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山东大学学报 (工学版) ›› 2019, Vol. 49 ›› Issue (2): 23-33.doi: 10.6040/j.issn.1672-3961.0.2017.503

• 机器学习与数据挖掘 • 上一篇    下一篇

自主驾驶的人机交互控制

邹启杰1,2(),李昊宇1,张汝波3,*(),裴腾达1,刘艳1   

  1. 1. 大连大学信息工程学院, 辽宁 大连 116000
    2. 国防科技大学无人研究所, 湖南 长沙 410000
    3. 大连民族大学机电工程学院, 辽宁 大连 116000
  • 收稿日期:2017-10-09 出版日期:2019-04-20 发布日期:2019-04-19
  • 通讯作者: 张汝波 E-mail:jessie_zou_zou@163.com;zhangrubo@dlnu.edu.cn
  • 作者简介:邹启杰(1978—)女,辽宁大连人,博士,副教授,主要研究方向为无人系统智能规划与决策,强化学习等. E-mail: jessie_zou_zou@163.com
  • 基金资助:
    国家自然科学基金面上资助项目(61673084);辽宁省自然科学基金资助项目(201602204)

Survey of human-robot interaction control for autonomous driving

Qijie ZOU1,2(),Haoyu LI1,Rubo ZHANG3,*(),Tengda PEI1,Yan LIU1   

  1. 1. College of Information Engineering, Dalian University, Dalian 116000, Liaoning, China
    2. Institute of Unmanned Systems, National University of Defense Technology, Changsha 410000, Hunan, China
    3. College of Mechanical and Electrical Engineering, Dalian Minzu University, Dalian 116000, Liaoning, China
  • Received:2017-10-09 Online:2019-04-20 Published:2019-04-19
  • Contact: Rubo ZHANG E-mail:jessie_zou_zou@163.com;zhangrubo@dlnu.edu.cn
  • Supported by:
    国家自然科学基金面上资助项目(61673084);辽宁省自然科学基金资助项目(201602204)

摘要:

本研究对自主驾驶的人机交互中机器学习方法进行综述。通过介绍自主驾驶中人机交互研究的价值和意义,明确了人机交互问题定义以及与机器学习之间的关系,构建了自主驾驶中人机交互团队的架构。围绕提出的人机交互的系统架构和研究方法展开讨论,提出了人机交互问题解决的通用架构。并且,重点针对自主系统和驾驶员两部分介绍了相关机器学习算法,对自主驾驶中人机交互控制的未来研究进行展望,并对本研究进行总结。

关键词: 人机交互, 自主驾驶, 驾驶员建模, 机器学习, 协同驾驶

Abstract:

This article summarizes the machine learning methods of human-robot interaction in autonomy vehicles. By introducing the value and significance of human-robot interaction, the relationship between the human-robot interaction problem definition and machine learning were identified, the human-robot interactions team framework was built. The frameworks of human-robot interaction and the research methods of autonomous driving system were reviewed, the general structure for solving human-robot interaction problems was presented. Furthermore, its machine learning algorithm from the two aspects of autonomous control system and driver modeling was introduced. The prospects of the future research direction were summarized.

Key words: human-robot interaction, autonomous driving, driver modeling, machine learning, cooperative driving

中图分类号: 

  • TP311

表1

自主车辆自主等级"

自主驾驶分级称呼
(SAE)
SAE定义主体
NHTSA SAE 驾驶操作 周边监控 支援 作用域
0 0 无自动化 由人类全权操作汽车,驾驶过程中可以得到警告和保护系统的辅助 人类驾驶员 人类驾驶员
1 1 辅助驾驶 系统在转向和制动中的一项操作提供支援,其余驾驶操作由人类完成 人类驾驶员 人类驾驶员 部分
2 2 部分自动化 系统在转向和制动中的多项操作提供支援,其余驾驶操作由人类完成 系统 人类驾驶员 部分
3 3 有条件自动化 由无人系统完成全部驾驶操作,人类驾驶员根据系统请求,完成适当应答 系统 系统 部分
4 4 高度自动化 由无人系统完成全部驾驶操作,遇到问题向人类求助时,并不一定会全部应答 系统 系统 系统 部分
5 完全自动化 由无人系统完成全部驾驶操作,人类可以接管操作,同时可以在全部环境下驾驶操作 系统 系统 系统 全域

图1

人机交互团队架构"

图2

人机交互问题解决的通用结构"

图3

四层递阶式自主驾驶智能控制系统结构"

1 鲍军鹏, 张选平. 人工智能导论[M]. 北京: 机械工业出版社, 2010.
2 史忠植. 高级人工智能[M]. 北京: 科学出版社, 1998.
3 SORIANO B C, DOUGHERTY S L, SOUBLET B G, et al. Autonomous vehicles: a perspective from the california department of motor vehicles[M]// Road Vehicle Automation. Cham: Springer International Publishing, 2014: 15-24.
4 MINDERHOUD M M. Impact of intelligent cruise control strategles and equipment route on road capacity[C]// Proceedings of the 5th World Congress on Intellgent Transport Systems. Seoul, Korea: Worldcat, 1998.
5 FANCHER P, BAREKET Z, PENG H, et al. Research on desirable adaptive cruise control behavior in traffic streams: Phase 2: Final Report[R]. Washington: National Highway Traffic Safety Administration, 2003.
6 ALKIM T P, BOOTSMA G, HOOGENDOORN S P. Field operational test "the assisted driver"[C]// 2007 IEEE Intelligent Vehicles Symposium, 13-15 June 2007.Istanbul, Turkey: IEEE, 2007: 1198-1203.
7 VITI F, HOOGENDOORN S P, ALKIM T P, et al. Driving behavior interaction with ACC: results from a field operational test in the Netherlands[C]// 2008 IEEE Intelligent Vehicles Symposium, 4-6 June 2008. Eindhoven, Netherland: IEEE, 2008: 745-750.
8 HOEDEMAEKER D M. Driving with intelligent vehicles: driving behaviour with adaptive cruise control and the acceptance by individual drivers[D]. Delft, Delft University of Technology, 1999.
9 COYTE J L, LI B Y, DU H P, et al. Decision tree assisted EKF for vehicle slip angle estimation using inertial motion sensors[C]// 2014 International Joint Conference on Neural Networks (IJCNN), 6-11 July 2014. Beijing: IEEE, 2014: 940-946.
10 APARICIO A, BAURÈS S, BARGALLÓ J, et al. Pre-crash performance of collision mitigation and avoidance systems: results from the assess project[M]// APARICIO A, BAURÈS S, BARGALLÓ J, et al. eds. Lecture Notes in Electrical Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012: 489-505.
11 BRAHMI M, SIEDERSBERGER K H, SIEGEL A, et al. Reference systems for environmental perception: requirements validation and metric-based evaluation[C]// 6th Conference on Driving Assistance, München, Germany: Technical University of Munich, 2013: 6.
12 TRAN C , TRIVEDI M M . 3-D posture and gesture recognition for interactivity in smart spaces[J]. IEEE Transactions on Industrial Informatics, 2012, 8 (1): 178- 187.
13 HOLTE M B , TRAN C , TRIVEDI M M , et al. Human pose estimation and activity recognition from multi-view videos: comparative explorations of recent developments[J]. IEEE Journal of Selected Topics in Signal Processing, 2012, 6 (5): 538- 552.
doi: 10.1109/JSTSP.2012.2196975
14 FU X P , GUAN X , PELI E , et al. Automatic calibration method for driver′s head orientation in natural driving environment[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 14 (1): 303- 312.
15 MURPHY-CHUTORIAN E , TRIVEDI M M . Head pose estimation and augmented reality tracking: An integrated system and evaluation for monitoring driver awareness[J]. IEEE Transactions on Intelligent Transportation Systems, 2010, 11 (2): 300- 311.
16 CHIANG H H , CHEN Y L , WU B F , et al. Embedded driver-assistance system using multiple sensors for safe overtaking maneuver[J]. IEEE Systems Journal, 2014, 8 (3): 681- 698.
doi: 10.1109/JSYST.2012.2212636
17 RYU D W, JEONG H B, LEE S H, et al. Development of driver-state estimation algorithm based on Hybrid Bayesian Network[C]// 2015 IEEE Intelligent Vehicles Symposium(Ⅳ), 28 June-1 July 2015. Seoul, South Korea: IEEE, 2015: 1282-1286.
18 WU J D , LIU P Y , HONG G L . Driver voice identification system using auto-correlation function and average magnitude difference function[J]. Applied Mechanics and Materials, 2014, 490/491, 1287- 1292.
doi: 10.4028/www.scientific.net/AMM.490-491
19 POWER M, RAFII-TARI H, BERGELES C, et al. A cooperative control framework for haptic guidance of bimanual surgical tasks based on Learning from demonstration[C]// 2015 IEEE International Conference on Robotics and Automation (ICRA), 26-30 May 2015. Seattle, USA: IEEE, 2015: 5330-5337.
20 SMISEK J, MUGGE W, SMEETS J B J, et al. Adapting haptic guidance authority based on user grip[C]// 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 5-8 Oct. 2014. San Diego, USA: IEEE, 2014: 1516-1521.
21 金会庆, 陈嵘, 张树林. 机动车驾驶员的速度估计、复杂反应判断和操纵机能特征研究[J]. 人类工效学, 1995, 1 (1): 13- 18.
JIN Huiqing , CHEN Rong , ZHANG Shulin . Study on occupational driver's characteristics of speed anticipation, discriminative reaction and action judgement[J]. Chinese Journal of Ergonomics, 1995, 1 (1): 13- 18.
22 李百川, 殷国祥, 苏如玉. 汽车驾驶员反应特性与交通事故关系的分析研究[J]. 人类工效学, 1995, 1 (2): 26- 31.
LI Baichuan , YIN Cuoxiang , SU Ruyu . Analysis and study on the relation between the driver's reaction character and traffic accident[J]. Chinese Journal of Ergonomics, 1995, 1 (2): 26- 31.
23 王兴伟, 李良明, 彭福敏, 等. 模拟应急条件下的反应时研究[J]. 人类工效学, 1996, 2 (1): 34- 37.
WANG Xingwei , LI Liangming , PENG Fumin , et al. The reaction under the condition of simulating air critical situation on the ground[J]. Chinese Journal of Ergonomics, 1996, 2 (1): 34- 37.
24 ROEHR T M, SHI Y. Using a self-confidence measure for a system-initiated switch between autonomy modes[C]// Proceedings of the 10th International Symposium on Artificial Intelligence, Robotics and Automation in Space. Sapporo, Japan: ISAIRAS, 2010: 507-514.
25 WEINBERGER M , WINNER H , BUBB H . Adaptive cruise control field operational test:the learning phase[J]. JSAE Review, 2001, 22 (4): 487- 494.
doi: 10.1016/S0389-4304(01)00142-4
26 RUDIN-BROWN C M , PARKER H A . Behavioural adaptation to adaptive cruise control (ACC): implications for preventive strategies[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2004, 7 (2): 59- 76.
27 GINDELE T , BRECHTEL S , DILLMANN R . Learning driver behavior models from traffic observations for decision making and planning[J]. IEEE Intelligent Transportation Systems Magazine, 2015, 7 (1): 69- 79.
28 BIRAL F, DA LIO M, BERTOLAZZI E. Combining safety margins and user preferences into a driving criterion for optimal control-based computation of reference maneuvers for an ADAS of the next generation[C]// Proceedings of the 2005 IEEE Intelligent Vehicles Symposium. Las Vegas, USA: IEEE, 2005: 36-41.
29 XIONG H M , BOYLE L N . Drivers′ adaptation to adaptive cruise control: examination of automatic and manual braking[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13 (3): 1468- 1473.
doi: 10.1109/TITS.2012.2192730
30 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.
31 HAJEK W , GAPONOVA I , FLEISCHER K H , et al. Workload-adaptive cruise control-a new generation of advanced driver assistance systems[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2013, 20, 108- 120.
32 MANN G A, SMALL N. Opportunities for enhanced robot control along the adjustable autonomy scale[C]// Proceedings of the 20125th International Conference on Human System Interactions. Perth, Australia: IEEE, 2012: 35-42.
33 ARGALL B D, MURPHEY T D. Computable trust in human instruction[C]// Proceedings of the AAAI Fall Symposium on Artificial Intelligence for Human-Robot Interactions. Arlington, USA: AAAI, 2014: 9.
34 王飞跃. 平行系统方法与复杂系统的管理和控制[J]. 控制与决策, 2004, 19 (5): 485- 489, 514.
doi: 10.3321/j.issn:1001-0920.2004.05.002
WANG Feiyue . Palallel system methods for management and control of complex systems[J]. Control and Decision, 2004, 19 (5): 485- 489, 514.
doi: 10.3321/j.issn:1001-0920.2004.05.002
35 CHOI S B, HEDRICK J K. Vehicle longitudinal control using an adaptive observer for automated highway systems[C]// Proceedings of 1995 American Control Conference-ACC'95. Seattle, USA: IEEE, 1995: 3106-3110.
36 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.
doi: 10.1109/TITS.2013.2248363
37 SUKKARIEH S, NEBOT E M, DURRANT-WHYTE H F. Achieving integrity in an INS/GPS navigation loop for autonomous land vehicle applications[C]// Proceedings of the 1998 IEEE International Conference on Robotics and Automation. Leuven, Belgium: IEEE, 1998, 4: 3437-3442.
38 SHLADOVER S E , DESOER C A , HEDRICK J K , et al. Automated vehicle control developments in the PATH program[J]. IEEE Transactions on Vehicular Technology, 1991, 40 (1): 114- 130.
39 赵亦林.车辆定位与导航系统[M].谭国真,译.北京:电子工业出版社, 1999.
40 王健.无人驾驶车辆运动控制方法研究[D].长沙:国防科学技术大学, 2013.
WANG Jian. Research on control methods for the locomotion of autonomous land vehicles[D]. ChangSha: National University of Defense Technology, 2013.
41 孙振平.自主驾驶汽车智能控制系统[D].长沙:国防科学技术大学, 2004.
SUN Zhenping. An intelligent control system for autonomous land vehicle[D]. Changsha: National University of Defense Technology, 2004.
42 HERNANDEZ-GRESS N, ESTEVE D. Multisensory fusion and neural networks methodology: Application to the active security in driving behavior[R]. Yokohama: Intelligent Transport Systems World Congress.1995.
43 WANG Wuhong , SHEN Zhongjie , DU Qiu . Modeling for action of recovering from erroneous driving condition based on revised decision tree[J]. Journal of Beijing Institute of Technology-English Edition, 2002, 11 (1): 61- 65.
44 郭孜政, 陈崇双, 王欣. 基于贝叶斯判别的驾驶行为危险状态辨识[J]. 西南交通大学学报, 2009, 44 (5): 771- 775.
doi: 10.3969/j.issn.0258-2724.2009.05.026
GUO Zizheng , CHEN Chongshuang , WANG Xin . Risk identification for driving behaviors based on bayesian discrimination[J]. Journal of Southwest Jiaotong University, 2009, 44 (5): 771- 775.
doi: 10.3969/j.issn.0258-2724.2009.05.026
45 D′AGOSTINO C, SAIDI A, SCOUARNEC G, et al. Learning-based driving events classification[C]// 16th International IEEE Conference on Intelligent Transportation Systems(ITSC 2013). Hague, Netherlands: IEEE, 2013: 1778-1783.
46 蔡旻融, 顾振宇, 董占勋. 基于非侵入行为监测技术的误踩油门踏板研究[J]. 微型机与应用, 2014, (1): 71- 73.
doi: 10.3969/j.issn.1674-7720.2014.01.022
CAI Minrong , GU Zhenyu , DONG Zhanxun . Accelerator pedal error research based on non-invasive behavior monitoring techniques[J]. Microcomputer & Its Applications, 2014, 33 (1): 71- 73.
doi: 10.3969/j.issn.1674-7720.2014.01.022
47 刘永涛, 乔洁, 魏朗, 等. 危险驾驶行为辨识算法研究[J]. 计算机工程与设计, 2014, 35 (4): 1322- 1326.
doi: 10.3969/j.issn.1000-7024.2014.04.039
LIU Yongtao , QIAO Jie , WEI Lang , et al. Research on identification algorithm of dangerous driving behavior[J]. Computer Engineering and Design, 2014, 35 (4): 1322- 1326.
doi: 10.3969/j.issn.1000-7024.2014.04.039
48 ISHIKAWA K, FUJINAMI T, SAKURAI A. Integration of constraint logic programming and artificial neural networks for driving robots[C]// Proceedings of the 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems.Expanding the Societal Role of Robotics in the the next Millennium (Cat. no.01CH37180). Maui, USA: IEEE. 2001, 2: 1011-1016.
49 孔令旗, 郭忠印. 基于人工神经网络的运行车速与道路安全性关系[J]. 同济大学学报(自然科学版), 2007, 35 (9): 1214- 1218.
doi: 10.3321/j.issn:0253-374X.2007.09.012
KONG Lingqi , GUO Zhongyin . Artificial neural network-based relation of operating speed and road safety[J]. Journal of Tongji University(Natural Science), 2007, 35 (9): 1214- 1218.
doi: 10.3321/j.issn:0253-374X.2007.09.012
50 FORBES J, HUANG T, KANAZAWA K, et al. The batmobile: towards a Bayesian automated taxi[C]// Proceedings of 1995 International Joint Conference on AI. Montreal, Canada: IJCAI, 1995: 1878-1885.
51 ARDELT M , COESTER C , KAEMPCHEN N . Highly automated driving on freeways in real traffic using a probabilistic framework[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13 (4): 1576- 1585.
doi: 10.1109/TITS.2012.2196273
52 SCHUBERT R , SCHULZE K , WANIELIK G . Situation assessment for automatic lane-change maneuvers[J]. IEEE Transactions on Intelligent Transportation Systems, 2010, 11 (3): 607- 616.
doi: 10.1109/TITS.2010.2049353
53 SCHUBERT R , WANIELIK G . A unified Bayesian approach for object and situation assessment[J]. IEEE Intelligent Transportation Systems Magazine, 2011, 3 (2): 6- 19.
54 SCHUBERT R . Evaluating the utility of driving: toward automated decision making under uncertainty[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13 (1): 354- 364.
55 NGAI D C K, YUNG N H C. Automated vehicle overtaking based on a multiple-goal reinforcement learning framework[C]// 2007 IEEE Intelligent Transportation Systems Conference. Seattle, USA: IEEE, 2007: 818-823.
56 NGAI D C K , YUNG N H C . A multiple-goal reinforcement learning method for complex vehicle overtaking maneuvers[J]. IEEE Transactions on Intelligent Transportation Systems, 2011, 12 (2): 509- 522.
57 刘春明.基于增强学习和车辆动力学的高速公路自主驾驶研究[D].长沙:国防科学技术大学, 2014.
LIU Chunming. Research on autonomous driving on highway roads based on reinforcement learning and vehicle dynamics[D]. Changsha: National University of Defense Technology, 2014.
58 KHAYYAM H , NAHAVANDI S , DAVIS S . Adaptive cruise control look-ahead system for energy management of vehicles[J]. Expert Systems with Applications, 2012, 39 (3): 3874- 3885.
59 JOCHEM T , POMERLEAU D . Life in the fast lane: the evolution of an adaptive vehicle control system[J]. AI magazine, 1996, 17 (2): 11.
60 OH S Y , LEE J H , CHOI D H . A new reinforcement learning vehicle control architecture for vision-based road following[J]. IEEE Transactions on Vehicular Technology, 2000, 49 (3): 997- 1005.
doi: 10.1109/25.845116
61 RIEDMILLER M, MONTEMERLO M, DAHLKAMP H. Learning to drive a real car in 20 minutes[C]// Frontiers in the Convergence of Bioscience and Information Technologies 2007. Jeju City, South Korea: IEEE, 2007: 645-650.
62 SARTER N B , WOODS D D . Situation awareness: a critical but ill-defined phenomenon[J]. The International Journal of Aviation Psychology, 1991, 1 (1): 45- 57.
doi: 10.1207/s15327108ijap0101_4
63 STANTON N A , YOUNG M S . A proposed psychological model of driving automation[J]. Theoretical Issues in Ergonomics Science, 2000, 1 (4): 315- 331.
doi: 10.1080/14639220052399131
64 PARASURAMAN R , SHERIDAN T B , WICKENS C D . Situation awareness, mental workload, and trust in automation: viable, empirically supported cognitive engineering constructs[J]. Journal of Cognitive Engineering and Decision Making, 2008, 2 (2): 140- 160.
doi: 10.1518/155534308X284417
65 ADAM E C. Fighter cockpits of the future[C]// Proceedings of the AIAA/IEEE Digital Avionics Systems Conference. Fort Worth, USA: IEEE, 1993: 318-323.
66 FULLER R . Towards a general theory of driver behaviour[J]. Accident Analysis & Prevention, 2005, 37 (3): 461- 472.
67 DE WINTER J C F , HAPPEE R , MARTENS M H , et al. Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2014, 27, 196- 217.
doi: 10.1016/j.trf.2014.06.016
68 CANNON D J , THOMAS G . Virtual tools for supervisory and collaborative control of robots[J]. Presence: Teleoperators and Virtual Environments, 1997, 6 (1): 1- 28.
doi: 10.1162/pres.1997.6.1.1
69 张维,王文军,成波.驾驶人不良驾驶行为的识别方法[C]//2011第十四届中国汽车安全技术学术会议论文集.北京: [出版者不详]. 2011: 208-212.
ZHANG Wei, WANG Wenjun, CHENG Bo. The method of identifying the driver′s bad driving behavior[C]// Proceedings of the 14th China Automotive Safety Technology Conference. Beijing: [s. n.]. 2011: 208-212.
70 HE L , ZONG C F , WANG C . Driving intention recognition and behaviour prediction based on a double-layer hidden Markov model[J]. Journal of Zhejiang University SCIENCE C, 2012, 13 (3): 208- 217.
doi: 10.1631/jzus.C11a0195
71 DEMEESTER E, NUTTIN M, VANHOOYDONCK D, et al. A model-based, probabilistic framework for plan recognition in shared wheelchair control: experiments and evaluation[C]// Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and System(IROS 2003). Las Vegas, USA: IEEE, 2003, 2: 1456-1461.
72 TIPPING M E . Sparse Bayesian learning and the relevance vector machine[J]. Journal of Machine Learning Research, 2001, (1): 211- 244.
73 MCCALL J C , WIPF D P , TRIVEDI M M , et al. Lane change intent analysis using robust operators and sparse Bayesian learning[J]. IEEE Transactions on Intelligent Transportation Systems, 2007, 8 (3): 431- 440.
doi: 10.1109/TITS.2007.902640
74 LEFÈVRE S, LAUGIER C, IBAÑEZ-GUZMÁN J. Risk assessment at road intersections: comparing intention and expectation[C]//2012 IEEE Intelligent Vehicles Symposium. Alcala de Henares, Spain: IEEE, 2012: 165-171.
75 GOODRICH M A, YI D Q. Toward task-based mental models of human-robot teaming: a bayesian approach[C]// International Conference on Virtual, Augmented and Mixed Reality. Las Vegas, USA: Springer Berlin Heidelberg, 2013: 267-276.
76 CHENG S Y , TRIVEDI M M . Turn-intent analysis using body pose for intelligent driver assistance[J]. IEEE Pervasive Computing, 2006, 5 (4): 28- 37.
doi: 10.1109/MPRV.2006.88
77 GINDELE T, BRECHTEL S, DILLMANN R. A probabilistic model for estimating driver behaviors and vehicle trajectories in traffic environments[C]// 13th International IEEE Conference on Intelligent Transportation Systems. Funchal, Portugal: IEEE, 2010: 1625-1631.
78 MUDGAL A , HALLMARK S , CARRIQUIRY A , et al. Driving behavior at a roundabout: a hierarchical Bayesian regression analysis[J]. Transportation Research Part D: Transport and Environment, 2014, 26, 20- 26.
doi: 10.1016/j.trd.2013.10.003
79 ZHANG Z L , RAO B D . Sparse signal recovery with temporally correlated source vectors using sparse Bayesian learning[J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5 (5): 912- 926.
doi: 10.1109/JSTSP.2011.2159773
80 CÔTÉ N, CANU A, BOUZID M, et al. Humans-robots sliding collaboration control in complex environments with adjustable autonomy[C]// Proceedings of the 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology: Volume 02. Macau, China: IEEE Computer Society, 2012: 146-153.
81 SHIMOSAKA M, KANEKO T, NISHI K. Modeling risk anticipation and defensive driving on residential roads with inverse reinforcement learning[C]// 17th International IEEE Conference on Intelligent Transportation Systems. Qingdao, China: IEEE, 2014: 1694-1700.
82 DEMEESTER E , HÜNTEMANN A , VANHOOYDONCK D , et al. User-adapted plan recognition and user-adapted shared control: a Bayesian approach to semi-autonomous wheelchair driving[J]. Autonomous Robots, 2008, 24 (2): 193- 211.
doi: 10.1007/s10514-007-9064-5
83 BAI H , HSU D , KOCHENDERFER M J , et al. Unmanned aircraft collision avoidance using continuous-state POMDPs[J]. Robotics: Science and Systems Ⅶ, 2012, 1, 1- 8.
84 BAI H Y, CAI S J, YE N, et al. Intention-aware online POMDP planning for autonomous driving in a crowd[C]// 2015 IEEE International Conference on Robotics and Automation (ICRA). Seattle, USA: IEEE, 2015: 454-460.
85 GALCERAN E, CUNNINGHAM A G, EUSTICE R M, et al. Multipolicy decision-making for autonomous driving via changepoint-based behavior prediction[C]// Robotics: Science and Systems. Rome, Italy: Sapienza University of Rome. 2015, 1(2).
86 AOUDE G S, LUDERS B D, LEE K K H, et al. Threat assessment design for driver assistance system at intersections[C]// 13th International IEEE Conference on Intelligent Transportation Systems. Funchal, Portugal: IEEE, 2010: 1855-1862.
87 AOUDE G, JOSEPH J, ROY N, et al. Mobile agent trajectory prediction using Bayesian nonparametric reachability trees[C]// AIAA Infotech@Aerospace Conference. Garden Grove, USA: AIAA, 2011: 1512.
88 DEL CAMPO I, FINKER R, MARTINEZ M V, et al. A real-time driver identification system based on artificial neural networks and cepstral analysis[C]// 2014 International Joint Conference on Neural Networks (IJCNN). Beijing: IEEE, 2014: 1848-1855.
89 MONTEMERLO M , BECKER J , BHAT S , et al. Junior: the stanford entry in the urban challenge[J]. Journal of Field Robotics, 2008, 25 (9): 569- 597.
doi: 10.1002/rob.v25:9
90 MILLER I , CAMPBELL M , HUTTENLOCHER D , et al. Team cornell's skynet: robust perception and planning in an urban environment[J]. Journal of Field Robotics, 2008, 25 (8): 493- 527.
doi: 10.1002/rob.v25:8
91 URMSON C , ANHALT J , BAGNELL D , et al. Autonomous driving in urban environments: boss and the urban challenge[J]. Journal of Field Robotics, 2008, 25 (8): 425- 466.
doi: 10.1002/rob.v25:8
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