Deep Generative Models for Vehicle Speed Trajectories
This work addresses a domain-specific need for improved trajectory generation in transportation, but it is incremental as it builds on existing deep generative methods.
The paper tackled the problem of generating realistic vehicle speed trajectories for fuel economy evaluation and self-driving car control by extending deep generative models, achieving accurate and scalable generation as demonstrated on GPS data from the Chicago metropolitan area.
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.