Good User Interfaces for Automated Driving Continues to be Necessary
In the study conducted by Dr Lewis Chuang and Ms. Christiane Glatz from the Max Planck Institute of Biological Cybernetics in Tübingen in collaboration with Dr Stas Krupenia from Scania CV employed EEG methods to clarify why auditory notifications, which were designed for task management in highly automated trucks, resulted in different performance behavior, when deployed in two different test settings: (a) student volunteers in a lab environment, (b) professional truck drivers in a realistic vehicle simulator.
Behavioral data showed that professional drivers were slower and less sensitive in identifying notifications compared to their counterparts. Such differences can be difficult to interpret and frustrates the deployment of implementations from the laboratory to more realistic settings. Our EEG recordings of brain activity reveal that these differences were not due to differences in the detection and recognition of the notifications. Instead, it was due to differences in EEG activity associated with response generation. Thus, the researchers show how measuring brain activity can deliver insights into how notifications are processed, at a finer granularity than can be afforded by behavior alone.
The study was awarded as best paper award at AutomotiveUI 2017 in Oldenburg in September 2017. Their studies were carried out in the Transregional Collaborative Research Centre (SFB-TRR 161), that also connects research groups at the Universities of Stuttgart and Konstanz developing quantitative methods for visual computing.We have asked Lewis more about their latest findings:
1. Why were you interested in this topic?
Novel vehicle interfaces are challenging to implement. It is rare for the results of laboratory user testing to replicate under more realistic settings. This is because many variables can influence user behaviour besides how the brain processes information e.g., age differences between truck drivers and university participants. Novel interfaces for automated trucks will be increasingly designed for how the brain responds and processes information, rather than manual vehicle handling. Thus, it makes sense to measure brain activity directly, as well as behaviour.
2. What should the average person take away from your study?
Although professional truck drivers in a truck simulator were slower than students in a laboratory, in responding to novel auditory notifications, designed for truck operations, this was not because of how their brains responded to the sounds. In other words, these sounds that were designed in an unrealistic setting, we're perceived in the same way, even in a virtual truck cabin by professional truck drivers. Thus, designers can invest their time on addressing other factors that could have caused slower reaction times in truck drivers, if necessary, possibly age differences or the response interface.
3. What is the added value of your study/paper for society?
Using neuroimaging techniques can help us more accurately identify where design problems may or may not exist in safety-critical interfaces e.g., vehicles.
4. Are there any major caveats? What questions still need to be addressed?
We presented notifications every 2 secs in this study. This was necessary to ensure that enough data was collected for meaningful EEG analyses. In the real world, notifications will arise less frequently. Thus, factors such as operational fatigue or surprise could have a strong impact on how these notifications are processed, which we did not address in the current study.
5. Is there anything else you would like to add?
It was a logistical miracle that we were able to coordinate a realistic truck simulator, recruit professional truck drivers, and assemble state-of-the-art EEG equipment all in the same place within 5 days. This was only achievable by having great colleagues and sponsors for expensive loan equipment.