Journal
ROBOTICS AND AUTONOMOUS SYSTEMS
Volume 115, Issue -, Pages 162-173Publisher
ELSEVIER
DOI: 10.1016/j.robot.2019.02.007
Keywords
Manoeuvre anticipation; ADAS; Deep learning; LSTM; Recurrent neural networks; Domain adaptation
Funding
- program of the Excellence Department on Robotics and Artificial Intelligence
- National Ministry for Education and Research (MIUR)
- Scuola Superiore Sant'Anna
- TeCIP institute
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Through deep learning and computer vision techniques, driving manoeuvres can be predicted accurately a few seconds in advance. Even though adapting a learned model to new drivers and different vehicles is key for robust driver-assistance systems, this problem has received little attention so far. This work proposes to tackle this challenge through domain adaptation, a technique closely related to transfer learning. A proof of concept for the application of a Domain-Adversarial Recurrent Neural Network (DA-RNN) to multi-modal time series driving data is presented, in which domain-invariant features are learned by maximising the loss of an auxiliary domain classifier. Our implementation is evaluated using a leave-one-driver-out approach on individual drivers from the Brain4Cars dataset, as well as using a new dataset acquired through driving simulations, yielding an average increase in performance of 30% and 114% respectively compared to no adaptation. We also show the importance of fine-tuning sections of the network to optimise the extraction of domain-independent features. The results demonstrate the applicability of the approach to driver-assistance systems as well as training and simulation environments. (C) 2019 Elsevier B.V. All rights reserved.
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