Analyzing and Mitigating Bias for Vulnerable Road Users by Addressing Class Imbalance in Datasets

作     者：Katare, Dewant Noguero, David Solans Park, Souneil Kourtellis, Nicolas Janssen, Marijn Ding, Aaron Yi 

作者机构：Delft Univ Technol Dept Engn Syst & Serv NL-2600 AA Delft Netherlands Telefonica Telefonica R&D Barcelona 08019 Spain 

出 版 物：《IEEE OPEN JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS》 (IEEE Open J. Intell. Transp. Syst.)

年 卷 期：2025年第6卷

页      面：590-604页

核心收录：

基　　金：European Union SPATIAL Project CONCORDIA Project Smart Networks and Services Joint Undertaking (SNS JU) through the European Union 

主　　题：Biological system modeling Roads Training Accuracy Measurement Autonomous vehicles Predictive models Visualization Prevention and mitigation Prediction algorithms Behaviour metrics class imbalance data disparities cost-sensitive learning sample representation object detection vision models 

摘      要：Vulnerable road users (VRUs), including pedestrians, cyclists, and motorcyclists, account for approximately 50% of road traffic fatalities globally, as per the World Health Organization. In these scenarios, the accuracy and fairness of perception applications used in autonomous driving become critical to reduce such risks. For machine learning models, performing object classification and detection tasks, the focus has been on improving accuracy and enhancing model performance metrics;however, issues such as biases inherited in models, statistical imbalances and disparities within the datasets are often overlooked. Our research addresses these issues by exploring class imbalances among vulnerable road users by focusing on class distribution analysis, evaluating model performance, and bias impact assessment. Using popular CNN models and Vision Transformers (ViTs) with the nuScenes dataset, our performance evaluation shows detection disparities for underrepresented classes. Compared to related work, we focus on metric-specific and cost-sensitive learning for model optimization and bias mitigation, which includes data augmentation and resampling. Using the proposed mitigation approaches, we see improvement in IoU(%) and NDS(%) metrics from 71.3 to 75.6 and 80.6 to 83.7 for the CNN model. Similarly, for ViT, we observe improvement in IoU and NDS metrics from 74.9 to 79.2 and 83.8 to 87.1. This research contributes to developing reliable models while addressing inclusiveness for minority classes in datasets. Code can be accessed at: BiasDet.