Identification of An “Appropriate” Drowsy Driver Detection Interface for Commercial Vehicle Operations

Author:

Ayoob Ellen M.1,Steinfeld Aaron2,Grace Richard3

Affiliation:

1. Human Computer Interaction Institute Carnegie Mellon University Pittsburgh, Pa

2. Robotics Institute Carnegie Mellon University Pittsburgh, Pa

3. Attention Technologies Pittsburgh, Pa

Abstract

Considerable progress has been made in measuring drowsiness and understanding its effects upon human performance in the laboratory and in simulated and operational driving conditions. This work builds upon previous research and identifies an appropriate design for a drowsy driver detection interface. A participatory design process was used that included both design experts and drivers in separate focus groups. One expert activity, evaluations of candidate interaction flow models, and two driver activities, critical incident interviews and a design exercise, are described here. The conflict that arose between the drivers' desires and the desires of the scientific community is that the drivers viewed the system as a loyal servant that would alert the driver when he became drowsy, while the scientific community viewed the system as a trusted advisor that would encourage the driver to stop and rest. The final design has many features to address both of these views.

Publisher

SAGE Publications

Subject

General Medicine,General Chemistry

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. First-Stage Evaluation of a Prototype Driver Distraction Human-Machine-Interface Warning System;Journal of Road Safety;2021-10-01

2. A Study on Tiredness Assessment by Using Eye Blink Detection;Jurnal Kejuruteraan;2019-10-31

3. Analysis of Traffic Related Factors and Vehicle Environment in Monitoring Driver’s Driveability;International Journal of Intelligent Transportation Systems Research;2019-08-17

4. Alertness Monitoring System for Vehicle Drivers using Physiological Signals;Advances in Civil and Industrial Engineering;2016

5. A Vision-Based System for Monitoring the Loss of Attention in Automotive Drivers;IEEE Transactions on Intelligent Transportation Systems;2013-12

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