Autonomous driving paper index
Collaborative Motion Planning Based on the Improved Ant Colony Algorithm for Multiple Autonomous Vehicles
One-line summary
To improve the online collaboration and planning capabilities between autonomous vehicles, this paper proposes a novel collaborative motion planning method.
Engineering notes
Key topics: autonomous driving, autonomous vehicle, motion planning, planning. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
To improve the online collaboration and planning capabilities between autonomous vehicles, this paper proposes a novel collaborative motion planning method. In this method, the ant colony algorithm was introduced and improved to achieve collaborative motion planning for multiple autonomous vehicles. First, independent subpopulations of the same size were generated according to the number of autonomous vehicles. Then, a multi-objective optimization function was established to optimize spatial collaboration and trajectory costs, and to update the pheromone in the ant colony algorithm. Meanwhile, the evaporation coefficient in the algorithm was adaptively adjusted to enhance the global search ability and improve the convergence speed of the algorithm. Finally, a feasible path was planned for each autonomous vehicle based on the path of each subpopulation. Simulation results show that the proposed method is effective and it can achieve stronger adaptability than the artificial potential field motion planning algorithm.
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