# Shadow Erosion and Nighttime Adaptability for Camera-Based Automated Driving Applications

arXiv: https://arxiv.org/abs/2504.08551 · HTML: https://mlsanity.com/paper/2504.08551/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 11 Apr 2025.

## Builds on (most cited first, 9 of 9)

- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [Deep Retinex Decomposition for Low-Light Enhancement](https://mlsanity.com/paper/1808.04560/) (2018)
- [Shadow Removal via Shadow Image Decomposition](https://mlsanity.com/paper/1908.08628/) (2019)
- [YOLOP: You Only Look Once for Panoptic Driving Perception](https://mlsanity.com/paper/2108.11250/) (2021)
- [Latent Feature-Guided Diffusion Models for Shadow Removal](https://mlsanity.com/paper/2312.02156/) (2023)
- [Deep Retinex Network for Estimating Illumination Colors with Self-Supervised Learning](https://mlsanity.com/paper/2102.04003/) (2021)
- [Multi-Task Learning for Automotive Foggy Scene Understanding via Domain Adaptation to an Illumination-Invariant Representation](https://mlsanity.com/paper/1909.07697/) (2019)
- [Interaction of Autonomous and Manually Controlled Vehicles Multiscenario Vehicle Interaction Dataset](https://mlsanity.com/paper/2403.08455/) (2024)
- [Inadequate contrast ratio of road markings as an indicator for ADAS failure](https://mlsanity.com/paper/2410.13320/) (2024)

## Abstract (arXiv)

Enhancement of images from RGB cameras is of particular interest due to its wide range of ever-increasing applications such as medical imaging, satellite imaging, automated driving, etc. In autonomous driving, various techniques are used to enhance image quality under challenging lighting conditions. These include artificial augmentation to improve visibility in poor nighttime conditions, illumination-invariant imaging to reduce the impact of lighting variations, and shadow mitigation to ensure consistent image clarity in bright daylight. This paper proposes a pipeline for Shadow Erosion and Nighttime Adaptability in images for automated driving applications while preserving color and texture details. The Shadow Erosion and Nighttime Adaptability pipeline is compared to the widely used CLAHE technique and evaluated based on illumination uniformity and visual perception quality metrics. The results also demonstrate a significant improvement over CLAHE, enhancing a YOLO-based drivable area segmentation algorithm.