An Ensemble Learning Framework for Vehicle Trajectory Prediction in Interactive Scenarios

An Ensemble Learning Framework for Vehicle Trajectory Prediction in Interactive Scenarios

Li, Z. (TU Delft Transport and Planning; Beijing Institute of Technology)
Lin, Yunlong (Beijing Institute of Technology)
Cheng, Gong (Beijing Institute of Technology)
Wang, X. (TU Delft Transport and Planning)
Liu, Qi (Beijing Institute of Technology)
Gong, Jianwei (Beijing Institute of Technology)
Lu, Chao (Beijing Institute of Technology)

2022

Precisely modeling interactions and accurately predicting trajectories of surrounding vehicles are essential to the decision-making and path-planning of intelligent vehicles. This paper proposes a novel framework based on ensemble learning to improve the performance of trajectory predictions in interactive scenarios. The framework is termed Interactive Ensemble Trajectory Predictor (IETP). IETP assembles interaction-aware trajectory predictors as base learners to build an ensemble learner. Firstly, each base learner in IETP observes historical trajectories of vehicles in the scene. Then each base learner handles interactions between vehicles to predict trajectories. Finally, an ensemble learner is built to predict trajectories by applying two ensemble strategies on the predictions from all base learners. Predictions generated by the ensemble learner are final outputs of IETP. In this study, three experiments using different data are conducted based on the NGSIM dataset. Experimental results show that IETP improves the predicting accuracy and decreases the variance of errors compared to base learners. In addition, IETP exceeds baseline models with 50% of the training data, indicating that IETP is data-efficient. Moreover, the implementation of IETP is publicly available at https://github.com/BIT-Jack/IETP.

http://resolver.tudelft.nl/uuid:710d628c-8363-41e6-b1f7-fe32341d271d

https://doi.org/10.1109/IV51971.2022.9827070

IEEE

2023-07-01

978-1-6654-8822-8

Proceedings of the 2022 IEEE Intelligent Vehicles Symposium, IV 2022

2022 IEEE Intelligent Vehicles Symposium, IV 2022, 2022-06-05 → 2022-06-09, Aachen, Germany

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2022 Z. Li, Yunlong Lin, Gong Cheng, X. Wang, Qi Liu, Jianwei Gong, Chao Lu