Radar-based road user detection is an important topic in the context of autonomous driving applications. The resolution of conventional automotive radar sensors results in a sparse data representation which is tough to refine during subsequent signal processing. On the other hand, a new sensor generation is waiting in the wings for its application in this challenging field. In this article, two sensors of different radar generations are evaluated against each other. The evaluation criterion is the performance on moving road user object detection and classification tasks. To this end, two data sets originating from an off-the-shelf radar and a high resolution next generation radar are compared. Special attention is given on how the two data sets are assembled in order to make them comparable. The utilized object detector consists of a clustering algorithm, a feature extraction module, and a recurrent neural network ensemble for classification. For the assessment, all components are evaluated both individually and, for the first time, as a whole. This allows for indicating where overall performance improvements have their origin in the pipeline. Furthermore, the generalization capabilities of both data sets are evaluated and important comparison metrics for automotive radar object detection are discussed. Results show clear benefits of the next generation radar. Interestingly, those benefits do not actually occur due to better performance at the classification stage, but rather because of the vast improvements at the clustering stage.

Reliable tracking of road users plays a critical part on the way to safe automated driving. In this paper, a machine learning based tracking approach on radar data is presented utilizing the radar target point clouds from multiple time steps as input to detect road users and to predict their tracking information. The detection and tracking of objects is achieved by applying a combination of known feature extractors from lidar and camera detection tasks. The generated feature maps are used as input to two branches - one branch for detection and one for tracking. In experiments on an extensive real-world radar data set, the proposed model achieves promising results in tracking performance compared to a basic clustering and a classification assisted tracking approach.

Semantic segmentation on radar point clouds is a new challenging task in radar data processing. We demonstrate how this task can be performed and provide results on a large data set of manually labeled radar reflections. In contrast to previous approaches where generated feature vectors from clustered reflections were used as an input for a classifier, now the whole radar point cloud is used as an input and class probabilities are obtained for every single reflection. We thereby eliminate the need for clustering algorithms and manually selected features.