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Assessment and Profiling of Driving Style and Skills

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User Experience Design in the Era of Automated Driving

Part of the book series: Studies in Computational Intelligence ((SCI,volume 980))

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Abstract

Although all drivers have in common the task of safe driving, as individuals, they are all unique. Each driver’s uniqueness depends on his or her driving skills and behavior, learning experiences, collision history, stress management, cultural environment, and consequently different expectations and judgments. The fact that these antecedents can be divided into more (e.g., culture, personality) as well as less stable driver characteristics (e.g., fatigue, stress), adds to the complexity of predicting driving performance and safety. In this chapter we report on methods and different approaches for the segmentation of driver types according to common response strategies in driving and the corresponding driver behavior model. The described exploratory data analysis reveals relationships between psychophysical, biometrical and neuropsychological factors and driving characteristics. We also explain how individual drivers react in different situations, and how stress and high cognitive workload impact driving performance as well as how this knowledge enables the profiling of drivers into different clusters based on their driving skills and styles.

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Authors and Affiliations

  1. Department of Psychology, Faculty of Arts, University of Maribor, Maribor, Slovenia

    Sara Tement, Bojan Musil, Nejc Plohl & Marina Horvat

  2. ICT department, Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia

    Kristina Stojmenova & Jaka Sodnik

Authors
  1. Sara Tement
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  2. Bojan Musil
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  3. Nejc Plohl
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  4. Marina Horvat
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  5. Kristina Stojmenova
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  6. Jaka Sodnik
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Corresponding author

Correspondence to Jaka Sodnik .

Editor information

Editors and Affiliations

  1. CARISSMA Institute of Automated Driving (C-IAD), Technische Hochschule Ingolstadt, Ingolstadt, Germany

    Andreas Riener

  2. Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA

    Myounghoon Jeon

  3. Systems and Software Research, Autonomous Driving Research Lab, Intel Labs, Portland, OR, USA

    Ignacio Alvarez

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Tement, S., Musil, B., Plohl, N., Horvat, M., Stojmenova, K., Sodnik, J. (2022). Assessment and Profiling of Driving Style and Skills. In: Riener, A., Jeon, M., Alvarez, I. (eds) User Experience Design in the Era of Automated Driving. Studies in Computational Intelligence, vol 980. Springer, Cham. https://doi.org/10.1007/978-3-030-77726-5_7

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