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
  • [11] Compensatory Behavior of Drivers When Conversing on a Cell Phone Investigation with Naturalistic Driving Data
    Fitch, Gregory M.
    Grove, Kevin
    Hanowski, Richard J.
    Perez, Miguel A.
    TRANSPORTATION RESEARCH RECORD, 2014, (2434) : 1 - 8
  • [12] Analyzing and Modeling Drivers' Deceleration Behavior from Normal Driving
    Deligianni, Stavroula Panagiota
    Quddus, Mohammed
    Morris, Andrew
    Anvuur, Aaron
    Reed, Steven
    TRANSPORTATION RESEARCH RECORD, 2017, (2663) : 134 - 141
  • [13] Cut-in Behavior Analyses Based on Naturalistic Driving Data
    Wang X.
    Yang M.
    2018, Science Press (46): : 1057 - 1063
  • [14] How Risky Are ADHD Teen Drivers? Analysis of ADHD Teen Drivers Using Naturalistic Driving Data
    Ankem, Gayatri
    Klauer, Charlie
    Ollendick, Thomas
    Dingus, Thomas
    Guo, Feng
    JOURNAL OF TRANSPORT & HEALTH, 2018, 9 : S13 - S13
  • [15] Driver Identification Using Vehicle Acceleration and Deceleration Events from Naturalistic Driving of Older Drivers
    Fung, Nathanael C.
    Wallace, Bruce
    Chan, Adrian D. C.
    Goubran, Rafik
    Porter, Michelle M.
    Marshall, Shawn
    Knoefel, Frank
    2017 IEEE INTERNATIONAL SYMPOSIUM ON MEDICAL MEASUREMENTS AND APPLICATIONS (MEMEA), 2017, : 33 - 38
  • [16] OLDER DRIVERS' COMFORT SCORES AND NATURALISTIC WINTER DRIVING BEHAVIOR
    Myers, A.
    Trang, A.
    Blanchard, R. A.
    Crizzle, A.
    GERONTOLOGIST, 2009, 49 : 320 - 321
  • [17] Learning the Driver Acceleration/Deceleration Behavior Under High-Speed Environments From Naturalistic Driving Data
    Liu, Chenhui
    Zhang, Wei
    IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE, 2022, 14 (03) : 78 - 91
  • [18] Identifying Distracted and Drowsy Drivers Using Naturalistic Driving Data
    Yadawadkar, Sujay
    Mayer, Brian
    Lokegaonkar, Sanket
    Islam, Mohammed Raihanul
    Ramakrishnan, Naren
    Song, Miao
    Mollenhauer, Michael
    2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2018, : 2019 - 2026
  • [19] Discretionary Cut-In Driving Behavior Risk Assessment Based on Naturalistic Driving Data
    Gao, Hongbo
    Hu, Chuan
    Xie, Guotao
    Han, Chao
    IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE, 2022, 14 (03) : 29 - 40
  • [20] ConvMLP for Driving Behavior Detection from Naturalistic Driving Data
    Gao, Jun
    Yi, Jiangang
    Murphey, Yi Lu
    2022 34TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC, 2022, : 640 - 645