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Report

Teaser, summary, work performed and final results

Periodic Reporting for period 1 - Detect and React (Distracted drivers in autonomous cars: Do drivers safely detect and react to unexpected warning signals?)

Teaser

Summary

\"Self-driving, or autonomous, cars promise sustained individual mobility while decreasing the risk of accidents due to human error. Their technological development tops the agendas of European governments and car manufacturers. With technology taking centre stage, it is easy to overlook the human driver. However, this would be a grave mistake, as autonomous vehicles still require human action. Specifically, the next frontier in autonomous vehicles is a car that controls the vehicle (e.g., steering, acceleration) and monitors the traffic environment, but that can signal a request for human intervention at any time. Little is known about how drivers detect and react to such unexpected signals. Research on lower levels of automation (e.g., cars with cruise control) suggests that reaction times to unexpected signals tend to be slow. It is, however, not clear what causes this slowdown, especially at higher levels of automation. Is this a failure to detect the signal, or a failure to react timely? My research has investigated under what conditions people (fail to) detect and react to unexpected audio intervention signals. To this end, I measured detection using cognitive neuroscience techniques (Event Related Brain Potentials) and reaction using reaction time in a driving simulator.

The results of our study demonstrated that driver\'s ability to detect alerts is reduced under automated driving conditions, especially when participants are passively (and less attentively) listening to the sounds. This is a challenge for current (semi-) automated vehicles, which rely on auditory alerts to warn drivers to take-over control of the car from the automated vehicle. Given the reduced ability to detect such alerts, drivers might miss these warnings.

Based on our results, I have further studied the effecive use of early warnings to warn a driver (also referred to as \"\"pre-alerts\"\"). The results demonstrated that such early warnings lead to better reactions to alerts by drivers in driving studies.

I also published a new framework (building on Hidden Markov Models) to consider human confusion when interacting with automated systems, and to guide further scientific dialogue on these matters.\"

Work performed

\"We conducted various studies that investigated how detection and reaction is affected by the level of automation and the level of distraction of the driver. We found that driver\'s ability to detect alerts is reduced under automated driving conditions, especially when participants are passively (and less attentively) listening to the sounds. This has lead to our proposal to use early warnings to warn a driver (also referred to as \"\"pre-alerts\"\"), which were demonstrated to lead to better reactions to alerts. I also proposed a new framework (building on Hidden Markov Models) to consider human confusion when interacting with automated systems.
The results have provided fundamental new insights about human behaviour in higher-level automated vehicles before these systems are released on the road. This knowledge will inform the design of safer technology and better policy for autonomous vehicles.

The work has been published in multiple scientific publications. I have also co-organized a workshop on human behavior in automated driving.

Apart from that, I have given presentations at international scientific conferences, workshops, and international research labs for in total over 1,000 people.
I have also given various public lectures on automation, artificial intelligence, and automated vehicles, for in total over 800 people. My work was also mentioned in various national and international news items.
Finally, I have started a public youtube channel in which I communicate the results from my scientific efforts through video.\"

Final results

Our studies used a unique method to measure the brain\'s ability to detect auditory alerts in automated driving settings. Preceding behavioral studies has showed that people respond slowly to auditory alerts. Our work demonstrates that such delays might be caused by a reduction in the brain\'s ability to detect auditory alerts. We also proposed novel methods (pre-alerts) to mitigate this problem.
The impact of this work is that it demonstrates that auditory alerts on their own are not sufficient per se to warn a driver of critical events. This is an important finding, as current systems rely heavily on auditory alerts.