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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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1370-4621
1573-773X
DOI:10.1007/s11063-022-10802-z