VANETs Meet Autonomous Vehicles: A Multimodal 3D Environment Learning\n Approach

2017/05/24 by Yassine Maalej, Sameh Sorour, Maalej, Yassine +5 · 1 citation
Computer Science · Engineering · #Advanced Neural Network Applications #Autonomous Vehicle Technology and Safety #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Video Surveillance and Tracking Methods

paper · pdf · doi:10.48550/arxiv.1705.08624

openalex publication_date 2017/05/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this paper, we design a multimodal framework for object detection,\nrecognition and mapping based on the fusion of stereo camera frames, point\ncloud Velodyne Lidar scans, and Vehicle-to-Vehicle (V2V) Basic Safety Messages\n(BSMs) exchanged using Dedicated Short Range Communication (DSRC). We merge the\nkey features of rich texture descriptions of objects from 2D images, depth and\ndistance between objects provided by 3D point cloud and awareness of hidden\nvehicles from BSMs' 3D information. We present a joint pixel to point cloud and\npixel to V2V correspondences of objects in frames from the Kitti Vision\nBenchmark Suite by using a semi-supervised manifold alignment approach to\nachieve camera-Lidar and camera-V2V mapping of their recognized objects that\nhave the same underlying manifold.\n

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• Cooperative Safety Intelligence in V2X-Enabled Transportation: A Survey

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