  Uncertainty estimation is crucial in safety-critical settings such as
automated driving as it provides valuable information for several downstream
tasks including high-level decision making and path planning. In this work, we
propose EvCenterNet, a novel uncertainty-aware 2D object detection framework
using evidential learning to directly estimate both classification and
regression uncertainties. To employ evidential learning for object detection,
we devise a combination of evidential and focal loss functions for the sparse
heatmap inputs. We introduce class-balanced weighting for regression and
heatmap prediction to tackle the class imbalance encountered by evidential
learning. Moreover, we propose a learning scheme to actively utilize the
predicted heatmap uncertainties to improve the detection performance by
focusing on the most uncertain points. We train our model on the KITTI dataset
and evaluate it on challenging out-of-distribution datasets including BDD100K
and nuImages. Our experiments demonstrate that our approach improves the
precision and minimizes the execution time loss in relation to the base model.

Computer Vision and Pattern Recognition;Artificial Intelligence