Analysis of drivers' deceleration behavior based on naturalistic driving data

被引:19
|
作者
Li, Shuang [1 ]
Li, Penghui [2 ,3 ,4 ]
Yao, Yao [5 ]
Han, Xiaofeng [1 ]
Xu, Yanhai [6 ]
Chen, Long [2 ,3 ]
机构
[1] Harbin Inst Technol, Control & Simulat Ctr, Harbin, Peoples R China
[2] Tsinghua Univ, Sch Vehicle & Mobil, State Key Lab Automot Safety & Energy, Beijing, Peoples R China
[3] China Automot Engn Res Inst Co Ltd, State Key Lab Vehicle NVH & Safety Technol, Chongqing, Peoples R China
[4] Univ Leeds, Inst Transport Studies, Leeds, W Yorkshire, England
[5] Minist Transport, Rd Safety Res Ctr, Res Inst Highway, Beijing, Peoples R China
[6] Xihua Univ, Sichuan Key Lab Automot Control & Safety, Chengdu, Peoples R China
基金
中国国家自然科学基金;
关键词
Deceleration mode; naturalistic driving data; logistic regression; intelligent vehicle; AUTOMATION;
D O I
10.1080/15389588.2019.1707194
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Objective: As one of the bases for designing a humanlike brake control system for the intelligent vehicle, drivers' deceleration behavior needs to be understood. There are two modes for drivers' deceleration behavior: (i) brake pedal input, by applying brake system to reduce the speed; (ii) no pedal input, by releasing the accelerator pedal without pressing the brake pedal, thus decelerating by naturalistic driving resistance. The deceleration behavior that drivers choose to press the brake pedal has been investigated in previous studies. However, releasing the accelerator pedal behavior has not received much attention. The objective of this study is to investigate factors that influence drivers' choice of the two deceleration modes using naturalistic driving data, which provide a theoretical foundation for the design of the brake control system. Methods: A logistic model was constructed to model drivers' deceleration mode, valued as "no pedal input" or "brake pedal input" for dependent variables. Factors such as Light condition, Intersection mode, Road alignment, Traffic flow, Traffic light, Ego-vehicle motion state, Lead vehicle motion state, Time headway (THW), and Ego-vehicle speed were considered in the model as independent variables. Results: 393 deceleration events were selected from the naturalistic driving data, which used as the database for the regression model. As a result, 6 remarkable factors were found to influence drivers' deceleration model, which include Traffic flow, Intersection mode, Lead vehicle motion state, Ego-vehicle motion state, Ego-vehicle speed and THW. Specifically, (1) the possibility of drivers choosing "no pedal input" is gradually increasing with the increase of THW and speed; (2) The drivers prefer to choose "no pedal input" when the lead vehicle is decelerating compared to it's stationary. This probability is relatively high when the lead vehicle is traveling along the road; (3) the possibility of choosing "no pedal input" at intersection is higher than roads without intersection; (4) the possibility of choosing "no pedal input" is higher when traveling with more traffic flow. Conclusion: The drivers' deceleration behavior can be divided into "no pedal input" and "brake pedal input." The following six factors significantly affect drivers' choice of deceleration mode: Traffic flow, Intersection mode, Lead vehicle motion state, Ego-vehicle motion state, Ego-vehicle speed and THW. The logistic regression model can quantify the influence of these six factors on drivers' deceleration behavior. This study provides a theoretical basis for the braking system design of ADAS (Advanced Driving Assistant System) and intelligent control system.
引用
收藏
页码:42 / 47
页数:6
相关论文
共 50 条
  • [1] Detecting Distraction Behavior of Drivers Using Naturalistic Driving Data
    Sun J.
    Zhang Y.-H.
    Wang J.-H.
    Zhongguo Gonglu Xuebao/China Journal of Highway and Transport, 2020, 33 (09): : 225 - 235
  • [2] Driving Style Recognition of Taxi Drivers Based on Naturalistic Driving Data
    Yan, Pengwei
    Zhao, Xiaohua
    Yao, Ying
    Ma, Xiaogang
    CICTP 2023: INNOVATION-EMPOWERED TECHNOLOGY FOR SUSTAINABLE, INTELLIGENT, DECARBONIZED, AND CONNECTED TRANSPORTATION, 2023, : 1225 - 1234
  • [3] Analysis of cut-in behavior based on naturalistic driving data
    Wang, Xuesong
    Yang, Minming
    Hurwitz, David
    ACCIDENT ANALYSIS AND PREVENTION, 2019, 124 : 127 - 137
  • [4] GIS Mapping of Driving Behavior Based on Naturalistic Driving Data
    Balsa-Barreiro, Jose
    Valero-Mora, Pedro M.
    Berne-Valero, Jose L.
    Varela-Garcia, Fco-Alberto
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2019, 8 (05):
  • [5] An Open-loop Model of Bus Drivers' Lane Change Behavior Based on Naturalistic Driving Data
    Huang, Xuyin
    Pi, Dawei
    Xie, Boyuan
    Wang, Hongliang
    PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021), 2021, : 4303 - 4310
  • [6] Driving Style Recognition Based on Lane Change Behavior Analysis Using Naturalistic Driving Data
    Gao, Zhen
    Liang, Yongchao
    Zheng, Jiangyu
    Chen, Junyi
    CICTP 2020: TRANSPORTATION EVOLUTION IMPACTING FUTURE MOBILITY, 2020, : 4449 - 4461
  • [7] Analysis and Identification of Drivers' Difference in Car-following Condition Based on Naturalistic Driving Data
    Liu Z.-Q.
    Zhang K.-D.
    Ni J.
    Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology, 2021, 21 (01): : 48 - 55
  • [8] Naturalistic rapid deceleration data: Drivers aged 75 years and older
    Chevalier, Anna
    Chevalier, Aran John
    Clarke, Elizabeth
    Coxon, Kristy
    Brown, Julie
    Rogers, Kris
    Boufous, Soufiane
    Ivers, Rebecca
    Keay, Lisa
    DATA IN BRIEF, 2016, 9 : 909 - 916
  • [9] Revision of the driver behavior questionnaire for Chinese drivers? aberrant driving behaviors using naturalistic driving data
    Jiao, Yujun
    Wang, Xuesong
    Hurwitz, David
    Hu, Gengdan
    Xu, Xiaoyan
    Zhao, Xudong
    ACCIDENT ANALYSIS AND PREVENTION, 2023, 187
  • [10] Examination of drivers' cell phone use behavior at intersections by using naturalistic driving data
    Xiong, Huimin
    Bao, Shan
    Sayer, James
    Kato, Kazuma
    JOURNAL OF SAFETY RESEARCH, 2015, 54 : 89 - 93