Recognition of the upper structure using the RCS characteristic of the automotive radar

2018 International Conference on Information Networking (ICOIN) Pub Date : 1900-01-01 DOI:10.1109/ICOIN.2018.8343264

J. Oh, Heemang Song, Hyun-Chool Shin

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引用次数: 2

Abstract

Recently, Automotive radar sensors achieve a greatly increasingly market influence and essentially used for providing driving environment information required by driver assistance systems. Although they are good at measuring the distance and the radial velocity of other objects even under bad weather circumstance, they have difficulty in distinguishing clearly upper structure and stationary vehicle for ambiguity in elevation. As a result, upper structure(e. g overpass, road signal, bridge so on) crossing lane of the ego vehicle may look similar to stationary vehicle. For this reason, AEB(Autonomous Emergency Braking) system fails to operate properly. As closer to the upper structure and the stationary vehicle, the pattern of RCS(Radar Cross Section) measured from the object is different. This difference of RCS pattern can be used to provide features to distinguish the upper structure and the stationary vehicle. There may exists certain features that can help classify them base only on RCS. Feature calculated from real FMCW(Frequency Modulated Continuos Wave) radar sensor data show potential availability in real traffic condition.

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利用汽车雷达的RCS特性识别上部结构

近年来，汽车雷达传感器的市场影响力越来越大，主要用于提供驾驶辅助系统所需的驾驶环境信息。虽然在恶劣天气条件下也能很好地测量其他物体的距离和径向速度，但由于高程模糊，难以清晰区分上层建筑和静止车辆。因此，上部结构(e。(立交桥、道路信号、桥梁等)自我车辆的交叉车道可能看起来与静止车辆相似。因此，自动紧急制动(AEB)系统无法正常运行。由于距离上层结构和静止车辆较近，从目标物测得的RCS(Radar Cross Section)模式不同。这种RCS模式的差异可以用来提供特征来区分上部结构和静止车辆。可能存在某些特征可以帮助仅基于RCS对它们进行分类。从实际FMCW(调频连续波)雷达传感器数据计算的特征显示了在实际交通条件下的潜在可用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2018 International Conference on Information Networking (ICOIN)

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