# Fairness in Autonomous Driving: Towards Understanding Confounding Factors in Object Detection under Challenging Weather

arXiv: https://arxiv.org/abs/2406.00219 · HTML: https://mlsanity.com/paper/2406.00219/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 35 references that are in the map.
- First posted on arXiv 31 May 2024.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [On Wasserstein Two Sample Testing and Related Families of Nonparametric Tests](https://mlsanity.com/paper/1509.02237/) (2015)
- [Disparities in Dermatology AI: Assessments Using Diverse Clinical Images](https://mlsanity.com/paper/2111.08006/) (2021)
- [Predictive Inequity in Object Detection](https://mlsanity.com/paper/1902.11097/) (2019)
- [M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network](https://mlsanity.com/paper/1811.04533/) (2018)
- [DAWN: Vehicle Detection in Adverse Weather Nature Dataset](https://mlsanity.com/paper/2008.05402/) (2020)
- [Gender Artifacts in Visual Datasets](https://mlsanity.com/paper/2206.09191/) (2022)
- [Age and gender bias in pedestrian detection algorithms](https://mlsanity.com/paper/1906.10490/) (2019)
- [How Do We Fail? Stress Testing Perception in Autonomous Vehicles](https://mlsanity.com/paper/2203.14155/) (2022)
- [Humans, AI, and Context: Understanding End-Users' Trust in a Real-World Computer Vision Application](https://mlsanity.com/paper/2305.08598/) (2023)
- [The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learning](https://mlsanity.com/paper/2405.03164/) (2024)
- [CF-YOLO: Cross Fusion YOLO for Object Detection in Adverse Weather with a High-quality Real Snow Dataset](https://mlsanity.com/paper/2206.01381/) (2022)
- [Uncertainty-Aware Online Merge Planning with Learned Driver Behavior](https://mlsanity.com/paper/2207.05228/) (2022)

## Built on by (most cited first, 2 of 2)

- [RESFL: An Uncertainty-Aware Framework for Responsible Federated Learning by Balancing Privacy, Fairness and Utility](https://mlsanity.com/paper/2503.16251/) (2025)
- [VLC Fusion: Vision-Language Conditioned Sensor Fusion for Robust Object Detection](https://mlsanity.com/paper/2505.12715/) (2025)

## Abstract (arXiv)

The deployment of autonomous vehicles (AVs) is rapidly expanding to numerous cities. At the heart of AVs, the object detection module assumes a paramount role, directly influencing all downstream decision-making tasks by considering the presence of nearby pedestrians, vehicles, and more. Despite high accuracy of pedestrians detected on held-out datasets, the potential presence of algorithmic bias in such object detectors, particularly in challenging weather conditions, remains unclear. This study provides a comprehensive empirical analysis of fairness in detecting pedestrians in a state-of-the-art transformer-based object detector. In addition to classical metrics, we introduce novel probability-based metrics to measure various intricate properties of object detection. Leveraging the state-of-the-art FACET dataset and the Carla high-fidelity vehicle simulator, our analysis explores the effect of protected attributes such as gender, skin tone, and body size on object detection performance in varying environmental conditions such as ambient darkness and fog. Our quantitative analysis reveals how the previously overlooked yet intuitive factors, such as the distribution of demographic groups in the scene, the severity of weather, the pedestrians' proximity to the AV, among others, affect object detection performance. Our code is available at https://github.com/bimsarapathiraja/fair-AV.