Emergency collision avoidance/mitigation system: motion planning and control of autonomous vehicle

Abstract

Despite globally overwhelming research and development efforts towards autonomous vehicle technologies, the number of collisions and driver interventions of autonomous vehicles tested in California seems to be reaching a plateau. One of the main reasons for this is the lack of defensive driving functionality

i.e. emergency collision avoidance when other human drivers make mistakes. In this study, a Collision Avoidance/Mitigation System (CAMS) is proposed to rapidly evaluate risks associated with all surrounding vehicles and then maneuver the vehicle into a safer region when faced with critically dangerous situations. The CAMS consists of predicting future situations, collision risk assessment, collision avoidance trajectory planning, and path tracking control.A risk assessment module, namely Predictive Occupancy Map (POM), is proposed in order to compute potential risks associated with surrounding vehicles based on relative position, velocity, and acceleration. In order to avoid dangerous situations, collision avoidance trajectory planning that considers the situation of the surrounding space is necessary. The safe trajectory planning method, which uses model predictive planning (MPP), is proposed. A model-based Linear Quadratic Gaussian (LQG) Control with adaptive Q-matrix is proposed to efficiently and systematically design the path tracking controller for any given target vehicle while effectively handling the noise and error problems arise from the localization and path planning algorithms. The performance limits and trade-offs between three performance criteria (lane tracking, stability robustness, and passenger comfort) are first investigated by exploring the entire design space of three prominent controllers. i.e.,PID, LQG, and $H_∞$. Through this study, the collision avoidance/mitigation system that avoid threat of surrounding vehicle is developed. The performance of the CAMS is validated based on scenario simulations. The performance of tracking controller is validated base on KAIST autonomous vehicle. It is expected that autonomous vehicles can drive safely without severe accident.