プロンプト駆動型ドメイン適応による悪天候下のEnd-to-End自動運転：In-Context強化学習の活用
Prompt-Driven Domain Adaptation for End-to-End Autonomous Driving via In-Context RL
悪天候など未知環境への適応を、モデル再学習や追加データ収集なしに、推論時に少数の軌跡をプロンプトとして与えるIn-Context強化学習で実現する手法を提案。
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著者: Aleesha Khurram, Amir Moeini, Shangtong Zhang, Rohan Chandra
分類: cs.RO, cs.LG
原文アブストラクト
Despite significant progress and advances in autonomous driving, many end-to-end systems still struggle with domain adaptation (DA), such as transferring a policy trained under clear weather to adverse weather conditions. Typical DA strategies in the literature include collecting additional data in the target domain or re-training the model, or both. Both these strategies quickly become impractical as we increase scale and complexity of driving. These limitations have encouraged investigation into few-shot and zero-shot prompt-driven DA at inference time involving LLMs and VLMs. These methods work by adding a few state-action trajectories during inference to the prompt (similar to in-context learning). However, there are two limitations of such an approach: $(i)$ prompt-driven DA methods are currently restricted to perception tasks such as detection and segmentation and $(ii)$ they require expert few-shot data. In this work, we present a new approach to inference-time few-shot prompt-driven DA for closed-loop autonomous driving in adverse weather condition using in-context reinforcement learning (ICRL). Similar to other prompt-driven DA methods, our approach does not require any updates to the model parameters nor does it require additional data collection in adversarial weather regime. Furthermore, our approach advances the state-of-the-art in prompt-driven DA by extending to closed driving using general trajectories observed during inference. Our experiments using the CARLA simulator show that ICRL results in safer, more efficient, and more comfortable driving policies in the target domain compared to state-of-the-art prompt-driven DA baselines.
