关键词: 自主驾驶, 多模态特征融合, 深度卷积神经网络, 低辨识目标, 智能感知

Abstract: Aiming at the problems of low-observable target detection in autonomous vehicles under real driving conditions, a target detection method was proposed based on multi-modal feature fusion. In order to improve the detection on the low-observable targets, the multi-modal deep convolutional neural network was designed based on Faster R-CNN to fuse the features of RGB images, polarized images and infrared images, the development of the multi-modal (three) image low-observable target real-time detection system was studied and the applications of multi-modal image feature fusion in the intelligent perception system of autonomous vehicles were explored herein. A manually labeled multi-modal image dataset of low-observable targets was built. The deep learning neural network was trained to optimize internal parameters to make the system capable for both pedestrians and vehicle recognition in the complex environments. The experimental results indicate that the deep convolutional neural network based on the multi-modal fusion has a better performance on the low-observable target detection and recognition in complex environments than that of traditional single-modal methods.

Key words: autonomous driving, multi-modal feature fusion, deep convolutional neural network, low-observable target, intelligent perception