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| Title | Urban Vehicle Trajectory Generation Based On Generative Adversarial Imitation Learning |
|---|---|
| ID_Doc | 60254 |
| Authors | Wang M.; Cui J.; Wong Y.W.; Chang Y.; Wu L.; Jin J. |
| Year | 2024 |
| Published | IEEE Transactions on Vehicular Technology, 73, 12 |
| DOI | http://dx.doi.org/10.1109/TVT.2024.3437412 |
| Abstract | With the rapid development of smart cities, the collection of vehicle trajectory data through sensors has increased significantly. While many studies have utilized calibrated physical car-following models (CFM) and machine learning techniques for trajectory prediction, these approaches often falter in complex, dynamic traffic scenarios. Addressing this gap, this paper introduces PS-TrajGAIL, a generative adversarial imitation learning framework tailored for urban vehicle trajectory generation. Contrary to conventional discriminative models, PS-TrajGAIL employs a generative model to capture the inherent distribution of urban vehicle trajectories. This framework models the tasks of trajectory generation as a partially observable Markov decision process based on imitation learning. PS-TrajGAIL's architecture features a generator, which simulates vehicle behavior to produce synthetic trajectories, and a discriminator that distinguishes between authentic and generated trajectories. In addition, the driving policy within the generator is fine-tuned using the Trust Region Policy Optimization (TRPO) algorithm, ensuring safety in vehicle driving. Experimental evaluations on both synthetic and real-world datasets highlight that PS-TrajGAIL notably surpasses existing baselines and state-of-the-art approaches in trajectory generation. © 1967-2012 IEEE. |
| Author Keywords | Generative adversarial learning; imitation learning; traffic simulation; trajectory data generation; urban vehicle trajectories |
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