Analyzing and Mitigating Bias for Vulnerable Classes: Towards Balanced Representation in Dataset

The accuracy and fairness of perception systems in autonomous driving are crucial, particularly for vulnerable road users. Mainstream research has looked into improving the performance metrics for classification accuracy. However, the hidden traits of bias inheritance in the AI models, class imbalances and disparities in the datasets are often overlooked. In this context, our study examines the class imbalances for vulnerable road users by focusing on class distribution analysis, performance evaluation, and bias impact assessment. We identify the concern of imbalances in class representation, leading to potential biases in detection accuracy. Utilizing popular CNN models and Vision Transformers (ViTs) with the nuScenes dataset, our performance evaluation reveals detection disparities for underrepresented classes. We propose a methodology for model optimization and bias mitigation, which includes data augmentation, resampling, and metric-specific learning. 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 respectively, 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 respectively. This research contributes to developing more reliable models and datasets, enhancing inclusiveness for minority classes.