计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 334-341.doi: 10.11896/jsjkx.200800066
曾伟良, 陈漪皓, 姚若愚, 廖睿翔, 孙为军
ZENG Wei-liang, CHEN Yi-hao, YAO Ruo-yu, LIAO Rui-xiang, SUN Wei-jun
摘要： 随着人工智能和大数据技术的快速发展,以深度学习为代表的自动驾驶轨迹预测是未来的热点研究方向。在混合交通场景下,如何准确地预测机动车与非机动车的轨迹,是实现自动驾驶技术中安全行驶和高效轨迹规划等问题的前提。针对交叉路口中不同运动对象之间发生交互时的轨迹预测问题,提出了基于图注意力网络的建模方案。所采用的模型结合了时间与空间上研究对象之间的相互作用,对机动车与非机动车的未来轨迹做出了更准确的预测,可应用于自动驾驶的轨迹规划方案,确保在复杂交通场景下,机动车与非机动车能够安全且高效地通过交叉路口。该模型在简单交互情况下,可取得较小的轨迹平均位移误差和最终位移误差,而在复杂交互情况下,可提供更为合理的规划路径。
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| [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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