RSS Model Calibration and Evaluation for AV Driving Safety based on Naturalistic Driving Data

被引:5
作者
Huang, Yiwen [1 ]
Elli, Maria Soledad [5 ]
Weast, Jack [5 ]
Lou, Yingyan [2 ]
Lu, Shi [3 ]
Chen, Yan [4 ]
机构
[1] Arizona State Univ, Sch Engn Matter Transport & Energy, Phoenix, AZ 85004 USA
[2] Arizona State Univ, Sch Sustainable Engn & Built Environm, Phoenix, AZ USA
[3] Arizona State Univ, Sch Elect Comp & Energy Engn, Phoenix, AZ USA
[4] Arizona State Univ, Polytech Sch, Phoenix, AZ USA
[5] Intel Corp, Mountain View, CA USA
来源
IFAC PAPERSONLINE | 2021年 / 54卷 / 20期
关键词
Responsibility sensitive safety; naturalistic driving; model calibration; optimization; and automated vehicles; CRASH;
D O I
10.1016/j.ifacol.2021.11.211
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The definition and evaluation of driving safety for automated vehicles (AVs) is a key element to enable safe and scalable AV applications. Although different distance based and/or time-based safety metrics have been proposed, a unified standard has not yet been recognized as a baseline to assess the safety of AV behavior. Moreover, the relationship between existing AV safety metrics (models) and naturalistic driving data, which indicate driving safety and comfort defined by human drivers, is not well explored. Utilizing the responsibility-sensitive safety (RSS) model, a new methodology is proposed to calibrate the RSS model based on naturalistic driving data. Without significantly relying on (large) safety-critical or collision data, the proposed method defines an optimization framework to calibrate the RSS model parameters and describes AV driving safety through both safe and safety-critical data in a cross-checking manner. Evaluation of the calibrated RSS model is discussed based on naturalistic driving data in Los Angeles, USA. Copyright (C) 2021 The Authors.
引用
收藏
页码:430 / 436
页数:7
相关论文
共 18 条
  • [1] Evaluation and Optimization of Responsibility-Sensitive Safety Models on Autonomous Car-Following Maneuvers
    Chai, Chen
    Zeng, Xianming
    Wu, Xiangbin
    Wang, Xuesong
    [J]. TRANSPORTATION RESEARCH RECORD, 2020, 2674 (11) : 662 - 673
  • [2] Chai C, 2019, IEEE INT C INTELL TR, P175, DOI 10.1109/ITSC.2019.8917421
  • [3] Dingus T., 2006, The 100-Car naturalistic driving study phase II-Results of the 100-Car field experiment
  • [4] Hayward J.C., 1971, M.S. Thesis
  • [5] Development and Performance Enhancement of an Overactuated Autonomous Ground Vehicle
    Huang, Yiwen
    Wang, Fengchen
    Li, Ao
    Shi, Yue
    Chen, Yan
    [J]. IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2021, 26 (01) : 33 - 44
  • [6] Naturalistic assessment of novice teenage crash experience
    Lee, Suzanne E.
    Simons-Morton, Bruce G.
    Klauer, Sheila E.
    Ouimet, Marie Claude
    Dingus, Thomas A.
    [J]. ACCIDENT ANALYSIS AND PREVENTION, 2011, 43 (04) : 1472 - 1479
  • [7] A Situation-Aware Collision Avoidance Strategy for Car-Following
    Li, Li
    Peng, Xinyu
    Wang, Fei-Yue
    Cao, Dongpu
    Li, Lingxi
    [J]. IEEE-CAA JOURNAL OF AUTOMATICA SINICA, 2018, 5 (05) : 1012 - 1016
  • [8] Application of proximal surrogate indicators for safety evaluation: A review of recent developments and research needs
    Mahmud S.M.S.
    Ferreira L.
    Hoque M.S.
    Tavassoli A.
    [J]. IATSS Research, 2017, 41 (04) : 153 - 163
  • [9] Mattas K., 2019, 98 ANN M TRANSP RES
  • [10] Mitchell M., 1998, INTRO GENETIC ALGORI