Abstract: In this study, we propose a dynamic local path planning framework for autonomous driving based on real-time environmental risk field so as to resolve the problem of local path planning of structured roads. To the best of our knowledge, the local path planning problem is categorized into lane decision and path planning for the first time. With respect to lane decision-making, a lane decision-making algorithm based on driving risk field and safety distance is proposed to maintain the driving speed of the self-vehicle and ensure that the vehicle is always in a low-risk driving environment to improve its safety. With respect to lane change path planning, a candidate path generation algorithm based on the uniform sampling of lane change time is proposed. In addition, we realize optimal path planning by proposing a cost function that comprehensively considers the instantaneity of lane change, speed smoothness, path smoothness, and comfort. Regarding single-vehicle path planning, we propose a cost function that considers safety, smoothness, and continuity to realize reasonable and safe dynamic planning of path and speed. Our experimental results show that the proposed local path dynamic planning framework can plan a low-risk, efficient, safe, reasonable, and smooth driving path and give a safe planning speed in the set local path planning task of structured roads. Thus, in other words, our results prove the effectiveness of the proposed local path dynamic planning algorithm.