@inproceedings{3e5327c2c2a44cafa03e794cad7dbb5d,
title = "Human-like longitudinal velocity control based on continuous reinforcement learning",
abstract = "Traditional intelligent driving systems suffer from low efficiency in the dynamic traffic environment because of their rigid planning modules. On the other hand, experienced human drivers can deal with dynamic situations adaptively without a complex planning system. This study aims to develop a learning-based driving system that can learn from human drivers and realize the human-like longitudinal control. The proposed system contains two main parts: a reinforcement learning module for learning human driving strategies and a PID control module for converting the strategies learned to control actions for vehicles. Experiments based on simulation are carried out to test the performance of the proposed system. A driving simulator based on the software PreScan is used to collect the driving data from human drivers and build the test scenarios. Experimental results show that the learning-based system can duplicate human driving strategies with acceptable errors in several predefined cases.",
keywords = "Artificial neural network, Human-like control, Intelligent driving systems, Reinforcement learning, Velocity planning",
author = "Xin Chen and Chao Lu and Jianwei Gong and Yong Zhai",
note = "Publisher Copyright: {\textcopyright} ASCE.; 17th COTA International Conference of Transportation Professionals: Transportation Reform and Change - Equity, Inclusiveness, Sharing, and Innovation, CICTP 2017 ; Conference date: 07-07-2017 Through 09-07-2017",
year = "2018",
doi = "10.1061/9780784480915.100",
language = "English",
series = "CICTP 2017: Transportation Reform and Change - Equity, Inclusiveness, Sharing, and Innovation - Proceedings of the 17th COTA International Conference of Transportation Professionals",
publisher = "American Society of Civil Engineers (ASCE)",
pages = "972--981",
editor = "Haizhong Wang and Jian Sun and Jian Lu and Lei Zhang and Yu Zhang and ShouEn Fang",
booktitle = "CICTP 2017",
address = "United States",
}