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<p>Vehicle trajectory prediction is fundamental to autonomous driving environment perception and decision control, directly impacting driving safety and traffic efficiency in complex interactive scenarios. However, existing deep learning-based trajectory prediction methods face three major challenges in real open road environments: (1) The lack of explicit modeling of multi-agent game interactions leads to a high rate of misjudgment in long-term intention prediction. (2) The coupling of multimodal uncertainty sources, including perceptual noise, behavioral ambiguity, and cognitive blind spots, prevents quantifiable risk assessment for downstream planning. (3) These models exhibit weak generalization ability in long-tail scenarios and cross-domain migration, while being constrained by the difficulty of real-time deployment on in-vehicle computing resources. To address these issues, this paper systematically reviews deep learning-driven vehicle trajectory prediction methods and highlights their application value in expert systems and intelligent transportation. The main contributions of this review are as follows: (1) Proposing a unified technical taxonomy that systematically analyzes the design principles, evolutionary logic, and inherent limitations of recurrent neural networks, convolutional neural networks, graph neural networks, Transformers, and deep generative models. (2) Revealing six major engineering gaps between simulation and real-world environments, namely the lack of game-theoretic interaction, uncertainty coupling, insufficient long-tail generalization, perception-prediction discrepancy, real-time constraints, and weak cross-scenario adaptability. (3) Summarizing mainstream public datasets and evaluation protocols to provide an empirical basis for algorithm validation. (4) Envisioning five future directions for next-generation expert systems: joint optimization of prediction and planning, decoupling of uncertainty quantification, game-theoretic multi-agent inference, lightweight adaptive deployment, and scenario-driven generalization evaluation. This review aims to provide a systematic reference for researchers and engineers, thereby promoting the development of safer, more interpretable, and deployable trajectory prediction for autonomous driving in open road environments.</p>

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Deep Learning for Vehicle Trajectory Prediction in Intelligent Transportation: Methods, Challenges, and Future Directions

  • 期刊论文

  • 发表日期：

    2026年5月20日

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  • Laixiang Xu,
  • Xiaowei Wang,
  • Xiangjun Chen,
  • Tiwei Zeng,
  • Hao Xi,
  • Xiaojie Du,
  • Junmin Zhao

摘要

Vehicle trajectory prediction is fundamental to autonomous driving environment perception and decision control, directly impacting driving safety and traffic efficiency in complex interactive scenarios. However, existing deep learning-based trajectory prediction methods face three major challenges in real open road environments: (1) The lack of explicit modeling of multi-agent game interactions leads to a high rate of misjudgment in long-term intention prediction. (2) The coupling of multimodal uncertainty sources, including perceptual noise, behavioral ambiguity, and cognitive blind spots, prevents quantifiable risk assessment for downstream planning. (3) These models exhibit weak generalization ability in long-tail scenarios and cross-domain migration, while being constrained by the difficulty of real-time deployment on in-vehicle computing resources. To address these issues, this paper systematically reviews deep learning-driven vehicle trajectory prediction methods and highlights their application value in expert systems and intelligent transportation. The main contributions of this review are as follows: (1) Proposing a unified technical taxonomy that systematically analyzes the design principles, evolutionary logic, and inherent limitations of recurrent neural networks, convolutional neural networks, graph neural networks, Transformers, and deep generative models. (2) Revealing six major engineering gaps between simulation and real-world environments, namely the lack of game-theoretic interaction, uncertainty coupling, insufficient long-tail generalization, perception-prediction discrepancy, real-time constraints, and weak cross-scenario adaptability. (3) Summarizing mainstream public datasets and evaluation protocols to provide an empirical basis for algorithm validation. (4) Envisioning five future directions for next-generation expert systems: joint optimization of prediction and planning, decoupling of uncertainty quantification, game-theoretic multi-agent inference, lightweight adaptive deployment, and scenario-driven generalization evaluation. This review aims to provide a systematic reference for researchers and engineers, thereby promoting the development of safer, more interpretable, and deployable trajectory prediction for autonomous driving in open road environments.