With the increasing level of vehicle automation, there is a growing demand for vehicles to autonomously perform obstacle avoidance in the field of autonomous driving. Therefore, to accurately, safely, and reasonably plan obstacle avoidance routes has become an important research direction in the field of automated driving. This paper elaborates the current research status of local path planning algorithms and their limitations in practical applications. Furthermore, the research status of improved path planning algorithms based on artificial potential field and sampling methods are reviewed in response to these limitations. The results show that this improved path planning algorithm performs well in experiments or practical applications and the path planning effect of multi-algorithm fusion is better than that of single algorithm in the future.

Automated driving  /  Obstacle avoidance  /  Trajectory planning