Control Transitions in Level 3 Automation: Safety Implications in Mixed-Autonomy Traffic

被引:0
|
作者
Alms, Robert [1 ]
Wagner, Peter [1 ,2 ]
机构
[1] German Aerosp Ctr DLR, Inst Transportat Syst, Rutherfordstr 2, D-12489 Berlin, Germany
[2] TU Berlin, Inst Land & Sea Transport Syst, Salzufer 17-19, D-10587 Berlin, Germany
关键词
automated vehicles (AVs); Level; 3; automation; mixed-autonomy traffic; surrogate safety measures (SSMs); take-over request (ToR); transition of control (ToC); GENERIC MULTILEVEL FRAMEWORK; TIME; COLLISION; VEHICLES; DRIVER; SIMULATION;
D O I
10.3390/safety10010001
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Level 3 automated driving systems could introduce challenges to traffic systems as they require a specific lead time in their procedures to ensure the safe return of vehicle control to the driver. These processes, called 'transitions of control', may particularly pose complications in accelerating traffic flows when regulations mandate control transitions due to an operational speed limitation of 60 km/h as established in recent certification processes based on UNECE regulations from 2021. To investigate these concerns, we conducted a comprehensive simulation study to examine potential safety implications arising from control transitions within mixed-autonomy traffic. The simulation results indicate adverse safety impacts due to increased safety-relevant interactions between vehicles caused by transitions of control in dynamic traffic flow conditions. Our findings also reveal that those effects could become stronger once string unstable ACC controllers are deployed as well.
引用
收藏
页数:22
相关论文
共 50 条
  • [1] Robust Safety for Mixed-Autonomy Traffic With Delays and Disturbances
    Zhao, Chenguang
    Yu, Huan
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2024, : 16522 - 16535
  • [2] Safety Critical Control of Mixed-autonomy Traffic via a Single Autonomous Vehicle
    Zhou, Jingyuan
    Yu, Huan
    2022 IEEE 25TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 2022, : 3089 - 3094
  • [3] Mixed-Autonomy Traffic Control with Proximal Policy Optimization
    Wei, Haoran
    Liu, Xuanzhang
    Mashayekhy, Lena
    Decker, Keith
    2019 IEEE VEHICULAR NETWORKING CONFERENCE (VNC), 2019,
  • [4] Hierarchical Joint Control for Urban Mixed-Autonomy Traffic Optimization
    Wu, Jia
    Li, ZiYan
    2020 IEEE 32ND INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI), 2020, : 700 - 705
  • [5] Optimal Control of Autonomous Vehicles for Flow Smoothing in mixed-autonomy Traffic
    Alanqary, Arwa
    Gong, Xiaoqian
    Keimer, Alexander
    Seibold, Benjamin
    Piccoli, Benedetto
    Bayen, Alexandre
    2023 62ND IEEE CONFERENCE ON DECISION AND CONTROL, CDC, 2023, : 105 - 111
  • [6] A spatiotemporal control method at isolated intersections under mixed-autonomy traffic conditions
    Dai, Rongjian
    Ding, Chuan
    Yu, Bin
    Hu, Jia
    COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, 2024, 39 (08) : 1159 - 1179
  • [7] Hybrid System Stability Analysis of Multilane Mixed-Autonomy Traffic
    Li, Sirui
    Dong, Roy
    Wu, Cathy
    IEEE Transactions on Robotics, 2024, 40 : 4469 - 4489
  • [8] Coupling Control of Traffic Signal and Entry Lane at Isolated Intersections Under the Mixed-Autonomy Traffic Environment
    Dai, Rongjian
    Ding, Chuan
    Wu, Xinkai
    Yu, Bin
    Lu, Guangquan
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2023, 24 (10) : 10628 - 10642
  • [9] Stackelberg Routing of Autonomous Cars in Mixed-Autonomy Traffic Networks
    Kolarich, Maxwell
    Mehr, Negar
    2022 AMERICAN CONTROL CONFERENCE, ACC, 2022, : 4654 - 4661
  • [10] Safe Reinforcement Learning for Mixed-Autonomy Platoon Control
    Zhou, Jingyuan
    Yan, Longhao
    Yang, Kaidi
    2023 IEEE 26TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS, ITSC, 2023, : 5744 - 5749