RACF: A Resilient Autonomous Car Framework with Object Distance Correction

2026-04-14

AI Summary

Autonomous driving perception systems are vulnerable to environmental disturbances and adversarial attacks, which can lead to inaccurate distance estimation and compromise driving safety. To address this challenge, this work proposes a resilient autonomous driving framework, RACF, that integrates depth cameras, LiDAR, and vehicle kinematic models. A key component of RACF is the Object Distance Correction Algorithm (ODCA), which employs cross-sensor gated triggering and leverages multimodal consistency checks to enable lightweight, real-time active distance correction. Experimental validation on the Quanser QCar 2 platform demonstrates that the proposed approach reduces the root mean square error (RMSE) of distance estimation by 35% under severe interference, significantly improving braking responsiveness and parking compliance.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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

Problem

• autonomous vehicles
• perception robustness
• distance estimation
• safety-critical systems
• adversarial perturbations

Innovation

• Resilient perception
• Object Distance Correction Algorithm (ODCA)
• Sensor fusion
• Autonomous driving safety
• Real-time robustness