# Worsening Perception: Real-time Degradation of Autonomous Vehicle Perception Performance for Simulation of Adverse Weather Conditions

arXiv: https://arxiv.org/abs/2103.02760 · HTML: https://mlsanity.com/paper/2103.02760/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 54 references that are in the map.
- First posted on arXiv 3 Mar 2021.

## Builds on (most cited first, 17 of 17)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [Image De-raining Using a Conditional Generative Adversarial Network](https://mlsanity.com/paper/1701.05957/) (2017)
- [Physics-Based Rendering for Improving Robustness to Rain](https://mlsanity.com/paper/1908.10335/) (2019)
- [Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene Understanding](https://mlsanity.com/paper/1901.01415/) (2019)
- [AADS: Augmented Autonomous Driving Simulation using Data-driven Algorithms](https://mlsanity.com/paper/1901.07849/) (2019)
- [Object Detection Under Rainy Conditions for Autonomous Vehicles: A Review of State-of-the-Art and Emerging Techniques](https://mlsanity.com/paper/2006.16471/) (2020)
- [Adversarial Training for Adverse Conditions: Robust Metric Localisation using Appearance Transfer](https://mlsanity.com/paper/1803.03341/) (2018)
- [I Can See Clearly Now : Image Restoration via De-Raining](https://mlsanity.com/paper/1901.00893/) (2019)
- [Benchmarking Image Sensors Under Adverse Weather Conditions for Autonomous Driving](https://mlsanity.com/paper/1912.03238/) (2019)
- [SoilingNet: Soiling Detection on Automotive Surround-View Cameras](https://mlsanity.com/paper/1905.01492/) (2019)
- [Dehaze-GLCGAN: Unpaired Single Image De-hazing via Adversarial Training](https://mlsanity.com/paper/2008.06632/) (2020)
- [Explorations and Lessons Learned in Building an Autonomous Formula SAE Car from Simulations](https://mlsanity.com/paper/1905.05940/) (2019)
- [Rainy screens: Collecting rainy datasets, indoors](https://mlsanity.com/paper/2003.04742/) (2020)

## Built on by (most cited first, 10 of 10)

- [Survey on LiDAR Perception in Adverse Weather Conditions](https://mlsanity.com/paper/2304.06312/) (2023)
- [Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know'](https://mlsanity.com/paper/2505.04950/) (2025)
- [Uncertainty-Aware Autonomous Vehicles: Predicting the Road Ahead](https://mlsanity.com/paper/2510.22680/) (2025)
- [Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racing](https://mlsanity.com/paper/2201.03246/) (2022)
- [RiWNet: A moving object instance segmentation Network being Robust in adverse Weather conditions](https://mlsanity.com/paper/2109.01820/) (2021)
- [Is That Rain? Understanding Effects on Visual Odometry Performance for Autonomous UAVs and Efficient DNN-based Rain Classification at the Edge](https://mlsanity.com/paper/2407.12663/) (2024)
- [Generating Clear Images From Images With Distortions Caused by Adverse Weather Using Generative Adversarial Networks](https://mlsanity.com/paper/2211.05234/) (2022)
- [UNet-Based Keypoint Regression for 3D Cone Localization in Autonomous Racing](https://mlsanity.com/paper/2602.21904/) (2026)
- [From Nominal Intensity to Equivalent Rainfall: A Path-Based Credibility Evaluation Framework for Simulated Rainfall in Autonomous-Driving Perception Tests](https://mlsanity.com/paper/2606.11989/) (2026)
- [Image Quality Dependent Degradation for AI Systems](https://mlsanity.com/paper/2607.25736/) (2026)

## Abstract (arXiv)

Autonomous vehicles rely heavily upon their perception subsystems to see the environment in which they operate. Unfortunately, the effect of variable weather conditions presents a significant challenge to object detection algorithms, and thus it is imperative to test the vehicle extensively in all conditions which it may experience. However, development of robust autonomous vehicle subsystems requires repeatable, controlled testing - while real weather is unpredictable and cannot be scheduled. Real-world testing in adverse conditions is an expensive and time-consuming task, often requiring access to specialist facilities. Simulation is commonly relied upon as a substitute, with increasingly visually realistic representations of the real-world being developed. In the context of the complete autonomous vehicle control pipeline, subsystems downstream of perception need to be tested with accurate recreations of the perception system output, rather than focusing on subjective visual realism of the input - whether in simulation or the real world. This study develops the untapped potential of a lightweight weather augmentation method in an autonomous racing vehicle - focusing not on visual accuracy, but rather the effect upon perception subsystem performance in real time. With minimal adjustment, the prototype developed in this study can replicate the effects of water droplets on the camera lens, and fading light conditions. This approach introduces a latency of less than 8 ms using compute hardware well suited to being carried in the vehicle - rendering it ideal for real-time implementation that can be run during experiments in simulation, and augmented reality testing in the real world.