Vehicle bounding box tracking (VBBT) is a new problem that is becoming increasingly important in autonomous driving. It is defined as a problem in which not only the position but also the size of a target vehicle is estimated using a sensor. In this paper, novel VBBT using a low-end three-dimensional (3D) laser scanner is proposed. Compared to previous methods, the proposed VBBT has three distinctions: (1) the center of a rectangular vehicle is defined as its position, and the motion model that uses the center of the vehicle as the state is developed; (2) a new measurement model is proposed that models the measured size of the target vehicle as a sample from a uniform distribution; and (3) a Bayesian filter for the proposed motion and measurement model is developed and it is named as the Pareto Kalman filter (PKF). Finally, the proposed method is applied to six scenarios, and its validity is demonstrated through experimentation.
|Number of pages||17|
|Journal||IEEE Transactions on Intelligent Transportation Systems|
|Publication status||Published - 2021 Jun|
Bibliographical noteFunding Information:
Manuscript received October 16, 2019; revised March 10, 2020; accepted May 8, 2020. Date of publication May 21, 2020; date of current version June 2, 2021. This work was supported by the National Research Foundation of Korea (NRF) through the Basic Science Research Program funded by the Ministry of Education, Science, and Technology under Grant NRF-2019R1A2C1007153. The Associate Editor for this article was L. M. Bergasa. (Corresponding author: Euntai Kim.) The authors are with the School of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, South Korea (e-mail: firstname.lastname@example.org; email@example.com). Digital Object Identifier 10.1109/TITS.2020.2994624
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All Science Journal Classification (ASJC) codes
- Automotive Engineering
- Mechanical Engineering
- Computer Science Applications