Ethical and Legal Dilemma of Autonomous Vehicles: Study on Driving Decision-Making Model under the Emergency Situations of Red Light-Running Behaviors

Abstract

Autonomous vehicles (AVs) are supposed to identify obstacles automatically and form appropriate emergency strategies constantly to ensure driving safety and improve traffic efficiency. However, not all collisions will be avoidable, and AVs are required to make difficult decisions involving ethical and legal factors under emergency situations. In this paper, the ethical and legal factors are introduced into the driving decision-making (DDM) model under emergency situations evoked by red light-running behaviors. In this specific situation, 16 factors related to vehicle-road-environment are considered as impact indicators of DDM, especially the duration of red light (RL), the type of abnormal target (AT-T), the number of abnormal target (AT-N) and the state of abnormal target (AT-S), which indicate legal and ethical components. Secondly, through principal component analysis, seven indicators are selected as input variables of the model. Furthermore, feasible DDM, including braking + going straight, braking + turning left, braking + turning right, is taken as the output variable of the model. Finally, the model chosen to establish DDM is the T-S fuzzy neural network (TSFNN), which has better performance, compared to back propagation neural network (BPNN) to verify the accuracy of TSFNN.