Research on Vehicle Trajectory Prediction Considering Vehicle-lane Interaction

doi:10.3901/JME.2024.10.076

Abstract

Abstract: Accurate obstacle trajectory prediction is a key to correct decision making and precise control of autonomous vehicle. Considering the influence of lane information and surrounding obstacles on vehicle motion in complex environment, a vehicle trajectory prediction model aggregated vehicle-lane information is proposed based on encoding-decoding framework. Firstly, the directed graph is adopted to describe the lane nodes. Then the target vehicle and surrounding obstacles are encoded via GRU. Simultaneously, the artificial potential field is introduced to represent the vehicle-vehicle interaction. By concatenating the lane node vector and repulsive force vector, the attention mechanism is utilized to explore the spatial-temporal coupling mechanism. Finally, the multi-modal trajectory prediction of obstacles is achieved by scoring and clustering the lane nodes through the strategy network. The proposed prediction model is trained on the nuScenes motion prediction dataset and evaluated with the state-of-the-art baseline models’ performance. The results demonstrated that the proposed prediction model has lower prediction error and better robustness. On the other hand, it has better interpretability by introducing the potential field into attention mechanism.

Key words: trajectory prediction, deep learning, encoding-decoding framework, potential field, attention mechanism

CLC Number:

TG156

LI Jiufa, ZOU Bowen, REN Yue. Research on Vehicle Trajectory Prediction Considering Vehicle-lane Interaction[J]. Journal of Mechanical Engineering, 2024, 60(10): 76-85.

References

[1] 李睿，李春明，苏杰，等. 考虑轨迹预测补偿的履带车辆滑动参数估计方法[J]. 清华大学学报(自然科学版)，2022，62(1):133-140. LI Rui，LI Chunming，SU Jie，et al. Slid parameter estimaters for tracked vehicles with trajectory prediction compensation[J]. Journal of Tsinghua University(Science and Technology)，2022，62(1):133-140.
[2] 张润生，黄小云，刘晶，等. 基于视觉复杂环境下车辆行驶轨迹预测方法[J]. 机械工程学报，2011，47(2):16-24. ZHANG Runsheng，HUANG Xiaoyun，LIU Jing，et al. Image vehicle motion trajectory prediction method under complex environment[J]. Journal of Mechanical Engineering，2011，47(2):16-24.
[3] ALAHI A，GOEL K，RAMANATHAN V. Social-LSTM:Human trajectory prediction in crowded spaces[C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR)，Las Vegas，USA，2016:961-971.
[4] SHENG Z，XU Y，XUE S，et al. Graph-based spatial-temporal convolutional network for vehicle trajectory prediction in autonomous driving[J]. IEEE Transactions on Intelligent Transportation System，2022，23(10):17654-17665.
[5] DEO N，TRIVEDI M M. Multi-modal trajectory prediction of surrounding vehicles with maneuver based LSTMs[C]//IEEE Intelligent Vehicles Symposium，Changshu ，China，2018:1179-1184.
[6] XU C，LI M，NI Z，et al. GroupNet:Multiscale hypergraph neural networks for trajectory prediction with relational reasoning[C]//IEEE Conference on Computer Vision and Pattern Recognition(CVPR)，New Orleans，USA，2022:6488-6497.
[7] KAOUTHER M，ITHERI Y，ANNE V. Attention based vehicle trajectory prediction[J]. IEEE Transactions on Intelligent Vehicles，2021，6(1):175-185.
[8] HUANG Y，BI H，LI Z. STGAT:Modeling spatial-temporal interactions for human trajectory prediction[C]//IEEE International Conference on Computer Vision(ICCV)，Seoul，Republic of Korea. 2019:6271-6280.
[9] LUO C，SUN L，DABIRI D. Probabilistic multi-modal trajectory prediction with lane attention for autonomous vehicles[C]//IEEE International Conference on Intelligent Robots and Systems (IROS)，Las Vegas，USA 2020:2370-2376.
[10] GUO H，MENG Q，CAO D. Vehicle trajectory prediction method coupled with ego vehicle motion trend under dual attention mechanism[J]. IEEE Transactions on Instrumentation and Measurement，2022，71:1-16.
[11] 黄玲，郭亨聪，张荣辉，等. 人机混驾环境下基于LSTM的无人驾驶车辆换道行为模型[J]. 中国公路学报，2020，33(7):156-166. HUANG Ling，GUO Hengcong，ZHANG Ronghui，et al. LSTM-based lane-changing behavior model for unmanned vehicle under environment of heterogeneous human-driven and autonomous vehicles[J]. China Journal of Highway and Transport，2020，33(7):156-166.
[12] GAO J，SUN C，ZHAO H. VectorNet:Encoding HD maps and agent dynamics from vectorized representation[C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR)，2020:11522-11530.
[13] LIANG M，YANG B，HU R. Learning lane graph representations for motion forecasting[C]//European Conference on Computer Vision(ECCV)，Glasgow，UK，2020:541-556.
[14] GU J，SUN C，ZHAO H. DenseTNT:End-to-end trajectory prediction from dense goal sets[C]//IEEE International Conference on Computer Vision (ICCV)，Montreal，Canada，2021:15283-15292.
[15] DEO N，WOLFF E，BEIJBOM O. Multimodal trajectory prediction conditioned on lane-graph traversals[C]//Proceedings of the 5th Conference on Robot Learning(PMLR)，London，UK，2022(164):203-212.
[16] 李文礼，肖凯文，石晓辉，等. 基于行人视野注意力场的人车微观交互模型[J]. 汽车工程，2022，44(6):808-820. LI Wenli，XIAO Kaiwen，SHI Xiaohui. Pedestrian-vehicle micro-interaction model based on attention field of pedestrian vision[J]. Automotive Engineering，2022，44(6):808-820.
[17] 陈无畏，胡振国，汪洪波，等. 基于可拓决策和人工势场法的车道偏离辅助系统研究[J]. 机械工程学报，2018，54(16):134-143. CHEN Wuwei，HU Zhenguo，WANG Hongbo，et al. Study on extension decision and artificial potential field based lane departure assistance system[J]. Journal of Mechanical Engineering，2018，54(16):134-143.
[18] HOLGER C，VARUN B，LANG A H，et al. NuScenes:A multimodal dataset for autonomous driving[C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR)，2020:11621-11631.
[19] PHAN-MINH T，GRIGORE E C，BOULTON F A. CoverNet:Multimodal behavior prediction using trajectory sets[C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR)，2020:14062-14071.
[20] SALZMANN T，IVANOVIC B，CHAKRAVARTY P. Trajectron++:Dynamically-feasible trajectory forecasting with heterogeneous data[C]//European Conference on Computer Vision(ECCV)，Glasgow，UK，2020:683-700.
[21] WANG C，WANG Y，XU M. Stepwise goal-driven networks for trajectory prediction[J]. IEEE Robotics and Automation Letters，2022，7(2):2716-2723.
[22] MESSAOUD K，DEO N，TRIVEDI M M. Trajectory prediction for autonomous driving based on multi-head attention with joint agent-map representation[C]//IEEE Intelligent Vehicles Symposium (IV)，NAGOYA，Japan，2021:165-170.
[23] DEO N，TRIVEDI M M. Trajectory forecasts in unknown environments conditioned on grid-based plans[J]. arXiv:2001.00735，2020.
[24] GILLES T，SABATINI S，TSISHKOU D. GOHOME:Graph-oriented heatmap output for future motion estimation[C]//International Conference on Robotics and Automation (ICRA)，Philadelphia，USA，2022:9107-9114.
[25] GIRGIS R，GOLEMO F，CODEVILLA F. Latent variable sequential set transformers for joint multi-agent motion prediction[C]//International Conference on Learning Representations(ICLR)，2022.