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The unique properties of radar sensors, such as their robustness to adverseweather conditions, make them an important part of the environment perceptionsystem of autonomous vehicles. One of the first steps during the processing ofradar point clouds is often the detection of clutter, i.e. erroneous pointsthat do not correspond to real objects. Another common objective is thesemantic segmentation of moving road users. These two problems are handledstrictly separate from each other in literature. The employed neural networksare always focused entirely on only one of the tasks. In contrast to this, weexamine ways to solve both tasks at the same time with a single jointly usedmodel. In addition to a new augmented multi-head architecture, we also devise amethod to represent a network's predictions for the two tasks with only oneoutput value. This novel approach allows us to solve the tasks simultaneouslywith the same inference time as a conventional task-specific model. In anextensive evaluation, we show that our setup is highly effective andoutperforms every existing network for semantic segmentation on the RadarScenesdataset.

Simultaneous Clutter Detection and Semantic Segmentation of Moving Objects for Automotive Radar Data