[1]赵靖文,李煊鹏,张为公.车辆多目标交互行为建模的轨迹预测方法[J].智能系统学报,2023,18(3):480-488.[doi:10.11992/tis.202201022]
ZHAO Jingwen,LI Xuanpeng,ZHANG Weigong.Vehicle trajectory prediction method based on modeling of multi-agent interaction behavior[J].CAAI Transactions on Intelligent Systems,2023,18(3):480-488.[doi:10.11992/tis.202201022]
点击复制
ZHAO Jingwen,LI Xuanpeng,ZHANG Weigong.Vehicle trajectory prediction method based on modeling of multi-agent interaction behavior[J].CAAI Transactions on Intelligent Systems,2023,18(3):480-488.[doi:10.11992/tis.202201022]
车辆多目标交互行为建模的轨迹预测方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
18
期数:
2023年第3期
页码:
480-488
栏目:
学术论文—机器感知与模式识别
出版日期:
2023-07-05
- Title:
- Vehicle trajectory prediction method based on modeling of multi-agent interaction behavior
- Keywords:
- trajectory prediction; attention mechanism; multi-agent interaction; multimodal trajectory; conditional variational auto-encoder; endpoint generating; inverse reinforcement learning; decision refinement
- 分类号:
- TP391
- 摘要:
- 预测周围智能体的运动轨迹是实现自动驾驶行为决策规划的关键。面对复杂的车辆交互影响和多模态驾驶意图所带来的难题，本文提出一种基于车辆多目标交互行为建模的轨迹预测方法。该方法采用条件变分自编码器生成轨迹终点的多模态结果，结合自注意力机制和多头注意力机制来捕捉车辆之间的群体交互影响，最终使用逆强化学习输出多模态轨迹的最优决策，实现了同步预测多个目标轨迹。在高速公路数据集NGSIM上的实验结果证明该模型的有效性，并且预测效果整体优于现有方法。
- Abstract:
- Predicting trajectories of surrounding agents is critical to realize the decision-making planning of autonomous driving behaviors. Facing the difficulties brought by complex vehicle interaction and multimodal driving intention, this paper proposes a trajectory prediction method based on vehicle multi-agent interaction behavior modeling. The method uses conditional variational autoencoder to generate multi-modal results of the trajectory endpoints. By combination with the self-attention mechanism and multi-head attention mechanism, the influence of group interaction between vehicles is captured. Finally, the inverse reinforcement learning is used to output the optimal decision of multi-modal trajectory, realizing synchronous prediction of multi-agent trajectory. An experiment has been carried out on the NGSIM, which is a real-world trajectory prediction dataset on the highway traffic scene. The results prove effectiveness of the model, and the prediction effect is better than existing methods as a whole.
- 参考文献/References:
-
[1] SIVARAMAN S, TRIVEDI M M. Dynamic probabilistic drivability maps for lane change and merge driver assistance[J]. IEEE transactions on intelligent transportation systems, 2014, 15(5): 2063–2073.
[2] HELBING D, MOLNáR P. Social force model for pedestrian dynamics[J]. Physical review E, statistical physics, plasmas, fluids, and related interdisciplinary topics, 1995, 51(5): 4282–4286.
[3] 乔少杰, 金琨, 韩楠, 等. 一种基于高斯混合模型的轨迹预测算法[J]. 软件学报, 2015, 26(5): 1048–1063
QIAO Shaojie, JIN Kun, HAN Nan, et al. Trajectory prediction algorithm based on Gaussian mixture model[J]. Journal of software, 2015, 26(5): 1048–1063
[4] 高建, 毛莺池, 李志涛. 基于高斯混合-时间序列模型的轨迹预测[J]. 计算机应用, 2019, 39(8): 2261–2270
GAO Jian, MAO Yingchi, LI Zhitao. Trajectory prediction based on Gauss mixture time series model[J]. Journal of computer applications, 2019, 39(8): 2261–2270
[5] 乔少杰, 韩楠, 朱新文, 等. 基于卡尔曼滤波的动态轨迹预测算法[J]. 电子学报, 2018, 46(2): 418–423
QIAO Shaojie, HAN Nan, ZHU Xinwen, et al. A dynamic trajectory prediction algorithm based on Kalman filter[J]. Acta electronica sinica, 2018, 46(2): 418–423
[6] SCHULZ J, HUBMANN C, L?CHNER J, et al. Multiple model unscented Kalman filtering in dynamic Bayesian networks for intention estimation and trajectory prediction[C]//2018 21st International Conference on Intelligent Transportation Systems. Maui: IEEE, 2018: 1467?1474.
[7] SUTSKEVER I, VINYALS O, LE Q V. Sequence to sequence learning with neural networks[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2. Montreal: ACM, 2014: 3104–3112.
[8] 孔玮, 刘云, 李辉, 等. 基于深度学习的行人轨迹预测方法综述[J]. 控制与决策, 2021, 36(12): 2841–2850
KONG Wei, LIU Yun, LI Hui, et al. Survey of pedestrian trajectory prediction methods based on deep learning[J]. Control and decision, 2021, 36(12): 2841–2850
[9] JEONG Y, KIM S, YI K. Surround vehicle motion prediction using LSTM-RNN for motion planning of autonomous vehicles at multi-lane turn intersections[J]. IEEE open journal of intelligent transportation systems, 2020, 1: 2–14.
[10] ZHANG Pu, XUE Jianru, ZHANG Pengfei, et al. Social-aware pedestrian trajectory prediction via states refinement LSTM[J]. IEEE transactions on pattern analysis and machine intelligence, 2022, 44(5): 2742–2759.
[11] PSALTA A, TSIRONIS V, KARANTZALOS K, et al. Social pooling with edge convolutions on local connectivity graphs for human trajectory prediction in crowded scenes[C]//2020 IEEE 23rd International Conference on Intelligent Transportation Systems. Rhodes: IEEE, 2020: 1?6.
[12] ALAHI A, GOEL K, RAMANATHAN V, et al. Social LSTM: human trajectory prediction in crowded spaces[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 961?971.
[13] DEO N, TRIVEDI M M. Convolutional social pooling for vehicle trajectory prediction[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Salt Lake City: IEEE, 2018: 1549?15498.
[14] MESSAOUD K, YAHIAOUI I, VERROUST-BLONDET A, et al. Non-local social pooling for vehicle trajectory prediction[C]//2019 IEEE Intelligent Vehicles Symposium (IV). Paris: IEEE, 2019: 975?980.
[15] VEMULA A, MUELLING K, OH J. Social attention: modeling attention in human crowds[C]//2018 IEEE International Conference on Robotics and Automation. New York: ACM, 2018: 1–7.
[16] MESSAOUD K, YAHIAOUI I, VERROUST-BLONDET A, et al. Attention based vehicle trajectory prediction[J]. IEEE transactions on intelligent vehicles, 2021, 6(1): 175–185.
[17] LIN Lei, LI Weizi, BI Huikun, et al. Vehicle trajectory prediction using LSTMs with spatial-temporal attention mechanisms[J]. IEEE intelligent transportation systems magazine, 2022, 14(2): 197–208.
[18] 孙亚圣, 姜奇, 胡洁, 等. 基于注意力机制的行人轨迹预测生成模型[J]. 计算机应用, 2019, 39(3): 668–674
SUN Yasheng, JIANG Qi, HU Jie, et al. Attention mechanism based pedestrian trajectory prediction generation model[J]. Journal of computer applications, 2019, 39(3): 668–674
[19] 李琳辉, 周彬, 连静, 等. 基于社会注意力机制的行人轨迹预测方法研究[J]. 通信学报, 2020, 41(6): 175–183
LI Linhui, ZHOU Bin, LIAN Jing, et al. Research on pedestrian trajectory prediction method based on social attention mechanism[J]. Journal on communications, 2020, 41(6): 175–183
[20] HUANG Yingfan, BI Huikun, LI Zhaoxin, et al. STGAT: modeling spatial-temporal interactions for human trajectory prediction[C]//2019 IEEE/CVF International Conference on Computer Vision. Seoul: IEEE, 2020: 6271?6280.
[21] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all You need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach: ACM, 2017: 6000–6010.
[22] MERCAT J, GILLES T, EL ZOGHBY N, et al. Multi-head attention for multi-modal joint vehicle motion forecasting[C]//2020 IEEE International Conference on Robotics and Automation. Paris: IEEE, 2020: 9638?9644.
[23] GIULIARI F, HASAN I, CRISTANI M, et al. Transformer networks for trajectory forecasting[C]//2020 25th International Conference on Pattern Recognition. Milan: IEEE, 2021: 10335?10342.
[24] FENG Xidong, CEN Zhepeng, HU Jianming, et al. Vehicle trajectory prediction using intention-based conditional variational autoencoder[C]//2019 IEEE Intelligent Transportation Systems Conference. Auckland: IEEE, 2019: 3514?3519.
[25] ROY D, ISHIZAKA T, MOHAN C K, et al. Vehicle trajectory prediction at intersections using interaction based generative adversarial networks[C]//2019 IEEE Intelligent Transportation Systems Conference. Auckland: IEEE, 2019: 2318?2323.
[26] GUPTA A, JOHNSON J, LI Feifei, et al. Social GAN: socially acceptable trajectories with generative adversarial networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 2255?2264.
[27] SADEGHIAN A, KOSARAJU V, SADEGHIAN A, et al. SoPhie: an attentive GAN for predicting paths compliant to social and physical constraints[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2020: 1349?1358.
[28] LEE N, CHOI W, VERNAZA P, et al. DESIRE: distant future prediction in dynamic scenes with interacting agents[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 2165?2174.
[29] MANGALAM K, GIRASE H, AGARWAL S, et al. It is not the journey but the destination: endpoint conditioned trajectory prediction[C]//Computer Vision – ECCV 2020: 16th European Conference. Glasgow: ACM, 2020: 759–776.
[30] KINGMA D P, MOHAMED S, REZENDE D J, et al. Semi-supervised learning with deep generative models[C]//Advances in Neural Information Processing Systems. Montreal: NIPS, 2014: 3581?3589.
[31] 陈希亮, 曹雷, 何明, 等. 深度逆向强化学习研究综述[J]. 计算机工程与应用, 2018, 54(5): 24–35
CHEN Xiliang, CAO Lei, HE Ming, et al. Overview of deep inverse reinforcement learning[J]. Computer engineering and applications, 2018, 54(5): 24–35
[32] COLYAR J, HALKIAS J. Us highway 101 dataset: federal highway administration research and technology fact sheet [EB/OL]. (2007?09?08)[2022?05?20].https://www.fhwa.dot.gov/publications/research/operations/07030/index.cfm.
[33] COLYAR J, HALKIAS J. Us highway 80 dataset, federal highway administration[EB/OL]. (2006?12?01) [2022?05?20].https://rosap.ntl.bts.gov/view/dot/38708.
- 相似文献/References:
-
[1]申凯,王晓峰,杨亚东.基于双向消息链路卷积网络的显著性物体检测[J].智能系统学报,2019,14(6):1152.[doi:10.11992/tis.201812003]
SHEN Kai,WANG Xiaofeng,YANG Yadong.Salient object detection based on bidirectional message link convolution neural network[J].CAAI Transactions on Intelligent Systems,2019,14():1152.[doi:10.11992/tis.201812003]
[2]赵文清,程幸福,赵振兵,等.注意力机制和Faster RCNN相结合的绝缘子识别[J].智能系统学报,2020,15(1):92.[doi:10.11992/tis.201907023]
ZHAO Wenqing,CHENG Xingfu,ZHAO Zhenbing,et al.Insulator recognition based on attention mechanism and Faster RCNN[J].CAAI Transactions on Intelligent Systems,2020,15():92.[doi:10.11992/tis.201907023]
[3]申翔翔,侯新文,尹传环.深度强化学习中状态注意力机制的研究[J].智能系统学报,2020,15(2):317.[doi:10.11992/tis.201809033]
SHEN Xiangxiang,HOU Xinwen,YIN Chuanhuan.State attention in deep reinforcement learning[J].CAAI Transactions on Intelligent Systems,2020,15():317.[doi:10.11992/tis.201809033]
[4]曾碧卿,韩旭丽,王盛玉,等.层次化双注意力神经网络模型的情感分析研究[J].智能系统学报,2020,15(3):460.[doi:10.11992/tis.201812017]
ZENG Biqing,HAN Xuli,WANG Shengyu,et al.Hierarchical double-attention neural networks for sentiment classification[J].CAAI Transactions on Intelligent Systems,2020,15():460.[doi:10.11992/tis.201812017]
[5]莫宏伟,田朋.基于注意力融合的图像描述生成方法[J].智能系统学报,2020,15(4):740.[doi:10.11992/tis.201910039]
MO Hongwei,TIAN Peng.An image caption generation method based on attention fusion[J].CAAI Transactions on Intelligent Systems,2020,15():740.[doi:10.11992/tis.201910039]
[6]鲍维克,袁春.面向推荐系统的分期序列自注意力网络[J].智能系统学报,2021,16(2):353.[doi:10.11992/tis.202005028]
BAO Weike,YUAN Chun.Recommendation system with long-term and short-term sequential self-attention network[J].CAAI Transactions on Intelligent Systems,2021,16():353.[doi:10.11992/tis.202005028]
[7]洪恺临,曹江涛,姬晓飞.改进Center-Net网络的自主喷涂机器人室内窗户检测[J].智能系统学报,2021,16(3):425.[doi:10.11992/tis.202005016]
HONG Kailin,CAO Jiangtao,JI Xiaofei.Indoor window detection of autonomous spraying robot based on improved CenterNet network[J].CAAI Transactions on Intelligent Systems,2021,16():425.[doi:10.11992/tis.202005016]
[8]张勇,高大林,巩敦卫,等.用于关系抽取的注意力图长短时记忆神经网络[J].智能系统学报,2021,16(3):518.[doi:10.11992/tis.202008036]
ZHANG Yong,GAO Dalin,GONG Dunwei,et al.Attention graph long short-term memory neural network for relation extraction[J].CAAI Transactions on Intelligent Systems,2021,16():518.[doi:10.11992/tis.202008036]
[9]陈新元,谢晟祎,陈庆强,等.结合卷积特征提取和路径语义的知识推理[J].智能系统学报,2021,16(4):729.[doi:10.11992/tis.202008007]
CHEN Xinyuan,XIE Shengyi,CHEN Qingqiang,et al.Knowledge-based inference on convolutional feature extraction and path semantics[J].CAAI Transactions on Intelligent Systems,2021,16():729.[doi:10.11992/tis.202008007]
[10]张恒,何文玢,何军,等.医学知识增强的肿瘤分期多任务学习模型[J].智能系统学报,2021,16(4):739.[doi:10.11992/tis.202010005]
ZHANG Heng,HE Wenbin,HE Jun,et al.Multi-task tumor stage learning model with medical knowledge enhancement[J].CAAI Transactions on Intelligent Systems,2021,16():739.[doi:10.11992/tis.202010005]
备注/Memo
收稿日期:2022-01-14。
基金项目:国家重点研发计划项目（2021YFB1600501）；国家自然科学基金项目（61906038）；中央高校基本科研业务费专项资金项目（2242021R41184）.
作者简介:赵靖文,硕士研究生,主要研究方向为道路场景下多目标轨迹预测。;李煊鹏,副教授,博士,主要研究方向为道路场景下的环境感知、行为预测及因果推理,主持和参与国家级及省部级项目8项。;张为公,教授,主要研究方向为智能汽车技术、车辆机电一体化技术、车辆测试技术。
通讯作者:李煊鹏.E-mail:li_xuanpeng@seu.edu.cn
更新日期/Last Update:
1900-01-01