基于高分辨率汽车雷达的自行车运动方向估计

Martin Stolz, Mingkang Li, Zhaofei Feng, M. Kunert, W. Menzel
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引用次数: 2

摘要

从传感器数据中预测物体的运动是汽车领域一个广泛的研究和发展课题。根据所使用的传感器类型,目标跟踪已经建立了几个测量周期。一个突出的例子是卡尔曼滤波器。在反应时间较短的时间关键场景中,在多个测量周期内跟踪是不合适的。为了在一个单一的测量周期内检测物体的运动,雷达传感器是一个候选,因为它能够通过使用多普勒效应即时测量物体的速度。本文介绍并说明了一种估算骑行者在一个测量周期内运动方向的新方法。它是基于对骑自行车的人的形状的近似。用两种不同的方法进行了近似,并对解进行了比较。为了验证方向估计的结果,对雷达数据进行了仿真和实测。
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Direction of Movement Estimation of Cyclists with a High-Resolution Automotive Radar
Prediction of the object movement from sensor data in the automotive sector is a widespread research and development topic. Dependent on the used sensor types, object tracking has been established over several measurement cycles. A prominent example of this is the Kalman filter. In time critical scenarios with less reaction time tracking over a number of measurement cycles is not suitable. To detect object movement within one single measurement cycle only the radar sensor is a candidate, due to the ability to measure the velocity of objects instantaneously by using the Doppler effect.A new approach to estimate the direction of movement of cyclists within one measurement cycle is introduced and explained in this paper. It is based on the approximation of the shape from a cyclist. The approximation is performed with two different methods and the solutions are compared with each other. To validate the results of the direction estimation, simulated and measured radar data are exercised.
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