Santosh Parit, Julia Hindel, Rohit Mohan, Georgi Pramatarov, Paul Newman, Daniele De Martini, Abhinav Valada · 2026 · arXiv
DOI: 10.48550/arXiv.2610.26816 · open-access PDF
在不同的环境条件下,强大的场景理解对于安全的自动驾驶至关重要,而摄像头和LiDAR在恶劣的天气和不良的照明下下降,频率调节连续波(FMCW)旋转雷达提供可靠的360°感知,以最低的计算成本,但实时FMCW雷达场景理解是具有挑战性的,因为复杂的信号返回,有限的时间分辨率和稀缺的注释数据集。
Machine translation (Meta M2M100); the English original follows.
Robust scene understanding under varying environmental conditions is essential for safe autonomous driving. While cameras and LiDAR degrade in adverse weather and poor lighting, Frequency-Modulated Continuous-Wave (FMCW) spinning radar provides dependable 360° perception at extended ranges with minimal compute cost. However, real-time FMCW radar scene understanding is challenging due to complex signal returns, limited temporal resolution, and scarce annotated datasets. We introduce StreamRPS, a novel framework for streaming FMCW radar panoptic segmentation, comprising an automated label-genera
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