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 utilizing 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：不确定性估计在自动驾驶等安全关键场景中至关重要，因为它为高级决策制定和路径规划等下游任务提供了宝贵信息。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感知二维目标检测框架。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的组合函数。通过引入类别平衡加权策略处理回归与热图预测，以缓解证据学习面临的类别不平衡问题。此外，我们提出一种学习方案，通过聚焦最不确定点主动利用预测热图不确定性来提升检测性能。我们在KITTI数据集上训练模型，并在包含BDD100K与nuImages的挑战性分布外数据集上评估性能。实验表明，本方法在保持基模型精度的同时提升了精确度并最小化了执行时间损失。