In this work, we introduce a novel Deep Learning-based method to perceive the environment of a vehicle based on radar scans while accounting for uncertainties in its predictions. The environment of the host vehicle is segmented into equally sized grid cells which are classified individually. Complementary to the segmentation output, our Deep Learning-based algorithm is capable of differentiating uncertainties in its predictions as being related to an inadequate model (epistemic uncertainty) or noisy data (aleatoric uncertainty). To this end, weights are described as probability distributions accounting for uncertainties in the model parameters. Distributions are learned in a supervised fashion using gradient descent. We prove that uncertainties in the model output correlate with the precision of its predictions. Compared to previous concepts, we show superior performance of our approach to reliably perceive the environment of a vehicle.

翻译：本文提出了一种新颖的基于深度学习的方法，用于通过雷达扫描感知车辆环境，同时考虑其预测中的不确定性。本车环境被划分为大小相等的网格单元，并逐个进行分类。作为分割输出的补充，我们的深度学习算法能够区分其预测中的不确定性是由模型不充分（认知不确定性）还是数据噪声（偶然不确定性）引起的。为此，权重被描述为概率分布，以解释模型参数中的不确定性。这些分布通过梯度下降以监督方式进行学习。我们证明，模型输出的不确定性与其预测精度相关。与先前概念相比，我们的方法在可靠感知车辆环境方面展现出更优的性能。