基于时空Transformer特征融合的车辆轨迹预测

赵文红; 王巍; 万子璐

doi:10.11959/j.issn.1000-436x.2024192

您当前的位置：

首页 >

文章列表页 >

基于时空Transformer特征融合的车辆轨迹预测

学术通信 | 更新时间：2024-12-24

- 基于时空Transformer特征融合的车辆轨迹预测
- Vehicle trajectory prediction based on spatio-temporal Transformer feature fusion
- 通信学报 2024年45卷第11期页码：267-276
- 作者机构：
  
  1.嘉兴南湖学院公共基础教学部，浙江嘉兴 314001
  2.电磁空间安全全国重点实验室，浙江嘉兴 314033
  3.浙江工业大学信息工程学院，浙江杭州 310013
- 作者简介：
  
  [ "赵文红（1981- ），女，河北衡水人，嘉兴南湖学院讲师，主要研究方向为优化计算。" ]
  [ "王巍（1980- ），男，博士，河北张家口人，电磁空间安全全国重点实验室研究员，主要研究方向为智能处理、网络优化。" ]
  [ "万子璐（2001- ），女，浙江乐清人，浙江工业大学硕士生，主要研究方向为智能驾驶、车辆控制。" ]
- 基金信息：
  
  国家自然科学基金资助项目(62231027;U19B2015;U21B2001);嘉兴南湖学院科研基金资助项目(62211ZL)
- DOI：10.11959/j.issn.1000-436x.2024192
  中图分类号： TP183
- 收稿：2024-06-28，
  
  修回：2024-10-17，
  
  纸质出版：2024-11-25
- 稿件说明：
移动端阅览
赵文红,王巍,万子璐.基于时空Transformer特征融合的车辆轨迹预测[J].通信学报,2024,45(11):267-276.

ZHAO Wenhong,WANG Wei,WAN Zilu.Vehicle trajectory prediction based on spatio-temporal Transformer feature fusion[J].Journal on Communications,2024,45(11):267-276.
赵文红,王巍,万子璐.基于时空Transformer特征融合的车辆轨迹预测[J].通信学报,2024,45(11):267-276. DOI： 10.11959/j.issn.1000-436x.2024192.

ZHAO Wenhong,WANG Wei,WAN Zilu.Vehicle trajectory prediction based on spatio-temporal Transformer feature fusion[J].Journal on Communications,2024,45(11):267-276. DOI： 10.11959/j.issn.1000-436x.2024192.

摘要

在复杂的交通环境下，自动驾驶汽车需要充分地分析周围交通物体的运动方向、运动速度等信息，并准确预测未来的轨迹。针对这个问题，提出了一种基于时空Transformer的网络模型。该模型首先利用空间自注意力机制，通过捕捉同一时刻下车辆间的空间相互作用，实现对多车空间关系交互性的精确建模；随后通过时间自注意力机制提取连续帧的时间依赖关系，以此生成一组能够反映车辆动态行为的时空特征；最后这些特征被送入解码器，以预测所有车辆在未来5 s内的运动轨迹。在公开的NGSIM数据集上进行了训练和验证，与其他的先进方案相比，该模型在未来5 s的轨迹预测中具有更高的准确性和精度，长期预测准确率比先进方案提高14.6%。

Abstract

In complex traffic environments

autonomous vehicles must thoroughly analyze the motion direction

speed

and other information of surrounding traffic objects to accurately predict future trajectories. A network model based on spatio-temporal Transformer was proposed to address this issue. The framework initially employs a spatial self-attention mechanism to capture the spatial interactions between vehicles at the same moment

achieving precise modeling of the spatial relationship interactivity among multiple vehicles. Subsequently

a temporal self-attention mechanism was utilized to extract the temporal dependencies between consecutive frames

thereby generating a set of spatiotemporal features that reflect the dynamic behavior of vehicles. These features were then fed into a decoder to predict the motion trajectories of vehicles over the next 5 s. The proposed model was trained and validated on the publicly available NGSIM dataset. Compared to other state-of-the-art schemes

our scheme demonstrates greater accuracy and precision in trajectory prediction over the subsequent 5 s. The long-term forecasting accuracy is increased by 14.6% compared to the advanced schemes.

关键词

Keywords

references

LUO W J , YANG B , URTASUN R . Fast and furious: real time end-to-end 3D detection, tracking and motion forecasting with a single convolutional net [C ] // Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE Press , 2018 : 3569 - 3577 .

温惠英 , 张伟罡 , 赵胜 . 基于生成对抗网络的车辆换道轨迹预测模型 [J ] . 华南理工大学学报(自然科学版) , 2020 , 48 ( 5 ): 32 - 40 .

WEN H Y , ZHANG W G , ZHAO S . Vehicle lane-change trajectory prediction model based on generative adversarial networks [J ] . Journal of South China University of Technology (Natural Science Edition) , 2020 , 48 ( 5 ): 32 - 40 .

KHOSROSHAHI A , OHN-BAR E , TRIVEDI M M . Surround vehicles trajectory analysis with recurrent neural networks [C ] // Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) . Piscataway : IEEE Press , 2016 : 2267 - 2272 .

PHILLIPS D J , WHEELER T A , KOCHENDERFER M J . Generalizable intention prediction of human drivers at intersections [C ] // Proceedings of the 2017 IEEE Intelligent Vehicles Symposium (IV) . Piscataway : IEEE Press , 2017 : 1665 - 1670 .

KIM B , KANG C M , KIM J , et al . Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network [C ] // Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) . Piscataway : IEEE Press , 2017 : 399 - 404 .

LEE N , CHOI W , VERNAZA P , et al . DESIRE: distant future prediction in dynamic scenes with interacting agents [C ] // Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE Press , 2017 : 2165 - 2174 .

蔡英凤 , 朱南楠 , 邰康盛 , 等 . 基于注意力机制的车辆行为预测 [J ] . 江苏大学学报(自然科学版) , 2020 , 41 ( 2 ): 125 - 130 .

CAI Y F , ZHU N N , TAI K S , et al . Vehicle behavior prediction based on attention mechanism [J ] . Journal of Jiangsu University (Natural Science Edition) , 2020 , 41 ( 2 ): 125 - 130 .

ALAHI A , GOEL K , RAMANATHAN V , et al . Social LSTM: human trajectory prediction in crowded spaces [C ] // Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE Press , 2016 : 961 - 971 .

GUPTA A , JOHNSON J , LI F F , et al . Social GAN: socially acceptable trajectories with generative adversarial networks [C ] // Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE Press , 2018 : 2255 - 2264 .

ZHANG P , OUYANG W L , ZHANG P F , et al . SR-LSTM: state refinement for LSTM towards pedestrian trajectory prediction [C ] // Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE Press , 2019 : 12077 - 12086 .

DEO N , TRIVEDI M M . Multi-modal trajectory prediction of surrounding vehicles with maneuver based LSTMs [C ] // Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV) . Piscataway : IEEE Press , 2018 : 1179 - 1184 .

DEO N , RANGESH A , TRIVEDI M M . How would surround vehicles move? A unified framework for maneuver classification and motion prediction [J ] . IEEE Transactions on Intelligent Vehicles , 2018 , 3 ( 2 ): 129 - 140 .

DEO N , TRIVEDI M M . Convolutional social pooling for vehicle trajectory prediction [C ] // Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) . Piscataway : IEEE Press , 2018 : 1549 - 15498 .

IVANOVIC B , PAVONE M . The trajectron: probabilistic multi-agent trajectory modeling with dynamic spatiotemporal graphs [C ] // Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV) . Piscataway : IEEE Press , 2019 : 2375 - 2384 .

ZHAO Z Y , FANG H W , JIN Z , et al . GISNet: graph-based information sharing network for vehicle trajectory prediction [C ] // Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN) . Piscataway : IEEE Press , 2020 : 1 - 7 .

LI X , YING X W , CHUAH M C . GRIP++: enhanced graph-based interaction-aware trajectory prediction for autonomous driving [J ] . arXiv Preprint , arXiv: 1907.07792 , 2019 .

XU D W , SHANG X T , LIU Y , et al . Group vehicle trajectory prediction with global spatio-temporal graph [J ] . IEEE Transactions on Intelligent Vehicles , 2023 , 8 ( 2 ): 1219 - 1229 .

XU D W , SHANG X T , PENG H , et al . MVHGN: multi-view adaptive hierarchical spatial graph convolution network based trajectory prediction for heterogeneous traffic-agents [J ] . IEEE Transactions on Intelligent Transportation Systems , 2023 , 24 ( 6 ): 6217 - 6226 .

VASWANI A , SHAZEER N , PARMAR N , et al . Attention is all you need [C ] // Proceeding of the 31st International Conference on Neural Information Processing Systems . California : MIT Press , 2017 : 6000 - 6010 .

GIULIARI F , HASAN I , CRISTANI M , et al . Transformer networks for trajectory forecasting [C ] // Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR) . Piscataway : IEEE Press , 2021 : 10335 - 10342 .

YU C J , MA X , REN J W , et al . Spatio-temporal graph transformer networks for pedestrian trajectory prediction [C ] // Proceedings of the 16th European Conference on Computer Vision (ECCV 2020) . Berlin : Springer , 2020 : 507 - 523 .

LIU Y C , ZHANG J H , FANG L J , et al . Multimodal motion prediction with stacked transformers [C ] // Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE Press , 2021 : 7573 - 7582 .

NGIAM J , CAINE B , VASUDEVAN V , et al . Scene Transformer: a unified architecture for predicting multiple agent trajectories [J ] . arXiv Preprint , arXiv: 2106.08417 , 2021 .

景荣荣 , 吴兰 , 张坤鹏 . 基于Transformer的自动驾驶交互感知轨迹预测 [J ] . 科学技术与工程 , 2023 , 23 ( 26 ): 11414 - 11423 .

JING R R , WU L , ZHANG K P . Transformer-based interaction-aware trajectory prediction for autonomous driving [J ] . Science Technology and Engineering , 2023 , 23 ( 26 ): 11414 - 11423 .

王庆荣 , 谭小泽 , 朱昌锋 , 等 . 基于门控循环单元和Transformer的车辆轨迹预测方法 [J ] . 汽车技术 , 2024 ( 7 ): 1 - 8 .

WANG Q R , TAN X Z , ZHU C F , et al . Vehicle trajectory prediction method based on GRU and transformer [J ] . Automobile Technology , 2024 ( 7 ): 1 - 8 .

KUEFLER A , MORTON J , WHEELER T , et al . Imitating driver behavior with generative adversarial networks [C ] // Proceedings of the 28th IEEE Intelligent Vehicles Symposium (IV) . Piscataway : IEEE Press , 2017 : 204 - 211 .

浏览量

955

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

群组感知的行人轨迹预测方法研究

面向车路云协同自动驾驶的大语言模型安全研究综述

基于多尺度卷积融合编码网络的调制识别方法

动态场景下自动驾驶运行时安全保障的自适应方法

基于SVM-RFE与Transformer-TBAM的高校邮件分析研究