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.

翻译：在自动驾驶等安全关键场景中，不确定性估计至关重要，它为高层决策和路径规划等多个下游任务提供有价值的信息。本文提出EvCenterNet——一种利用证据学习直接估计分类与回归不确定性的新型2D目标检测框架。为将证据学习应用于目标检测，我们针对稀疏热图输入设计了证据损失与焦点损失相结合的函数。针对证据学习中遇到的类别不平衡问题，我们引入了用于回归和热图预测的类别平衡加权机制。此外，我们提出了一种主动利用预测热图不确定性来改进检测性能的学习方案，通过聚焦于最不确定的点来提升检测表现。我们在KITTI数据集上训练模型，并在包括BDD100K和nuImages的挑战性分布外数据集上进行评估。实验表明，与基础模型相比，我们的方法在提升精度的同时最小化了执行时间损失。