Prior work in 3D object detection evaluates models using offline metrics like
average precision since closed-loop online evaluation on the downstream driving
task is costly. However, it is unclear how indicative offline results are of
driving performance. In this work, we perform the first empirical evaluation
measuring how predictive different detection metrics are of driving performance
when detectors are integrated into a full self-driving stack. We conduct
extensive experiments on urban driving in the CARLA simulator using 16 object
detection models. We find that the nuScenes Detection Score has a higher
correlation to driving performance than the widely used average precision
metric. In addition, our results call for caution on the exclusive reliance on
the emerging class of `planner-centric' metrics.

在本研究中，我们通过对 16 种物体检测模型在 CARLA 模拟器上的大量实验，首次实证评估了不同检测指标对驾驶性能的预测能力。我们发现 nuScenes 检测得分与驾驶性能的相关性高于广泛采用的平均精度指标，并呼吁谨慎依赖新兴的以规划为中心的指标类别。