Resolving Elevation Ambiguity in 1D Radar Array Measurements using Deep Learning Motivated by requirements for future automotive radar, we study the problem of resolving target elevation from measurements by a onedimensional horizontal radar antenna array. This is a challenging and illposed problem, since such measurements contain only indirect and highly ambiguous elevation cues. As a consequence, traditional modelbased approaches fail. We instead propose to use a machinelearningbased approach that learns to exploit the subtle elevation cues and prior knowledge of the scene from the data. We design an encoderdecoder structured deep convolutional neural network that takes a radar return intensity image in the rangeazimuth plane as input and produces a depth image in the elevationazimuth plane as output. We train the network with over 200 000 radar frames collected in highway environments. Through experimental evaluations, we demonstrate the feasibility of resolving the highly ambiguous elevation information in such environments.

Deep, spatially coherent Inverse Sensor Models with Uncertainty Incorporation using the evidential Framework. To perform high speed tasks, sensors of autonomous cars have to provide as much information in as few time steps as possible. However, radars, one of the sensor modalities autonomous cars heavily rely on, often only provide sparse, noisy detections. These have to be accumulated over time to reach a high enough confidence about the static parts of the environment. For radars, the state is typically estimated by accumulating inverse detection models (IDMs). We employ the recently proposed evidential convolutional neural networks which, in contrast to IDMs, compute dense, spatially coherent inference of the environment state. Moreover, these networks are able to incorporate sensor noise in a principled way which we further extend to also incorporate model uncertainty. We present experimental results that show This makes it possible to obtain a denser environment perception in fewer time steps.