Pedestrian detection plays a critical role in autonomous driving (AD), where ensuring safety and reliability is important. While many detection models aim to reduce miss-rates and handle challenges such as occlusion and long-range recognition, fairness remains an underexplored yet equally important concern. In this work, we systematically investigate how variations in the pedestrian pose--including leg status, elbow status, and body orientation--as well as individual joint occlusions, affect detection performance. We evaluate five pedestrian-specific detectors (F2DNet, MGAN, ALFNet, CSP, and Cascade R-CNN) alongside three general-purpose models (YOLOv12 variants) on the EuroCity Persons Dense Pose (ECP-DP) dataset. Fairness is quantified using the Equal Opportunity Difference (EOD) metric across various confidence thresholds. To assess statistical significance and robustness, we apply the Z-test. Our findings highlight biases against pedestrians with parallel legs, straight elbows, and lateral views. Occlusion of lower body joints has a more negative impact on the detection rate compared to the upper body and head. Cascade R-CNN achieves the lowest overall miss-rate and exhibits the smallest bias across all attributes. To the best of our knowledge, this is the first comprehensive pose- and occlusion-aware fairness evaluation in pedestrian detection for AD.

翻译：行人检测在自动驾驶中扮演着关键角色，确保其安全性与可靠性至关重要。尽管许多检测模型致力于降低漏检率并应对遮挡、远距离识别等挑战，公平性仍是一个尚未充分探索但同等重要的问题。本研究系统性地探究了行人姿态变化（包括腿部状态、肘部状态及身体朝向）以及单个关节遮挡如何影响检测性能。我们在EuroCity Persons Dense Pose（ECP-DP）数据集上评估了五个行人专用检测器（F2DNet、MGAN、ALFNet、CSP和Cascade R-CNN）以及三个通用模型（YOLOv12变体）。公平性通过在不同置信度阈值下使用机会均等差异（EOD）指标进行量化。为评估统计显著性与鲁棒性，我们采用了Z检验。研究结果揭示了模型对双腿并拢、肘部伸直及侧身视角的行人存在检测偏差。与上半身及头部相比，下半身关节的遮挡对检测率产生更显著的负面影响。Cascade R-CNN实现了最低的整体漏检率，并在所有属性中表现出最小的偏差。据我们所知，这是首次在自动驾驶行人检测领域进行的全面、兼顾姿态与遮挡感知的公平性评估。