DRIVING RHYTHM METHOD FOR DRIVING COMFORT ANALYSIS ON RURAL HIGHWAYS

被引:8
|
作者
Yu, Bo [1 ]
Chen, Yuren [1 ]
机构
[1] Tongji Univ, Key Lab Rd & Traff Engn, Minist Educ, Transportat Engn Sch, 4800 Caoan Highway, Shanghai 201804, Peoples R China
来源
PROMET-TRAFFIC & TRANSPORTATION | 2017年 / 29卷 / 04期
基金
美国国家科学基金会;
关键词
driving comfort; driver's visual lane model; driving rhythm; BP neural network; wavelet transform; RIDE COMFORT; VERTICAL ALIGNMENT; INJURY SEVERITY; NETWORK; 2-LANE; SAFETY; ROADS;
D O I
10.7307/ptt.v29i4.2217
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
Driving comfort is of great significance for rural highways, since the variation characteristics of driving speed are comparatively complex on rural highways. Earlier studies about driving comfort were usually based on the actual geometric road alignments and automobiles, without considering the driver's visual perception. However, some scholars have shown that there is a discrepancy between actual and perceived geometric alignments, especially on rural highways. Moreover, few studies focus on rural highways. Therefore, in this paper the driver's visual lane model was established based on the Catmull-Rom spline, in order to describe the driver's visual perception of rural highways. The real vehicle experiment was conducted on 100 km rural highways in Tibet. The driving rhythm was presented to signify the information during the driving process. Shape parameters of the driver's visual lane model were chosen as input variables to predict the driving rhythm by BP neural network. Wavelet transform was used to explore which part of the driving rhythm is related to the driving comfort. Then the probabilities of good, fair and bad driving comfort can be calculated by wavelets of the driving rhythm. This work not only provides a new perspective into driving comfort analysis and quantifies the driver's visual perception, but also pays attention to the unique characteristics of rural highways.
引用
收藏
页码:371 / 379
页数:9
相关论文
共 50 条
  • [41] Optimization of Driving Comfort of Golf Cart
    Jirova, Radka
    Pesik, Lubomir
    Svoboda, Roman
    [J]. CURRENT METHODS OF CONSTRUCTION DESIGN, 2020, : 89 - 94
  • [43] Research on probability index of road driving comfort based on driving vibration distribution
    Chen, Guanghua
    Zhang, Jinxi
    Liu, Pengfei
    Liang, Liduan
    [J]. ROAD MATERIALS AND PAVEMENT DESIGN, 2023, 24 (12) : 2994 - 3012
  • [44] Crashes and near-crashes on horizontal curves along rural two-lane highways: Analysis of naturalistic driving data
    Wang, Bo
    Hallmark, Shauna
    Savolainen, Peter
    Dong, Jing
    [J]. JOURNAL OF SAFETY RESEARCH, 2017, 63 : 163 - 169
  • [45] Low cost sensing for autonomous car driving in highways
    Goncalves, Andre
    Godinho, Andre
    Sequeira, Joao
    [J]. ICINCO 2007: PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS, VOL RA-2: ROBOTICS AND AUTOMATION, VOL 2, 2007, : 370 - 377
  • [46] A New Method and Results for Analyzing Decision-Making Processes in Automated Driving on Highways
    Altendorf, Eugen
    Schreck, Constanze
    Flemisch, Frank
    [J]. ADVANCES IN HUMAN ASPECTS OF TRANSPORTATION, 2017, 484 : 571 - 583
  • [47] A driving simulation study to examine the impact of available sight distance on driver behavior along rural highways
    Bassani, M.
    Catani, L.
    Salussolia, A.
    Yang, C. Y. D.
    [J]. ACCIDENT ANALYSIS AND PREVENTION, 2019, 131 : 200 - 212
  • [48] A hybrid controller for autonomous vehicles driving on automated highways
    Girault, A
    [J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2004, 12 (06) : 421 - 452
  • [49] Recognizing Temporary Changes on Highways for Reliable Autonomous Driving
    Seo, Young-Woo
    Wettergreen, David
    Zhang, Wende
    [J]. PROCEEDINGS 2012 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2012, : 3027 - 3032
  • [50] Legible Model Predictive Control for Autonomous Driving on Highways
    Bruedigam, Tim
    Ahmic, Kenan
    Leibold, Marion
    Wollherr, Dirk
    [J]. IFAC PAPERSONLINE, 2018, 51 (20): : 215 - 221