# Benchmarking Image Sensors Under Adverse Weather Conditions for Autonomous Driving

arXiv: https://arxiv.org/abs/1912.03238 · HTML: https://mlsanity.com/paper/1912.03238/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2019.

## Built on by (most cited first, 21 of 21)

- [Emergent Visual Sensors for Autonomous Vehicles](https://mlsanity.com/paper/2205.09383/) (2022)
- [SAMFusion: Sensor-Adaptive Multimodal Fusion for 3D Object Detection in Adverse Weather](https://mlsanity.com/paper/2508.16408/) (2025)
- [Worsening Perception: Real-time Degradation of Autonomous Vehicle Perception Performance for Simulation of Adverse Weather Conditions](https://mlsanity.com/paper/2103.02760/) (2021)
- [RestoreX-AI: A Contrastive Approach towards Guiding Image Restoration via Explainable AI Systems](https://mlsanity.com/paper/2204.01719/) (2022)
- [Cross-spectral Gated-RGB Stereo Depth Estimation](https://mlsanity.com/paper/2405.12759/) (2024)
- [Gated Stereo: Joint Depth Estimation from Gated and Wide-Baseline Active Stereo Cues](https://mlsanity.com/paper/2305.12955/) (2023)
- [Task-Driven Deep Image Enhancement Network for Autonomous Driving in Bad Weather](https://mlsanity.com/paper/2110.07206/) (2021)
- [Gated2Gated: Self-Supervised Depth Estimation from Gated Images](https://mlsanity.com/paper/2112.02416/) (2021)
- [EpiTESTER: Testing Autonomous Vehicles with Epigenetic Algorithm and Attention Mechanism](https://mlsanity.com/paper/2312.00207/) (2023)
- [A Benchmark for Spray from Nearby Cutting Vehicles](https://mlsanity.com/paper/2108.10800/) (2021)
- [ZeroScatter: Domain Transfer for Long Distance Imaging and Vision through Scattering Media](https://mlsanity.com/paper/2102.05847/) (2021)
- [DeepQTest: Testing Autonomous Driving Systems with Reinforcement Learning and Real-world Weather Data](https://mlsanity.com/paper/2310.05170/) (2023)
- [Benchmarking the Robustness of Panoptic Segmentation for Automated Driving](https://mlsanity.com/paper/2402.15469/) (2024)
- [Gated Fields: Learning Scene Reconstruction from Gated Videos](https://mlsanity.com/paper/2405.19819/) (2024)
- [LED: Light Enhanced Depth Estimation at Night](https://mlsanity.com/paper/2409.08031/) (2024)
- [Pix2Next: Leveraging Vision Foundation Models for RGB to NIR Image Translation](https://mlsanity.com/paper/2409.16706/) (2024)
- [6D Camera Relocalization in Visually Ambiguous Extreme Environments](https://mlsanity.com/paper/2207.06333/) (2022)
- [Towards a Unified Approach to Single Image Deraining and Dehazing](https://mlsanity.com/paper/2103.14204/) (2021)
- [GenRadar: Self-supervised Probabilistic Camera Synthesis based on Radar Frequencies](https://mlsanity.com/paper/2107.08948/) (2021)
- [Stereo 4D Radar for 3D Object Detection: Integrating Geometric Alignment and Absolute Velocity Estimation](https://mlsanity.com/paper/2609.02560/) (2026)
- [GRADE: Single-Frame Generative Radar Depth Estimation Under Visual Degradation](https://mlsanity.com/paper/2609.10756/) (2026)

## Abstract (arXiv)

Adverse weather conditions are very challenging for autonomous driving because most of the state-of-the-art sensors stop working reliably under these conditions. In order to develop robust sensors and algorithms, tests with current sensors in defined weather conditions are crucial for determining the impact of bad weather for each sensor. This work describes a testing and evaluation methodology that helps to benchmark novel sensor technologies and compare them to state-of-the-art sensors. As an example, gated imaging is compared to standard imaging under foggy conditions. It is shown that gated imaging outperforms state-of-the-art standard passive imaging due to time-synchronized active illumination.