Path Planning and Path Tracking for Collision Avoidance of Autonomous Ground Vehicles

TLDR: An advanced collision avoidance framework is proposed to avoid collision efficiently with road friction estimation and dynamic stability control and hardware-in-the-loop platform tests are conducted to validate that the proposed controller can not only avoid collisions effectively, but also have a good performance on keeping the vehicle dynamic stability with accurateRoad friction estimation.

Abstract: How to plan the collision-free path effectively is crucial for autonomous ground vehicles to avoid collisions. The vehicle may lose its lateral dynamic stability due to high speed, time-varying speed and multifriction road during tracking the collision-free path. In this article, an advanced collision avoidance framework is proposed to avoid collision efficiently with road friction estimation and dynamic stability control. First, an improved A* algorithm is constructed to generate a desired trajectory for collision avoidance, which is capable of observing road regulations and overcoming the vehicle mechanical drawbacks. Next, a model predictive control-based path-tracking controller is established to solve the tracking task as a multiconstraint and multitarget optimization problem, where optimized steering angle and additional yaw moment can be calculated. Meanwhile, a novel long short-term memory based road friction coefficient estimator is built to observe road friction. Moreover, electric power steering system controls the steering motor to realize the desired steering angle. The additional yaw moment can be obtained by differential braking. Finally, hardware-in-the-loop platform tests are conducted to validate that the proposed controller can not only avoid collisions effectively, but also have a good performance on keeping the vehicle dynamic stability with accurate road friction estimation.