Spatiotemporal Prediction of Vehicle Movement Using Artificial Neural Networks
Autoři
Rok
2022
Publikováno
Proceedings of 2022 IEEE Intelligent Vehicles Symposium (IV). Piscataway: IEEE, 2022. p. 734-739. ISSN 1931-0587. ISBN 978-1-6654-8821-1.
Typ
Stať ve sborníku
Anotace
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.