Prediction of the movement of all traffic participants is a very important task in autonomous driving. Well-predicted behavior of other cars and actors is crucial for safety. A sequence of bird’s-eye view artificially rasterized frames are used as input to neural networks which are trained to predict the future behavior of the participants. The Lyft Motion Prediction for Autonomous Vehicles dataset is explored and adapted for this task. We developed and applied a novel approach where the prediction problem is viewed as a problem of spatiotemporal prediction and we use methods based on convolutional recurrent neural networks.
Spatiotemporal Prediction of Vehicle Movement Using Artificial Neural Networks
2022-06-05
2866330 byte
Conference paper
Electronic Resource
English
Prediction of vehicle reliability performance using artificial neural networks
Tema Archive | 2008
|Road Traffic Prediction Using Artificial Neural Networks
IEEE | 2018
|Prediction of Runoff Using Artificial Neural Networks
HENRY – Federal Waterways Engineering and Research Institute (BAW) | 2010
|DGPS Correction Prediction Using Artificial Neural Networks
Online Contents | 2007
|Wing-Fuselage Drag Prediction Using Artificial Neural Networks
British Library Conference Proceedings | 2012
|