It is crucial to quantify the uncertainty of these dynamics models when using them for planning.

Optimizing proper loss functions is popularly believed to yield predictors with good calibration properties; the intuition being that for such losses, the global optimum is to predict the ground-truth probabilities, which is indeed calibrated.

Then, we present the error bar plots with mean D-ECE and std deviation (Sec. The error in particular detection is computed as it satisfies the false positive criteria. We report D-ECE on these challenging out-domain scenarios. (Figure 1). We show the bar plots depicting mean D-ECE with respective standard deviations.

Specifically, we consider an individual calibration objective for characterizing the quantiles of the prediction model.