Generative Foundation Models for Multi-Agent Trajectory Prediction and Scene Reconstruction in Autonomous Driving
Keywords:
autonomous driving, generative foundation models, trajectory prediction, scene reconstruction, world models, multi-agent systems, robustness, AI governanceAbstract
The increasing complexity of urban driving environments has motivated a shift from narrowly supervised predictive modules toward large-scale generative foundation models that learn multimodal representations of traffic scenes. This paper examines the systems-level implications of applying generative foundation models to multi-agent trajectory prediction and scene reconstruction in autonomous driving. Rather than focusing on a single algorithmic contribution, the analysis addresses architectural structuring, data infrastructure, training strategies, evaluation maturity, robustness, fairness, deployment sustainability, and governance. The discussion emphasizes that generative models can serve as unified predictive infrastructures by jointly capturing agent interactions, road geometry, sensor observations, and environmental uncertainty. However, such unification introduces structural trade-offs between representational flexibility and real-time inference cost, between generative diversity and safety-critical determinism, and between large-scale pretraining and domain-specific verification. The paper further considers how foundation models alter the design of motion forecasting stacks, scene memory systems, simulation environments, and regulatory assurance processes. Through a cross-domain synthesis of methods from language modeling, image synthesis, neural rendering, and autonomous systems, the article argues that generative foundation models should be understood not merely as improved predictors but as sociotechnical infrastructures that reshape operational requirements, risk management, and policy frameworks. The analysis concludes by outlining future research directions in calibrated generation, resource-efficient deployment, and certification-oriented evaluation for safety-critical autonomous systems.
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