Capturing the interactions between moving objects is vital in traffic analysis, sports, and animal behavior, but remains challenging because of subtle spatiotemporal dynamics. This paper introduces HAIMO (Hybrid Analysis of the Interaction of Moving Objects), a conceptual framework that combines knowledge-driven AI for interpretable, symbolic interaction representations with data-driven models trained through self-supervised learning (SSL) on large sets of unlabeled trajectory data. In HAIMO, we propose using transformer architectures to model complex spatiotemporal dependencies while maintaining interpretability through symbolic reasoning. To illustrate the feasibility of this hybrid approach, we present a basic proof-of-concept using elite tennis rallies, where the knowledge-driven component identifies interaction patterns between players and the ball, and we outline how SSL-enhanced transformer models could support and strengthen movement prediction. By bridging symbolic reasoning and self-supervised data-driven learning, HAIMO provides a conceptual foundation for future GeoAI and spatiotemporal analytics, especially in applications where both pattern discovery and explainability are crucial.