RAMP-CNN：自動車レーダー物体認識を強化する新規ニューラルネットワーク
RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object Recognition
レーダーの距離-速度-角度ヒートマップ列を複数の低次元CNNで処理し、4D畳み込み並みの性能を低計算コストで実現した物体検出・認識モデル。夜間でも堅牢に動作する。
著者: Xiangyu Gao, Guanbin Xing, Sumit Roy, Hui Liu
分類: eess.SP, cs.AI, cs.CV, cs.LG, cs.RO
原文アブストラクト
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.
