# RACF: A Resilient Autonomous Car Framework with Object Distance Correction Source: [RACF: A Resilient Autonomous Car Framework with Object Distance Correction]() Collection index: [llms.txt]() Data updated: 2026-04-16T05:00:04.684Z Authors: Chieh Tsai, Hossein Rastgoftar, Salim Hariri Published: 2026-04-14T08:06:09.000Z ## Abstract Autonomous vehicles are increasingly deployed in safety-critical applications, where sensing failures or cyberphysical attacks can lead to unsafe operations resulting in human loss and/or severe physical damages. Reliable real-time perception is therefore critically important for their safe operations and acceptability. For example, vision-based distance estimation is vulnerable to environmental degradation and adversarial perturbations, and existing defenses are often reactive and too slow to promptly mitigate their impacts on safe operations. We present a Resilient Autonomous Car Framework (RACF) that incorporates an Object Distance Correction Algorithm (ODCA) to improve perception-layer robustness through redundancy and diversity across a depth camera, LiDAR, and physics-based kinematics. Within this framework, when obstacle distance estimation produced by depth camera is inconsistent, a cross-sensor gate activates the correction algorithm to fix the detected inconsistency. We have experiment with the proposed resilient car framework and evaluate its performance on a testbed implemented using the Quanser QCar 2 platform. The presented framework achieved up to 35% RMSE reduction under strong corruption and improves stop compliance and braking latency, while operating in real time. These results demonstrate a practical and lightweight approach to resilient perception for safety-critical autonomous driving ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Distance estimation on QCar 2 Multi-Sensor Driving Benchmark (test)](): Distance Estimation; QCar 2 Multi-Sensor Driving Benchmark (test); RMSE: 0.111. - [Distance Estimation on QCar 2 indoor map (strong attacks)](): Distance Estimation; QCar 2 indoor map (strong attacks); RMSE: 0.323. - [Closed-loop stop-sign response on QCar real-world trials 2](): Closed-loop stop-sign response; QCar real-world trials 2; Stop Compliance Rate (SCR): 80. ## Original sources and implementation [Original paper]() [PDF]()