RACF: A resilient autonomous vehicle framework with target distance correction

The researchers propose the Resilient Autonomous Car Framework (RACF), which uses the Object Distance Correction Algorithm (ODCA) to withstand environmental degradation and adversarial perturbations. The framework makes a depth camera, LiDAR and physics-based kinematics mutually redundant; when the depth camera's obstacle distance estimates are inconsistent, cross-sensor gating triggers the correction algorithm to repair them. On the Quanser QCar 2 platform testbed, the framework reduces RMSE by up to 35% under strong interference, improves parking compliance and braking latency, and can run in real time.