Pei Yu Chang

Pei Yu Chang, Vishnu Renganathan, Qadeer Ahmed

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.

Pei Yu Chang, Qizhe Xu, Vishnu Renganathan et al.

Safe control in dynamic traffic environments remains a major challenge for autonomous vehicles (AVs), as ego vehicle and obstacle states are inherently affected by sensing noise and estimation uncertainty. However, existing studies have not sufficiently addressed how uncertain multi-modal sensing information can be systematically incorporated into tail-risk-aware safety-critical control. To address this gap, this paper proposes a risk-aware safe control framework that integrates probabilistic state estimation with a conditional value-at-risk (CVaR) control barrier function (CBF) safety filter. Obstacle detections from cameras, LiDAR, and vehicle-to-everything (V2X) communication are combined using a Wasserstein barycenter (WB) to obtain a probabilistic state estimate. A model predictive controller generates the nominal control, which is then filtered through a CVaR-CBF quadratic program to enforce risk-aware safety constraints. The approach is evaluated through numerical studies and further validated on a full-scale AV. Results demonstrate improved safety and robustness over a baseline MPC-CBF design, with an average improvement of 12.7\% in success rate across the evaluated scenarios.