[1] XIE G, GAO H, QIAN L, et al. Vehicle trajectory prediction by integrating physics and maneuver-based approaches using interactive multiple models[J]. IEEE Transactions on Industrial Electronics, 2017, 65(7): 5999-6008. [2] DING W, CHEN J, SHEN S. Predicting vehicle behaviors over an extended horizon using behavior interaction network[C]. 2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 8634-8640. [3] 熊帅. 快速路汇入行为分析及合流区组织优化[D]. 长春:吉林大学, 2019. XIONG S. Analysis of expressway merging behavior and optimization of merging area organization [D]. Changchun: Jilin University, 2019. [4] 党彤. 基于贝叶斯网络的车辆变道行为分析[D]. 西安:西安理工大学, 2018. DANG T. Lane changing behavior analysis of vehicles based on Bayesian network [D]. Xi'an: Xi'an University of Technology, 2018. [5] 祝俪菱, 刘澜, 赵新朋, 等. 基于支持向量机的车辆驾驶行为识别研究[J]. 交通运输系统工程与信息, 2016, 17(1): 91- 97. ZHU L, LIU L, ZHAO X, et al. Research on vehicle driving behavior recognition based on support vector machine [J]. Transportation System Engineering and Information, 2016, 17 (1): 91- 97. [6] 李建平. 面向智能驾驶的交通车辆运动预测方法研究[D]. 长春:吉林大学, 2018. LI J. Research on intelligent driving oriented traffic vehicle motion prediction method [D]. Changchun: Jilin University, 2018. [7] GENG X, LIANG H, YU B, et al. A scenario-adaptive driving behavior prediction approach to urban autonomous driving[J]. Applied Sciences, 2017, 7(4): 426-447. [8] XU D, HE X, ZHAO H, et al. Ego-centric traffic behavior understanding through multi-level vehicle trajectory analysis[C]. 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017: 211-218. [9] 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(8):858-864. LIU Z, WU X, NI J, et al. Driving intention recognition based on HMM and SVM cascade algorithm [J]. Automotive Engineering, 2018,40(8): 858-864. [10] 李创. 智能汽车对前方车辆的运动感知与换道意图辨识[D]. 西安:西安理工大学, 2018. LI C. Motion perception and lane change intention identification for intelligent vehicles [D]. Xi'an: Xi'an University of Technology, 2018. [11] DENG Q, WANG J, SOFFKER D. Prediction of human driver behaviors based on an improved HMM approach[C]. 2018 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2018: 2066-2071. [12] LI K, WANG X, XU Y, et al. Lane changing intention recogni-tion based on speech recognition models[J]. Transportation Research Part C: Emerging Technologies, 2016, 69: 497-514. [13] 王一鸣, 陈恳. 基于稀疏DBN和双向LSTM的视觉语音识别算法[J]. 数据通信, 2019(3) :9-15. WANG Y, CHEN K. Visual speech recognition algorithm based on sparse DBN and bidirectional LSTM [J]. Data Communica-tion, 2019(3): 9-15. [14] HUANG X, TAN H, LIN G, et al. A LSTM-based bidirectional translation model for optimizing rare words and terminologies[C]. 2018 International Conference on Artificial Intelligence and Big Data (ICAIBD). IEEE, 2018: 185-189. [15] 姚朝, 辛平安, 施卜今, 等. 基于 LSTM 时间递归神经网络的短期电力负荷预测[J]. 云南水力发电, 2019,35(3): 163- 165. YAO C, XIN P, SHI B, et al. Short term power load forecasting based on LSTM time recurrent neural network [J]. Yunnan Hydropower, 2019,35(3): 163-165. [16] 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, FEI C, HE X, et al. Driving intention recognition and vehicle trajectory prediction based on LSTM network [J]. China Journal of Highway and Transport, 2019, 32 (6): 34-42. [17] ALAHI A, GOEL K, RAMANATHAN V, et al. Social LSTM: human trajectory prediction in crowded spaces[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 961-971. [18] KRAJEWSKI R, BOCK J, KLOEKER L, et al. The HighD dataset: a drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems[C]. 2018 21st International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2018: 2118-2125. |