[1] 郑赟. 2020中国智能电动汽车发展报告[R]. 广州: 罗兰贝格, 2020.
ZHENG Y. 2020 China intelligent electric vehicle development report[R]. Guangzhou: Roland Berger, 2020.
[2] LITMAN T. Autonomous vehicle implementation predictions: implications for transport planning[C]//Transportation Research Board Annual Meeting, 2015.
[3] LEFèVRE S, VASQUEZ D, LAUGIER C. A survey on motion prediction and risk assessment for intelligent vehicles[J]. ROBOMECH Journal, 2014, 1(1): 1-14.
[4] LI G, LI S E, LIAO Y, et al. Lane change maneuver recognition via vehicle state and driver operation signals—results from naturalistic driving data[C]//2015 IEEE Intelligent Vehicles Symposium (IV), 2015: 865-870.
[5] KUMAR P, PERROLLAZ M, LEFEVRE S, et al. Learning-based approach for online lane change intention prediction[C]//2013 IEEE Intelligent Vehicles Symposium (IV), 2013: 797-802.
[6] MORRIS B, DOSHI A, TRIVEDI M. Lane change intent prediction for driver assistance: on-road design and evaluation[C]//2011 IEEE Intelligent Vehicles Symposium (IV), 2011: 895-901.
[7] DING J, DANG R, WANG J, et al. Driver intention recognition method based on comprehensive lane-change environment assessment[C]//2014 IEEE Intelligent Vehicles Symposium Proceedings, 2014: 214-220.
[8] ZONG C, WANG C, YANG D, et al. Driving intention identification and maneuvering behavior prediction of drivers on cornering[C]//2009 International Conference on Mechatronics and Automation, 2009: 4055-4060.
[9] CHEN C, LIU L, QIU T, et al. Driver’s intention identification and risk evaluation at intersections in the Internet of vehicles[J]. IEEE Internet of Things Journal, 2018, 5(3): 1575-1587.
[10] WANG S, Fujii H, Yoshimura S. Accurate and efficient driving intention inference based on traffic environment information and FES-XGB framework[J]. Journal of Information Processing, 2022, 30: 30-41.
[11] WU Z, LIANG K, LIU D, et al. Driver lane change intention recognition based on attention enhanced residual-MBi-LSTM network[J]. IEEE Access, 2022, 10: 58050-58061.
[12] LI L, ZHAO W, XU C, et al. Lane-change intention inference based on RNN for autonomous driving on highways[J]. IEEE Transactions on Vehicular Technology, 2021, 70(6): 5499-5510.
[13] XU G, LI L, SONG Z. Driver behavior analysis based on Bayesian network and multiple classifiers[C]//IEEE International Conference on Intelligent Computing & Intelligent Systems, 2010.
[14] 毕胜强, 梅德纯, 刘志强, 等. 面向驾驶行为预警的换道意图辨识模型研究[J]. 中国安全科学学报, 2016, 26(2): 91-95.
BI S Q, MEI D C, LIU Z Q, et al. Research on lane change intention identification model for driving behavior warning [J]. China Safety Science Journal, 2016, 26(2): 91-95.
[15] MCCALL J C, TRIVEDI M M, WIPF D, et al. Lane change intent analysis using robust operators and sparse bayesian learning[C]//2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 2006.
[16] 姜顺明, 匡志豪, 王奕轩, 等. 基于贝叶斯网络的车辆并线意图识别[J]. 河南科技大学学报 (自然科学版), 2021, 42(5): 39-44.
JIANG S M, KUANG Z H, WANG Y X, et al. Vehicles and lines based on Bayesian network intention recognition[J]. Journal of Henan University of Science and Technology (Natural Science Edition), 2021, 42 (5): 39-44.
[17] 刘志强, 吴雪刚, 倪捷, 等. 基于 HMM 和SVM 级联算法的驾驶意图识别[J]. 汽车工程, 2018, 40(7): 858-864.
LIU Z Q, WU X G, NI J, et al. Driving intention recognition based on HMM and SVM cascade algorithm[J]. Automotive Engineering, 2018, 40 (7): 858-864.
[18] 张海伦, 付锐. 高速场景相邻前车驾驶行为识别及意图预测[J]. 交通运输系统工程与信息, 2020, 20(1): 40-46.
ZHANG H L, FU R. Driving behavior recognition and intention prediction of adjacent front vehicles in high-speed scene[J]. Transportation System Engineering and Information, 2020, 20(1): 40-46.
[19] 王亚伦, 陈焕明, 赵岩. 基于条件随机场的驾驶意图识别研究[J]. 青岛大学学报 (工程技术版), 2021, 36(4): 88-94.
WANG Y L, CHEN H M, ZHAO Y. Research on driving intention recognition based on conditional random field[J]. Journal of Qingdao University (Engineering and Technology Edition), 2021, 36(4): 88-94.
[20] 季学武, 费聪, 何祥坤, 等. 基于LSTM网络的驾驶意图识别及车辆轨迹预测[J]. 中国公路学报, 2019, 32(6): 34-42.
JI X W, FEI C, HE X K, et al. LSTM network based driver intention recognition and vehicle track prediction[J]. China Journal of Highway and Transport, 2019, 32(6): 34-42.
[21] 宋威龙. 城区动态环境下智能车辆行为决策研究[D]. 北京: 北京理工大学, 2016.
SONG W L. Research on intelligent vehicle behavior decision in urban dynamic environment[D]. Beijing: Beijing Institute of Technology, 2016.
[22] 宋晓琳, 曾艳兵, 曹昊天, 等. 基于长短期记忆网络的换道意图识别方法[J]. 中国公路学报, 2021, 34(11): 236-245.
SONG X L, ZENG Y B, CAO H T, et al. Lane change intention recognition method based on long short-term memory network[J]. China Journal of Highway and Transport, 2021, 34(11): 236-245.
[23] LAFFERTY J, MCCALLUM A, PEREIRA F. Conditional random fields: probabilistic models for segmenting and labeling sequence data[C]//Proceedings of the 18th International Conference on Machine Learning, 2001.
[24] XIA Y, QU Z, SUN Z, et al. A human-like model to understand surrounding vehicles’ lane changing intentions for autonomous driving[J]. IEEE Transactions on Vehicular Technology, 2021, 70(5): 4178-4189.
[25] JIN H, DUAN C, LIU Y, et al. Gauss mixture hidden Markov model to characterise and model discretionary lane‐change behaviours for autonomous vehicles[J]. IET Intelligent Transport Systems, 2020, 14(5): 401-411. |