A Multimodal Spatio-Temporal Transformer for Trajectory-Aware Long Video Event Understanding in Intelligent Transportation Systems
Keywords:
multimodal transformer, long video understanding, trajectory prediction, intelligent transportation systems, spatio-temporal attention, edge-cloud deployment, fairness, sustainabilityAbstract
The increasing deployment of intelligent transportation systems (ITS) demands robust, real-time video understanding that extends beyond isolated frame analysis to capture long-range spatio-temporal dependencies in traffic scenes. This paper proposes a multimodal spatio-temporal transformer architecture specifically designed for trajectory-aware long video event understanding. The framework integrates heterogeneous sensor streams, including visual, LiDAR, and radar data, into a unified token representation that preserves spatial topology and temporal continuity. A hierarchical attention mechanism is introduced to process video segments of arbitrary length while maintaining computational tractability through windowed self-attention and cross-modal fusion layers. The architecture explicitly encodes trajectory priors from a dedicated motion encoder, enabling the model to reason about agent interactions over extended time horizons. This paper examines the structural trade-offs inherent in such system design, including model depth versus inference latency, modality alignment cost, and the balance between local and global receptive fields. Deployment considerations for edge-cloud hybrid infrastructures are analyzed, with emphasis on sustainability, energy efficiency, and real-time constraints. Robustness to sensor noise and adversarial perturbations is addressed through a discussion of redundancy and failover mechanisms. Fairness and governance issues arising from biased training data and uneven coverage across demographic groups are critically assessed. Policy implications for regulatory compliance, privacy preservation, and public accountability are outlined. The proposed architecture demonstrates how trajectory-aware multi-modal transformers can achieve state-of-the-art performance in tasks such as traffic accident anticipation, pedestrian intention recognition, and congestion pattern evolution, while also highlighting the need for responsible deployment strategies that prioritize both performance and equity.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
