CHEN, Xianda (HKUST(GZ)), Tiu, PakHin (The Hong Kong University of Science and Technology (Guangzhou)), HAN, XU (Hong Kong University of Science and Technology (Guangzhou)), Chen, Junjie (the Hong Kong University of Science and Technology (Guangzhou)), Wu, Yuanfei (The Hong Kong University of Science and Technology (Guangzhou)), Zheng, Xinhu (The HongKong University of Science and Technology (Guangzhou)), Zhu, Meixin (HKUST)

Continual Learning for Adaptable Car-Following in Dynamic Traffic Environments

Scheduled for presentation during the Invited Session "AI-Enhanced Safety-Certifiable Autonomous Vehicles" (WeBT3), Wednesday, September 25, 2024, 14:50−15:10, Salon 6

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 3, 2024

Abstract

The continual evolution of autonomous driving technology requires car-following models that can adapt to diverse and dynamic traffic environments. Traditional learning-based models often suffer from performance degradation when encountering unseen traffic patterns due to a lack of continual learning capabilities. This paper proposes a novel car-following model based on continual learning that addresses this limitation. Our framework incorporates Elastic Weight Consolidation (EWC) and Memory Aware Synapses (MAS) techniques to mitigate catastrophic forgetting and enable the model to learn incrementally from new traffic data streams. We evaluate the performance of the proposed model on the Waymo and Lyft datasets which encompass various traffic scenarios. The results demonstrate that the continual learning techniques significantly outperform the baseline model, achieving 0% collision rates across all traffic conditions. This research contributes to the advancement of autonomous driving technology by fostering the development of more robust and adaptable car-following models.