Driving Characteristics of Drivers in a State of Low Alertness when an Autonomous System Changes from Autonomous Driving to Manual Driving

2015-01-1407

04/14/2015

Event
SAE 2015 World Congress & Exhibition
Authors Abstract
Content
This study investigated the driving characteristics of drivers when the system changes from autonomous driving to manual driving in the case of low driver alertness. The analysis clarified the difference in driving characteristics between cases of normal and low driver alertness. In the experiments, driver's alertness states varied from completely alert (level 1) to asleep (level 5).
The experimental scenario was that the host vehicle drives along a highway at 27.8 m/s (100km/h) under the control of the autonomous system. The operation of the autonomous system is suspended, and the mode of autonomous driving changes to a mode of manual driving as the other vehicle pulls in front of the host vehicle. The driver then avoids a collision with the other vehicle with him/herself in control. The alertness level of drivers was determined from a previously developed method of examining video of the driver's face and their actions. The alertness level was compared to biological measurements (EEG and heart rate).
In the results, a significant difference was observed in the reaction time and the brake pedal force for brake pedal operation in the low alertness state, compared to the normal alertness state of the driver, and there were instances when the driver was unable to perform an adequate avoidance operation. Therefore, taking into consideration the state of the driver, it is necessary to have a prior warning or an interface that ensures smooth switching, when the system changes from autonomous driving to manual driving.
Meta TagsDetails
DOI
https://doi.org/10.4271/2015-01-1407
Pages
11
Citation
Hirose, T., Kitabayashi, D., and Kubota, H., "Driving Characteristics of Drivers in a State of Low Alertness when an Autonomous System Changes from Autonomous Driving to Manual Driving," SAE Technical Paper 2015-01-1407, 2015, https://doi.org/10.4271/2015-01-1407.
Additional Details
Publisher
Published
Apr 14, 2015
Product Code
2015-01-1407
Content Type
Technical Paper
Language
English