换道过程中驾驶人感知操作的模式发现与规则挖掘

摘要/Abstract

摘要：

为发现高速公路下自由换道过程中眼睛感知-手脚操作之间的时序关联性，探索感知与操作相互作用的内在机理，采用驾驶模拟舱进行高速公路驾驶实验，采集眼动数据和车辆运行数据；分别提取换道瞬时和换道全过程的眼睛感知-手脚操作的特征；采用Aprior算法从换道瞬时和换道全过程两个角度发现眼睛感知-手脚操作的频繁模式，挖掘它们的关联规则。对于瞬时感知-操作，左换道发现13种频繁3项集模式，右换道发现18种频繁3项集模式；对于全过程感知- 操作，左换道发现4种频繁模式，右换道发现3种频繁模式。左右换道各自挖掘到6条有实际价值的关联规则。对频繁模式和规则分析发现：右换道比左换道需要较多的感知时间、较复杂的手脚操作行为。发现的频繁模式和挖掘的关联规则描述了自由换道过程中感知操作的特征和它们之间的关联性，能够为安全换道提供参考，为无人驾驶换道操作提供支撑。

关键词: 交通工程, 关联规则, Aprior算法, 换道行为, 模式发现

Abstract:

To find the correlation between eye perceiving and hand- foot operating during the process of discretionary lane changing on the highway, and to explore the mechanism of the interaction between perception and operation, a driving simulator was used to carry out a highway driving experiment. Eye movement data and vehicle operation data were collected. Features of eye perceiving and hand-foot operating for an instant and during the whole process of lane changing were extracted. The Aprior algorithm was used to find frequent patterns and mine association rules of eye perceiving and hand- foot operating. For the instantaneous perception- operation, 13 frequent 3-itemset patterns were found in left lane changing and 18 frequent 3- itemset patterns were found in right lane changing. For the whole process, 4 frequent patterns were found in left lane changing and 3 frequent patterns were found in right lane changing. Six valuable association rules were found in the left lane and the right lane, respectively. Through the analysis of frequent patterns and association rules, right lane changing needs more perception time and more complicated handfoot operating behavior than left lane changing. The frequent patterns and association rules describe the characteristics of perception- operation and the association between them in the process of discretionary lane changing, which can provide the reference for safe lane changing and support for lane changing operations of unmanned vehicles.

Key words: traffic engineering, association rules, Aprior algorithm, lane changing behavior, pattern discovery

中图分类号:

U491

龙彦，黄建玲，赵晓华. 换道过程中驾驶人感知操作的模式发现与规则挖掘[J]. 交通运输系统工程与信息, 2021, 21(3): 237-246.

LONG Yan，HUANG Jian-ling，ZHAO Xiao-hua. Pattern Discovery and Rule Mining of Drivers' Perception and Operation During Lane Changing Process[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(3): 237-246.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.tseit.org.cn/CN/abstract/abstract30266.shtml

http://www.tseit.org.cn/CN/Y2021/V21/I3/237

参考文献

[1] 谷新平, 韩云鹏, 于俊甫.基于决策机理与支持向量机的车辆换道决策模型[J]. 哈尔滨工业大学学报, 2020, 52(7): 111-121. [GU X P, HAN Y P, YU J F. Vehicle lane- changing decision model based on decision mechanism and support vector machine[J]. Journal of Harbin Institute of Technology (Harbin Inst Technol), 2020, 52(7): 111-121.]

[2] 陆建, 李英帅. 车辆换道行为建模的回顾与展望[J]. 交通运输系统工程与信息, 2017, 17(4): 48-55. [LU J, LI Y S. Review and outlook of modeling of lane changing behavior[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(4): 48-55.]

[3] LIU A, PENTLAND A. Towards real-time recognition of driver intentions[C]. IEEE Conference on Intelligent Transportation System, Boston, IEEE, 1997.

[4] PENTLAND A, LIU A. Modeling and prediction of human behavior[J]. Neural Computation, 2014, 11(1): 229-242.

[5] KUGE N, et al. A driver behavior recognition method based on a driver model framework[R]. Warrendale PA: SAE, 2000.

[6] 宗长富, 王畅, 何磊, 等. 基于双层隐式马尔科夫模型的驾驶意图辨识[J]. 汽车工程, 2011, 33(8): 701-706. [ZONG C F, WANG C, HE L, et al. Driving intention recognition based on double-layer HMM[J]. Automotive Engineering, 2011, 33(8): 701-706.]

[7] 王一男. 基于隐马尔科夫模型的驾驶员换道驾驶意图识别方法研究[D]. 长春: 吉林大学, 2020. [WANG Y N. Drivers' lane changing intention recognition method research based on Hidden Markov Model[D]. Changchun: Jilin University, 2020.]

[8] 胡少伟. 基于驾驶意图识别的主动换道系统研究[D]. 北京: 清华大学, 2019. [HU S W. Research on active lane change system based on driving intention recognition [D]. Beijing: Tsinghua University, 2019.]

[9] MANDALIA H M, SALVUCCI D D. Using support vector machines for lane- change detection[C]. Orlando, FL, United States: Proceedings of the Human Factors &Ergonomics Society Annual Meeting, 2005.

[10] KUMAR P, et al. Learning-based approach for online lane change intention prediction[C]. Gold Coast, QLD: IEEE Intelligent Vehicles Symposium, 2013.

[11] LIU L, XU G, SONG Z. Driver lane changing behavior analysis based on parallel Bayesian networks[C]. Yantai, China: the 6th International Conference on Natural Computation, IEEE, 2010.

[12] PENG J, et al. Multi- parameter prediction of drivers' lane-changing behaviour with neural network model[J]. Applied Ergonomics, 2015, 50: 207-217.

[13] WIRTHMUELLER F, et al. Predicting the Time until a vehicle changes the lane using LSTM-based recurrent neural networks[J]. IEEE Robotics and Automation Letters, 2021, DOI 10.1109/LRA.2021.3058930.

[14] GEBERT P, et al. End- to- end prediction of driver intention using 3D convolutional neural networks[C]. Paris, France: Institute of Electrical and Electronics Engineers Inc, IEEE Intelligent Vehicles Symposium, 2019.

[15] GIPPS P G. A model for the structure of lane- changing decisions[J]. Transportation Research Part B: Methodological, 1986, 20(5): 403-414.

[16] TOLEDO T, CHOUDHURY C F, BEN- AKIVA M E. Lane- changing model with explicit target lane choice[J]. Transportation Research Record, 2005(1934): 157-165.

[17] 许伦辉, 倪艳明, 罗强, 等. 基于最小安全距离的车辆换道模型研究[J]. 广西师范大学学报(自然科学版), 2011, 29(4): 1-6. [XU L H, NI Y M, LUO Q, et al. Lanechanging model based on minimum safety distance[J]. Journal of Guangxi Normal University(Natural Science Edition), 2011, 29(4): 1-6.

[18] MORIDPOUR S, et al. Lane-changing decision model for heavy vehicle drivers[J]. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 2012, 16(1): 24-35.

[19] 刘晨强. 车辆轨迹数据与换道行为特性研究[D]. 北京: 北京工业大学, 2018. [LIU C Q. Research on vehicle trajectory and the characteristics of lanechanging behavior[D]. Beijing: Beijing University of Technology, 2018.

[20] ZHAO X, et al. The "PNE" driving simulator- based training model founded on the theory of planned behavior [J]. Cognition Technology & Work, 2019, 21(2): 287-300.

[21] 郑淑欣. 基于循环神经网络的车辆换道轨迹评价方法研究[D]. 北京: 北京工业大学, 2020. [ZHENG S X. Research on evaluation method of lane-changing trajectory based on recurrent neural network[D]. Beijing: Beijing University of Technology, 2020.]

[22] 李慧轩. 基于驾驶行为动态获取的换道行为微观建模及仿真校验研究[D]. 北京: 北京交通大学, 2016. [LI H X. Research on microscopic modeling and simulation validation of lane changing behavior based on dynamic acquisition of driving behavior[D]. Beijing: Beijing Jiaotong University, 2016.]

[23] 向馗, 蒋静坪. 时间序列的符号化方法研究[J]. 模式识别与人工智能, 2007, 20(2): 154-161. [XIANG K, JIANG J P. Study on symbolization analysis of time series [J]. Pattern Recognition and Artificial Intelligence, 2007, 20(2): 154-161.]