共形予測によるリスク認識ナビゲーションのための微分可能最適化階層型安全制御
Differentiable Optimization Layered Safety-Critical Control for Risk-Aware Navigation via Conformal Prediction
未知環境での自律走行のリスク認識ナビゲーションのために、共形予測で不確実性を扱い、微分可能な最適化層で制御障壁関数を構築する安全制御手法を提案した。
著者: Jinyang Dong, Shizhen Wu, Yongchun Fang
分類: eess.SY, cs.AI
原文アブストラクト
Risk-aware navigation in unknown environments is a fundamental challenge for autonomous vehicles operating in complex urban systems. To address this issue, this paper presents a differentiable optimization layered safety-critical control method based on conformal prediction. First, to handle uncertainties arising from sensor noise, the conformal prediction method is employed to generate risk-aware obstacle ellipsoids around an elliptical-shaped robot. Second, two nested differentiable optimization layers are introduced to build the control barrier functions for obstacle avoidance and feasibility guarantee, respectively. Then, a quadratic program based safety-critical control law is proposed to integrate the above control barrier function constraints as well as input constraints. In the end, the effectiveness of the proposed framework is demonstrated through numerical simulations.