Generative Models for Vehicle Speed Trajectories

• Activity Number: 512 - Risk Assessment for Autonomous Vehicles
• Type: Topic Contributed
• Date/Time: Thursday, August 11, 2022 : 8:30 AM to 10:20 AM
• Sponsor: Transportation Statistics Interest Group
• Author(s): Vadim Sokolov*
• Companies: George Mason University
• Keywords: self driving; generation; deep learning; bayesian

Abstract

Generating realistic vehicle speed trajectories is a crucial component in evaluating vehicle fuel economy as well as in predictive control in self-driving cars. Traditional statistical models rely on Markov chain methods and can produce accurate synthetic trajectories but are subject to 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.