attack in V2X <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> BIB002 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> Cyber Attack in V2X <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> BIB003
Fuzzy Detector Fuzzy Logic To address security issues of a system of connected vehicles (CVs), a fuzzy detector was also introduced that detects possibility of a cyber threat and takes proper actions in response to the specific attack. Results show the designed system can detect any adversary access to the system and can prevent subsequent crashes by adjusting the safe following distance. BIB003 Road and Obstacle Detection Sensor Fusion Fuzzy Logic A high-level data-fusion strategy has been devised, which is based on the identification and representation of the descriptive and procedural knowledge required. Such a strategy yields better recognition results by merging the various hypotheses generated by the separate channels and solving possible conflicts through a fuzzy representation of knowledge when compared to a benchmark. In addition, the data-fusion system has performed a more accurate segmentation process by using goal-driven low-level procedures, according to which the image regions have been assigned relative fuzzy memberships to the object to be recognized. In this paper, three methods were compared based on three trajectories in the low noise and noisy environments. The GMM based method had the best performance in a low noise environment. In practice, there's always unexpected noise around a robot, implying the GMM based method was not practical for real environments. The CHMM based method was suitable for turning trajectories, while The DHMM based method was more robust for straight trajectories. The proposed method has been shown to give significantly better vehicle volume counts than both multiple targets moving object tracking and VDL on a dataset of over 88 hours of video. On this testing set, the proposed method achieved a median 5-minute-bin error of 0.0686 for this counting task while the multiple target motion tracking and VDL implementations had median errors of 0.0957 and 0.2290 respectively. The proposed method was also more reliable having fewer and less severe occurrences of 5-minute-bin errors throughout the testing set. [36] Safety-warning and driver-assistance system and an automatic pilot for rural and urban traffic environments The average performance of the system considering only speed bump with proper marking is 85%. BIB002 Obstacle clusteering and tracking Lidar + split-andmerge algorithm RamerDouglasPeucker or RamerDouglas algorithm Paper presents a robust platform for implementing a perception system for ground vehicles using a LIDAR sensor and two cameras has been designed. Tests were performed using this platform and different implementations, and the results were checked with the real-world scenes, demonstrating the technique validity. [23] Predicts the CPU usage patterns of software tasks running on a selfdriving car Methods for learning the patterns of tasks' CPU utilizations in given driving contexts Regression Based Models A feature vector was designed to represent the internal and external states of a self-driving car and five regression methods were used to predict the CPU usage patterns of four software tasks running on a self-driving car. Through testing with the actual driving data, the results showed a regression method could be used to predict a software task's dynamic BIB001 CPU utilization. The paper presented a collision and driving scenario classification system based on the Doppler profile which could potentially decouple the safety benefits of V2V communications from relying on SM content. The Doppler profile in V2V networks showed rich data about the vehicles and their environments and could be exploited to potentially provide a reliable collision avoidance service directly from the radio front-end. No experimental results were presented.  Road Detection The experiments showed that virtualworld data is effective for training visionbased pedestrian detectors which can be adapted to operate in real scenarios. The different adaptation procedures have shown to provide adapted detectors that improve those trained only on virtual data, as well as those trained using only the real-world data available for the adaptation (which constrained to save a ∼90% annotation effort along the experiments). [37]