To improve the solving speed and success rate of the lane change path planning problem for autonomous vehicles, a fast and stable planning method (FSDETS) is proposed in this paper. This lane change path planning scheme is formulated as an optimal control problem according to the three-segment lane change model. Then the B-spline curve is applied to smooth the path. The core contribution is that a method based on hybrid intelligent algorithms is proposed to solve the optimal control problem fast and stably. First, an extended differential evolution is proposed to quickly provide a tough initial guess for the subsequent search. Then two criteria are set to judge whether the tabu search can start. After satisfying them, tabu search seeks a better solution based on the initial guess. Finally, a 1000 cycles simulation experiment is run to verify its stability and real-time performance. Simulation results show that the average time consumption is 0.7417ms, the success rate is 99.97% and the average maximum curvature is 0.13947m−1. Besides, it is also compared with other intelligent optimization algorithms as well as the optimization solver MIDACO. The result shows that our method surpasses other algorithms in comprehensive performances.


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    Title :

    A Fast Stable Lane Change Path Planning Method Based On Hybrid Intelligent Algorithms


    Contributors:
    Ruan, Xinyao (author) / Yu, Zhuoping (author) / Xiong, Lu (author) / Zeng, Dequan (author) / Fu, Zhiqiang (author) / Xiao, Kui (author)


    Publication date :

    2021-10-22


    Size :

    711303 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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