Deep Generative Models for Vehicle Speed Trajectories

TL;DR

This paper introduces advanced deep generative models with recurrent and feed-forward architectures, trained adversarially, to generate realistic vehicle speed trajectories efficiently, overcoming limitations of traditional methods.

Contribution

It presents novel deep generative model architectures that enable scalable and accurate vehicle trajectory generation with conditional inputs.

Findings

Models generate realistic GPS-based trajectories

Outperform traditional Markov chain methods

Effective in urban vehicle speed prediction

Abstract

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.