Fuzzy Fusion for Collision Avoidance

  • Mahdi Rezaei
  • Reinhard Klette
Part of the Computational Imaging and Vision book series (CIVI, volume 45)


In this chapter we discuss how to assess the risk level in a given driving scenario based on the eight possible inputs: driver’s direction of attention (yaw, roll, pitch), signs of fatigue or drowsiness (yawning, head nodding, eye closure), and from road situations (distance, and the angle of the detected vehicles to the ego-vehicle). Using a fuzzy-logic inference system, we develop an integrated solution to fuse, to interpret, and to process all of the above information. The ultimate goal is to prevent a traffic accident by fusing all the existing “in-out” data from inside the car cockpit and outside on the road. We aim to warn the driver in case of high-risk driving conditions and to prevent an imminent crash.


Membership Function Fuzzy Inference System Fuzzy Logic System Lead Vehicle Driving Scenario 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mahdi Rezaei
    • 1
  • Reinhard Klette
    • 2
  1. 1.Department of Computer EngineeringQazvin Islamic Azad UniversityQazvinIran
  2. 2.Department of Electrical and Electronic EngineeringAuckland University of TechnologyAucklandNew Zealand

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