Large Language Models Powered Context-aware Motion Prediction

no code implementations • 17 Mar 2024 • Xiaoji Zheng, Lixiu Wu, Zhijie Yan, Yuanrong Tang, Hao Zhao, Chen Zhong, Bokui Chen, Jiangtao Gong

Traditional methods of motion forecasting primarily encode vector information of maps and historical trajectory data of traffic participants, lacking a comprehensive understanding of overall traffic semantics, which in turn affects the performance of prediction tasks.

Motion Forecasting motion prediction +1

P2DT: Mitigating Forgetting in task-incremental Learning with progressive prompt Decision Transformer

no code implementations • 22 Jan 2024 • Zhiyuan Wang, Xiaoyang Qu, Jing Xiao, Bokui Chen, Jianzong Wang

Catastrophic forgetting poses a substantial challenge for managing intelligent agents controlled by a large model, causing performance degradation when these agents face new tasks.

Incremental Learning reinforcement-learning

INCPrompt: Task-Aware incremental Prompting for Rehearsal-Free Class-incremental Learning

no code implementations • 22 Jan 2024 • Zhiyuan Wang, Xiaoyang Qu, Jing Xiao, Bokui Chen, Jianzong Wang

This paper introduces INCPrompt, an innovative continual learning solution that effectively addresses catastrophic forgetting.

Class Incremental Learning General Knowledge +1