  Automotive radar provides reliable environmental perception in all-weather
conditions with affordable cost, but it hardly supplies semantic and geometry
information due to the sparsity of radar detection points. With the development
of automotive radar technologies in recent years, instance segmentation becomes
possible by using automotive radar. Its data contain contexts such as radar
cross section and micro-Doppler effects, and sometimes can provide detection
when the field of view is obscured. The outcome from instance segmentation
could be potentially used as the input of trackers for tracking targets. The
existing methods often utilize a clustering-based classification framework,
which fits the need of real-time processing but has limited performance due to
minimum information provided by sparse radar detection points. In this paper,
we propose an efficient method based on clustering of estimated semantic
information to achieve instance segmentation for the sparse radar detection
points. In addition, we show that the performance of the proposed approach can
be further enhanced by incorporating the visual multi-layer perceptron. The
effectiveness of the proposed method is verified by experimental results on the
popular RadarScenes dataset, achieving 89.53% mean coverage and 86.97% mean
average precision with the IoU threshold of 0.5, which is superior to other
approaches in the literature. More significantly, the consumed memory is around
1MB, and the inference time is less than 40ms, indicating that our proposed
algorithm is storage and time efficient. These two criteria ensure the
practicality of the proposed method in real-world systems.
