Path Planning and Tracking for Autonomous Vehicle Collision Avoidance with Consideration of Tire-Road Friction Coefficient

Abstract

Abstract Autonomous vehicles (AVs) have attracted a lot of attention in recent years and fully-autonomous vehicles are expected on road in the near future. Collision avoidance is one of the key driving tasks for autonomous driving which consists of path planning and tracking control. The main problem discussed in this paper is the development of a path planning and tracking framework based on model predictive control (MPC) with consideration of the estimated tire-road friction coefficient (TRFC). The planned path in terms of lateral position is generated based on the safety distance between the host and the obstacle vehicle, which is related to TRFC and vehicle speed. A new structure of MPC is further designed so that only lateral position is required to track the planned path. Moreover, the adaptive weights on the outputs to a wide range of vehicle speeds have been identified. The effectiveness of the proposed planning and tracking framework is validated through CarSim-MATLAB/Simulink co-simulations on both high- and low-friction roads.