Deep generative models for vehicle speed trajectories

• Published in: Applied stochastic models in business and industry Vol. 39; no. 5; pp. 701 - 719
• Main Authors: Behnia, Farnaz, Karbowski, Dominik, Sokolov, Vadim
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.09.2023; Wiley Subscription Services, Inc; Wiley
• Subjects: generative adversarial networks; Markov chains; Metropolitan areas; Predictive control; Production methods; speed profile; time series; Traffic speed; Trajectories; vehicle trajectory
• ISSN: 1524-1904; 1526-4025
• DOI: 10.1002/asmb.2816

Generating realistic vehicle speed trajectories is a crucial component in evaluating vehicle fuel economy and in predictive control of self‐driving cars. Traditional generative models rely on Markov chain methods and can produce accurate synthetic trajectories but are subject to the curse of dimensionality. They do not allow to include conditional input variables into the generation process. In this paper, we show how extensions to deep generative models allow accurate and scalable generation. Proposed architectures involve recurrent and feed‐forward layers and are trained using adversarial techniques. Our models are shown to perform well on generating vehicle trajectories using a model trained on GPS data from Chicago metropolitan area.