On-Road Object Collision Point Estimation by Radar Sensor Data Fusion

This paper proposes an object collision point estimation scheme by developing a new data fusion method in a multi-radar network environment. In order to reduce radar's estimation error due to measurement uncertainty, we first design radar accuracy models determined by the position of each objec...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2022-09, Vol.23 (9), p.14753-14763
Main Authors: Choi, Woo Young, Lee, Seung-Hi, Chung, Chung Choo
Format: Article
Language:English
Subjects:
autonomous driving
data fusion
Data integration
Data models
Error analysis
Estimation
interacting multiple model
Model accuracy
Multisensor fusion
Object estimation
Radar
Radar data
Radar detection
Radar measurements
Radar networks
Sensor fusion
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Summary:This paper proposes an object collision point estimation scheme by developing a new data fusion method in a multi-radar network environment. In order to reduce radar's estimation error due to measurement uncertainty, we first design radar accuracy models determined by the position of each object. Then, an interacting multiple model (IMM) filter based on occupancy zones is designed for accurate object estimation. For a multi-radar network's object estimation, we also design a radar data fusion method using the estimated object information through the IMM instead of the object estimation information given by the radars. A collision point identification problem, where multiple sensors calculate the different vehicle surface points of the same object, is solved by developing the data fusion method to estimate the object surface's collision point closest to the ego vehicle center. The utility of the proposed scheme was validated through a scenario-based object estimation experiment. We confirmed that the proposed data fusion method produced substantially improved error distributions over conventional methods.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2021.3133290