Event-Triggered Parallel Control Using Deep Reinforcement Learning With Application to Comfortable Autonomous Driving

被引:5
作者
Lu, Jingwei [1 ,2 ]
Li, Lefei [1 ]
Wang, Fei-Yue [2 ,3 ]
机构
[1] Tsinghua Univ, Dept Ind Engn, Beijing 100084, Peoples R China
[2] Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, 100190, Peoples R China
[3] Macau Univ Sci & Technol, Fac Innovat Engn, Macau 999078, Peoples R China
来源
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES | 2024年 / 9卷 / 03期
关键词
Autonomous driving; comfort driving; deep deterministic policy gradient (DDPG); event-triggered control (ETC); parallel control; TRACKING; MODEL;
D O I
10.1109/TIV.2024.3372522
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A novel event-triggered control (ETC) method, called deep event-triggered parallel control (deep-ETPC), is presented to achieve path tracking for comfortable autonomous driving (CAD) using parallel control and deep deterministic policy gradient (DDPG). Based on parallel control, the developed deep-ETPC method constructs a dynamic control policy by introducing variation rates of controls. By employing variation rates of controls, the developed deep-ETPC method is capable of indicating communication loss and comfortable driving indices in the reward, and then enables reinforcement learning (RL) agents to learn comfortable ETC driving policies directly. Moreover, the communication loss, which reflects ETC, is integrated into the reward, so there is no need to additionally design/train triggering conditions, which can be considered a type of multi-tasking learning. Furthermore, an ETPC-oriented DDPG algorithm is developed to achieve the developed deep-ETPC method, making DDPG applicable to ETC. Empirical results, including tracking a simple straight line trajectory and a complicated sinusoidal trajectory, demonstrate the effectiveness of the developed deep-ETPC method.
引用
收藏
页码:4470 / 4479
页数:10
相关论文
共 52 条
  • [1] Deep Reinforcement Learning With NMPC Assistance Nash Switching for Urban Autonomous Driving
    Alighanbari, Sina
    Azad, Nasser L.
    [J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 2023, 8 (03): : 2604 - 2615
  • [2] Arzen K.-E., 1999, Proceedings of the 14th World Congress. International Federation of Automatic Control, P423
  • [3] Toward a Comfortable Driving Experience for a Self-Driving Shuttle Bus
    Bae, Il
    Moon, Jaeyoung
    Seo, Jeongseok
    [J]. ELECTRONICS, 2019, 8 (09)
  • [4] Baumann D, 2018, IEEE DECIS CONTR P, P943, DOI 10.1109/CDC.2018.8619335
  • [5] Safe driving envelopes for path tracking in autonomous vehicles
    Brown, Matthew
    Funke, Joseph
    Erlien, Stephen
    Gerdes, J. Christian
    [J]. CONTROL ENGINEERING PRACTICE, 2017, 61 : 307 - 316
  • [6] Comparison of Event-Triggered Model Predictive Control for Autonomous Vehicle Path Tracking
    Chen, Jun
    Yi, Zonggen
    [J]. 5TH IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (IEEE CCTA 2021), 2021, : 808 - 813
  • [7] Choi JM, 2015, 2015 EUROPEAN CONTROL CONFERENCE (ECC), P2132, DOI 10.1109/ECC.2015.7330855
  • [8] Robust Event Triggered Control for Lateral Dynamics of Intelligent Vehicle With Designable Inter-Event Times
    Chu, Xing
    Liu, Zhi
    Mao, Lei
    Jin, Xin
    Peng, Zhaoxia
    Wen, Guoguang
    [J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, 2022, 69 (11) : 4349 - 4353
  • [9] The Road Ahead: DAO-Secured V2X Infrastructures for Safe and Smart Vehicular Management
    Dai, Xingyuan
    Vallati, Mauro
    Guo, Rongge
    Wang, Yutong
    Han, Shuangshuang
    Lin, Yilun
    [J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 2023, 8 (12): : 4674 - 4677
  • [10] Event-Triggered Model Predictive Control With Deep Reinforcement Learning for Autonomous Driving
    Dang, Fengying
    Chen, Dong
    Chen, Jun
    Li, Zhaojian
    [J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 2024, 9 (01): : 459 - 468