On-Road Object Collision Point Estimation by Radar Sensor Data Fusion
- Authors
- Choi, W.Y.; 이승희; Chung, C.C.
- Issue Date
- 1-Sep-2022
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- autonomous driving.; data fusion; Data integration; Data models; Estimation; interacting multiple model; Object estimation; Radar; radar; Radar detection; Radar measurements; Sensor fusion
- Citation
- IEEE Transactions on Intelligent Transportation Systems, v.23, no.9, pp 14753 - 14763
- Pages
- 11
- Journal Title
- IEEE Transactions on Intelligent Transportation Systems
- Volume
- 23
- Number
- 9
- Start Page
- 14753
- End Page
- 14763
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/24468
- DOI
- 10.1109/TITS.2021.3133290
- ISSN
- 1524-9050
1558-0016
- Abstract
- 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. IEEE
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- Appears in
Collections - College of Engineering > Department of Mechanical and System Design Engineering > 1. Journal Articles
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