Cross-Modal Trajectory Prediction and Scene Understanding in Autonomous Driving Videos via Hierarchical Motion Encoding
Keywords:
autonomous driving, trajectory prediction, scene understanding, hierarchical motion encoding, cross-modal fusion, system architecture, robustness, fairness, infrastructure deploymentAbstract
Autonomous driving systems rely on accurate trajectory prediction and comprehensive scene understanding to operate safely in dynamic environments. This paper presents a system-level investigation of cross-modal trajectory prediction and scene understanding in autonomous driving videos, focusing on a hierarchical motion encoding paradigm that integrates multiple sensor modalities through structured abstraction layers. We argue that existing approaches often treat motion prediction and scene semantics as separate pipelines, leading to inefficiencies in capturing long-range dependencies and cross-modal interactions. The proposed hierarchical framework decomposes motion information into successive levels of abstraction, from raw sensor data to behavioral intention, enabling the system to jointly reason about spatial configurations, temporal dynamics, and semantic context. We examine architectural trade-offs between early fusion, late fusion, and hierarchical integration, and discuss how each design choice influences computational cost, robustness to sensor failure, and generalization across diverse driving scenarios. The paper also addresses critical infrastructural considerations such as real-time deployment on embedded platforms, energy efficiency, and the governance of prediction uncertainty under safety-critical constraints. Through a cross-domain comparison with established models in video understanding and pedestrian prediction, we illustrate how a hierarchical motion encoding strategy improves long-horizon forecasting and scene comprehension. Furthermore, we explore the implications of such systems for fairness, accountability, and regulatory compliance, particularly in urban environments with heterogeneous traffic participants. The study concludes by proposing future research directions that emphasize modular design, learning from limited labeled data, and the integration of causal reasoning into hierarchical motion representations.
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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.
