One of the major challenges of autonomous vehicles (AV) is their interaction with pedestrians. Unofficial interactions such as gestures, eye contact, waving, and flashing lights are very common behavioral patterns for drivers to express their intent to give priority. In our research we composed a virtual reality experiment for a pedestrian crossing in an urban environment in order to test pedestrians’ reactions on an LED light display mounted on a virtual AV. Our main research interest was to investigate whether communication patterns influence the decision making of pedestrians when crossing the road. In a VR environment, four scenarios were created with a vehicle approaching a pedestrian crossing with different speeds and displaying a special red/green sign to pedestrians. Here, 51 persons participating in the experiment had to decide when crossing is safe. Results show that the majority of people indicated they would cross in the time windows when it was actually safe to cross. Male subjects made their decision to cross slightly faster but no significant differences were found in the decision making by gender. It was found that age is not an influencing factor, either. Overall, a quick learning process was experienced proving that explicit communication patterns are self-explaining.

The advent of autonomous vehicles (AV) could revolutionize the automotive industry by significantly improving safety, efficiency, and accessibility. Despite their potential to improve traffic safety by reducing human error, their integration into existing transportation systems presents significant challenges. This is particularly evident in scenarios involving takeover events, where there is a transition of control from the vehicle to the human driver. Our driving simulator study, involving 14 drivers in a work-zone environment, provides critical insights into the takeover performance of level 3 to level 5 AVs. The findings suggest that the successful integration of AVs depends on their seamless incorporation into existing systems and the readiness of drivers to adapt to this emerging technology.

The Automated Driving Data Analyzer (ADDA) is a modular application written in Python for the synchronized acquisition and analysis of physiological, vehicle, and interface data. It provides a managed data acquisition process and one-click data analysis. It also provides raw data storage and systematic archiving of data sets. ADDA integrates real-time data from a BeamNG vehicle simulation, Pupil Labs eye-tracking system, hand-tracking, and cardiac data. This integration allows the simultaneous recording and analysis of multiple data streams, which can be visualized and controlled through a graphical user interface (GUI) built with Tkinter. The application is designed to help researchers and engineers analyze driving behavior under different conditions, enabling a deeper understanding of the interactions between the driver and automated driving functions.

Current research and development in understanding road users’ driving behaviors play a key role in improving traffic safety. Recently, several driving simulators have been employed as a suitable approach to investigate several drivers’ responses in challenging traffic scenarios. Although professional drivers represent a particular category among driving populations, the body of literature about their comparative behavioral and psychological characteristics remains limited. This study examined the differences in driving performance and visual and physiological responses between civilian and professional drivers in a simulated environment. A total of 30 drivers, with an equal split between professional and civilian categories, took part in a series of driving simulations. The simulations incorporated various infrastructure types, including four cone avoidance tasks and a high-speed motorway task. This study collected comprehensive data on performance metrics, hand usage, heart rate, and eye movements. Eye-tracking technology was used to measure visual attention. The findings revealed that during cone avoidance scenarios, civilian drivers exhibited a similar performance, visual behavior, and physiological response, except for the speed, experiment duration, and throttle, to professional drivers. In the motorway scenario, all metrics showed no significant difference between the two driver groups. These results highlight the need for cautious interpretation, particularly given the limitations of the sample. Revalidation is needed in larger studies, especially for understanding the differences between drivers’ metrics, which is crucial to elevate drivers’ safety, and assessing training programs in Hungary.