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

Energies

Olivér Rákos¹  Szilárd Aradi¹  Tamás Bécsi¹  Péter Gáspár² 

[1] Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics, 1111 Budapest, Hungary;Systems and Control Laboratory, Institute for Computer Science and Control, 1111 Budapest, Hungary;

关键词: NGSIM;    occupancy grid;    convolutional variational autoencoder;   

DOI  :  10.3390/en14175232

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown