Learning-based Extended Object Tracking Using Hierarchical Truncation Measurement Model with Automotive Radar
(2021) In IEEE Journal on Selected Topics in Signal Processing 15(4). p.1013-1029- Abstract
This paper presents a data-driven measurement model for extended object tracking (EOT) with automotive radar. Specifically, the spatial distribution of automotive radar measurements is modeled as a hierarchical truncated Gaussian (HTG) with structural geometry parameters, e.g., truncation bounds, orientation, and scaling, that can be learned from the training data. The HTG measurement model provides an adequate resemblance to the spatial distribution of real-world automotive radar measurements. Moreover, large-scale radar datasets can be leveraged to learn the geometry-related model parameters and offload the computationally demanding model parameter estimation from the state update step. The learned HTG measurement model is further... (More)
This paper presents a data-driven measurement model for extended object tracking (EOT) with automotive radar. Specifically, the spatial distribution of automotive radar measurements is modeled as a hierarchical truncated Gaussian (HTG) with structural geometry parameters, e.g., truncation bounds, orientation, and scaling, that can be learned from the training data. The HTG measurement model provides an adequate resemblance to the spatial distribution of real-world automotive radar measurements. Moreover, large-scale radar datasets can be leveraged to learn the geometry-related model parameters and offload the computationally demanding model parameter estimation from the state update step. The learned HTG measurement model is further incorporated into a random matrix based EOT approach with two (multi-sensor) measurement updates: one is based on a factorized Gaussian inverse-Wishart density representation and the other is based on a Rao-Blackwellized particle density representation. The effectiveness of the proposed approaches is verified on both synthetic data and real-world automotive radar nuScenes dataset over 300 trajectories.
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- author
- Xia, Yuxuan ; Wang, Pu ; Berntorp, Karl Oskar Erik LU ; Svensson, Lennart ; Granstrom, Karl ; Mansour, Hassan ; Boufounos, Petros T. and Orlik, Philip
- organization
- publishing date
- 2021-02-09
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Automotive engineering, Automotive radar, autonomous driving, Computational modeling, Density measurement, extended object tracking, nuScenes, Radar, Radar measurements, random matrix, Sensors, Time measurement
- in
- IEEE Journal on Selected Topics in Signal Processing
- volume
- 15
- issue
- 4
- pages
- 17 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- scopus:85100862277
- ISSN
- 1932-4553
- DOI
- 10.1109/JSTSP.2021.3058062
- language
- English
- LU publication?
- yes
- id
- dbb84c8b-bb5c-42c5-a458-9ac4598ebb39
- date added to LUP
- 2021-03-04 13:42:21
- date last changed
- 2022-04-27 00:32:52
@article{dbb84c8b-bb5c-42c5-a458-9ac4598ebb39, abstract = {{<p>This paper presents a data-driven measurement model for extended object tracking (EOT) with automotive radar. Specifically, the spatial distribution of automotive radar measurements is modeled as a hierarchical truncated Gaussian (HTG) with structural geometry parameters, e.g., truncation bounds, orientation, and scaling, that can be learned from the training data. The HTG measurement model provides an adequate resemblance to the spatial distribution of real-world automotive radar measurements. Moreover, large-scale radar datasets can be leveraged to learn the geometry-related model parameters and offload the computationally demanding model parameter estimation from the state update step. The learned HTG measurement model is further incorporated into a random matrix based EOT approach with two (multi-sensor) measurement updates: one is based on a factorized Gaussian inverse-Wishart density representation and the other is based on a Rao-Blackwellized particle density representation. The effectiveness of the proposed approaches is verified on both synthetic data and real-world automotive radar nuScenes dataset over 300 trajectories.</p>}}, author = {{Xia, Yuxuan and Wang, Pu and Berntorp, Karl Oskar Erik and Svensson, Lennart and Granstrom, Karl and Mansour, Hassan and Boufounos, Petros T. and Orlik, Philip}}, issn = {{1932-4553}}, keywords = {{Automotive engineering; Automotive radar; autonomous driving; Computational modeling; Density measurement; extended object tracking; nuScenes; Radar; Radar measurements; random matrix; Sensors; Time measurement}}, language = {{eng}}, month = {{02}}, number = {{4}}, pages = {{1013--1029}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, series = {{IEEE Journal on Selected Topics in Signal Processing}}, title = {{Learning-based Extended Object Tracking Using Hierarchical Truncation Measurement Model with Automotive Radar}}, url = {{http://dx.doi.org/10.1109/JSTSP.2021.3058062}}, doi = {{10.1109/JSTSP.2021.3058062}}, volume = {{15}}, year = {{2021}}, }