|Table of Contents|

Overview of recognition and evaluation of driving characteristics and their applications in intelligent vehicles(PDF)

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

Issue:
2021年02期
Page:
7-20
Research Field:
综述
Publishing date:

Info

Title:
Overview of recognition and evaluation of driving characteristics and their applications in intelligent vehicles
Author(s):
GUO Lie MA Yue YUE Ming QIN Zeng-ke
(School of Automotive Engineering, Dalian University of Technology, Dalian 116024, Liaoning, China)
Keywords:
automotive engineering intelligent vehicle driving characteristic assistant driving human-machine co-driving
PACS:
U471
DOI:
10.19818/j.cnki.1671-1637.2021.02.002
Abstract:
The methods for the recognition of driving characteristics, the research progress on driver takeover ability, and the application of driving characteristics to the field of intelligent vehicles were studied. The driver condition monitoring was divided into driver fatigue, distraction, and bad driving behavior monitoring. The research targets, methods, accuracy, judgment standards, and advantages and disadvantages of driver condition monitoring were summarized. The differences in various detection signals in the driver fatigue monitoring method were compared and analyzed. The methods for driver intention identification and prediction based on the fuzzy recognition and hidden Markov models were discussed and evaluated. The main steps and features of typical identification methods for driving style classification and identification were summarized. The influencing factors and evaluation criteria for driver takeover ability were analyzed. The major ways that driving characteristics were used to develop assistant driving systems with high user acceptance and excellent human-machine interaction performance were expounded. The approach considering the driving characteristics in human-machine co-driving cooperative control was summarized. Analysis result shows that driver condition monitoring methods based on the multi-sensor signal fusion can effectively avoid the disadvantages of single sensor-based methods, and increase the detection accuracy, and decrease the false alarms. Combining traditional prediction models with hybrid intelligent learning is the main solution for the online recognition and prediction of driving intentions. The identification of driving characteristics under complex conditions is the primary research focus. The research on driver takeover ability needs to be theoretical and systematic. Developing an integrated assistant driving technology based on driving characteristics and realizing the interaction of intention and control strategy between the driver and the assistant driving system under typical road conditions is a future research trend. Considering the driving characteristics of personalized drivers in the design of co-driving coefficients helps to improve the personalization, intelligence level, and environmental adaptability of human-machine co-driving systems. 4 tabs, 5 figs, 82 refs.

References:

[1] 陈 虹,郭露露,边 宁.对汽车智能化进程及其关键技术的思考[J].科技导报,2017,35(11):52-59.
CHEN Hong, GUO Lu-lu, BIAN Ning. On automobile intelligentization and key technologies[J]. Science and Technology Review, 2017, 35(11): 52-59.(in Chinese)
[2] 杜明博.基于人类驾驶行为的无人驾驶车辆行为决策与运动规划方法研究[D].合肥:中国科学技术大学,2016.
DU Ming-bo. Research on behavioral decision making and motion planning methods of autonomous vehicle based on human driving behavior[D]. Hefei: University of Science and Technology of China, 2016.(in Chinese)
[3] 《中国公路学报》编辑部.中国汽车工程学术研究综述·2017[J].中国公路学报,2017,30(6):1-197.
Editorial Department of China Journal of Highway and Transport. Review on China's automotive engineering research progress: 2017[J]. China Journal of Highway and Transport, 2017, 30(6): 1-197.(in Chinese)
[4] WANG Wu-hong, MAO Yan, JIN Jing, et al. Driver's
various information process and multi-ruled decision-making mechanism: a fundamental of intelligent driving shaping model[J]. International Journal of Computational Intelligence Systems, 2011, 4(3): 297-305.
[5] LIN Na, ZONG Chang-fu, TOMIZUKA M, et al. An overview on study of identification of driver behavior characteristics for automotive control[J]. Mathematical Problems in Engineering, 2014(5): 1-15.
[6] PAPACOSTAS C S, SYNODINOS N E. Dimensions of
driving behaviour and driver characteristics[J]. Applied Psychology, 2008, 37(1): 3-13.
[7] ANGKITITRAKUL P, RYUTA T, WAKITA T, et al.
Evaluation of driver-behavior models in real-world car-following task[C]∥IEEE. Proceedings of IEEE International Conference on Vehicular Electronics and Safety. New York: IEEE, 2009: 113-118.
[8] 刘明周,蒋倩男,扈 静.基于面部几何特征及手部运动特征的驾驶人疲劳检测[J].机械工程学报,2019,55(2):18-26.
LIU Ming-zhou, JIANG Qian-nan, HU Jing. Based on facial geometric features and hand motion characteristics driver fatigue detection[J]. Journal of Mechanical Engineering, 2019, 55(2): 18-26.(in Chinese)
[9] 张玉峰.驾驶人状态检测及其在人机共驾中的应用[D].重庆:重庆大学,2018.
ZHANG Yu-feng. Research on driver's state detection and application on man-machine shared driving[D]. Chongqing: Chongqing University, 2018.(in Chinese)
[10] LIN C T, CHANG C J, LIN B S, et al. A real-time wireless brain-computer interface system for drowsiness detection[J]. IEEE Transactions on Biomedical Circuits and Systems, 2010, 4(4): 214-222.
[11] LI Ming-ai, ZHANG Cheng, YANG Jin-fu. An EEG-based method for detecting drowsy driving state[C]∥IEEE. International Conference on Fuzzy Systems and Knowledge Discovery. New York: IEEE, 2010: 2164-2167.
[12] VICENTE J, LAGUNA P, BARTRA A, et al. Drowsiness detection using heart rate variability[J]. Medical and Biological Engineering and Computing, 2016, 54(6): 927-937.
[13] CHEN Rong-hua. Sitting behaviour-based pattern recognition for predicting driver fatigue[D]. Victoria: Deakin University, 2013.
[14] TAKEI Y, FURUKAWA Y. Estimate of driver's fatigue through steering motion[C]∥IEEE. International Conference on Systems, Man and Cybernetics. New York: IEEE, 2005: 1765-1770.
[15] LIU C C, HOSKING S G, LENN M G. Predicting driver drowsiness using vehicle measures: recent insights and future challenges[J]. Journal of Safety Research, 2009, 40(4): 239-245.
[16] AREDT J T, WILDE G J S, MUNT P W. How do
prolonged wakefulness and alcohol compare in the decrements they produce on a simulated driving task?[J]. Accident Analysis and Prevention, 2001, 33(3): 337-344.
[17] LENNAE'G M G, TRIGGS T J, REDMAN J R. Interactive effects of sleep deprivation, time of day, and driving experience on a driving task[J]. Sleep, 1998(1): 38-44.
[18] 王 加,陈 慧.基于驾驶人操纵及车辆运动轨迹信息的驾驶分心辨识方法[J].汽车技术,2013(10):14-18.
WANG Jia, CHEN Hui. Recognition of distracted driving based on driver operation signals and vehicle trajectory[J]. Automobile Technology, 2013(10): 14-18.(in Chinese)
[19] KAPLAN S, GUVENSAN M A, YAVUZ A G, et al.
Driver behavior analysis for safe driving: a survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(6): 3017-3032.
[20] 李 力.基于CNNs和LSTM 的驾驶人疲劳和分心状态识别研究[D].长沙:湖南大学,2018.
LI Li. A study recognizing driver fatigue and distraction state by means of CNNs and LSTM[D]. Changsha: Hunan University, 2018.(in Chinese)
[21] YEO M, LI Xiao-ping, SHEN K, et al. Can SVM be used for automatic EEG detection of drowsiness during car driving?[J]. Safety Science, 2009, 47: 115-124.
[22] YAN Shi-yang, TENG Yu-xuan, SMITH J S, et al. Driver behavior recognition based on deep convolutional neural networks [C]∥IEEE. International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery. New York: IEEE, 2016: 636-641.
[23] XING Yang, LYU Chen, ZHANG Zhao-zhong, et al.
Identification and analysis of driver postures for in-vehicle driving activities and secondary tasks recognition[J]. IEEE Transactions on Computational Social Systems, 2017, 5(1): 95-108.
[24] XING Yang, LYU Chen, WANG Hua-ji, et al. Driver
activity recognition for intelligent vehicles: a deep learning approach[J]. IEEE Transactions on Vehicular Technology, 2019, 68(6): 5379-5390.
[25] XING Yang, LYU Chen, WANG Hua-ji, et al. Driver lane change intention inference for intelligent vehicles: framework, survey, and challenges[J]. IEEE Transactions on Vehicular Technology, 2019, 68(5): 4377-4390.
[26] OHASHI L, YAMAGUCHI T, TAMAI I. Humane automotive system using driver intention recognition[C]∥IEEE. SICE 2004 Annual Conference. New York: IEEE, 2004: 1164-1167.
[27] WU H, LI Yan, WU Chao-zhong, et al. A longitudinal minimum safety distance model based on driving intention and fuzzy reasoning[C]∥IEEE. International Conference on Transportation Information and Safety. New York: IEEE, 2017: 158-162.
[28] DING Jie-yun, DANG Rui-na, WANG Jian-qiang, et al.
Driver intention recognition method based on comprehensive lane-change environment assessment[C]∥IEEE. IEEE Intelligent Vehicles Symposium Proceedings. New York: IEEE, 2014: 214-220.
[29] SCHMIDT K, BEGGIATO M, HOFFMANN K H, et al.
A mathematical model for predicting lane changes using the steering wheel angle[J]. Journal of Safety Research, 2014, 49: 85-90.
[30] DRIGGS-CAMPBELL K, BAJCSY R. Identifying modes of intent from driver behaviors in dynamic environments[J]. Computer Science, 2015, 23: 529-45.
[31] 张泽星,宗长富,马福良,等.基于多维高斯隐马尔科夫模型的驾驶人转向行为辨识方法[J].汽车技术,2011(7):1-3.
ZHANG Ze-xing, ZONG Chang-fu, MA Fu-liang, et al. Driving intention recognition on cornering based on multi-dimensional Gaussian hidden Markov model[J]. Automobile Technology, 2011(7): 1-3.(in Chinese)
[32] TAKANO W, MATSUSHITA A, IWAO K, et al.
Recognition of human driving behaviors based on stochastic symbolization of time series signal[C]∥IEEE. IEEE/RSJ International Conference on Intelligent Robots and Systems. New York: IEEE, 2008: 167-172.
[33] ZOU Xi, LEVINSON D M. Modeling pipeline driving behaviors: hidden Markov model approach[J]. Transportation Research Record, 2006(1980): 16-23.
[34] LANSDOWN T C. Individual differences during driver secondary task performance: verbal protocol and visual allocation findings[J]. Accident Analysis and Prevention, 2002, 34(5): 655-662.
[35] YI De-wei, SU Jin-ya, LIU Cun-jia, et al. Trajectory clustering aided personalized driver intention prediction for intelligent vehicles[J]. IEEE Transactions on Industrial Informatics, 2019, 15(6): 3693-3702.
[36] WOO H, MADOKORO H, SATO K, et al. Prediction of
following vehicle trajectory considering operation characteristics of a human driver[C]∥IEEE. 2020 IEEE/SICE International Symposium on System Integration. New York: IEEE, 2020: 712-717.
[37] KIM H, MARTIN S, TAWARI A, et al. Toward real-time estimation of driver situation awareness: an eye-tracking approach based on moving objects of interest[C]∥IEEE. 2020 IEEE Intelligent Vehicles Symposium. New York:IEEE, 2020: 1035-1041.
[38] DOSHI A, TRIVEDI M M. Tactical driver behavior
prediction and intent inference: a review[C]∥IEEE. 14th International IEEE Conference Intelligent Transport System. New York: IEEE, 2011: 1892-1897.
[39] 王 畅.基于隐形马尔科夫模型的驾驶人意图辨识方法研究[D].长春:吉林大学,2011.
WANG Chang. Research on driving intention identification based in hidden Markov model[D]. Changchun: Jilin University, 2011.(in Chinese)
[40] SERTTA瘙塁 T N, GEREK Ö N, HOCAOAGˇGLU F O. Driver classification using K-means clustering of within-car accelerometer data[C]∥IEEE. Signal Processing and Communications Applications Conference. New York: IEEE, 2019: 1-4.
[41] FERNANDEZ S, ITO T. Driver classification for intelligent transportation systems using fuzzy logic[C]∥IEEE. International Conference on Intelligent Transportation Systems. New York: IEEE, 2016: 1212-1216.
[42] 万单盼.驾驶人转向行为特性辨识方法研究[D].长春:吉林大学,2017.
WAN Shan-pan. Research on the identification method of driver steering behavior characteristics[D]. Changchun: Jilin University, 2017.(in Chinese)
[43] 林 娜.“车适应人”线控汽车驾驶人特性辨识算法研究[D].长春:吉林大学,2015.
LIN Na. Research on the identification algorithm of driver behavior characteristics for “car adapts to driver” X-by-wire vehicle[D]. Changchun: Jilin University, 2015.(in Chinese)
[44] HU Lin, BAO Xing-qian, WU He-quan, et al. A study on correlation of traffic accident tendency with driver characters using in-depth traffic accident data[J]. Journal of Advanced Transportation, 2020(6): 1-7.
[45] CHRISTIAN G, QUINTERO M, JOSÉOÑATE LÓPEZ, et al. Driver behavior classification model based on an intelligent driving diagnosis system[C]∥IEEE. International Conference on Intelligent Transportation Systems. New York: IEEE, 2012: 894-899.
[46] 李高超.驾驶人特性辨识及在四轮独立驱动与转向电动汽车上的应用研究[D].锦州:辽宁工业大学,2018.
LI Gao-chao. Research on identification of driver characteristics and application on four wheel independent drive and steering electric vehicle[D]. Jinzhou: Liaoning University of Technology, 2018.(in Chinese)
[47] 李 刚,李高超,韩海兰,等.考虑驾驶人特性的四轮独立驱动电动汽车转向控制研究[J].科学技术与工程,2016,16(28):288-293.
LI Gang, LI Gao-chao, HAN Hai-lan, et al. Study on steering control for wheel independent drive electrical vehicle considering driver characteristic[J]. Science Technology and Engineering, 2016, 16(28): 288-293.(in Chinese)
[48] SUN Bo-hua, DENG Wei-wen, WU Jian, et al. Research on the classification and identification of driver's driving style[C]∥IEEE. International Symposium on Computational Intelligence and Design. New York: IEEE, 2017: 28-32.
[49] AOUDE G S, DESARAJU V R, STEPHENS L H, et al. Driver behavior classification at intersections and validation on large naturalistic data set[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(2): 724-736.
[50] NAUMOV V. EWMA based classification of driver state[C]∥IEEE. 11th International Conference on ITS Telecommunication. New York: IEEE, 2011: 103-106.
[51] ZHU H B, ZHOU Y J, WU W J. Modeling traffic flow
mixed with automated vehicles considering drivers' character difference[J]. Physica A: Statistical Mechanics and its Applications, 2020, 549: 124337-1-11.
[52] 吴超仲,吴浩然,吕能超.人机共驾智能汽车的控制权切换与安全性综述[J].交通运输工程学报,2018,18(6):131-141.
WU Chao-zhong, WU Hao-ran, LYU Neng-chao. Review of control switch and safety of human-computer driving intelligent vehicle[J]. Journal of Traffic and Transportation Engineering, 2018, 18(6): 131-141.(in Chinese)
[53] 胡云峰,曲 婷,刘 俊,等.智能汽车人机协同控制的研究现状与展望[J].自动化学报,2019,45(7):1261-1280.
HU Yun-feng, QU Ting, LIU Jun, et al. Human-machine cooperative control of intelligent vehicle: recent developments and future perspectives[J]. Acta Automatica Sinica, 2019,45(7): 1261-1280.(in Chinese)
[54] ZEEB K, BUCHNER A, SCHRAUF M. Is take-over time all that matters? The impact of visual-cognitive load on driver take-over quality after conditionally automated driving[J]. Accident Analysis and Prevention, 2016, 92: 230-239.
[55] PAUWELUSSEN J, FEENSTRA P J. Driver behavior
analysis during ACC activation and deactivation in a real traffic environment[J]. IEEE Transactions on Intelligent Transportation Systems, 2010, 11(2): 329-338.
[56] HAPPEE R, GOLD C, RADLMAYR J, et al. Take-over performance in evasive manoeuvers[J].Accident Analysis and Prevention, 2017, 106: 211-222.
[57] WANDTNER B, SCHÖMIG N, SCHMIDT G. Effects of
non-driving related task modalities on takeover performance in highly automated driving[J]. Human Factors, 2018, 60(6): 870-881.
[58] 鲁光泉,赵鹏云,王兆杰,等.自动驾驶中视觉次任务对年轻驾驶人接管时间的影响[J].中国公路学报,2018,31(4):165-171.
LU Guang-quan, ZHAO Peng-yun, WANG Zhao-jie, et al. Impact of visual secondary task on young drivers' take-over time in automated driving[J]. China Journal of Highway and Transport, 2018, 31(4): 165-171.(in Chinese)
[59] LARSSON A F L, KIRCHER K, HULTGREN J A.
Learning from experience: familiarity with ACC and responding to a cut-in situation in automated driving[J]. Transportation Research Part F: Psychology and Behaviour, 2014, 27: 229-237.
[60] KLEIN G. Naturalistic decision making[J]. Human Factors, 2008, 50(3): 456-460.
[61] WAARD D D, HULST M V D, HOEDEMAEKER M, et al. Driver behavior in an emergency situation in the automated highway system[J]. Transportation Human Factors, 1999, 1(1): 67-82.
[62] STRAND N, NILSSON J, KARLSSON M A, et al. Semi-automated versus highly automated driving in critical situations caused by automation failures[J]. Transportation Research Part F: Psychology and Behaviour, 2014, 27: 218-228.
[63] BANKS V A, ERIKSSON A, O DONOGHUE J, et al. Is partially automated driving a bad idea? Observations from an on-road study[J]. Applied Ergonomics, 2018, 68: 138-145.
[64] FLEMISCH F O, BENGLER K, BUBB H, et al. Towards cooperative guidance and control of highly automated vehicles: h-mode and conduct-by-wire[J]. Ergonomics, 2014, 57(3): 343-360.
[65] 周孝吉.驾驶人特性和实时驾驶能力评估研究[D].重庆:重庆大学,2018.
ZHOU Xiao-ji. Research on driver characteristics and driving capacity real-time evaluation[D]. Chongqing: Chongqing University, 2018.(in Chinese)
[66] 贾立山.体现驾驶人特性的车道偏离预警系统关键技术研究[D].武汉:华中科技大学,2011.
JIA Li-shan. Research of key technologies for lane departure warning system considering the driver characteristics[D]. Wuhan: Huazhong University of Science and Technology,2011.(in Chinese)
[67] 胡满江,边有钢,许 庆,等.自适应驾驶人行为特征的车道偏离防范系统[J].汽车工程,2017,39(10):1152-1157.
HU Man-jiang, BIAN You-gang, XU Qing, et al. Lane departure prevention system adapted to driver behavior characteristics[J]. Automotive Engineering, 2017, 39(10): 1152-1157.(in Chinese)
[68] 姬生远.基于驾驶人特性的电动汽车自适应巡航系统[D].长春:吉林大学,2018.
JI Sheng-yuan. Adaptive cruise control system of electric vehicle based on driver characteristics[D]. Changchun: Jilin University, 2018.(in Chinese)
[69] 刘 伟,卫 璐,孙芳岑,等.基于驾驶人实际跟车特性的自适应巡航系统研究[J].北京汽车,2018(3):1-3,28.
LIU Wei, WEI Lu, SUN Fang-cen, et al. Research on adaptive cruise control system based on car-following behavior of driver[J]. Beijing Automotive Engineering, 2018(3): 1-3, 28.(in Chinese)
[70] 张 磊.基于驾驶人特性自学习方法的车辆纵向驾驶辅助系统[D].北京:清华大学,2009.
ZHANG Lei. A vehicle longitudinal driving assistance system based on self-learning method of driver characteristics[D]. Beijing: Tsinghua University, 2009.(in Chinese)
[71] 胡远志,杨喜存,刘 西,等.基于驾驶人特性的主动避撞分级制动策略与验证[J].汽车工程,2019,41(3):298-306.
HU Yuan-zhi, YANG Xi-cun, LIU Xi, et al. Hierarchic braking strategy for active collision avoidance and its verification based on driver's characteristics[J]. Automotive Engineering, 2019, 41(3): 298-306.(in Chinese)
[72] 于 佳.基于不同驾驶人特性的汽车防撞安全距离算法研究[D].大连:大连理工大学,2018.
YU Jia. Research on vehicle collision avoidance safety distance algorithm based on driver characteristics[D]. Dalian: Dalian University of Technology, 2018.(in Chinese)
[73] 吴 涛.考虑驾驶人避撞行为特性的汽车前方防碰撞系统研究[D].长春:吉林大学,2014.
WU Tao. Study on forward collision avoidance system considering drivers' collision avoidance behavior characteristic[D]. Changchun: Jilin University, 2014.(in Chinese)
[74] CAO Ming-chun, WU Gang, YAN Shen-xiang, et al.
Control strategy of vehicle anti-rollover considering driver's characteristic[J]. IEEE Access, 2020, 8: 128264-128281.
[75] YANG Wei, ZHENG Ling, LI Yi-nong, et al. Automated
highway driving decision considering driver characteristics[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(6): 2350-2359.
[76] SENTOUH C, DEBERNARD S, POPIEUL J, et al.
Toward a shared lateral control between driver and steering assist controller[C]∥Elsevier. 11th IFAC/IFIP/IFORS/IEA Symposium on Analysis, Design, and Evaluation of Human-Machine Systems. Amsterdam: Elsevier, 2010: 404-409.
[77] SALEH L, CHEVREL P, CLAVEAU F, et al. Shared
steering control between a driver and an automation: stability in the presence of driver behavior uncertainty[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(2): 974-983.
[78] 高振刚,陈无畏,谈东奎,等.考虑驾驶人操纵失误的车道偏离辅助人机协同控制[J].机械工程学报,2019,55(16):91-103.
GAO Zhen-gang, CHEN Wu-wei, TAN Dong-kui, et al. Human-machine cooperative lane departure assist control considering driver manipulate failure[J]. Journal of Mechanical Engineering, 2019, 55(16): 91-103.(in Chinese)
[79] 郭 烈,葛平淑,夏文旭,等.基于人机共驾的车道保持辅助控制系统研究[J].中国公路学报,2019,32(12):46-57.
GUO Lie, GE Ping-shu, XIA Wen-xu, et al. Lane-keeping control systems based on human-machine cooperative driving[J]. China Journal of Highway and Transport, 2019, 32(12): 46-57.(in Chinese)
[80] WANG Xuan-yao, CHENG Yi. Lane departure avoidance by man-machine cooperative control based on EPS and ESP systems[J]. Journal of Mechanical Science and Technology, 2019, 33: 2929-2940.
[81] WANG Jian-qiang, ZHANG Lei, ZHANG De-zhao, et al.
An adaptive longitudinal driving assistance system based on driver characteristics[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(1): 1-12.
[82] 谈东奎,陈无畏,王家恩,等.基于人机共享和分层控制的车道偏离辅助系统[J].机械工程学报,2015,51(22):98-110.
TAN Dong-kui, CHEN Wu-wei, WANG Jia-en, et al. Human-machine sharing and hierarchical control based lane departure assistance system[J]. Journal of Mechanical Engineering, 2015, 51(22): 98-110.(in Chinese)

Memo

Memo:
-
Last Update: 2021-06-01