# Bayesian-Based Traffic Safety Evaluation Study for Driverless Infiltration
###### Abstract  
Although driverless technology belongs to the frontier of science and technology, there is no sufficient actual data. From the lack of a comprehensive systematic evaluation method of traffic safety under driverless penetration, considering the impact of the three core systems of driverless perception, decision-making, control, and the complex road factors on the safety of driving, we review the main risk causal factors through the analysis of the accident causal model STAMP and put forward the fusion of the Leaky Noisy-OR Gate and Bayesian network model. The Bayesian network professional analysis tool GeNIe 2.0 was used to simulate, analyze, and evaluate the driverless traffic risk Bayesian network model, which accurately assessed the traffic safety risk under driverless penetration and diagnosed and identified the sensitive risk factors. The results of this study concluded that, in order to effectively deal with the future traffic safety risks of driverless vehicles, vehicle enterprises, research institutions, software and hardware suppliers in the field of driverless driving should strengthen the research and development and manufacturing of key components such as perception, and enhance the depth of research and development of AI decision-making software, which provides a new way of thinking about the management of the safety risk of driverless traffic and a theoretical basis for the development and implementation of risk control measures. driverless vehicles; safety evaluation; STAMP; Bayesian networks; Leaky Noisy-OR gate model; GeNIe; sensitivity analysis +
Footnote †: journal: Medical  
accidents when traveling, and have even caused injuries and deaths, which has sounded a safety alarm for the whole driverless technology industry, and road traffic safety under driverless infiltration has become a hotspot of concern both at home and abroad. In road traffic safety under driverless penetration, it is especially important to carry out a risk assessment of the key parts of the driverless vehicle and the surrounding environment, to find out the higher risk of influencing factors and then put forward rectification proposals and improve them, to reduce the occurrence of traffic accidents, reduce the loss of life and property brought about by traffic accidents, and accurate risk assessment can ensure that the vehicle drives smoothly and safely so that passengers can enjoy a fast, safe and efficient traveling experience. Traffic safety accident causation is complex and diverse, combined with the characteristics of autonomous perception, control, and decision-making of the AI system of driverless vehicles. Firstly, the driving environment includes characterizing the impact of stationary objects and moving targets on the road on driving safety. Secondly, the risk of AI driving includes the impact of perception, decision-making, and control on driving safety. The classic road traffic safety management model, traffic accident causation analysis is to view the human-vehicle-road-environment as a whole, and common analysis methods and theories include fuzzy hierarchical analysis, neural networks, Bayesian networks, fault tree analysis, etc. Sun Xianglong et al. [4] in the three-dimensional evaluation model of traffic safety risk of general trunk highways, the weight determination method based on the relevance of indicators (CRITIC) and fuzzy hierarchical analysis (Fuzzy-AHP) are combined to establish a comprehensive evaluation model of traffic safety risk, and different improvement measures should be taken according to different risk levels of road sections, in order to effectively improve the level of traffic safety of general trunk highways. Yue Long [5] in the analysis of factors affecting traffic accidents on highways based on radial basis neural network, radial basis neural network is used to predict traffic accidents with high feasibility and accuracy (91.6%). Cheng Wei et al.