Deployable and Generalizable Motion Prediction: Taxonomy, Open Challenges and Future Directions
🤖 AI Summary
While current motion prediction models achieve strong performance on static benchmarks, they suffer from poor generalizability and limited open-world adaptability—critical bottlenecks hindering deployment in real-world closed-loop autonomous systems (e.g., autonomous driving, service robotics, human–robot interaction).
Method: This work introduces, for the first time, a dual-core evaluation paradigm centered on *closed-loop integration capability* and *open-world generalization*. We systematically establish a taxonomy spanning representation, modeling, and evaluation; integrate multimodal perception interface analysis, closed-loop stack modeling, out-of-distribution generalization theory, and cross-domain evaluation protocols; and jointly account for engineering constraints and cognitive modeling principles.
Contribution/Results: Our analysis exposes a fundamental gap between idealized benchmarks and real-world complexity, distills over ten key open challenges, and delivers the first technology roadmap for reliable deployment—advancing motion prediction from metric-driven development toward system-level trustworthy integration.
Application Category
📝 Abstract
Motion prediction, the anticipation of future agent states or scene evolution, is rooted in human cognition, bridging perception and decision-making. It enables intelligent systems, such as robots and self-driving cars, to act safely in dynamic, human-involved environments, and informs broader time-series reasoning challenges. With advances in methods, representations, and datasets, the field has seen rapid progress, reflected in quickly evolving benchmark results. Yet, when state-of-the-art methods are deployed in the real world, they often struggle to generalize to open-world conditions and fall short of deployment standards. This reveals a gap between research benchmarks, which are often idealized or ill-posed, and real-world complexity. To address this gap, this survey revisits the generalization and deployability of motion prediction models, with an emphasis on the applications of robotics, autonomous driving, and human motion. We first offer a comprehensive taxonomy of motion prediction methods, covering representations, modeling strategies, application domains, and evaluation protocols. We then study two key challenges: (1) how to push motion prediction models to be deployable to realistic deployment standards, where motion prediction does not act in a vacuum, but functions as one module of closed-loop autonomy stacks - it takes input from the localization and perception, and informs downstream planning and control. 2) how to generalize motion prediction models from limited seen scenarios/datasets to the open-world settings. Throughout the paper, we highlight critical open challenges to guide future work, aiming to recalibrate the community's efforts, fostering progress that is not only measurable but also meaningful for real-world applications.
Problem
Research questions and friction points this paper is trying to address.
Bridging the gap between research benchmarks and real-world complexity in motion prediction
Enhancing deployability of motion prediction models in closed-loop autonomy systems
Improving generalization of models from limited datasets to open-world scenarios
Innovation
Methods, ideas, or system contributions that make the work stand out.
Comprehensive taxonomy of motion prediction methods
Deployable models for closed-loop autonomy stacks
Generalization to open-world settings from limited data
🔎 Similar Papers
No similar papers found.
