Abstract: To address the lane change planning problem for autonomous vehicles in scenarios with obstacles during changing lanes, an improved whale optimization algorithm (IWOA) was proposed and applied to lane change path planning. The proposed IWOA incorporated a three-layer adaptive mechanism: in the initialization layer, Tent mapping was used to generate a population with high fractal dimension, enhancing early exploration diversity; in the iterative control layer, the dynamic mapping function for the nonlinear convergence factor was constructed to extend the global search cycle; in the mutation enhancement layer, the adaptive Cauchy perturbation strategy based on information entropy was introduced, significantly improving the algorithm’s ability to escape local optima. During the path generation phase, cubic spline curves were used to model control points, and the comprehensive cost function was constructed, which included path smoothness, obstacle safety distance, lane boundary constraints. Simulation and real-vehicle test results show that compared to the traditional whale algorithm, average convergence speed and path smoothness of the proposed IWOA is improved by 18.5% and 18.7%, respectively. The research results indicate the proposed algorithm demonstrates superior convergence accuracy, escape capability and path feasibility in multiple typical lane-changing path planning scenarios.

Key words: vehicle engineering; intelligent vehicles; whale optimization algorithm; adaptive optimization; cubic spline curve; lane change path planning

摘要： 针对自动驾驶车辆在换道过程中有障碍物场景下的路径规划问题，提出了一种改进的鲸鱼优化算法（IWOA），并将其应用在换道路径规划中。所提出的IWOA算法引入三层自适应机制：在初始化层采用Tent映射生成高分形维度的种群，增强早期探索多样性；在迭代调控层构建非线性收敛因子动态映射函数，延长全局搜索周期；在变异增强层引入基于信息熵的自适应柯西扰动策略，显著提升算法跳出局部最优能力。在路径生成阶段采用三次样条曲线对控制点进行建模，并构建包含路径平滑性、障碍物安全距离、车道边界约束的综合代价函数。仿真和实车测试结果表明，相较于传统鲸鱼算法，平均收敛速度提高18.5%，路径平滑度提升18.7%。研究结果表明：该算法在多个典型换道路径规划场景中均表现出更优的收敛精度、跳出能力与路径可行性。

关键词: 车辆工程；智能车辆；鲸鱼优化算法；自适应优化；三次样条曲线；换道路径规划