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.

翻译：自动驾驶感知系统的准确性和公平性至关重要，尤其对弱势道路使用者而言。主流研究已聚焦于提升分类精度的性能指标，但人工智能模型中偏差继承的隐性特征、数据集中的类别不平衡与差异性问题常被忽视。本研究通过类别分布分析、性能评估及偏差影响评估，重点探讨弱势道路使用者的类别不平衡问题。我们识别出类别表征不平衡会导致检测精度的潜在偏差。基于nuScenes数据集，采用主流CNN模型与Vision Transformers（ViTs）进行性能评估，揭示了低表征类别的检测差异。我们提出包含数据增强、重采样及度量特定学习的模型优化与偏差缓解方法。采用所提缓解策略后，CNN模型的IoU(%)与NDS(%)指标分别从71.3提升至75.6、80.6提升至83.7；ViT模型的IoU与NDS指标分别从74.9提升至79.2、83.8提升至87.1。本研究为开发更可靠的模型与数据集、提升少数类别的包容性提供了方法支撑。