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Personalised neural networks for a driver intention prediction: communication as enabler for automated driving

Personalised neural networks for a driver intention prediction: communication as enabler for... AbstractIn everyday traffic, pedestrians rely on informal communication with other road users. In case of automated vehicles, this communication can be replaced by light signals, which need to be learned beforehand. Prior to an extensive introduction of automated vehicles, a learning phase for these light signals can be set up in manual driving with help of a driver intention prediction. Therefore, a three-staged algorithm consisting of a neural network, a random forest and a conditional stage, is implemented. Using this algorithm, a true-positive rate (TPR) of 94.0% for a 5.0% false-positive rate (FPR) can be achieved. To improve this process, a personalization procedure is implemented, using driver-specific behaviours, resulting in TPRs ranging from 91.5 to 96.6% for a FPR of 5.0%. Transfer learning of neural networks improves the prediction accuracy of almost all drivers. In order to introduce the implemented algorithm in today’s traffic, especially the FPR has to be improved considerably. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advanced Optical Technologies de Gruyter

Personalised neural networks for a driver intention prediction: communication as enabler for automated driving

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Publisher
de Gruyter
Copyright
© 2020 Walter de Gruyter GmbH, Berlin/Boston
ISSN
2192-8584
eISSN
2192-8584
DOI
10.1515/aot-2020-0035
Publisher site
See Article on Publisher Site

Abstract

AbstractIn everyday traffic, pedestrians rely on informal communication with other road users. In case of automated vehicles, this communication can be replaced by light signals, which need to be learned beforehand. Prior to an extensive introduction of automated vehicles, a learning phase for these light signals can be set up in manual driving with help of a driver intention prediction. Therefore, a three-staged algorithm consisting of a neural network, a random forest and a conditional stage, is implemented. Using this algorithm, a true-positive rate (TPR) of 94.0% for a 5.0% false-positive rate (FPR) can be achieved. To improve this process, a personalization procedure is implemented, using driver-specific behaviours, resulting in TPRs ranging from 91.5 to 96.6% for a FPR of 5.0%. Transfer learning of neural networks improves the prediction accuracy of almost all drivers. In order to introduce the implemented algorithm in today’s traffic, especially the FPR has to be improved considerably.

Journal

Advanced Optical Technologiesde Gruyter

Published: Dec 16, 2020

Keywords: automotive lighting; learning signals; recurrent neural networks; time sequence processing; vehicle-pedestrian-communication

References