Differentiable Optimization Layered Safety-Critical Control for Risk-Aware Navigation via Conformal Prediction
It addresses risk-aware navigation for autonomous vehicles in complex urban environments, but the contribution appears incremental as it combines existing techniques without clear quantitative improvements.
This paper proposes a differentiable optimization layered safety-critical control method using conformal prediction for risk-aware navigation in unknown environments. Numerical simulations demonstrate its effectiveness in handling sensor noise and ensuring obstacle avoidance.
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.