Conditional Variational Autoencoder Networks for Autonomous Vehicle Path Prediction

Mobility of autonomous vehicles is a challenging task to implement. Under the given traffic circumstances, all agent vehicles’ behavior is to be understood and their paths for a short future needs to be predicted to decide upon the maneuver of the ego vehicle. We explore variational autoencoder netw...

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Veröffentlicht in:	Neural processing letters Jg. 54; H. 5; S. 3965 - 3978
Hauptverfasser:	Jagadish, D. N., Chauhan, Arun, Mahto, Lakshman
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.10.2022 Springer Nature B.V
Schlagworte:	Artificial Intelligence Complex Systems Computational Intelligence Computer Science Networks Neural networks Path predictors Semantics Sensors Traffic control Traffic flow Vehicles Conditional variational autoencoder Deep learning Autonomous vehicle path
ISSN:	1370-4621, 1573-773X
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Abstract	Mobility of autonomous vehicles is a challenging task to implement. Under the given traffic circumstances, all agent vehicles’ behavior is to be understood and their paths for a short future needs to be predicted to decide upon the maneuver of the ego vehicle. We explore variational autoencoder networks to get multimodal predictions of agents. In our work, we condition the network on past trajectories of agents and traffic scenes as well. The latent space representation of traffic scenes is achieved by using another variational autoencoder network. The proposed networks are trained for varied prediction horizon. The performance of a network is compared with other networks trained on the dataset.
AbstractList	Mobility of autonomous vehicles is a challenging task to implement. Under the given traffic circumstances, all agent vehicles’ behavior is to be understood and their paths for a short future needs to be predicted to decide upon the maneuver of the ego vehicle. We explore variational autoencoder networks to get multimodal predictions of agents. In our work, we condition the network on past trajectories of agents and traffic scenes as well. The latent space representation of traffic scenes is achieved by using another variational autoencoder network. The proposed networks are trained for varied prediction horizon. The performance of a network is compared with other networks trained on the dataset.
Author	Chauhan, Arun Mahto, Lakshman Jagadish, D. N.
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SubjectTerms	Artificial Intelligence Complex Systems Computational Intelligence Computer Science Networks Neural networks Path predictors Semantics Sensors Traffic control Traffic flow Vehicles
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Title	Conditional Variational Autoencoder Networks for Autonomous Vehicle Path Prediction
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