Risk Aware Safe Control with Multi-Modal Sensing for Dynamic Obstacle Avoidance
This addresses the problem of dynamic obstacle avoidance for autonomous vehicles, but it is incremental as it builds on existing MPC-CBF methods with risk-aware enhancements.
The paper tackled safe control for autonomous vehicles in dynamic traffic by integrating probabilistic state estimation from multi-modal sensors with a risk-aware safety filter, resulting in a 12.7% average improvement in success rate over a baseline.
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.