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La popularité est une évaluation communautaire de chaque livre calculée sur une échelle de 0 à 100.

Cette évaluation repose sur une agrégation de diverses quantités qui reflètent la vie de chaque livre au sein de la communauté des lecteurs. Cette vie s'exprime en fréquence de lecture, nombre d'étagères publiques et privées dans lesquelles le livre est présent.

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Advanced Driver Intention Inference: Theory and Design describes one of the most important function for future ADAS, namely, the driver intention inference. The book contains the state-of-art knowledge on the construction of driver intention inference system, providing a better understanding on how the human driver intention mechanism will contribute to a more naturalistic on-board decision system for automated vehicles.

Advanced Driver Intention Inference

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Theory and Design

Advanced Driver Intention Inference: Theory and Design describes one of the most important function for future ADAS, namely, the driver intention inference. The book contains the state-of-art knowledge on the construction of driver intention inference system, providing a better understanding on how

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Auteur(s): Xing, Yang • Lv, Chen • Cao, Dongpu

Editeur: Elsevier Science

Année de Publication: 2020

pages: 536

Langue: Anglais

ISBN: 978-0-12-819113-2

eISBN: 978-0-12-819114-9

Advanced Driver Intention Inference: Theory and Design describes one of the most important function for future ADAS, namely, the driver intention inference. The book contains the state-of-art knowledge on the construction of driver intention inference system, providing a better understanding on how

Advanced Driver Intention Inference: Theory and Design describes one of the most important function for future ADAS, namely, the driver intention inference. The book contains the state-of-art knowledge on the construction of driver intention inference system, providing a better understanding on how the human driver intention mechanism will contribute to a more naturalistic on-board decision system for automated vehicles.

  • Features examples of using machine learning/deep learning to build industry products
  • Depicts future trends for driver behavior detection and driver intention inference
  • Discuss traffic context perception techniques that predict driver intentions such as Lidar and GPS

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