Extreme Gradient Boosting (XGBoost) Model for Vehicle Trajectory Prediction in Connected and Autonomous Vehicle Environment
Abstract
Connected and autonomous vehicles (CAVs) have the ability to receive information on their leading vehicles through multiple sensors and vehicle-to-vehicle (V2V) technology and then predict their future behaviour thus to improve roadway safety and mobility. This study presents an innovative algorithm for connected and autonomous vehicles to determine their trajectory considering surrounding vehicles. For the first time, the XGBoost model is developed to predict the acceleration rate that the object vehicle should take based on the current status of both the object vehicle and its leading vehicle. Next Generation Simulation (NGSIM) datasets are utilised for training the proposed model. The XGBoost model is compared with the Intelligent Driver Model (IDM), which is a prior state-of-the-art model. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are applied to evaluate the two models. The results show that the XGBoost model outperforms the IDM in terms of prediction errors. The analysis of the feature importance reveals that the longitudinal position has the greatest influence on vehicle trajectory prediction results.
References
Helbing D, Treiber M, Kesting A, Schönhof M. Theoretical vs. empirical classification and prediction of congested traffic states. The European Physical Journal B. 2009;69(4): 583-598. DOI: 10.1140/epjb/e2009-00140-5
Prayogo D, Cheng MY, Wu YW, Tran DH. Combining machine learning models via adaptive ensemble weighting for prediction of shear capacity of reinforced-concrete deep beams. Engineering with Computers. 2019;36: 1135-1153. DOI: 10.1007/s00366-019-00753-w
Zhang D, et al. A data-driven design for fault detection of wind turbines using random forests and XGboost. IEEE Access. 2018;6: 21020-21031. DOI: 10.1109/ACCESS.2018.2818678
Chen T, Guestrin C. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13-17 August 2016, San Francisco, California, USA; 2016. p. 785-794.
Colyar J, Halkias J. US highway 101 dataset. Federal Highway Administration (FHWA). Tech. Rep. FHWA-HRT-07-030, 2007.
Polychronopoulos A, Tsogas M, Amditis AJ, Andreone L. Sensor fusion for predicting vehicles' path for collision avoidance systems. IEEE Transactions on Intelligent Transportation Systems. 2007;8(3): 549-562. DOI: 10.1109/TITS.2007.903439
Schubert R, Richter E, Wanielik G. Comparison and evaluation of advanced motion models for vehicle tracking. 2008 11th International Conference on Information Fusion, 30 June-3 July 2008, Cologne, Germany. IEEE; 2008. p. 1-6.
Liu P, Fan W. Exploring the impact of connected and autonomous vehicles on freeway capacity using a revised Intelligent Driver Model. Transportation Planning and Technology. 2020;43(3): 279-292. DOI: 10.1080/03081060.2020.1735746
Qiao S, et al. A self-adaptive parameter selection trajectory prediction approach via hidden Markov models. IEEE Transactions on Intelligent Transportation Systems. 2014;16(1): 284-296. DOI: 10.1109/TITS.2014.2331758
Liu P, Kurt A, Özgüner Ü. Trajectory prediction of a lane changing vehicle based on driver behavior estimation and classification. 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), 8-11 Oct. 2014, Qingdao, China. IEEE; 2014. p. 942-947. DOI: 10.1109/ITSC.2014.6957810
Ye N, Zhang Y, Wang R, Malekian R. Vehicle trajectory prediction based on Hidden Markov Model. KSII Transactions on Internet and Information Systems. 2016; 10(7). DOI: 10.3837/tiis.2016.07.016
Tran Q, Firl J. Online maneuver recognition and multimodal trajectory prediction for intersection assistance using non-parametric regression. 2014 IEEE Intelligent Vehicles Symposium Proceedings, 8-11 June 2014, Dearborn, MI, USA. IEEE; 2014. p. 918-923. DOI: 10.1109/IVS.2014.6856480
Goli SA, Far BH, Fapojuwo AO. Vehicle trajectory prediction with Gaussian Process Regression in Connected Vehicle environment. 2018 IEEE Intelligent Vehicles Symposium (IV), 26-30 June 2018, Changshu, China. IEEE; 2018. p. 550-555. DOI: 10.1109/IVS.2018.8500614
Schreier M, Willert V, Adamy J. An integrated approach to maneuver-based trajectory prediction and criticality as sessment in arbitrary road environments. IEEE Transactions on Intelligent Transportation Systems. 2016;17(10): 2751-2766. DOI: 10.1109/TITS.2016.2522507
Xiao Z, et al. GOI: A novel design for vehicle positioning and trajectory prediction under urban environments. IEEE Sensors Journal. 2018;18(13), 5586-5594. DOI: 10.1109/JSEN.2018.2826000
Izquierdo R, et al. Vehicle trajectory and lane change prediction using ANN and SVM classifiers. 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 16-19 Oct. 2017, Yokohama, Japan. IEEE; 2017. p. 1-6. DOI: 10.1109/ITSC.2017.8317838
Boubezoul A, Koita A, Daucher D. Vehicle trajectories classification using Support Vectors Machines for failure trajectory prediction. 2009 International Conference on Advances in Computational Tools for Engineering Applications, 15-17 July 2009, Beirut, Lebanon. IEEE; 2009. p. 486-491. DOI: 10.1109/ACTEA.2009.5227873
Woo H, et al. Trajectory prediction of surrounding vehicles using LSTM network. 2013 IEEE Intelligent Vehicles Symposium (IV), Gold Coast, QLD, Australia, 26–28 June 2013.
Messaoud K, Yahiaoui I, Verroust-Blondet A, Nashashibi F. Relational recurrent neural networks for vehicle trajectory prediction. 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 27-30 Oct. 2019, Auckland, New Zealand. IEEE; 2019. p. 1813-1818. DOI: 10.1109/ITSC.2019.8916887
Dai S, Li L, Li Z. Modeling vehicle interactions via modified LSTM models for trajectory prediction. IEEE Access. 2019;7: 38287-38296. DOI: 10.1109/ACCESS.2019.2907000
Kim B, et al. Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network. 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 16-19 Oct. 2017, Yokohama, Japan. IEEE; 2017. p. 399-404. DOI: 10.1109/ITSC.2017.8317943
Park SH, et al. Sequence-to-sequence prediction of vehicle trajectory via LSTM encoder-decoder architecture. 2018 IEEE Intelligent Vehicles Symposium (IV), 26-30 June 2018, Changshu, China. IEEE; 2018. p. 1672-1678. DOI: 10.1109/IVS.2018.8500658
Ju C, et al. Interaction-aware Kalman Neural Networks for trajectory prediction. arXiv preprint arXiv: 1902.10928. 2019.
Nikhil N, Morris BT. Convolutional neural network for trajectory prediction. Proceedings of the European Conference on Computer Vision (ECCV); 2018.
Han F, Tan Y, Eledath J. Preceding vehicle trajectory prediction by multi-cue integration. Proceedings of the IAPR Conference on Machine Vision Applications (IAPR MVA 2007), 16-18 May 2007, Tokyo, Japan. 2007. p. 575-578.
Jiang H, Chang L, Li Q, Chen D. Trajectory prediction of vehicles based on deep learning. 2019 4th International Conference on Intelligent Transportation Engineering (ICITE), 5-7 Sep. 2019, Singapore. IEEE; 2019. p. 190-195. DOI: 10.1109/ICITE.2019.8880168
Xing Y, Lv C, Cao D. Personalized vehicle trajectory prediction based on joint time series modeling for connected vehicles. IEEE Transactions on Vehicular Technology. 2019;69(2): 1341-1352. DOI: 10.1109/TVT.2019.2960110
Song K, et al. A steel property optimization model based on the XGBoost algorithm and improved PSO. Computational Materials Science. 2020;174: 109472. DOI: 10.1016/j.commatsci.2019.109472
Parsa AB, et al. Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis. Accident Analysis & Prevention. 2020;136: 105405. DOI: 10.1016/j.aap.2019.105405
Dong W, Huang Y, Lehane B, Ma G. XGBoost algorithm-based prediction of concrete electrical resistivity for structural health monitoring. Automation in Construction. 2020;114: 103155. DOI: 10.1016/j.autcon.2020.103155
Lim S, Chi S. Xgboost application on bridge management systems for proactive damage estimation. Advanced Engineering Informatics. 2019;41: 100922. DOI: 10.1016/j.aei.2019.100922
Xu Y, Zhao X, Chen Y, Yang Z. Research on a mixed gas classification algorithm based on extreme random tree. Applied Sciences. 2019;9(9): 1728. DOI: 10.3390/app9091728
Treiber M, Hennecke A, Helbing D. Congested traffic states in empirical observations and microscopic simulations. Physical review E. 2000;62(2); 1805-1824. DOI: 10.1103/PhysRevE.62.1805
Tomar RS, Verma S, Tomar GS. Prediction of lane change trajectories through neural network. 2010 International Conference on Computational Intelligence and Communication Networks, 26-28 Nov. 2010, Bhopal, India. IEEE; 2010. p. 249-253. DOI: 10.1109/CICN.2010.59
Ding C, Wang W, Wang X, Baumann M. A neural network model for driver’s lane-changing trajectory prediction in urban traffic flow. Mathematical Problems in Engineering. 2013;2013: Article ID 967358. DOI: 10.1155/2013/967358
Altché F, de La Fortelle A. An LSTM network for highway trajectory prediction. 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 16-19 Oct. 2017, Yokohama, Japan. IEEE; 2017. p. 353-359. DOI: 10.1109/ITSC.2017.8317913
Deo N, Trivedi MM. Multi-modal trajectory prediction of surrounding vehicles with maneuver based LSTMs. 2018 IEEE Intelligent Vehicles Symposium (IV), 26-30 June 2018, Changshu, China. IEEE; 2018. p. 1179-1184. DOI: 10.1109/IVS.2018.8500493
Li J, Ma H, Zhan W, Tomizuka M. Coordination and trajectory prediction for vehicle interactions via Bayesian generative modeling. 2019 IEEE Intelligent Vehicles Symposium (IV), 9-12 June 2019, Paris, France. IEEE; 2019. p. 2496-2503. DOI: 10.1109/IVS.2019.8813821
Copyright (c) 2021 Pengfei Liu, Wei Fan
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
