The process for calculating these metrics is described in Appendix C. Moreover, to ensure the comparability between prediction performance metrics and driving performance metrics in the radar plot, we normalize all metrics to the scale of [0, 1]. In the subsequent section, we provide an overview of the DESPOT planner. These two values can only be inferred from history. The safety is represented by the normalized collision rate.

While performing abest-first search, compilation errors constitute 88.7% ofprogramfailures.

LinearEvaluationProtocol Inlinear evaluation, wefollowthecommon setting [6,5]tofreeze the backbone of ResNet-50 and train a supervised linear classifier on the global average pooling features for100 epochs. Note that, the2-layer head inunsupervised pre-training isnotused inthe linear evaluation stage. During training, we augment the image with random scaling from 0.5 to 2.0, crop size of 769 and random flip. The top-1 and top-5 accuracyresults are reported inTable9. From the perspective of optimization goals, the only difference between the parametric instance classification framework and supervised classification framework is how to define the classes for each instance.