Title: A review of different strategies for vehicle collision avoidance

Authors: Saloni; Umesh Dutta; Sudesh Pahal

Addresses: Manav Rachna International Institute of Research and Studies (MRIIRS), Faridabad, Haryana, India ' Manav Rachna International Institute of Research and Studies (MRIIRS), Faridabad, Haryana, India ' Maharaja Surajmal Institute of Technology, Janakpuri, New Delhi, India

Abstract: Vehicular Networks (V2X) technologies play an important role in improving road safety, traffic efficiency, and communication among vehicles. The paper outlines the key role of trajectory prediction in V2X systems for anticipating vehicle movements. Analytical and data-driven methods, including machine learning and deep learning, are investigated for improving prediction accuracy. Furthermore, the paper investigates early-warning collision systems, which leverage trajectory prediction and risk assessment to provide timely alerts to drivers and autonomous vehicles. Various methodologies, such as sensor-based systems and V2V/V2I communication protocols, are analysed for their effectiveness in collision prevention. The proposed framework is structured into four integral components: (1) trajectory prediction, (2) dynamic risk assessment, (3) early warning notification, and (4) vehicle-to-vehicle (V2V) communication. By exploring the intricate interplay between trajectory prediction, risk assessment and early warning collision systems, this review paper offers insight into the state-of-art methods and technologies shaping the future of vehicular network.

Keywords: collision avoidance; vehicle trajectory prediction; risk assessment; early warning systems; vehicular communication; machine learning; neural networks; deep learning and V2X.

DOI: 10.1504/IJVICS.2025.147512

International Journal of Vehicle Information and Communication Systems, 2025 Vol.10 No.3, pp.299 - 324

Received: 18 Dec 2023
Accepted: 10 Jan 2025

Published online: 18 Jul 2025 *

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