Quantification of Uncertainties in Deep Learning-based Environment Perception
This addresses the need for reliable uncertainty estimation in autonomous driving perception, though it appears incremental as it builds on existing deep learning segmentation methods.
The paper tackles the problem of environment perception for vehicles using radar scans by introducing a deep learning method that quantifies both epistemic and aleatoric uncertainties in predictions, showing superior performance compared to previous concepts.
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.