2022, Volume 19, Issue 12
Engineering >> 2022, Volume 19, Issue 12 doi: 10.1016/j.eng.2021.12.020
A Probabilistic Architecture of Long-Term Vehicle Trajectory Prediction for Autonomous Driving
State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
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Abstract
In mixed and dynamic traffic environments, accurate long-term trajectory forecasting of surrounding vehicles is one of the indispensable preconditions for autonomous vehicles (AVs) to accomplish reasonable behavioral decisions and guarantee driving safety. In this paper, we propose an integrated probabilistic architecture for long-term vehicle trajectory prediction, which consists of a driving inference model (DIM) and a trajectory prediction model (TPM). The DIM is designed and employed to accurately infer the potential driving intention based on a dynamic Bayesian network. The proposed DIM incorporates the basic traffic rules and multivariate vehicle motion information. To further improve the prediction accuracy and realize uncertainty estimation, we develop a Gaussian process (GP)-based TPM, considering both the short-term prediction results of the vehicle model and the driving motion characteristics. Afterward, the effectiveness of our novel approach is demonstrated by conducting experiments on a public naturalistic driving dataset under lane-changing scenarios. The superior performance on the task of long-term trajectory prediction is presented and verified by comparing with other advanced methods.
Keywords
Autonomous driving ; Dynamic Bayesian network ; Driving intention recognition ; Gaussian process ; Vehicle trajectory prediction
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References
[ 1 ] Zhou J, Zhou Y, Wang B, Zang J. Human–cyber–physical systems (HCPSs) in the context of new-generation intelligent manufacturing. Engineering 2019;5 (4):624–36. link1
[ 2 ] Seif HG, Hu X. Autonomous driving in the iCity—HD maps as a key challenge of the automotive industry. Engineering 2016;2(2):159–62. link1
[ 3 ] Krüger M, Novo AS, Nattermann T, Bertram T. Interaction-aware trajectory prediction based on a 3D spatio–temporal tensor representation using convolutional–recurrent neural networks. In: Proceedings of 2020 IEEE Intelligent Vehicles Symposium (IV); 2020 Oct 19–Nov 13; Las Vegas, NV, USA. New York City: IEEE; 2020. p. 1122–7. link1
[ 4 ] Huang H, Wang J, Fei C, Zheng X, Yang Y, Liu J, et al. A probabilistic risk assessment framework considering lane-changing behavior interaction. Sci China Inf Sci 2020;63(9):1–15. link1
[ 5 ] Lefèvre S, Vasquez D, Laugier C. A survey on motion prediction and risk assessment for intelligent vehicles. ROBOMECH J 2014;1:1. link1
[ 6 ] Mozaffari S, Al-Jarrah OY, Dianati M, Jennings P, Mouzakitis A. Deep learningbased vehicle behaviour prediction for autonomous driving applications: a review. IEEE Trans Intell Transp Syst 2020;23(1):33–47. link1
[ 7 ] Rudenko A, Palmieri L, Herman M, Kitani KM, Gavrila DM, Arras KO. Human motion trajectory prediction: a survey. Int J Robot Res 2020;39(8):895–935. link1
[ 8 ] Sorstedt J, Svensson L, Sandblom F, Hammarstrand L. A new vehicle motion model for improved predictions and situation assessment. IEEE Trans Intell Transp Syst 2011;12(4):1209–19. link1
[ 9 ] Schubert R, Richter E, Wanielik G. Comparison and evaluation of advanced motion models for vehicle tracking. In: Proceedings of 2008 11th International Conference on Information Fusion; 2008 Jun 30–Jul 3; Cologne, Germany. New York City: IEEE; 2008. p. 1–6. link1
[10] Lin CF, Ulsoy AG, LeBlanc DJ. Vehicle dynamics and external disturbance estimation for vehicle path prediction. IEEE Trans Control Syst Technol 2000;8 (3):508–18. link1
[11] Kamann A, Bielmeier JB, Hasirlioglu S, Schwarz UT, Brandmeier T. Object tracking based on an extended Kalman filter in high dynamic driving situations. In: Proceedings of 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC); 2017 Oct 16–19; Yokohama, Japan. New York City: IEEE; 2017. p. 1–6. link1
[12] Caveney D. Stochastic path prediction using the unscented transform with numerical integration. In: Proceedings of 2007 IEEE Intelligent Transportation Systems Conference; 2007 Sep 30–Oct 3; Bellevue, WA, USA. New York: IEEE; 2007. p. 848–53. link1
[13] Berntorp K, Hoang T, Di Cairano S. Motion planning of autonomous road vehicles by particle filtering. IEEE Trans Intell Veh 2019;4(2):197–210. link1
[14] Schreier M, Willert V, Adamy J. Bayesian, maneuver-based, long-term trajectory prediction and criticality assessment for driver assistance systems. In: Proceedings of 17th International IEEE Conference on Intelligent Transportation Systems (ITSC); 2014 Oct 8–11; Qingdao, China. New York City: IEEE; 2014. p. 334–41. link1
[15] Liu P, Kurt A, Özgüner Ü. Trajectory prediction of a lane changing vehicle based on driver behavior estimation and classification. In: Proceedings of 17th International IEEE Conference on Intelligent Transportation Systems (ITSC); 2014 Oct 8–11; Qingdao, China. New York City: IEEE; 2014. p. 942–7. link1
[16] Deo N, Rangesh A, Trivedi MM. How would surround vehicles move? A unified framework for maneuver classification and motion prediction. IEEE Trans Intell Veh 2018;3(2):129–40. link1
[17] Kumar P, Perrollaz M, Lefèvre S, Laugier C. Learning-based approach for online lane change intention prediction. In: Proceedings of 2013 IEEE Intelligent Vehicles Symposium (IV); 2013 Jun 23–26; Gold Coast, QLD, Australia. New York City: IEEE; 2013. p. 797–802. link1
[18] Streubel T, Hoffmann KH. Prediction of driver intended path at intersections. In: Proceedings of 2014 IEEE Intelligent Vehicles Symposium Proceedings; 2014 Jun 8–11; New York City: IEEE; 2014. p. 134–9. link1
[19] Brown K, Driggs-Campbell K, Kochenderfer MJ. Modeling and prediction of human driver behavior: a survey. 2020. arXiv:2006.08832.
[20] Xing Y, Lv C, Wang H, Wang H, Ai Y, Cao D, et al. Driver lane change intention inference for intelligent vehicles: framework, survey, and challenges. IEEE Trans Veh Technol 2019;68(5):4377–90. link1
[21] Wiest J, Höffken M, Kreßel U, Dietmayer K. Probabilistic trajectory prediction with Gaussian mixture models. In: Proceedings of 2012 IEEE Intelligent Vehicle Symposium; 2012 Jun 3–7; Madrid, Spain. New York City: IEEE; 2012. p. 141–6. link1
[22] Gao H, Zhu J, Zhang T, Xie G, Kan Z, Hao Z, et al. Situational assessment for intelligent vehicles based on stochastic model and Gaussian distributions in typical traffic scenarios. IEEE Trans Syst Man Cybern Syst. In press.
[23] Schreier M, Willert V, Adamy J. An integrated approach to maneuver-based trajectory prediction and criticality assessment in arbitrary road environments. IEEE Trans Intell Transp Syst 2016;17(10):2751–66. link1
[24] Li J, Wu J, Sun H, Jiang Y, Deng W, Zhu B. Traffic modeling considering motion uncertainties. SAE Internat 2017. link1
[25] Houenou A, Bonnifait P, Cherfaoui V, Yao W. Vehicle trajectory prediction based on motion model and maneuver recognition. In: Proceedings of 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems; 2013 Nov 3–7; Tokyo, Japan. New York City: IEEE; 2013. p. 4363–9. link1
[26] Xie G, Gao H, Qian L, Huang B, Li K, Wang J. Vehicle trajectory prediction by integrating physics- and maneuver-based approaches using interactive multiple models. IEEE Trans Ind Electron 2018;65(7):5999–6008. link1
[27] Schlenoff C, Madhavan R, Kootbally Z. PRIDE: a hierarchical, integrated prediction framework for autonomous on-road driving. In: Proceedings of 2006 IEEE International Conference on Robotics and Automation; 2006 May 15–19; Orlando, FL, USA. New York City: IEEE; 2006. p. 2348–53. link1
[28] Deo N, Trivedi MM. Convolutional social pooling for vehicle trajectory prediction. In: Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshop (CVPRW); 2018 Jul 18–22; Salt Lake City, UT, USA. New York City: IEEE; 2018. p. 1468–76. link1
[29] Messaoud K, Yahiaoui I, Verroust-Blondet A, Nashashibi F. Relational recurrent neural networks for vehicle trajectory prediction. In: Proceedings of 2019 IEEE Intelligent Transportation Systems Conference (ITSC); 2019 Oct 27–30; Auckland, New Zealand. New York City: IEEE; 2019. p. 1813–8. link1
[30] Yan J, Peng Z, Yin H, Wang J, Wang X, Shen Y, et al. Trajectory prediction for intelligent vehicles using spatial-attention mechanism. IET Intell Transp Syst 2020;14(13):1855–63. link1
[31] Mo X, Xing Y, Lv C. Interaction-aware trajectory prediction of connected vehicles CNN-LSTM networks. In: Proceedings of IECON 2020 46th Annual Conference of the IEEE Industrial Electronics Society; 2020 Oct 18–21; Singapore. New York City: IEEE; 2020. p. 5057–62. link1
[32] Xing Y, Lv C, Cao D. Personalized vehicle trajectory prediction based on joint time-series modeling for connected vehicles. IEEE Trans Veh Technol 2020;69 (2):1341–52. link1
[33] Xing Y, Lv C, Cao D, Lu C. Energy oriented driving behavior analysis and personalized prediction of vehicle states with joint time series modeling. Appl Energy 2020;261:114471. link1
[34] Murphy KP. Dynamic Bayesian networks: representation, inference and learning [dissertation]. Berkeley: University of California, Berkeley; 2002. link1
[35] Rabiner LR. A tutorial on hidden Marko models and selected applications in speech recognition. Proc IEEE 1989;77(2):257–86. link1
[36] Minka TP. Expectation propagation for approximate Bayesian inference. In: Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence; 2001 Aug 2–5; Seattle, WA, USA. San Francisco: Morgan Kaufmann Publishers Inc.; 2001. p. 362–9. link1
[37] Liu J, Luo Y, Xiong H, Wang T, Huang H, Zhong Z. An integrated approach to probabilistic vehicle trajectory prediction via driver characteristic and intention estimation. In: Proceedings of 2019 IEEE Intelligent Transportation Systems Conference (ITSC); 2019 Oct 27–30; Auckland, New Zealand. New York City: IEEE; 2019. p. 3526–32. link1
[38] Xiao W, Zhang L, Meng D. Vehicle trajectory prediction based on motion model and maneuver model fusion with interactive multiple models. SAE Int J Adv Curr Pract Mobil 2020;2:3060–71. link1
[39] Williams CK, Rasmussen CE. Gaussian processes for machine learning. Cambridge: MIT Press; 2006. link1
[40] Krajewski R, Bock J, Kloeker L, Eckstein L. The highD dataset: A drone dataset of naturalistic vehicle trajectories on German highways for validation of highly automated driving systems. In: Proceedings of 2018 21st International Conference on Intelligent Transportation Systems (ITSC); 2018 Nov 4–7; Maui, HI, USA. New York City: IEEE; 2018. p. 2118–25. link1
[41] Bishop CM. Pattern recognition and machine learning. New York: Springer; 2006. link1
[42] Wang H, Wang W, Yuan S, Li X. Uncovering interpretable internal states of merging tasks at highway on-ramps for autonomous driving decision-making, 2021. arXiv: 2102.07530. link1
[43] Yang T, Nan Z, Zhang H, Chen S, Zheng N. Traffic agent trajectory prediction using social convolution and attention mechanism. 2020. arXiv: 2007.02515.
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