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Background
Automated driving is often proposed as a solution to human errors. However, fully automated driving has not yet reached the point where it can be implemented in real traffic. This study focused on adaptively allocating steering control either to the driver or to an automated pilot based on momentary driver distraction measured from an eye tracker.
Methods
Participants (N = 31) steered a simulated vehicle with a fixed speed, and at specific moments were required to perform a visual secondary task (i.e., changing a CD). Three conditions were tested: (1) Manual driving (Manual), in which participants steered themselves. (2) An automated backup (Backup) condition, consisting of manual steering except during periods of visual distraction, where the driver was backed up by automated steering. (3) A forced manual drive (Forced) condition, consisting of automated steering except during periods of visual distraction, where the driver was forced into manual steering. In all three conditions, the speed of the vehicle was automatically kept at 70 km/h throughout the drive.
Results
The Backup condition showed a decrease in mean and maximum absolute lateral error compared to the Manual condition. The Backup condition also showed the lowest self-reported workload ratings and yielded a higher acceptance rating than the Forced condition. The Forced condition showed a higher maximum absolute lateral error than the Backup condition.
Discussion
In conclusion, the Backup condition was well accepted, and significantly improved performance when compared to the Manual and Forced conditions. Future research could use a higher level of simulator fidelity and a higher-quality eye-tracker.
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Background
Automated driving is often proposed as a solution to human errors. However, fully automated driving has not yet reached the point where it can be implemented in real traffic. This study focused on adaptively allocating steering control either to the driver or to an automated pilot based on momentary driver distraction measured from an eye tracker.
Methods
Participants (N = 31) steered a simulated vehicle with a fixed speed, and at specific moments were required to perform a visual secondary task (i.e., changing a CD). Three conditions were tested: (1) Manual driving (Manual), in which participants steered themselves. (2) An automated backup (Backup) condition, consisting of manual steering except during periods of visual distraction, where the driver was backed up by automated steering. (3) A forced manual drive (Forced) condition, consisting of automated steering except during periods of visual distraction, where the driver was forced into manual steering. In all three conditions, the speed of the vehicle was automatically kept at 70 km/h throughout the drive.
Results
The Backup condition showed a decrease in mean and maximum absolute lateral error compared to the Manual condition. The Backup condition also showed the lowest self-reported workload ratings and yielded a higher acceptance rating than the Forced condition. The Forced condition showed a higher maximum absolute lateral error than the Backup condition.
Discussion
In conclusion, the Backup condition was well accepted, and significantly improved performance when compared to the Manual and Forced conditions. Future research could use a higher level of simulator fidelity and a higher-quality eye-tracker.
Automated driving vehicles of the future will most likely include multiple modes and levels of operation and thus include various transitions of control (ToC) between human and machine. Traditional activation devices (e.g., knobs, switches, buttons, and touchscreens) may be confused by operators among other system setting manipulators and also susceptible to inappropriate usage. Non-intrusive eye-tracking measures may assess driver states (i.e., distraction, drowsiness, and cognitive overload) automatically to trigger manual-to-automation ToC and serve as a driver readiness verification during automation-to-manual ToC. Our integrated driver state monitor is overviewed here within the scope of this brief system description/demonstration paper. It combines gaze position, gaze variability, eyelid opening, as well as external environmental complexity from the driving scene to facilitate ToC in automated driving. As both driver facing and forward facing cameras become increasingly commonplace and even legally mandated within various automated driving vehicles, our integrated system helps inform relevant future research and development towards improved human-computer interaction and driving safety.
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Automated driving vehicles of the future will most likely include multiple modes and levels of operation and thus include various transitions of control (ToC) between human and machine. Traditional activation devices (e.g., knobs, switches, buttons, and touchscreens) may be confused by operators among other system setting manipulators and also susceptible to inappropriate usage. Non-intrusive eye-tracking measures may assess driver states (i.e., distraction, drowsiness, and cognitive overload) automatically to trigger manual-to-automation ToC and serve as a driver readiness verification during automation-to-manual ToC. Our integrated driver state monitor is overviewed here within the scope of this brief system description/demonstration paper. It combines gaze position, gaze variability, eyelid opening, as well as external environmental complexity from the driving scene to facilitate ToC in automated driving. As both driver facing and forward facing cameras become increasingly commonplace and even legally mandated within various automated driving vehicles, our integrated system helps inform relevant future research and development towards improved human-computer interaction and driving safety.
