Citation

Rashid, Sidra and Khattak, Muazzam A. Khan and Akram, Muhammad Usman and Mumtaz, Raheel and Syed, Toqeer Ali and Lee, It Ee and Wali, Qamar (2026) Evolution of Deep Learning Techniques and Challenges for Vehicle Trajectory Prediction. IEEE Open Journal of Intelligent Transportation Systems, 7. pp. 1364-1385. ISSN 2687-7813

Text 12.pdf - Published Version Restricted to Repository staff only Download (3MB)

Abstract

Accurate vehicle trajectory prediction (VTP) plays a fundamental role in the advancement of intelligent transportation systems and autonomous driving. It improves situational awareness, optimizes decision-making and safety in complex traffic scenarios. Although several surveys have reviewed vehicle trajectory prediction approaches, a comprehensive review of recent deep learning-based approaches from a system and modeling perspective is still lacking. To bridge this gap, we conducted a systematic review of the literature on deep learning-based methods by critically analyzing emerging trends in model design. We introduce a system-oriented taxonomy of vehicle trajectory prediction methods which includes classical, reinforcement learning-based, collaborative, and deep learning-based methods. We evaluate their capacity to model uncertainty, inter-agent interactions, and dynamic traffic behavior. This survey emphasizes three key perspectives that drive performance in deep learning-based models including data preprocessing and feature extraction, model creation and attention mechanisms. Deep-learning-based methods are critically analyzed in design dimensions, e.g. temporal modeling strategy, interaction encoding, modeling capability, suitable deployment scenarios and their key strengths for trajectory prediction. Finally, we discuss open research challenges and propose practical recommendations for next-generation VTP in autonomous driving.

Item Type:	Article
Uncontrolled Keywords:	Attention mechanism, deep learning model, data pre-processing, taxonomy, vehicle trajectory prediction
Subjects:	T Technology > TL Motor vehicles. Aeronautics. Astronautics
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	05 Jun 2026 05:44
Last Modified:	05 Jun 2026 05:44
URII:	http://shdl.mmu.edu.my/id/eprint/16030

Downloads

Edit (login required)