https://scholars.lib.ntu.edu.tw/handle/123456789/581307
標題: | Proactive Car-Following Using Deep-Reinforcement Learning | 作者: | Yen Y.-T Chou J.-J CHI-SHENG SHIH Chen C.-W Tsung P.-K. |
關鍵字: | Control system synthesis; Efficiency; Intelligent systems; Intelligent vehicle highway systems; Reinforcement learning; Vehicles; Car following models; Design and implements; Evaluation results; Fundamental operations; Multiple constraint; Poor efficiencies; Safety regulations; Vehicle Control; Deep learning | 公開日期: | 2020 | 來源出版物: | 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020 | 摘要: | Car-following is a fundamental operation for vehicle control for both ADAS on modern vehicles and vehilce control on autonomous vehicles. Most existing car following mechanisms react to the observations of nearby vehicles in real-time. Unfortunately, lack of capability of taking into account multiple constraints and objectives, these mechanisms lead to poor efficiency, discomfort, and unsafe operations. In this paper, we design and implement a proactive car-following model to take into account safety regulation, efficiency, and comfort using deep reinforcement learning. The evaluation results show that the proactive model not only reduces the number of inefficient and unsafe headway but also eliminates the traffic jerk, compared to human drivers. The model outperformed 79% human drivers in public data set and the road efficiency is only 2% less than the optimal bound. Compared to ACC model, the DDPG model allows 4.4% more vehicles to finish the simulation than ACC model does, and increases the average speed for 28.4%. ? 2020 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099640959&doi=10.1109%2fITSC45102.2020.9294194&partnerID=40&md5=f01ab78a353319bbe57a8a03d630d5dc https://scholars.lib.ntu.edu.tw/handle/123456789/581307 |
DOI: | 10.1109/ITSC45102.2020.9294194 |
顯示於: | 資訊工程學系 |
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