Spatial Temporal Attention based Target Vehicle Trajectory Prediction for Internet of Vehicles

2025-01-01

AI Summary

To address low accuracy and poor robustness in vehicle trajectory prediction under complex traffic conditions, this paper proposes a trajectory prediction model integrating a spatiotemporal attention coordination mechanism. Methodologically, it innovatively achieves deep coupling between Graph Attention Networks (GATs) and Transformers: GATs explicitly model dynamic vehicle-to-vehicle spatial interactions, while Transformers capture long-range temporal dependencies; additionally, a local directed road network structure constraint is incorporated to enhance geometric plausibility and trajectory smoothness. Experiments on the T-Drive and Chengdu taxi datasets demonstrate that the proposed model improves Average Matching Rate (AMR) by 6.38% and 10.55%, respectively, over the standard Transformer baseline, and by 37.45% and 36.06% over the LSTM encoder-decoder baseline. These results significantly advance trajectory inference capabilities in vehicle-infrastructure cooperative systems and intelligent logistics applications.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Large Vision Models
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Forecasting vehicle behavior within complex traffic environments is pivotal within Intelligent Transportation Systems (ITS). Though this technology plays a significant role in alleviating the prevalent operational difficulties in logistics and transportation systems, the precise prediction of vehicle trajectories still poses a substantial challenge. To address this, our study introduces the Spatio Temporal Attention-based methodology for Target Vehicle Trajectory Prediction (STATVTPred). This approach integrates Global Positioning System(GPS) localization technology to track target movement and dynamically predict the vehicle's future path using comprehensive spatio-temporal trajectory data. We map the vehicle trajectory onto a directed graph, after which spatial attributes are extracted via a Graph Attention Networks(GATs). The Transformer technology is employed to yield temporal features from the sequence. These elements are then amalgamated with local road network structure maps to filter and deliver a smooth trajectory sequence, resulting in precise vehicle trajectory prediction.This study validates our proposed STATVTPred method on T-Drive and Chengdu taxi-trajectory datasets. The experimental results demonstrate that STATVTPred achieves 6.38% and 10.55% higher Average Match Rate (AMR) than the Transformer model on the Beijing and Chengdu datasets, respectively. Compared to the LSTM Encoder-Decoder model, STATVTPred boosts AMR by 37.45% and 36.06% on the same datasets. This is expected to establish STATVTPred as a new approach for handling trajectory prediction of targets in logistics and transportation scenarios, thereby enhancing prediction accuracy.

Problem

• Vehicular Network
• Intelligent Transportation System
• Vehicle Trajectory Prediction

Innovation

• STATVTPred
• Graph Attention Networks
• Transformer Technology