Self-Supervised Representation Learning for Large-Scale Vehicle Motion Prediction
Keywords:
self-supervised learning; vehicle motion prediction; large-scale systems; representation learning; autonomous driving; data curation; robustness; governance; sustainabilityAbstract
Large-scale vehicle motion prediction has become a central problem for autonomous driving, intelligent transportation, and safety-critical urban mobility. The field increasingly depends on representation learning that can exploit massive unlabeled or weakly labeled trajectory corpora without requiring exhaustive manual annotation. This paper examines self-supervised representation learning as a system-level strategy for vehicle motion prediction at scale. It argues that the principal challenges are not only model architecture but also data infrastructure, curation, evaluation, deployment, governance, robustness, fairness, and long-term sustainability. Beginning from conceptual foundations in transformer architectures, masked modeling, contrastive learning, and redundancy reduction, the discussion develops an integrated view of how self-supervised objectives can structure multi-agent interaction, map context, temporal dependency, and uncertainty. The paper then analyzes architectural trade-offs among centralized, hierarchical, and scene-centric representations, and it considers how data pipelines, benchmark design, and density-balanced evaluation shape model behavior. Particular attention is given to the tension between generalization and operational reliability, because motion prediction systems must perform under distribution shift, sensor degradation, edge cases, and heterogeneous traffic norms. The paper further assesses deployment constraints involving latency, memory, fleet-scale inference, and continuous learning, as well as sustainability concerns related to compute, data storage, and lifecycle maintenance. Fairness and policy implications are treated as first-order engineering concerns rather than external constraints, especially where predictive systems allocate risk across pedestrians, cyclists, and underserved communities. Through case illustrations from autonomous driving datasets and cross-domain comparisons with language and vision pretraining, the paper outlines a research agenda for self-supervised motion prediction that is technically rigorous, institutionally accountable, and socially legible. The conclusion emphasizes that scalable representation learning must be embedded within broader socio-technical infrastructures if it is to support dependable vehicle motion prediction.
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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.
