潜在状態拡散モデルを統合した自動運転の安全性向上：エンドツーエンドナビゲーション
Enhanced Safety in Autonomous Driving: Integrating Latent State Diffusion Model for End-to-End Navigation
制約付きマルコフ決定過程として定式化した自動運転の制御最適化に対し、CVaRベースのSoft Actor-Criticと潜在拡散モデルを組み合わせ、将来軌道を予測しながら安全な方策を学習する手法を提案した。
著者: Detian Chu, Linyuan Bai, Jianuo Huang, Zhenlong Fang, Peng Zhang, Wei Kang, Haifeng Lin
分類: cs.AI, cs.CV, cs.RO
原文アブストラクト
With the advancement of autonomous driving, ensuring safety during motion planning and navigation is becoming more and more important. However, most end-to-end planning methods suffer from a lack of safety. This research addresses the safety issue in the control optimization problem of autonomous driving, formulated as Constrained Markov Decision Processes (CMDPs). We propose a novel, model-based approach for policy optimization, utilizing a conditional Value-at-Risk based Soft Actor Critic to manage constraints in complex, high-dimensional state spaces effectively. Our method introduces a worst-case actor to guide safe exploration, ensuring rigorous adherence to safety requirements even in unpredictable scenarios. The policy optimization employs the Augmented Lagrangian method and leverages latent diffusion models to predict and simulate future trajectories. This dual approach not only aids in navigating environments safely but also refines the policy's performance by integrating distribution modeling to account for environmental uncertainties. Empirical evaluations conducted in both simulated and real environment demonstrate that our approach outperforms existing methods in terms of safety, efficiency, and decision-making capabilities.
