# RGB cameras failures and their effects in autonomous driving applications

arXiv: https://arxiv.org/abs/2008.05938 · HTML: https://mlsanity.com/paper/2008.05938/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2020.

## Built on by (most cited first, 12 of 12)

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- [PriorEye: Geospatial Visual Priors for End-to-End Autonomous Driving](https://mlsanity.com/paper/2606.31830/) (2026)
- [t-READi: Transformer-Powered Robust and Efficient Multimodal Inference for Autonomous Driving](https://mlsanity.com/paper/2410.09747/) (2024)
- [FLYNN: Robust Neural Network for Robot Navigation using Fly Brain Topology](https://mlsanity.com/paper/2607.00025/) (2026)
- [Driving Through Uncertainty: Risk-Averse Control with LLM Commonsense for Autonomous Driving under Perception Deficits](https://mlsanity.com/paper/2503.07020/) (2025)
- [A Survey on Deep Multi-Task Learning in Connected Autonomous Vehicles](https://mlsanity.com/paper/2508.00917/) (2025)
- [EPOFusion: Exposure aware Progressive Optimization Method for Infrared and Visible Image Fusion](https://mlsanity.com/paper/2603.16130/) (2026)
- [Geometry-Aware Representation Denoising for Robust Multi-view 3D Reconstruction](https://mlsanity.com/paper/2605.26230/) (2026)
- [DeLux: Cross-Modal Local Artifact Restoration in Video Using Neuromorphic Data](https://mlsanity.com/paper/2606.27576/) (2026)

## Abstract (arXiv)

RGB cameras are one of the most relevant sensors for autonomous driving applications. It is undeniable that failures of vehicle cameras may compromise the autonomous driving task, possibly leading to unsafe behaviors when images that are subsequently processed by the driving system are altered. To support the definition of safe and robust vehicle architectures and intelligent systems, in this paper we define the failure modes of a vehicle camera, together with an analysis of effects and known mitigations. Further, we build a software library for the generation of the corresponding failed images and we feed them to six object detectors for mono and stereo cameras and to the self-driving agent of an autonomous driving simulator. The resulting misbehaviors with respect to operating with clean images allow a better understanding of failures effects and the related safety risks in image-based applications.