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.

翻译：汽车雷达以较低的成本提供全天候条件下的可靠环境感知，但由于雷达检测点的稀疏性，其难以提供语义和几何信息。随着近年来汽车雷达技术的发展，利用汽车雷达实现实例分割成为可能。其数据包含雷达散射截面和微多普勒效应等上下文信息，有时还能在视野受阻时提供检测结果。实例分割的输出可潜在地用作跟踪器进行目标跟踪的输入。现有方法通常采用基于聚类的分类框架，这满足了实时处理的需求，但由于稀疏雷达检测点提供的有限信息，其性能受到限制。本文提出了一种基于语义信息估计聚类的高效方法，实现了对稀疏雷达检测点的实例分割。此外，我们展示了通过融入视觉多层感知器可进一步提升所提方法的性能。通过在流行的RadarScenes数据集上的实验结果验证了所提方法的有效性，在IoU阈值为0.5的情况下，实现了89.53%的平均覆盖率和86.97%的平均精确率，优于文献中的其他方法。更重要的是，其内存消耗约为1MB，推理时间小于40ms，表明我们提出的算法在存储和时间上均高效。这两个标准确保了所提方法在现实世界系统中的实用性。