|Table of Contents|

Speed estimation model during lane-changing decision

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2015年06期
Page:
83-91
Research Field:
交通运输规划与管理
Publishing date:

Info

Title:
Speed estimation model during lane-changing decision
Author(s):
WANG Chang1 FU Rui1 ZHANG Qiong2
1. School of Automobile, Chang’an University, Xi’an 710064, Shaanxi, China; 2. China Communications Press Co., Ltd., Beijing 100011, China
Keywords:
lane-changing decision speed estimation driver perception characteristic regression analysis
PACS:
U491
DOI:
-
Abstract:
In order to research the driver’s estimation characteristic for rear-vehicle speed during lane-changing decision, small passenger car was used as test platform and established by using microwave radar, vehicle CAN-bus data logger, audio-video monitoring system. 15 drivers were recruited, and the estimation test of rear-vehicle speed was carried out in a normal highway. The speeds of test vehicle were set as 60, 70, 80, 90 km·h-1 separately, and 1 625 sets of data were obtained finally. The impact characteristics of relative speed, rear-vehicle speed, and relative distance on driver’s speed estimation behavior were analyzed by using significance analysis method. A driver’s speed estimation model was established by using multiple linear regression theory, and the model was examined. Analysis result shows that 60% of absolute values of speed estimate errors are no more than 10 km·h-1, and the driver’s speed estimation error follows a normal distribution. Driver’s speed estimation error decreases with the increase of relative speed and rear-vehicle speed, when the relative speed or rear-vehicle speed is lower, rear-vehicle speed is overestimated, and when the relative speed or rear-vehicle’s speed is higher, rear-vehicle speed is underestimated. When the relative distance of vehicles increase, the driver’s speed estimation error change very small. However, when the relative distance is smaller, rear-vehicle speed is overestimated by driver. The average estimation errors of speed estimation model is -0.56 km·h-1, so the model is feasible. 3 tabs, 12 figs, 23 refs.

References:

[1] MILANéS V, ALONSO J, BOURAOUI L, et al. Cooperative maneuvering in close environments among cybercars and dual-mode cars[J]. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(1): 15-24.
[2] RISTO M, MARTENS M H. Driver headway choice: a comparison between driving simulator and real-road driving[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2014, 25(3): 1-9.
[3] 程国柱,韩 娟.高速公路夜间最高车速限制研究[J].中国公路学报,2013,26(2):147-153.CHENG Guo-zhu, HAN Juan.Maximum speed limit on expressway at night[J]. China Journal of Highway and Transport, 2013, 26(2): 147-153.(in Chinese)
[4] 程国柱,胡立伟,韩 娟.高速公路驾驶员昼夜感知速度变化规律[J].东南大学学报:自然科学版,2012,42(3):547-550.CHENG Guo-zhu, HU Li-wei, HAN Juan. Variation rule of driver’s perception speed on freeway during daytime and nighttime[J]. Journal of Southeast University: Natural Science Editon, 2012, 42(3): 547-550.(in Chinese)
[5] HAGLUND M, ?BERG L. Speed choice in relation to speed limit and influences from other drivers[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2000, 3(1): 39-51.
[6] SUH W, PARK P Y J, PARK C H, et al. Relationship between speed, lateral placement, and drivers’ eye movement at two-lane rural highways[J]. Journal of Transportation Engineering, 2006, 132(8): 649-653.
[7] 廖律超,蒋新华,林铭榛,等.基于交通轨迹数据挖掘的道路限速信息识别方法[J].交通运输工程学报,2015,15(5):118-126.LIAO Lu-chao, JIANG Xin-hua, LIN Ming-zhen, et al. Recognition method of road speed limit information based on data mining of traffic trajectory[J]. Journal of Traffic and Transportation Engineering, 2015, 15(5): 118-126.(in Chinese)
[8] KONSTANTOPOULOS P, CHAPMAN P, CRUNDALL D. Driver’s visual attention as a function of driving experience and visibility. Using a driving simulator to explore drivers’ eye movements in day, night and rain driving[J]. Accident Analysis and Prevention, 2010, 42(3): 827-834.
[9] THEOFILATOS A, YANNIS G. A review of the effect of traffic and weather characteristics on road safety[J]. Accident Analysis and Prevention, 2014, 72(1): 244-256.
[10] AARTS L, SCHAGEN I V. Driving speed and the risk of road crashes: a review[J]. Accident Analysis and Prevention, 2006, 38(2): 215-224.
[11] 王 畅,付 锐,张 琼,等.换道预警系统中参数TTC特性研究[J].中国公路学报,2015,28(8):91-100,108.WANG Chang, FU Rui, ZHANG Qiong, et al. Research on parameter TTC characteristics of lane change warning system[J]. China Journal of Highway and Transport, 2015, 28(8): 91-100, 108.(in Chinese)
[12] 康国祥.地面动态环境中驾驶员空间距离判识的研究[D].西安:长安大学,2005.KANG Guo-xiang. The study of driver’s space distance judgment on ground dynamic environment[D]. Xi’an: Chang’an University, 2005.(in Chinese)
[13] 魏建国,赵炜华,熊保林.不同因素对驾驶人夜间视认距离影响[J].中国安全科学学报,2013,23(12):21-27.WEI Jian-guo, ZHAO Wei-hua, XIONG Bao-lin. Effects of different factors on visual cognition distance of drivers at night[J]. China Safety Science Journal, 2013, 23(12): 21-27.(in Chinese)
[14] 周立军.基于驾驶员信息处理特性的跟驰及换道模型研究[D].长春:吉林大学,2008.ZHOU Li-jun. Research on car-following and lane-changing model based on driver’s information processing characteristics[D]. Changchun: Jilin University, 2008.(in Chinese)
[15] 彭金栓.基于视觉特性与车辆相对运动的驾驶人换道意图识别方法[D].西安:长安大学,2012.PENG Jin-shuan. Driver’s lane change intent identification based on visual characteristics and vehicles’ relative movements[D]. Xi’an: Chang’an University, 2012.(in Chinese)
[16] PENG Jin-shuan, GUO Ying-shi, FU Rui, et al. Multi-parameter prediction of drivers’ lane-changing behaviour with neural network model[J]. Applied Ergonomics, 2015, 50(1): 207-217.
[17] SNOWDEN R J, STIMPSON N, RUDDLE R A. Speed perception fogs up as visibility drops[J]. Nature, 1998, 392(6675): 450.
[18] DELUCIA P R. Effects of size on collision perception and implications for perceptual theory and transportation safety[J]. Current Directions in Psychological Science, 2013, 22(3): 199-204.
[19] JONASSON J K, ROOTZEN H. Internal validation of near-crashes in naturalistic driving studies: a continuous and multivariate approach[J]. Accident Analysis and Prevention, 2014, 62(5): 102-109.
[20] HAHNEL U J J, HECHT H. The impact of rear-view mirror distance and curvature on judgements relevant to road safety[J]. Ergonomics, 2012, 55(1): 23-36.
[21] KIEFER R J, FLANNAGAN C A, JEROME C J. Time-to-collision judgments under realistic driving conditions[J]. Human Factors, 2006, 48(2): 334-345.
[22] LEE S E, SIMONS-MORTON B G, KLAUER S E, et al. Naturalistic assessment of novice teenage crash experience[J]. Accident Analysis and Prevention, 2011, 43(4): 1472-1479.
[23] 李瑞敏,马 玮.基于BP神经网络与D-S证据理论的路段平均速度融合方法[J].交通运输工程学报,2014,14(5):111-118.LI Rui-min, MA Wei. Fusion method of road section average speed based on BP neural network and D-S evidence theory[J]. Journal of Traffic and Transportation Engineering, 2014, 14(5): 111-118.(in Chinese)

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