提出轮廓误差评估方法，更精准衡量自动驾驶中多目标跟踪的定位与朝向差异。

Open-loop performance evaluation of 3D multi-object tracking in autonomous driving requires matching criteria that effectively penalize translational, shape, and orientation errors from the ego vehicle perspective. The prevailing criteria for determining true positives are Intersection over Union (IoU) and Centre-Point Distances (CPD). When IoU is extended from the 2D image plane to 3D volumetric overlap, it often falls below its acceptance threshold even with minor yaw misalignments, whereas CPD disregards orientation entirely. To address this limitation, we propose Contour Errors (CE) as an ego-centric criterion that employs Hausdorff-type reasoning to sparse bounding-box corner geometry by selecting the k-nearest ego-centric corners. This method provides a graded orientation sensitivity between the extremes of IoU, which overpenalizes, and CPD, which is orientation-blind. We evaluate Contour Errors against six baselines using the HOTA evaluation protocol on the nuScenes dataset, conditioned on proximity, yaw error, and a confidence threshold. At the standard IoU vehicle threshold, 47% of car and 75% of pedestrian CE-valid matches are rejected by IoU despite close contour proximity, while fewer than 0.1% of IoU-valid matches fail CE. These results establish the ego-centric matching criterion as a primary factor for improving open-loop perception evaluation in safety-critical autonomous driving.