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 sort long-term situation prediction, which are crucial for intelligent Whatever method used to make forecasts, vehicles’ dynamic environment must processed accurate forecasting. In present article, a proposed preprocess freeway traffic situation. uses structured data surrounding vehicles transforms it an occupancy grid Convolutional Variational Autoencoder (CVAE) processes. grids (2048 pixels) compressed 64-dimensional latent vector by encoder reconstructed decoder. output pixel intensities interpreted as probabilities corresponding field occupied vehicle. This method’s benefit represent lower-dimensional that any further tasks built on it. representation not handmade or heuristic but extracted from database patterns unsupervised way.