Differentiable Optimization Layered Safety-Critical Control for Risk-Aware Navigation via Conformal Prediction

TL;DR

This paper introduces a differentiable optimization control framework using conformal prediction for risk-aware navigation and obstacle avoidance in autonomous vehicles, validated through simulations.

Contribution

It presents a novel layered safety-critical control method combining conformal prediction with differentiable optimization for risk-aware navigation.

Findings

Effective obstacle avoidance demonstrated in simulations

Risk-aware obstacle ellipsoids improve safety margins

Layered control ensures feasibility and safety constraints

Abstract

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.