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Driving Performance and Technology Acceptance Evaluation in Real Traffic of a Smartphone-Based Driver Assistance System

Author

Listed:
  • Gheorghe-Daniel Voinea

    (Department of Automotive and Transport Engineering, Transilvania University of Brasov, 500036 Brasov, Romania)

  • Cristian Cezar Postelnicu

    (Department of Automotive and Transport Engineering, Transilvania University of Brasov, 500036 Brasov, Romania)

  • Mihai Duguleana

    (Department of Automotive and Transport Engineering, Transilvania University of Brasov, 500036 Brasov, Romania)

  • Gheorghe-Leonte Mogan

    (Department of Automotive and Transport Engineering, Transilvania University of Brasov, 500036 Brasov, Romania)

  • Radu Socianu

    (General Magic Technology, 500090 Brasov, Romania)

Abstract

Technological advances are changing every aspect of our lives, from the way we work, to how we learn and communicate. Advanced driver assistance systems (ADAS) have seen an increased interest due to the potential of ensuring a safer environment for all road users. This study investigates the use of a smartphone-based ADAS in terms of driving performance and driver acceptance, with the aim of improving road safety. The mobile application uses both cameras of a smartphone to monitor the traffic scene and the driver’s head orientation, and offers an intuitive user interface that can display information in a standard mode or in augmented reality (AR). A real traffic experiment consisting of two driving conditions (a baseline scenario and an ADAS scenario), was conducted in Brasov, Romania. Objective and subjective data were recorded from twenty-four participants with a valid driver’s license. Results showed that the use of the ADAS influences the driving performance, as most of them adopted an increased time headway and lower mean speeds. The technology acceptance model (TAM) questionnaire was used to assess the users’ acceptance of the proposed driver assistance system. The results showed significant interrelations between acceptance factors, while the hierarchical regression analysis indicates that the variance of behavioral intention (BI) can be predicted by attitude toward behavior.

Suggested Citation

  • Gheorghe-Daniel Voinea & Cristian Cezar Postelnicu & Mihai Duguleana & Gheorghe-Leonte Mogan & Radu Socianu, 2020. "Driving Performance and Technology Acceptance Evaluation in Real Traffic of a Smartphone-Based Driver Assistance System," IJERPH, MDPI, vol. 17(19), pages 1-20, September.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:19:p:7098-:d:420764
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    References listed on IDEAS

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    1. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    2. Rui Fu & Yunxing Chen & Qingjin Xu & Yuxi Guo & Wei Yuan, 2020. "A Comparative Study of Accident Risk Related to Speech-Based and Handheld Texting during a Sudden Braking Event in Urban Road Environments," IJERPH, MDPI, vol. 17(16), pages 1-18, August.
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    1. Yunshun Zhang & Qishuai Xie & Minglei Gao & Yuchen Guo, 2023. "The Impact of In-Vehicle Traffic Lights on Driving Characteristics in the Presence of Obstructed Line-of-Sight," Sustainability, MDPI, vol. 15(10), pages 1-26, May.
    2. Ma. Janice J. Gumasing & Frances Jeann Charlize S. Bermejo & Keisha Taranee C. Elpedes & Lady Fatima E. Gonzales & Aaron Chastine V. Villajin, 2023. "Antecedents of Waze Mobile Application Usage as a Solution for Sustainable Traffic Management among Gen Z," Sustainability, MDPI, vol. 15(13), pages 1-18, June.

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