  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.
