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Review

A Review of Tracking and Trajectory Prediction Methods for Autonomous Driving

Faculty of Automatic Control and Computer Engineering, “Gheorghe Asachi” Technical University of Iaşi, Bd. Mangeron 27, 700050 Iaşi, Romania
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Mathematics 2021, 9(6), 660; https://doi.org/10.3390/math9060660
Submission received: 28 January 2021 / Revised: 9 March 2021 / Accepted: 17 March 2021 / Published: 19 March 2021

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 autonomous driving; object tracking; trajectory prediction; deep neural networks; stochastic methods

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MDPI and ACS Style

Leon, F.; Gavrilescu, M. A Review of Tracking and Trajectory Prediction Methods for Autonomous Driving. Mathematics 2021, 9, 660. https://doi.org/10.3390/math9060660

AMA Style

Leon F, Gavrilescu M. A Review of Tracking and Trajectory Prediction Methods for Autonomous Driving. Mathematics. 2021; 9(6):660. https://doi.org/10.3390/math9060660

Chicago/Turabian Style

Leon, Florin, and Marius Gavrilescu. 2021. "A Review of Tracking and Trajectory Prediction Methods for Autonomous Driving" Mathematics 9, no. 6: 660. https://doi.org/10.3390/math9060660

APA Style

Leon, F., & Gavrilescu, M. (2021). A Review of Tracking and Trajectory Prediction Methods for Autonomous Driving. Mathematics, 9(6), 660. https://doi.org/10.3390/math9060660

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