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

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...

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Bibliographic Details
Published in:Energies (Basel) Vol. 14; no. 17; p. 5232
Main Authors: Rákos, Olivér, Bécsi, Tamás, Aradi, Szilárd, Gáspár, Péter
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.09.2021
Subjects:
convolutional variational autoencoder
Neural networks
NGSIM
occupancy grid
Physics
Planning
Vehicles
ISSN:1996-1073, 1996-1073
Online Access:Get full text
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Summary: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.
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content type line 14
ISSN:1996-1073
1996-1073
DOI:10.3390/en14175232