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

Joobin Jin; Seokjun Hong; Gyeongseon Baek; Yeeun Kim; Byeongjoon Noh

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arXiv · 2507.17418

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

Joobin Jin·Seokjun Hong·Gyeongseon Baek·Yeeun Kim·Byeongjoon Noh

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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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Joobin Jin, Seokjun Hong, Gyeongseon Baek, Yeeun Kim, Byeongjoon Noh. 2025-07-23. Ctx2TrajGen: Traffic Context-Aware Microscale Vehicle Trajectories using Generative Adversarial Imitation Learning. https://arxiv.org/abs/2507.17418

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