A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> 7% <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> BIB001 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> 7% <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> BIB002 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> 7% <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> BIB003 </s> A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety <s> 7% <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> BIB004
Collision avoidance when no action is taken by driver; Safety, interpretability, and compliance; Pedestrian Detection; Design of driving assistance system; BIB001 , BIB003 ,  , BIB004 Fuzzy Logic 5 8% Haar-like feature detector 3 5% Developing remotecontrolled car with some automation to deal with traffic light detection, obstacle avoidance system and lane detection system to be driven from anywhere over a secured internet connection; Vehicle Detection and Counting; How to "automate" manual annotation for images to train visual perception for AVs;  , BIB002 ,  Histogram of Oriented Gradient (HOG)