Continual Interactive Behavior Learning With Traffic Divergence Measurement: A Dynamic Gradient Scenario Memory Approach

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

  • Yunlong Lin - , Beijing Institute of Technology (Autor:in)
  • Zirui Li - , Beijing Institute of Technology, Technische Universität Dresden (Autor:in)
  • Cheng Gong - , Beijing Institute of Technology (Autor:in)
  • Chao Lu - , Beijing Institute of Technology (Autor:in)
  • Xinwei Wang - , Queen Mary University of London (Autor:in)
  • Jianwei Gong - , Beijing Institute of Technology (Autor:in)

Abstract

Developing autonomous vehicles (AVs) helps improve the road safety and traffic efficiency of intelligent transportation systems (ITS). Accurately predicting the trajectories of traffic participants is essential to the decision-making and motion planning of AVs in interactive scenarios. Recently, learning-based trajectory predictors have shown state-of-the-art performance in highway or urban areas. However, most existing learning-based models trained with fixed datasets may perform poorly in continuously changing scenarios. Specifically, they may not perform well in learned scenarios after learning the new one. This phenomenon is called “catastrophic forgetting”. Few studies investigate trajectory predictions in continuous scenarios, where catastrophic forgetting may happen. To handle this problem, first, a novel continual learning (CL) approach for vehicle trajectory prediction is proposed in this paper. Then, inspired by brain science, a dynamic memory mechanism is developed by utilizing the measurement of traffic divergence between scenarios, which balances the performance and training efficiency of the proposed CL approach. Finally, datasets collected from different locations are used to design continual training and testing methods in experiments. Experimental results show that the proposed approach achieves consistently high prediction accuracy in continuous scenarios without re-training, which mitigates catastrophic forgetting compared to non-CL approaches. The implementation of the proposed approach is publicly available at https://github.com/BIT-Jack/D-GSM .

Details

OriginalspracheEnglisch
Seiten (von - bis)2355 - 2372
Seitenumfang18
FachzeitschriftIEEE Transactions on Intelligent Transportation Systems
Jahrgang25
Ausgabenummer3
PublikationsstatusVeröffentlicht - 23 Okt. 2023
Peer-Review-StatusJa

Externe IDs

Scopus 85181579705

Schlagworte

Ziele für nachhaltige Entwicklung

Schlagwörter

  • Continual learning, autonomous vehicles, intelligent transportation systems, interactive behavior modeling, trajectory prediction