Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (6A): 334-341.doi: 10.11896/jsjkx.200800066

• Intelligent Computing • Previous Articles     Next Articles

Application of Spatial-Temporal Graph Attention Networks in Trajectory Prediction for Vehicles at Intersections

ZENG Wei-liang, CHEN Yi-hao, YAO Ruo-yu, LIAO Rui-xiang, SUN Wei-jun   

  1. School of Automation,Guangdong University of Technology,Guangzhou 510006,China
  • Online:2021-06-10 Published:2021-06-17
  • About author:ZENG Wei-liang,born in 1986,Ph.D associate professor.His main research interests include routing problem in complex network,traffic simulation,and big data visualization for smart city.
    SUN Wei-jun,born in 1975,Ph.D,is a member of CCF YOCSEF AC.His main research interests include internet of thing and machine learning.
  • Supported by:
    National Natural Science Foundation of China(61803100,U1911401),Guangdong Provincial Key Laboratory of Intelligent Transportation System(202005003),Science and Technology Planning Project of Guangdong Province,China(2019B010121001,2019B010118001,2019B01019001),Industrial Internet Innovation and Development Project of MIIT(TC190A3X9-2-2) and National Key Research and Development Project(2018YFB1802400).

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Abstract: With the rapid development of artificial intelligence and big data technology,the application of deep learning to trajectory prediction on autonomous driving has become a hot topic in recent years.The premise of keeping the safety of navigation and efficient path planning in autonomous driving is to make trajectory predictions of motor and non-motor vehicles accurate,especially in mixed traffic scenes.Aiming at tackling the problem related to path planning when interactions happen among different research objects at an intersection,a modelling scheme based on graph attention networks is proposed.The model applied combines spatial and temporal interactions among traffic-agents to improve the accuracy of trajectory predictions for motor and non-motor vehicles.In addition,the proposed model can be applied to path planning of autonomous driving,ensuring that motor and non-motor vehicles are capable of passing the interactions safely and efficiently in complex traffic scenes.In the case of simple interactions,the average displacement error and final displacement error of the trajectories derived from the model reach relatively small.And in the case of complex interactions,the future paths provided by the model are more reasonable than the future ground truths.

Key words: Autonomous driving, Deep learning, Graph Attention Networks, Trajectory prediction

CLC Number: 

  • TP391
[1] WU Y,SCHUSTER M,CHEN Z,et al.Google's neural machine translation system:bridging the gap between human and machine translation[J].arXiv:1609.08144,2016.
[2] BAHDANAU D,CHO K,BENGIO Y.Neural machine translation by jointly learning to align and translate[J].arXiv:1409.0473,2014.
[3] GRAVES A,JAITLY N.Towards end-to-end speech recogni-tion with recurrent neural networks[C]//International Conference on Machine Learning.2014:1764-1772.
[4] CHOROWSKI J,BAHDANAU D,CHO K,et al.End-to-endcontinuous speech recognition using attention-based recurrent nn:First results[J].arXiv:1412.1602,2014.
[5] XU K,BAJ,KIROS R,et al.Show,attend and tell:Neural image caption generation with visual attention[C]//International Conference on Machine Learning.2015:2048-2057.
[6] LAROCA R,SEVERO E,ZANLORENSI L A,et al.A robustreal-time automatic license plate recognition based on the yolodetector[C]//International JointConference on Neural Networks.2018:1-10.
[7] BERGER C.From a competition for self-driving miniature cars to a standardized experimental platform:concept,models,architecture,and evaluation[J].arXiv:1406.7768,2014.
[8] HELBING D,MOLNAR P.Social force model for pedestriandynamics [J].Physical Review E Statistical Physics Plasmas Fluids & Related Interdisciplinary Topics,1998,51(5):4282.
[9] TAY C,LAUGIER C.Modelling smooth paths using gaussian processes[C]//Springer Tracts in Advanced Robotics,2007,42:381-390.
[10] CHAN Y T,HU A G C,PLANT J B.A kalmanfilter basedtracking scheme with input estimation[J].IEEE Transactions on Aerospace and Electronic Systems,1979,AES-15(2):237-244.
[11] ALAHI A,GOEL K,RAMANATHAN V,et al.Social lstm:human trajectory prediction in crowded spaces[C]//IEEE Conference on Computer Vision and Pattern Recognition.2016.
[12] ILYA S,ORIOL V,QUOC V L.Sequence tosequence learning with neural networks [C]//Advances in Neural Information Processing Systems.2014:3104-3112.
[13] AGRIM G,JUSTIN J,LI F,et al.Social gan:socially acceptable trajectories with generative adversarial networks[C]//IEEE Conference on Computer Vision and Pattern Recognition.2018.
[14] JAVAD A,JEAN-BERNARD H,JULIAN P.Social ways:learning multi-modal distributions of pedestrian trajectories with gans[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.2019.
[15] AMIR S,VINEET K,ALI S,et al.SoPhie:an attentive gan for predicting paths compliant to social and physical constraints[C]//IEEE Conference on Computer Vision and Pattern Recognition.2018.
[16] ZHAO T,XU Y,MONFORT M,et al.Multi-agent tensor fusion for contextual trajectory prediction[C]//IEEE Conference on Computer Vision and Pattern Recognition.2019.
[17] ASHISH V,NOAM S,NIKI P,et al.Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems.2017,30.
[18] VEMULA A,MUELLING K,OH J.Social attention:modeling attention in human crowds[C]//2018 IEEE international Conference on Robotics and Automation (ICRA).IEEE,2018.
[19] XU K,HU W,LESKOVEC J,et al.How powerful are graphneural networks?[J].arXiv:1810.00826.
[20] SIJIE Y,YUANJUN X,DAHUA L.Spatial temporal graphconvolutional networks for skeleton-based action recognition[C]//Thirty-Second AAAI Conference on ArtificialIntelligence.2018.
[21] YAN S J,XIONG Y J,LIN D H.Spatial temporal graph convolutional networks for skeleton-based action recognition[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2018(1).
[22] CHENYANG S,WENTAO C,WEI W,et al.An attention enhanced graph convolutional lstm network for skeleton-basedaction recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2019:1227-1236.
[23] VELIKOVI P,CUCURULL G,CASANOVA A,et al.Graph attention networks[J].arXiv:1710.10903.
[24] HUANG Y,BI H,LI Z,et al.STGAT:modeling spatial-temporal interactions for human trajectory prediction[C]//IEEE International Conference on Computer Vision.2019:6272-6281.
[25] CHO K,VAN M B,GULCEHRE C,et al.Learning phrase representations usingrnnencoder-decoder for statistical machine translation[J].arXiv:1406.1078.
[26] HASIM S,ANDREW S,FRANCOISE B.Long short-termmemory based recurrent neural network architectures for large vocabulary speech recognition[J].arXiv:1402.1128.
[27] SUZUKI K,NAKAMURA H.Traffic Analyzer-the integratedvideo image processing system for traffic flow analysis[C]//Proceedings of the 13th World Congress on Intelligent Transportation Systems.2006.
[28] LI X,CHEN S,HU X,YANG J.Understanding the disharmony between dropout and batch normalization by variance shift[C]//IEEE Conference on Computer Vision and Pattern Recognition.2019:2677-2685.
[29] NWANKPA C,IJOMAH W,GACHAGAN A,et al.Activation functions:comparison of trends in practice and research for deep learning[J].arXiv:1811.03378.
[30] BELLO I,ZOPH B,VASUDEVAN V,et al.Neural optimizer search with reinforcement Learning[C]//International Conference on Machine Learning.2016:459-468.
[31] HOJJATS,SHARN S,JOSEPH B.Recent advances in recurrent neural networks[J].arXiv:1801.01078,2017.
[32] WOJCIECH Z,ILYA S,ORIOL V.Recurrent neural network regularization[J].arXiv:1409.2329.
[33] SEPP H,JURGEN S.Long short-term memory[J].NeuralComputation,1997,9(8):1735-1780.
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