Life-Long Multi-Task Learning of Adaptive Path Tracking Policy for Autonomous Vehicle

TL;DR

This paper introduces a life-long, multi-task learning approach for autonomous vehicle path tracking that continually adapts and improves without forgetting previous knowledge, demonstrated through high-fidelity vehicle simulations.

Contribution

It presents a model-free, continual learning method for adaptive path tracking that prevents catastrophic forgetting and optimizes memory for evolving tasks.

Findings

Effective adaptation to new environments

Surpasses baseline methods in performance

Maintains knowledge without catastrophic forgetting

Abstract

This paper proposes a life-long adaptive path tracking policy learning method for autonomous vehicles that can self-evolve and self-adapt with multi-task knowledge. Firstly, the proposed method can learn a model-free control policy for path tracking directly from the historical driving experience, where the property of vehicle dynamics and corresponding control strategy can be learned simultaneously. Secondly, by utilizing the life-long learning method, the proposed method can learn the policy with task-incremental knowledge without encountering catastrophic forgetting. Thus, with continual multi-task knowledge learned, the policy can iteratively adapt to new tasks and improve its performance with knowledge from new tasks. Thirdly, a memory evaluation and updating method is applied to optimize memory structure for life-long learning which enables the policy to learn toward selected directions. Experiments are conducted using a high-fidelity vehicle dynamic model in a complex curvy road to evaluate the performance of the proposed method. Results show that the proposed method can effectively evolve with continual multi-task knowledge and adapt to the new environment, where the performance of the proposed method can also surpass two commonly used baseline methods after evolving.