Open AccessMulti‐vehicle multi‐sensor occupancy grid map fusion in vehicular networks
Author(s) -
Meng Xi,
Duan Dongliang,
Feng Tao
Publication year - 2022
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/cmu2.12314
Subject(s) - sensor fusion , occupancy grid mapping , computer science , occupancy , grid , real time computing , point cloud , lidar , process (computing) , wireless sensor network , kernel (algebra) , kernel density estimation , advanced driver assistance systems , artificial intelligence , engineering , remote sensing , geography , computer network , architectural engineering , statistics , mathematics , geodesy , combinatorics , estimator , robot , mobile robot , operating system
Sensing is an essential part in autonomous driving and intelligent transportation systems. It enables the vehicle to better understand itself and its surrounding environment. Vehicular networks support information sharing among different vehicles and hence enable the multi‐vehicle multi‐sensor cooperative sensing, which can greatly improve the sensing performance. However, there are a couple of issues to be addressed. First, the multi‐sensor data fusion needs to deal with heterogeneous data formats. Second, the cooperative sensing process needs to deal with low data quality and perception blind spots for some vehicles. In order to solve the above problems, in this paper the occupancy grid map is adopted to facilitate the fusion of multi‐vehicle and multi‐sensor data. The dynamic target detection frame and pixel information of the camera data are mapped to the static environment of the LiDAR point cloud, and the space‐based occupancy probability distribution kernel density estimation characterization fusion data is designed , and the occupancy grid map based on the probability level and the spatial level is generated. Real‐world experiments show that the proposed fusion framework is better compatible with the data information of different sensors and expands the sensing range by involving the collaborations among multiple vehicles in vehicular networks.