STGT-Gen: Spatio-Temporal Graph Transformer for Multi-Vehicle Traffic Scenario Generation Beijing Institute of Technology

Format:

Book

Conference/Event

Author/Creator:

Qin, Xupeng, author.

Contributor:

Fan, Sizhe

Gong, Jianwei

Lü, Chao

Song, Ze

Wei, Yangyang

Conference Name:

SAE 2025 Intelligent and Connected Vehicles Symposium (2025-09-19 : Shanghai, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

In the testing and validation of autonomous driving systems, scenario-based simulation is crucial to address the high costs and insufficient scene coverage of real-road testing. However, existing simulators rely on handcrafted rules to generate traffic scenarios, failing to capture the complexity of multi-agent interactions and physical rationality in real traffic. This paper proposes STGT-Gen, a data-driven Spatio-Temporal Graph Transformer framework, to generate realistic and diverse multi-vehicle traffic scenarios by integrating spatio-temporal interaction modeling, physical constraints, and high-definition (HD) map information.STGT-Gen adopts an encoder-decoder architecture: The encoder captures temporal dependencies of vehicle trajectories and spatial interactions via a Temporal Transformer and a Spatial Graph Transformer, respectively, while a hierarchical map encoding module fuses lane topologies and traffic rules. The decoder ensures physical feasibility during long-term trajectory generation through the Separating Axis Theorem (SAT) for collision detection and dynamic constraints (acceleration and steering angle limits). Experiments on real-world rounD and highD traffic datasets show that compared with the LSTM baseline model and recent Transformer-based methods, STGT-Gen achieves three-dimensional optimization: the Average Displacement Error (ADE) is reduced by 34.6%40.7% compared to LSTM and by 12.3%18.5% compared to Transformer baselines, the collision rate decreases by 62%, and the lane deviation rate drops by 81%. These results significantly enhance the trajectory accuracy, physical safety, and map compliance of generated scenarios, providing an efficient solution for high-fidelity scenario testing of autonomous driving systems

Notes:

Vendor supplied data

Publisher Number:

2025-01-7316

Access Restriction:

Restricted for use by site license