No abstract

Nowadays, the transport industry plays an important role in human well-being and the functioning of any settlement. Transport systems are involved in almost all areas of production and services. Therefore, any failure in its operation can lead to significant material costs. One of the most important such systems is "driver -vehicle -road -environment". It should be noted, that the main link in it is "driver". The correctness and duration of decision-making in different road situations depend on the driver`s functional state. This directly affects the level of traffic safety. Consequently, the tasks of modern transport research are the introduction of methods of the vehicle driver`s conditions monitoring and the detection of his fatigue in its early stages. That`s why the actuality of studying the human operator role in the transport process and the creation of modern means of driving assistance are increasing now.

No 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.

No abstract