RGB Cameras Failures and Their Effects in Autonomous Driving Applications

Ceccarelli, Andrea; Secci, Francesco · 2023 · Crossref

DOI: 10.1109/tdsc.2022.3156941

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This paper addresses the critical safety risks posed by RGB camera failures in autonomous driving systems. As cameras are primary sensors for perception tasks like object detection and lane recognition, their malfunction can lead to unsafe vehicle behavior. The authors identify a gap in existing research, which often focuses on adversarial attacks rather than realistic, accidental hardware or environmental failures. The study aims to systematically define camera failure modes, analyze their effects on downstream AI/ML agents, and provide tools to assess system robustness. The methodology involves a Failure Modes and Effects Analysis (FMEA) applied to the five main components of an RGB camera: the lens, camera body, Bayer filter, image sensor, and Image Signal Processor (ISP). This analysis identified 28 distinct failure modes, ranging from environmental issues like ice, rain, and condensation to hardware defects such as broken lenses, dead pixels, and electrical overloads. To evaluate the impact of these failures, the authors developed a Python library capable of synthesizing images with these specific defects. They applied these synthetic failures to images from the KITTI Vision Benchmark Suite. The altered images were then processed by six different object detectors for both mono and stereo cameras, with performance measured using Average Precision. Additionally, the failures were injected into a self-driving agent within a simulation environment to observe behavioral consequences during dynamic driving scenarios. The results demonstrate that camera failures significantly degrade the performance of object detection algorithms. The study quantifies the reduction in detection accuracy for each failure mode, identifying specific failures as more dangerous than others. For instance, failures that result in no image output or severe visual distortion (such as heavy condensation or broken lenses) cause the most critical drops in detection capability. The analysis reveals common trends across the six tested detectors, showing that while some failures have negligible impacts, others lead to substantial misbehaviors in the autonomous driving agent, such as missed obstacles or incorrect precedence decisions. The authors also provide a comprehensive review of existing mitigations for each failure mode, noting that while individual countermeasures exist, there is a lack of orchestrated approaches to handle the entire set of potential failures. The significance of this work lies in its contribution to the safety and robustness of autonomous vehicle architectures. By providing a systematic classification of camera failures and quantitative data on their impact, the paper offers a reference for designing safer systems and evaluating the resilience of image-based AI applications. The publicly available software library and dataset enable further research into robustness assessment. The findings underscore the necessity of considering realistic hardware and environmental failures in the development of autonomous driving systems, moving beyond the focus on adversarial robustness to address practical safety risks.

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• automated enforcement cameras