Autonomous Intersection Management by Using Reinforcement Learning

被引:4
|
作者
Karthikeyan, P. [1 ]
Chen, Wei-Lun [1 ]
Hsiung, Pao-Ann [1 ]
机构
[1] Natl Chung Cheng Univ, Dept Comp Sci & Informat Engn, Chiayi 621301, Taiwan
关键词
reinforcement learning; autonomous vehicles; traffic control; intersection; DRLAIM;
D O I
10.3390/a15090326
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Developing a safer and more effective intersection-control system is essential given the trends of rising populations and vehicle numbers. Additionally, as vehicle communication and self-driving technologies evolve, we may create a more intelligent control system to reduce traffic accidents. We recommend deep reinforcement learning-inspired autonomous intersection management (DRLAIM) to improve traffic environment efficiency and safety. The three primary models used in this methodology are the priority assignment model, the intersection-control model learning, and safe brake control. The brake-safe control module is utilized to make sure that each vehicle travels safely, and we train the system to acquire an effective model by using reinforcement learning. We have simulated our proposed method by using a simulation of urban mobility tools. Experimental results show that our approach outperforms the traditional method.
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页数:22
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