A Review of Tracking and Trajectory Prediction Methods for Autonomous Driving

Florin Leon and Marius Gavrilescu
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Florin Leon: Faculty of Automatic Control and Computer Engineering, “Gheorghe Asachi” Technical University of Iaşi, Bd. Mangeron 27, 700050 Iaşi, Romania
Marius Gavrilescu: Faculty of Automatic Control and Computer Engineering, “Gheorghe Asachi” Technical University of Iaşi, Bd. Mangeron 27, 700050 Iaşi, Romania

Mathematics, 2021, vol. 9, issue 6, 1-37

Abstract: This paper provides a literature review of some of the most important concepts, techniques, and methodologies used within autonomous car systems. Specifically, we focus on two aspects extensively explored in the related literature: tracking, i.e., identifying pedestrians, cars or obstacles from images, observations or sensor data, and prediction, i.e., anticipating the future trajectories and motion of other vehicles in order to facilitate navigating through various traffic conditions. Approaches based on deep neural networks and others, especially stochastic techniques, are reported.

Keywords: autonomous driving; object tracking; trajectory prediction; deep neural networks; stochastic methods (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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