RACF: A Resilient Autonomous Car Framework with Object Distance Correction

TL;DR

This paper introduces RACF, a framework enhancing autonomous vehicle perception robustness by integrating multiple sensors and a correction algorithm, significantly reducing distance estimation errors under adverse conditions.

Contribution

The paper presents RACF with ODCA, a novel approach combining sensor redundancy and correction for resilient perception in autonomous vehicles, addressing environmental and adversarial challenges.

Findings

Achieved up to 35% RMSE reduction under sensor corruption.

Improved stop compliance and braking latency in tests.

Operates effectively in real-time on a testbed.

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