A Recurrent Neural Network Solution for Predicting Driver Intention at Unsignalized Intersections

In this letter, we present a system capable of inferring intent from observed vehicles traversing an unsignalized intersection, a task critical for the safe driving of autonomous vehicles, and beneficial for advanced driver assistance systems. We present a prediction method based on recurrent neural...

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Published inIEEE robotics and automation letters Vol. 3; no. 3; pp. 1759 - 1764
Main Authors Zyner, Alex, Worrall, Stewart, Nebot, Eduardo
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.07.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Advanced driver assistance systems
Algorithms
Automobiles
Datasets
deep learning in robotics and automation
Driver behavior
Drivers
Intelligent transportation systems
Intelligent vehicles
Laser radar
Neural networks
Predictions
Recurrent neural networks
Roads
Roundabouts
Sensors
Task analysis
Tracking systems
Traffic intersections
Urban areas
Online AccessGet full text
ISSN2377-3766
2377-3766
DOI10.1109/LRA.2018.2805314

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Abstract In this letter, we present a system capable of inferring intent from observed vehicles traversing an unsignalized intersection, a task critical for the safe driving of autonomous vehicles, and beneficial for advanced driver assistance systems. We present a prediction method based on recurrent neural networks that takes data from a Lidar-based tracking system similar to those expected in future smart vehicles. The model is validated on a roundabout, a popular style of unsignalized intersection in urban areas. We also present a very large naturalistic dataset recorded in a typical intersection during two days of operation. This comprehensive dataset is used to demonstrate the performance of the algorithm introduced in this letter. The system produces excellent results, giving a significant 1.3-s prediction window before any potential conflict occurs.
AbstractList In this letter, we present a system capable of inferring intent from observed vehicles traversing an unsignalized intersection, a task critical for the safe driving of autonomous vehicles, and beneficial for advanced driver assistance systems. We present a prediction method based on recurrent neural networks that takes data from a Lidar-based tracking system similar to those expected in future smart vehicles. The model is validated on a roundabout, a popular style of unsignalized intersection in urban areas. We also present a very large naturalistic dataset recorded in a typical intersection during two days of operation. This comprehensive dataset is used to demonstrate the performance of the algorithm introduced in this letter. The system produces excellent results, giving a significant 1.3-s prediction window before any potential conflict occurs.
Author Worrall, Stewart
Nebot, Eduardo
Zyner, Alex
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  givenname: Eduardo
  surname: Nebot
  fullname: Nebot, Eduardo
  email: e.nebot@acfr.usyd.edu.au
  organization: Australian Centre for Field Robot., Univ. of Sydney, Sydney, NSW, Australia
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Snippet In this letter, we present a system capable of inferring intent from observed vehicles traversing an unsignalized intersection, a task critical for the safe...
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SubjectTerms Advanced driver assistance systems
Algorithms
Automobiles
Datasets
deep learning in robotics and automation
Driver behavior
Drivers
Intelligent transportation systems
Intelligent vehicles
Laser radar
Neural networks
Predictions
Recurrent neural networks
Roads
Roundabouts
Sensors
Task analysis
Tracking systems
Traffic intersections
Urban areas
Title A Recurrent Neural Network Solution for Predicting Driver Intention at Unsignalized Intersections
URI https://ieeexplore.ieee.org/document/8290702
https://www.proquest.com/docview/2299371161
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