A novel temporal-spatial object classification neural network model is proposed to improve the classification capability of tracked objects. It takes queued points of tracked objects using multiple frames as input, utilizes spatial and temporal information from these points for sampling and grouping as well as extracts hierarchical temporal-spatial features for target classification. Experimental results on a proprietary 4D Imaging Radar dataset and open-source 2D RadarScenes dataset demonstrate that the proposed tracker-cued radar point-cloud target classification method allows the model to learn meaningful appearance and motion features from sparse radar points data, and achieves accurate classification output as compared to a baseline method, while being efficient to run on edge hardware with limited resources.

This paper focuses on the challenge of estimating the 2D instantaneous ego -motion of vehicles equipped with an automotive radar. To further improve our previous study based on the weighted least squares (wLSQ) method and purpose-designed neural networks (NNs), this work proposes a new network architecture that supports local and global feature extraction as well as point-wise dynamic feature channel mixing. Compared with our previous work, the proposed method provides better estimation accuracy, lighter network size, and faster runtime performance.

A single frame radar-based multi-object tracker that aims to improve data association for better tracking performance is proposed. Firstly, a baseline tracker based on track-by-detection paradigm was implemented for automotive radar. Secondly, investigation on the performance of the tracker when tracking individual classes separately versus all classes together was performed. Thirdly, appearance features were extracted from a neural network and added as an additional metric to the cost matrix for improved data association. Extensive experiments on the 2D RadarScenes dataset and a 3D proprietary Lunewave dataset (in partnership with NXP Semiconductors) showed a consistent improvement in the tracking performance using the approach proposed by adding features extracted from a neural network.