Moon Sunghoon, Kim Daehyun, Kim Younglok
Department of Electronic Engineering, Sogang University, Seoul 04107, Republic of Korea.
Sensors (Basel). 2024 Oct 3;24(19):6409. doi: 10.3390/s24196409.
The development of autonomous driving vehicles has increased the global demand for robust and efficient automotive radar systems. This study proposes an automotive radar-based ego-vehicle speed detection network (AVSD Net) model using convolutional neural networks for estimating the speed of the ego vehicle. The preprocessing and postprocessing methods used for vehicle speed correction are presented in detail. The AVSD Net model exhibits characteristics that are independent of the angular performance of the radar system and its mounting angle on the vehicle, thereby reducing the loss of the maximum detection range without requiring a downward or wide beam for the elevation angle. The ego-vehicle speed is effectively estimated when the range-velocity spectrum data are input into the model. Moreover, preprocessing and postprocessing facilitate an accurate correction of the ego-vehicle speed while reducing the complexity of the model, enabling its application to embedded systems. The proposed ego-vehicle speed correction method can improve safety in various applications, such as autonomous emergency braking systems, forward collision avoidance assist, adaptive cruise control, rear cross-traffic alert, and blind spot detection systems.
自动驾驶车辆的发展增加了全球对强大且高效的汽车雷达系统的需求。本研究提出了一种基于汽车雷达的自车速度检测网络（AVSD Net）模型，该模型使用卷积神经网络来估计自车的速度。详细介绍了用于车速校正的预处理和后处理方法。AVSD Net模型具有独立于雷达系统的角度性能及其在车辆上的安装角度的特性，从而在不需要仰角向下或宽波束的情况下减少了最大检测范围的损失。当将距离-速度谱数据输入模型时，可以有效地估计自车速度。此外，预处理和后处理有助于准确校正自车速度，同时降低模型的复杂性，使其能够应用于嵌入式系统。所提出的自车速度校正方法可以提高各种应用中的安全性，例如自动紧急制动系统、前碰撞避免辅助、自适应巡航控制、后方交叉交通警报和盲点检测系统。
