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.
From: Jelena Trisovic [view email]
[v1]
Mon, 4 Aug 2025 15:04:39 UTC (5,587 KB)
[v2]
Tue, 28 Jul 2026 08:33:04 UTC (6,373 KB)
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