论文标题
坡道-CNN：一种用于增强汽车雷达对象识别的新型神经网络
RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object Recognition
论文作者
论文摘要
毫米波雷达越来越多地集成到商用车辆中，以通过实现健壮和高性能的对象检测，本地化以及识别 - 新环境感知的关键组成部分，以支持新的高级驾驶员辅助系统。在本文中，我们提出了一种新型的雷达多角度卷积神经网络（RAMP-CNN），该网络（RAMP-CNN）基于进一步处理范围内速率 - 角度（RVA）热图序列来提取对象的位置和类别。为了绕过4D卷积神经网络（NN）的复杂性，我们建议将几种低维NN模型结合在我们的坡道-CNN模型中，尽管如此，这些模型仍然以较低的复杂性接近性能上限。广泛的实验表明，在所有测试方案中，提出的坡道-CNN模型比先前的作品获得了更好的平均召回和平均精度。此外，在夜间下，坡道-CNN模型已被验证可在夜间稳健工作，这可以使低成本雷达作为在严重条件下纯粹的光学传感的潜在替代品。
Millimeter-wave radars are being increasingly integrated into commercial vehicles to support new advanced driver-assistance systems by enabling robust and high-performance object detection, localization, as well as recognition - a key component of new environmental perception. In this paper, we propose a novel radar multiple-perspectives convolutional neural network (RAMP-CNN) that extracts the location and class of objects based on further processing of the range-velocity-angle (RVA) heatmap sequences. To bypass the complexity of 4D convolutional neural networks (NN), we propose to combine several lower-dimension NN models within our RAMP-CNN model that nonetheless approaches the performance upper-bound with lower complexity. The extensive experiments show that the proposed RAMP-CNN model achieves better average recall and average precision than prior works in all testing scenarios. Besides, the RAMP-CNN model is validated to work robustly under nighttime, which enables low-cost radars as a potential substitute for pure optical sensing under severe conditions.
