Formulating Vehicle Aggressiveness Towards Social Cognitive Autonomous Driving

Accurately identifying the driving threat could greatly improve the driving safety for autonomous vehicles in the mixed traffic, where the human-driven and driverless, as well as different types of vehicles coexist. The existing safety evaluation methods merely focus on the possibility of collision, which is deficient to evaluate the hazard level due to the symmetry for both the interactive vehicles. Thus, the vehicle aggressiveness model is proposed in this paper based on the asymmetric interactions between different types of vehicles from the perspective of the social cognitions in the human driving. Firstly, a new conceptual framework of the vehicle aggressiveness is constructed, and the factors are analyzed. Secondly, the general mathematic formulation of the aggressiveness is deduced elaborately based on the analogy with the mechanical wave. Thirdly, the simplified formulation is derived by introducing resonant assumption, and an illustration of aggressiveness distribution is presented and discussed. The mathematical analysis and simulation results indicate that the proposed model could explicitly describe the asymmetric characteristics as regards the vehicle mass, motion states and position. Finally, the potential applications in safety assessment, decision-making and motion planning of the social cognitive autonomous driving are discussed. The aggressiveness model provides a new perspective in asymmetric driving safety evaluation and heterogeneous driving behavior model under complex and mixed traffic environments.