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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| Vydáno v: | Neural processing letters Ročník 54; číslo 5; s. 3965 - 3978 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
Springer US
01.10.2022
Springer Nature B.V |
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| 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. |
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| 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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| Keywords | Conditional variational autoencoder Deep learning Autonomous vehicle path |
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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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