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A Review of Tracking and Trajectory Prediction Methods for Autonomous Driving

Author

Listed:
  • 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)

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.

Suggested Citation

  • Florin Leon & Marius Gavrilescu, 2021. "A Review of Tracking and Trajectory Prediction Methods for Autonomous Driving," Mathematics, MDPI, vol. 9(6), pages 1-37, March.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:6:p:660-:d:520526
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    References listed on IDEAS

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    1. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    2. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    3. Mehdi Foumani & Asghar Moeini & Michael Haythorpe & Kate Smith-Miles, 2018. "A cross-entropy method for optimising robotic automated storage and retrieval systems," International Journal of Production Research, Taylor & Francis Journals, vol. 56(19), pages 6450-6472, October.
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    Cited by:

    1. Andreea-Iulia Patachi & Florin Leon, 2023. "Multiagent Multimodal Trajectory Prediction in Urban Traffic Scenarios Using a Neural Network-Based Solution," Mathematics, MDPI, vol. 11(8), pages 1-25, April.
    2. Renbo Huang & Guirong Zhuo & Lu Xiong & Shouyi Lu & Wei Tian, 2023. "A Review of Deep Learning-Based Vehicle Motion Prediction for Autonomous Driving," Sustainability, MDPI, vol. 15(20), pages 1-43, October.
    3. Florin Leon & Mircea Hulea & Marius Gavrilescu, 2022. "Preface to the Special Issue on “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning”," Mathematics, MDPI, vol. 10(10), pages 1-4, May.
    4. Olivér Hornyák & László Barna Iantovics, 2023. "AdaBoost Algorithm Could Lead to Weak Results for Data with Certain Characteristics," Mathematics, MDPI, vol. 11(8), pages 1-24, April.
    5. Wang, Ziwei & Peng, Pai & Geng, Keke & Cheng, Xiaolong & Zhu, Xiaoyuan & Chen, Jiansong & Yin, Guodong, 2023. "Analysis of pedestrian crossing behavior based on Centralized Unscented Kalman Filter and pedestrian awareness based social force model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    6. Shi, Xiaowei & Li, Xiaopeng, 2023. "Trajectory Planning for an Autonomous Vehicle with Conflicting Moving Objects Along a Fixed Path – An Exact Solution Method," Transportation Research Part B: Methodological, Elsevier, vol. 173(C), pages 228-246.

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