Abstract
The evaluation of car drivers’ stress condition is gaining interest as research on Autonomous Driving Systems (ADS) progresses. The analysis of the stress response can be used to assess the acceptability of ADS and to compare the driving styles of different autonomous drive algorithms. In this contribution, we present a system based on the analysis of the Electrodermal Activity Skin Potential Response (SPR) signal, aimed to reveal the driver’s stress induced by different driving situations. We reduce motion artifacts by processing two SPR signals, recorded from the hands of the subjects, and outputting a single clean SPR signal. Statistical features of signal blocks are sent to a Supervised Learning Algorithm, which classifies between stress and normal driving (non-stress) conditions. We present the results obtained from an experiment using a professional driving simulator, where a group of people is asked to undergo manual and autonomous driving on a highway, facing some unexpected events meant to generate stress. The results of our experiment show that the subjects generally appear more stressed during manual driving, indicating that the autonomous drive can possibly be well received by the public. During autonomous driving, however, significant peaks of the SPR signal are evident during unexpected events. By examining the electrocardiogram signal, the average heart rate is generally higher in the manual case compared to the autonomous case. This further supports our previous findings, even if it may be due, in part, to the physical activity involved in manual driving.
Highlights
Sophisticated assisted and autonomous vehicles are becoming readily available.Autonomous driving is categorized in relation to the human involvement during driving
In order to do that, since every subject has a different electrodermal activity signal, we compute the Root Mean Square (RMS) value of the Skin Potential Response (SPR) signal for each subject considering only the time intervals corresponding to the different tasks, and we normalize this value by dividing it by the RMS of the SPR signal computed on the whole driving track
This is certainly due in part to the physical activity involved in manual driving, which, makes it difficult to directly compare the two scenarios using Heart Rate (HR) information
Summary
Autonomous driving is categorized in relation to the human involvement during driving. It varies from level zero, where the driver controls everything in the car and there is no automation, up to level five, where there is full automation of the control, and the expectation is that the performance of the autonomous system matches the one of a human driver. The impact of assisted or autonomous cars on humans (either drivers or passengers) is not predictable. In this scenario, there is an increasing interest in the technologies that provide real-time monitoring of the driver psycho-physiological reactions to vehicle dynamics under direct or automated control. Among the aspects that can be examined, the study of the human’s robotic acceptance, the evaluation of the difference between self and assisted/autonomous driving in the same scenarios, and the analysis of the impact of on-the-fly setup variations of the vehicle, when it is autonomously driven, are of paramount interest
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