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DA-IVE: MLP Based Data Association Method for Instantaneous Velocity Estimation Using Multi-Radar: An Experimental Validation Study
Technical Paper
2021-01-0092
ISSN: 0148-7191, e-ISSN: 2688-3627
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SAE WCX Digital Summit
Language:
English
Abstract
This paper describes a novel Multi-Layer Perceptrons (MLP) learning-based association algorithm that is used in conjunction with an Instantaneous Velocity Estimator (IVE) to estimate the velocity of a surrounding vehicle using multi-radar sensors. The IVE algorithm requires at least two targets to be able to provide a velocity estimate. The approach suggested in this paper performs three stages of filtering on a list of targets available for the association to a given track. The algorithm identifies the one pair of targets that will provide the best instantaneous velocity estimation from all possible pairs. The three stages of filtering described ahead are, I - Semantic gating, II - MLP scoring, and III - Algebraic scoring. The IVE algorithm performs linear regression on the pair of targets it is finally provided to come up with a velocity estimation. This research also describes a novel method of labeling radar targets for use in the training of the neural network in association stage II. A thorough analysis of the correlation between a radar target’s quality and attributes is performed and presented here. The performance of the proposed algorithm is evaluated using real-world data collected through the ZF Automated Driving prototype vehicle.
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Citation
Shakibajahromi, B., Krishnan, A., Ati, D., Jabalameli, A. et al., "DA-IVE: MLP Based Data Association Method for Instantaneous Velocity Estimation Using Multi-Radar: An Experimental Validation Study," SAE Technical Paper 2021-01-0092, 2021, https://doi.org/10.4271/2021-01-0092.Data Sets - Support Documents
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