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With the gradual improvement of intelligent transportation system, unmanned driving, as a representative product of the intelligent development of automobiles, has attracted much attention for its broad application prospects and influence. Technically speaking, path planning is the key to the realization of unmanned driving, and it is also one of the research hotspots in the field of unmanned driving technology. Aiming at the problems of complicated calculation, low efficiency and unsmooth path in the path planning process of unmanned cars, this paper will focus on the global trajectory planning algorithm, take Rapidly-exploring Random Tree (RRT) algorithm as the research object, and put forward corresponding optimization strategies, so as to improve the overall performance of the vehicle path planning algorithm. Practice has proved that the improved RRT algorithm has obvious advantages in sampling time, the number of points used, the number of sharp points, the path cost and other indicators after MATLAB simulation test. Compared with the commonly used original RRT algorithm and RRT* algorithm, it can solve the optimal path of unmanned cars more quickly.
Qu et al. (Thu,) studied this question.