Collision Avoidance Control for Autonomous Driving With Multiple Dynamic Obstacles in IoV: A Prediction-Enhanced APF-Based Approach

Zenghui Qian, Ruoyang Chen, Changyan Yi, Xiangping Zhai, Bing Chen

Abstract: With the rapid development of autonomous driving, how to enable unmanned vehicles (UVs) to efficiently avoid multiple dynamically moving obstacles, especially obstacle vehicles (OVs), has become a vital issue in the context of the Internet of Vehicles (IoV). This requires not only high-level adaptability to dynamic and complex traffic environments, but also extraordinarily agility in reacting to possible collision hazards with safer and proactive collision avoidance. Conventional methods, e.g., artificial potential field (APF), may overreact to distant targets which have no risk in collision, generating a false evasion direction when facing multiple OVs. To this end, we propose a novel improved APF-based algorithm along with the trajectory prediction. Specifically, to measure the safety distance for vehicle maneuvering, a trajectory prediction method integrated with unscented kalman filter (UKF) is developed. Then, an obstacle filtering method utilizing sensor information and trajectory prediction results is applied for wiping off collision-free targets. Afterwards, by employing APF method combining with avoidance strategies based on virtual forces and window-based collision detection, the potential pushing effect caused by multiple OVs is mitigated. Experimental results show that, given the scenario of collision avoidance with multiple OVs, the proposed solution can achieve an obstacle avoidance success rate of around 90%, which is about 20% higher than the best benchmark algorithms, simultaneously demonstrating advantages in efficiency and safety.