Uncertainty-Aware Decision Making and Planning for ICV Based on Asymmetric Driving Aggressiveness

Abstract

Precisely assessing driving threat of road segments could significantly enhance the driving efficiency of intelligent connected vehicles (ICV) within mixed traffic scenarios. Existing methods primarily concentrate on collision probabilities, resulting in an insufficient appraisal of asymmetrical hazard levels attributed to the various interactions. Meanwhile, the uncertainty and communication delay have great influence on ICV, and it is an issue that must be addressed when designing decision-making and planning model. Thus, this study proposes and formulates a new driving aggressiveness model after analyzing asymmetric interactions behaviors among vehicles with different types. Subsequently, aims to verify the capability of generating asymmetric interaction, the driving aggressiveness model is applied on lane-change decision-making and planning. Concretely, the aggressiveness-sensitive lanes-selection model is designed based on game theory, and the uncertainty-aware trajectory planning is developed by utilizing stochastic model predictive control (MPC) and the asymmetric driving aggressiveness. Finally, two naturalistic driving scenarios are utilized to verify the performance of the decision-making and planning model. The outcomes of simulations illustrate that the driving aggressiveness model introduces a novel perspective to assess the asymmetric driving threat. Meanwhile, the uncertainty-aware decision making and planning model can reduce the influence on interactive vehicles, and it has superior adaptability for dynamic and connected traffic environments.