Analyzing and Mitigating Bias for Vulnerable Classes: Towards Balanced
  Representation in Dataset
Dewant Katare David Solans Noguero Souneil Park Nicolas Kourtellis Marijn Janssen Aaron Yi Ding
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.