Ctx2TrajGen: Traffic Context-Aware Microscale Vehicle Trajectories using Generative Adversarial Imitation Learning

• URL: http://arxiv.org/abs/2507.17418v1
• Date: Wed, 23 Jul 2025 11:21:27 GMT
• Title: Ctx2TrajGen: Traffic Context-Aware Microscale Vehicle Trajectories using Generative Adversarial Imitation Learning
• Authors: Joobin Jin, Seokjun Hong, Gyeongseon Baek, Yeeun Kim, Byeongjoon Noh,
• Abstract summary: Ctx2TrajGen is a context-aware trajectory generation framework that synthesizes realistic urban driving behaviors using GAIL.<n>By explicitly conditioning on surrounding vehicles and road geometry, Ctx2TrajGen generates interaction-aware trajectories aligned with real-world context.
• Score: 1.2023648183416153
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Precise modeling of microscopic vehicle trajectories is critical for traffic behavior analysis and autonomous driving systems. We propose Ctx2TrajGen, a context-aware trajectory generation framework that synthesizes realistic urban driving behaviors using GAIL. Leveraging PPO and WGAN-GP, our model addresses nonlinear interdependencies and training instability inherent in microscopic settings. By explicitly conditioning on surrounding vehicles and road geometry, Ctx2TrajGen generates interaction-aware trajectories aligned with real-world context. Experiments on the drone-captured DRIFT dataset demonstrate superior performance over existing methods in terms of realism, behavioral diversity, and contextual fidelity, offering a robust solution to data scarcity and domain shift without simulation.

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