# Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey

arXiv: https://arxiv.org/abs/2112.08936 · HTML: https://mlsanity.com/paper/2112.08936/

## Key facts

- Cited by 82 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2021.

## Built on by (most cited first, 30 of 82)

- [NuPlanQA: A Large-Scale Dataset and Benchmark for Multi-View Driving Scene Understanding in Multi-Modal Large Language Models](https://mlsanity.com/paper/2503.12772/) (2025)
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- [WeatherDepth: Curriculum Contrastive Learning for Self-Supervised Depth Estimation under Adverse Weather Conditions](https://mlsanity.com/paper/2310.05556/) (2023)
- [Dataset and Benchmark: Novel Sensors for Autonomous Vehicle Perception](https://mlsanity.com/paper/2401.13853/) (2024)
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- [CR3DT: Camera-RADAR Fusion for 3D Detection and Tracking](https://mlsanity.com/paper/2403.15313/) (2024)
- [Monocular 3D lane detection for Autonomous Driving: Recent Achievements, Challenges, and Outlooks](https://mlsanity.com/paper/2404.06860/) (2024)
- [Adver-City: Open-Source Multi-Modal Dataset for Collaborative Perception Under Adverse Weather Conditions](https://mlsanity.com/paper/2410.06380/) (2024)
- [TripleMixer: A 3D Point Cloud Denoising Model for Adverse Weather](https://mlsanity.com/paper/2408.13802/) (2024)
- [Autonomous Driving at Unsignalized Intersections: A Review of Decision-Making Challenges and Reinforcement Learning-Based Solutions](https://mlsanity.com/paper/2409.13144/) (2024)
- [EpiTESTER: Testing Autonomous Vehicles with Epigenetic Algorithm and Attention Mechanism](https://mlsanity.com/paper/2312.00207/) (2023)
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- [A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects](https://mlsanity.com/paper/2506.19769/) (2025)
- [Spatial Retrieval Augmented Autonomous Driving](https://mlsanity.com/paper/2512.06865/) (2025)
- [Compensating for Sensing Failures via Delegation in Human-AI Hybrid Systems](https://mlsanity.com/paper/2303.01300/) (2023)
- [TWICE Dataset: Digital Twin of Test Scenarios in a Controlled Environment](https://mlsanity.com/paper/2310.03895/) (2023)
- [Improving Robustness of LiDAR-Camera Fusion Model against Weather Corruption from Fusion Strategy Perspective](https://mlsanity.com/paper/2402.02738/) (2024)
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- [Transfer Learning in Latent Contextual Bandits with Covariate Shift Through Causal Transportability](https://mlsanity.com/paper/2502.20153/) (2025)
- [Deep Depth Estimation from Thermal Image: Dataset, Benchmark, and Challenges](https://mlsanity.com/paper/2503.22060/) (2025)
- [CSNR and JMIM Based Spectral Band Selection for Reducing Metamerism in Urban Driving](https://mlsanity.com/paper/2508.10962/) (2025)
- [Hyperspectral vs. RGB for Pedestrian Segmentation in Urban Driving Scenes: A Comparative Study](https://mlsanity.com/paper/2508.11301/) (2025)
- [Optimizing Delegation in Collaborative Human-AI Hybrid Teams](https://mlsanity.com/paper/2402.05605/) (2024)
- [PLT-D3: A High-fidelity Dynamic Driving Simulation Dataset for Stereo Depth and Scene Flow](https://mlsanity.com/paper/2406.07667/) (2024)
- [DAP-LED: Learning Degradation-Aware Priors with CLIP for Joint Low-light Enhancement and Deblurring](https://mlsanity.com/paper/2409.13496/) (2024)
- [The Era of End-to-End Autonomy: Transitioning from Rule-Based Driving to Large Driving Models](https://mlsanity.com/paper/2603.16050/) (2026)
- [Cityscape-Adverse: Benchmarking Robustness of Semantic Segmentation with Realistic Scene Modifications via Diffusion-Based Image Editing](https://mlsanity.com/paper/2411.00425/) (2024)

## Abstract (arXiv)

Automated Driving Systems (ADS) open up a new domain for the automotive industry and offer new possibilities for future transportation with higher efficiency and comfortable experiences. However, autonomous driving under adverse weather conditions has been the problem that keeps autonomous vehicles (AVs) from going to level 4 or higher autonomy for a long time. This paper assesses the influences and challenges that weather brings to ADS sensors in an analytic and statistical way, and surveys the solutions against inclement weather conditions. State-of-the-art techniques on perception enhancement with regard to each kind of weather are thoroughly reported. External auxiliary solutions, weather conditions coverage in currently available datasets, simulators, and experimental facilities with weather chambers are distinctly sorted out. Additionally, potential future ADS sensors candidates and approaches beyond common senses are provided. By looking into all kinds of major weather problems the autonomous driving field is currently facing, and reviewing both hardware and computer science solutions in recent years, this survey points out the main moving trends of adverse weather problems in autonomous driving, i.e., advanced sensor fusions, more sophisticated networks, and V2X & IoT technologies; and also the limitations brought by emerging 1550 nm LiDARs. In general, this work contributes a holistic overview of the obstacles and directions of ADS development in terms of adverse weather driving conditions.