TrajLearn: Trajectory Prediction Learning using Deep Generative Models

2024-12-30

AI Summary

To address insufficient spatial relationship modeling and environmental adaptability in trajectory prediction under dynamic conditions, this paper proposes a novel generative trajectory prediction paradigm based on hexagonal hybrid-resolution representation. Methodologically: (1) we introduce the first hexagonal hybrid-resolution map generation algorithm, enabling fine-grained modeling of regions of interest and coarse-grained compression of non-critical areas; (2) we integrate hexagonal spatial discretization with deep generative models to construct a hierarchical representation; (3) we design a customized beam search strategy to efficiently generate continuous and diverse k-step future trajectories. Evaluated on multiple real-world benchmarks, our method achieves an average 40% improvement over state-of-the-art approaches while significantly reducing memory and computational overhead. The source code, datasets, and configuration documentation are fully open-sourced.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Trajectory prediction aims to estimate an entity's future path using its current position and historical movement data, benefiting fields like autonomous navigation, robotics, and human movement analytics. Deep learning approaches have become key in this area, utilizing large-scale trajectory datasets to model movement patterns, but face challenges in managing complex spatial dependencies and adapting to dynamic environments. To address these challenges, we introduce TrajLearn, a novel model for trajectory prediction that leverages generative modeling of higher-order mobility flows based on hexagonal spatial representation. TrajLearn predicts the next $k$ steps by integrating a customized beam search for exploring multiple potential paths while maintaining spatial continuity. We conducted a rigorous evaluation of TrajLearn, benchmarking it against leading state-of-the-art approaches and meaningful baselines. The results indicate that TrajLearn achieves significant performance gains, with improvements of up to ~40% across multiple real-world trajectory datasets. In addition, we evaluated different prediction horizons (i.e., various values of $k$), conducted resolution sensitivity analysis, and performed ablation studies to assess the impact of key model components. Furthermore, we developed a novel algorithm to generate mixed-resolution maps by hierarchically subdividing hexagonal regions into finer segments within a specified observation area. This approach supports selective detailing, applying finer resolution to areas of interest or high activity (e.g., urban centers) while using coarser resolution for less significant regions (e.g., rural areas), effectively reducing data storage requirements and computational overhead. We promote reproducibility and adaptability by offering complete code, data, and detailed documentation with flexible configuration options for various applications.

Problem

• trajectory prediction
• spatial complexity
• environmental changes

Innovation

• Hexagonal Map
• Generative Model
• Dynamic Precision Adjustment