Abstract
Radar sensors are low cost, long-range, and weather-resilient. Therefore, they are widely used for driver assistance functions, and are expected to be crucial for the success of autonomous driving in the future. In many perception tasks only pre-processed radar point clouds are considered. In contrast, radar spectra are a raw form of radar measurements and contain more information than radar point clouds. However, radar spectra are rather difficult to interpret. In this work, we aim to explore the semantic information contained in spectra in the context of automated driving, thereby moving towards better interpretability of radar spectra. To this end, we create a radar spectra-language model, allowing us to query radar spectra measurements for the presence of scene elements using free text. We overcome the scarcity of radar spectra data by matching the embedding space of an existing vision-language model (VLM). Finally, we explore the benefit of the learned representation for scene parsing, and obtain improvements in free space segmentation and object detection merely by injecting the spectra embedding into a baseline model.
Abstract (translated)
雷达传感器具有低成本、远距离和天气耐用性。因此，它们广泛应用于驾驶辅助功能，并预计将在未来自动驾驶的成功中扮演关键角色。在许多感知任务中，只考虑预处理后的雷达点云。相比之下，雷达频谱是一种原始的雷达测量形式，含有比雷达点云更多的信息。然而，雷达频谱的解读相当困难。在这项工作中，我们旨在探索频谱中包含的语义信息，从而在自动驾驶中实现更好的可解释性。为此，我们创建了一个雷达频谱-语言模型，使我们能够使用自由文本查询雷达频谱测量中是否存在场景元素。我们通过将现有的视觉语言模型（VLM）的嵌入空间与雷达频谱数据进行匹配来克服雷达频谱数据的稀缺性。最后，我们探讨了学习表示对场景解析的好处，只需将频谱嵌入注入到基线模型中，就能获得仅通过在自由空间分割和目标检测方面实现提高。
URL
https://arxiv.org/abs/2406.02158
