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Human-like driving behaviour emerges from a risk-based driver model.

Sarvesh Kolekar1, Joost de Winter2, David Abbink2

  • 1Department of Cognitive Robotics, Faculty of Mechanical, Maritime and Materials Engineering (3mE), Delft University of Technology, Mekelweg 2, 2628 CD, Delft, The Netherlands. s.b.kolekar@tudelft.nl.

Nature Communications
|September 30, 2020
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Summary
This summary is machine-generated.

We introduce the Driver's Risk Field (DRF), a novel model for predicting driving behavior across diverse scenarios. This approach unifies various driving situations by managing perceived risk, offering a generalizable principle for human and automated driving.

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Area of Science:

  • Human-Computer Interaction
  • Robotics
  • Transportation Science

Background:

  • Existing driving behavior models are scenario-specific, limiting their applicability.
  • A unified principle for diverse driving scenarios is needed for advanced vehicle systems.

Purpose of the Study:

  • To propose a generalizable model for emergent driving behavior.
  • To investigate an underlying principle that explains human driving across various situations.

Main Methods:

  • Development of the Driver's Risk Field (DRF) model, quantifying perceived risk.
  • Human-in-the-loop and computer simulations to validate the DRF model.
  • Testing the model's predictions against established driving behavior literature.

Main Results:

  • Human-like driving behavior emerged when perceived risk was maintained below a threshold.
  • The DRF model accurately predicted behavior in seven distinct driving scenarios.
  • The model's predictions showed strong concurrence with existing literature.

Conclusions:

  • The Driver's Risk Field offers a scientifically robust and generalizable model for driving behavior.
  • This unified approach has significant implications for the development of automated vehicles.