Predictive Control for Driving under Uncertain Road Geometry Estimated from Onboard Vision

Abstract

Autonomous vehicles driving on unknown roads must estimate the road geometry from onboard sensors and follow the resulting reference while respecting road boundaries. When the perception-induced estimation error is non-negligible relative to the lateral constraint margins, treating the estimated reference as ground-truth can lead to safety constraint violations. This paper proposes a perception-based control framework that integrates road geometry estimation and the resulting geometric uncertainty with constrained control. A parametric model of the road curvature is identified from RGB-D measurements via constrained nonlinear optimization, enforcing geometric consistency. The residual perception uncertainty is then captured by constructing a set of curvature profiles consistent with measurements by perturbing the estimated parameters. Using the Frenet-frame vehicle model, the curvature uncertainty is propagated through the model via a scenario model predictive control scheme as parametric model uncertainty, thus enforcing constraints across all sampled curvature realizations. The curvature estimation module is evaluated both in high-fidelity simulation and on real-world image data, confirming reliable operation under realistic visual and depth noise. The full perception-to-control pipeline is validated in simulation, demonstrating that the uncertainty-aware controller maintains tighter adherence to road boundaries compared to its nominal counterpart.