SafePath: 安全なLLMベース自律ナビゲーションのためのConformal Prediction
SafePath: Conformal Prediction for Safe LLM-Based Autonomous Navigation
LLMによる経路計画にConformal Predictionを組み合わせ、ユーザー定義の確率で安全な軌道を保証しつつ、不確実性が高い場合には人間に制御を委ねる枠組みを提案。
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著者: Achref Doula, Max Mühlhäuser, Alejandro Sanchez Guinea
分類: cs.LG, cs.RO
原文アブストラクト
Large Language Models (LLMs) show growing promise in autonomous driving by reasoning over complex traffic scenarios to generate path plans. However, their tendencies toward overconfidence, and hallucinations raise critical safety concerns. We introduce SafePath, a modular framework that augments LLM-based path planning with formal safety guarantees using conformal prediction. SafePath operates in three stages. In the first stage, we use an LLM that generates a set of diverse candidate paths, exploring possible trajectories based on agent behaviors and environmental cues. In the second stage, SafePath filters out high-risk trajectories while guaranteeing that at least one safe option is included with a user-defined probability, through a multiple-choice question-answering formulation that integrates conformal prediction. In the final stage, our approach selects the path with the lowest expected collision risk when uncertainty is low or delegates control to a human when uncertainty is high. We theoretically prove that SafePath guarantees a safe trajectory with a user-defined probability, and we show how its human delegation rate can be tuned to balance autonomy and safety. Extensive experiments on nuScenes and Highway-env show that SafePath reduces planning uncertainty by 77\% and collision rates by up to 70\%, demonstrating effectiveness in making LLM-driven path planning more safer.
