山东大学学报 (工学版) ›› 2021, Vol. 51 ›› Issue (1): 100-107.doi: 10.6040/j.issn.1672-3961.0.2020.247
肖浩1(
),廖祝华1,2,*(),刘毅志1,2,刘思林1,刘建勋1,2
Hao XIAO1(),Zhuhua LIAO1,2,*(),Yizhi LIU1,2,Silin LIU1,Jianxun LIU1,2
摘要：
实际交通环境规划最优路径的重要问题是无人车智能导航, 而无人车全局路径规划研究主要在于模拟环境中算法求解速度的提升, 考虑大部分仅路径距离最优或局限于当前道路的自身状况, 本研究针对实际环境中的其他因素及其未来的变化和动态路网中无人车全局路径规划的复杂任务, 基于预测后再规划的思想提出面向实际环境的无人车驾驶系统框架, 并结合深度Q学习和深度预测网络技术提出一种快速全局路径规划方法(deep prediction network and deep Q network, DP-DQN), 从而利用时空、天气等道路特征数据来预测未来交通状况、求解全局最优路径。基于公开数据集的试验和评价后发现, 本研究提出的方法与Dijkstra、A*等算法相比, 行车时间最高降低了17.97%。
中图分类号:
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