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

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
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. 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.