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Deep Generative Models for Vehicle Speed Trajectories

arXiv 2112.08361 ↗First posted 14 Dec 2021cs.LG stat.APcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 26 of its 69 references that are in the map. First posted on arXiv 14 Dec 2021.

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References · 26

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14 more
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