Learning Latent Representation of Freeway Traffic Situations from Occupancy Grid Pictures Using Variational Autoencoder

Olivér Rákos, Tamás Bécsi, Szilárd Aradi and Péter Gáspár
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Olivér Rákos: Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics, 1111 Budapest, Hungary
Tamás Bécsi: Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics, 1111 Budapest, Hungary
Szilárd Aradi: Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics, 1111 Budapest, Hungary
Péter Gáspár: Systems and Control Laboratory, Institute for Computer Science and Control, 1111 Budapest, Hungary

Energies, 2021, vol. 14, issue 17, 1-15

Abstract: Several problems can be encountered in the design of autonomous vehicles. Their software is organized into three main layers: perception, planning, and actuation. The planning layer deals with the sort and long-term situation prediction, which are crucial for intelligent vehicles. Whatever method is used to make forecasts, vehicles’ dynamic environment must be processed for accurate long-term forecasting. In the present article, a method is proposed to preprocess the dynamic environment in a freeway traffic situation. The method uses the structured data of surrounding vehicles and transforms it to an occupancy grid which a Convolutional Variational Autoencoder (CVAE) processes. The grids (2048 pixels) are compressed to a 64-dimensional latent vector by the encoder and reconstructed by the decoder. The output pixel intensities are interpreted as probabilities of the corresponding field is occupied by a vehicle. This method’s benefit is to preprocess the structured data of the dynamic environment and represent it in a lower-dimensional vector that can be used in any further tasks built on it. This representation is not handmade or heuristic but extracted from the database patterns in an unsupervised way.

Keywords: NGSIM; occupancy grid; convolutional variational autoencoder (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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