Data-Centric Optimization of Autonomous Driving Trajectory Prediction via Intelligent Dataset Generation
Keywords:
autonomous driving, trajectory prediction, data-centric artificial intelligence, dataset generation, simulation, robustness, fairness, governance, infrastructure, safetyAbstract
Autonomous driving trajectory prediction has largely advanced through model-centric innovations, yet its real-world reliability remains constrained by data scarcity, imbalance, distribution shift, and weak representativeness. This paper examines data-centric optimization of trajectory prediction through intelligent dataset generation as a system-level strategy rather than a narrow preprocessing technique. It argues that dataset generation must be treated as an infrastructural capability that couples perception, simulation, curation, validation, governance, and deployment feedback. By synthesizing literature on driving datasets, motion forecasting, simulation, data-centric artificial intelligence, and safety governance, the paper identifies structural trade-offs among realism, coverage, cost, fairness, and maintainability. It further analyzes architectural implications for autonomous driving stacks, including how generated data can support rare-event augmentation, interactive scenario stress testing, and continuous lifecycle improvement. Special attention is given to robustness, fairness, sustainability, and policy because generated datasets can either amplify or mitigate long-standing asymmetries in road-user representation. The paper also considers evaluation regimes that move beyond aggregate error metrics toward density balance, scenario validity, and socio-technical accountability. The central claim is that intelligent dataset generation should be governed as a public-interest infrastructure, with transparent provenance, versioning, auditing, and cross-institutional coordination. Without such governance, data-centric gains may remain brittle and difficult to certify. With it, trajectory prediction can become more adaptable, equitable, and deployable across diverse urban and regulatory contexts. The conclusion outlines a research agenda spanning synthetic-real hybrid pipelines, simulation fidelity, evaluation pluralism, and lifecycle sustainability.
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