This paper presents a hybrid control framework with a risk-budgeted monitor for safety-certified autonomous driving. A sliding-window monitor tracks insufficient barrier residuals and triggers switching from a relaxed control barrier function (R-CBF) to a more conservative conditional value-at-risk CBF (CVaR-CBF) when the safety margin deteriorates. Two real-time triggers are considered: feasibility-triggered (FT), which activates CVaR-CBF when the R-CBF problem is reported infeasible, and quality-triggered (QT), which switches when the residual falls below a prescribed safety margin. The framework is evaluated with model predictive control (MPC) under vehicle localization noise and obstacle position uncertainty across multiple AV-pedestrian interaction scenarios with 1,500 Monte Carlo runs. In the most challenging case with 5 m pedestrian detection uncertainty, the proposed method achieves a 94--96\% collision-free success rate over 300 trials while maintaining the lowest mean cross-track error (CTE = 3.2--3.6 m), indicating faster trajectory recovery after obstacle avoidance and a favorable balance between safety and performance.
@article{arxiv.2510.10442,
title = {Risk-Budgeted Control Framework for Balanced Performance and Safety in Autonomous Vehicles},
author = {Pei Yu Chang and Vishnu Renganathan and Qadeer Ahmed},
journal= {arXiv preprint arXiv:2510.10442},
year = {2026}
}