中圖分類號： TP181
文獻標識碼： A
DOI： 10.19358/j.issn.2096-5133.2022.06.008
引用格式： 曹歡. 交通場景下基于深度強化學習的感知型路徑分配算法[J].信息技術與網絡安全，2022，41(6)：43-49.

Perceptual path allocation algorithm based on deep reinforcement learning in traffic scene

Abstract： Path allocation problem is one of the important research directions of traffic digital twin system. Its focus is to comprehensively consider the dynamic changes of driving demand and the real-time changes of road network information, so as to realize efficient and reasonable path planning. At present, some classical allocation algorithms such as particle swarm optimization, Dijkstra and their optimization models can only achieve global static optimization, ignoring the complex changes in real traffic. Although various depth learning algorithms gradually introduced can carry out comprehensive spatio-temporal prediction, they are difficult to be applied on a large scale due to the inductive analysis of massive historical data and high operation cost. In view of this, this paper proposes a perceptual path allocation algorithm based on the combination of static allocation algorithm and deep reinforcement learning algorithm. During driving, the global path is dynamically redistributed and updated according to the real-time road network information and the current state of vehicles. The accuracy and efficiency of the proposed algorithm are verified in the simulation experiment.

Key words : path allocation；deep reinforcement learning；road network；traffic perception