AI 中文总结
该研究提出基于锚的AI模型，结合雷达微多普勒特征与创新雷达图像膨胀技术，提升碰撞前目标检测性能，在动态场景中优于现有汽车雷达跟踪方法。
AI 中文摘要
高级自动驾驶为现代汽车安全系统带来巨大潜力，但高度依赖约束系统的可靠激活。前瞻传感器对于即时且精确的目标检测至关重要。汽车雷达技术的最新进展实现了详细的环境检测和高分辨率特征（如微多普勒特征）的识别。结合先进的人工智能技术，这些特征显著增强了目标检测能力并提高了运动学参数估计的准确性，这对于早期且可靠地激活智能安全气囊、自适应安全带等不可逆安全系统至关重要。因此，本文提出了一种基于锚的人工智能模型，旨在处理高分辨率雷达数据，重点关注微多普勒特征以改进碰撞前目标检测。此外，这些特征可提高运动学目标参数估计的准确性，减少假阴性，尤其在关键近场区域。为解决雷达点云稀疏且波动的挑战，研究人员开发了一种针对特征输入通道的创新雷达图像膨胀技术，以放大微多普勒特征等局部雷达模式。因此，该方法提高了系统的可靠性，增强了在雷达多路径反射和虚假目标情况下检测碰撞前场景中目标的能力。为研究该模型的适用性并将其性能与先进的汽车雷达跟踪方法进行比较，研究人员记录了使用量产传感器且与碰撞前场景相关的雷达数据集。结果表明，基于锚的人工智能模型相较于已有的跟踪方法具有优势，在动态场景中估计目标参数方面表现出色，并强调其能有效处理不同数据集的能力。
英文摘要
Advanced automated driving presents significant potential to improve modern automotive safety systems, but it depends highly on the reliable activation of restraint systems. Forward-looking sensors are crucial for immediate and precise object detection. Recent developments in automotive radar technology enable detailed environment detection and the recognition of high-resolution features, such as micro-Doppler signatures. Combined with advanced AI techniques, these features significantly enhance object detection and improve the accuracy of kinematic parameter estimation. This is essential for the early and reliable activation of irreversible safety systems, such as smart airbags and adaptive seat belts. Therefore, an anchor-based AI model is presented, designed to process high-resolution radar data with an explicit focus on micro-Doppler signatures to improve pre-crash object detection. Furthermore, these signatures can improve the accuracy of kinematic object parameter estimation and reduce false negatives, especially in the critical near-field. To address the challenges of sparse and fluctuating radar point clouds, an innovative radar-image dilation technique on the feature input channels was developed to amplify local radar patterns, like micro-Doppler features. Therefore, this approach increases the system's reliability and increases its ability to detect objects in pre-crash scenarios despite radar multipath reflections and ghost objects. In order to investigate the applicability and compare the model's performance with advanced automotive radar tracking methods, a radar data set using series sensors and pre-crash relevant scenarios was recorded. The results demonstrate the advantages of the anchor-based AI model over established tracking approaches. It excels at estimating object parameters in dynamic scenarios and underscores its ability to process different data sets effectively.
