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Beyond One Model Fits All: Ensemble Deep Learning for Autonomous Vehicles
Moreover, by exploring the significance of each modality, this study offers a roadmap for future research in autonomous driving, emphasizing the importance of leveraging multiple models to achieve robust performance.
KARNet: Kalman Filter Augmented Recurrent Neural Network for Learning World Models in Autonomous Driving Tasks
For this purpose, deep learning models can be used to learn compact latent representations from a stream of incoming data.
CARNet: A Dynamic Autoencoder for Learning Latent Dynamics in Autonomous Driving Tasks
Thus, there is a need for deep learning models that explicitly consider the temporal dependence of the data in their architecture.
Selective Eye-gaze Augmentation To Enhance Imitation Learning In Atari Games
This paper presents the selective use of eye-gaze information in learning human actions in Atari games.
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