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.

本研究提出了一种基于深度学习的新方法，通过考虑预测中的不确定性来感知车辆环境，对车辆环境进行了等大小单元格的分割并进行了逐个分类，证明预测结果的不确定性与其精度相关，相比之前的概念，展示了更优秀的表现展示了更优秀的表现。

基于深度学习的环境感知中的不确定性量化