Obstacle Avoidance, Path Planning and Control for Autonomous Vehicles

Abstract

Obstacle avoidance requires three main levels in autonomous vehicles, namely, perception, path planning and guidance control. In this paper, a global architecture is proposed by taking into account the link between the three levels. On the environment perception level, an evidential occupancy-grid-based approach is used for dynamic obstacle detection. The poses of objects are therefore considered for trajectory generation. The latter is based on a smooth trajectory sigmoid function. Finally, the control guidance employs this obstacle avoidance trajectory to generate the appropriate steering angle. The whole strategy is validated on our experimental test car. The experimental results show the effectiveness of the proposed approach.