Understanding how human drivers interact in dynamic traffic situations is a crucial step toward the safe and seamless integration of automated vehicles (AVs) into everyday traffic. A common setting for these interactions is the four way single-lane roundabout. Here, drivers must make quick decisions about who yields and who proceeds, based not just on traffic rules but also on subtle cues and shared expectations. These decisions rely heavily on gap acceptance, where each driver evaluates whether there is enough space and time to enter the roundabout safely. It often depends on mutual negotiation and split-second judgments, shaped by visual contact and behavioral feedback.
While earlier studies have explored driver gaze behavior in controlled environments, little is known about how gaze correlates with decision-making in continuous and mutual encounters, especially at roundabouts. This study fills that gap by studying human-human interactions during roundabout entry in a novel experimental setup. Using a coupled virtual reality driving simulator, two participants navigated a single-lane roundabout under varying approach speeds and distances. Eye-tracking was used to measure where and how long each driver fixated at the other vehicle. Control input data captured how drivers reacted in the seconds following these gaze events.
The results show that both entry distance and speed had a strong influence on who proceeded first. Drivers who started closer to the roundabout or moved faster were more likely to take priority. Drivers positioned closer to the conflict zone looked at the other vehicle for longer durations, indicating stronger visual engagement. Furthermore, drivers often responded with throttle or brake inputs shortly after looking at the other vehicle, especially when distance to the roundabout was small.
This study offers insight into how gaze behavior, positioning and control decisions shape mutual negotiation at roundabouts. These findings move beyond the idea of gap acceptance as a one-sided decision and highlight the importance of real-time interaction.

Cyclist route choice can lead to uncomfortable and dangerous situations. Therefore, it is important to research ways to influence this. This study explores how infrastructural nudging can be used to influence cyclist route choice. Using virtual reality and a bicycle simulator the impact of visual road hierarchy, visual obstruction and herding through street art is studied. A virtual urban environment was designed with 11 T-intersections. In 9 of the 11 intersection these methods were applied in three different ways, to see whether the nudge impacted cyclist route choice. The gathered data was then analysed with descriptive data analysis and discrete choice modelling. This study shows that cyclists follow nudges using visual road hierarchy and herding. However, they do not follow nudges with visual obstruction. This effect is the same for people of all ages, genders, heights etc. Though less experienced cyclists seem to react more heavily to obstruction methods. The results also indicate that cyclists have a significant right-handed tendency. This effect is not influenced by eye or hand dominance. The effect becomes slightly weaker when nudging is applied but does not go away. Future research should validate these results in a physical environment before this is used in practice.

This paper investigates cycling route preferences, with a focus on the cycling environment. To represent the cycling environment street-level images were used. A recently proposed model incorporates computer vision into a traditional discrete choice model to accommodate choice tasks involving numerical attributes and images. This computer vision-enriched discrete choice model (cv-dcm) was applied using a stated choice experiment, where respondents had to choose between two cycling routes. Each route was defined by three attributes, including commute time, number of traffic lights, and the cycling environment, the latter visualised using street-level images. While the cv-dcm relies on a neural network, making interpretability challenging, this study addressed this by collecting detailed cycling environment attributes. Results showed that the cycling environment was the most influential factor, with cyclists preferring green areas and separated cycling lanes. On average, cyclists were willing to take a 1.5-minute detour for a cycle trip of 11 minutes to use a separated cycling lane instead of a mixed-traffic road. These insights offer valuable insights for policymakers aiming to design cycling environments that align with cyclists’ preferences.

Electric vehicles (EVs) are required according to the EU legislation from 2014 to install Acoustic Vehicle Alerting Systems (AVAS) to convey information about the EVs through sound signals to other road users, ensuring their safety. Meanwhile,autonomous vehicles (AVs) are also recommended to be equipped with an External Human-Machine Interface (eHMI) system, enabling better interaction with other road users. . However, for the special group of autonomous electric vehicles (AEVs), considering that equipping both eHMI and AVAS, given their overlapping functionality in conveying information, might result in a waste of resources, it remains unclear whether it's feasible to use only eHMI for communication with road users as a cost-saving measure, thereby omitting AVAS or perhaps adding only a simple sound signal to replace AVAS. This study aims to explore whether autonomous electric vehicles need to add extra noise to provide more information to other vulnerable road users, such as cyclists and pedestrians. In this study, we will use Virtual Reality (VR) technology for simulation experiments. Utilizing VR, as opposed to real-world experiments, allows for precise control over the variables in each experiment, ensuring that experiments can be conducted under the same conditions multiple times, thus enhancing reliability. On the other hand, VR can ensure the safety of experiment participants while providing them with an immersive experience, especially in experiments related to traffic safety research.

In this experiment, we will utilize the Unreal Engine to create a simulated testing environment, focusing on observing participants' (acting as cyclists) reactions to a series of variables. These include different environmental noises, the warning distance of sound signals emitted by autonomous vehicles, and the autonomous vehicles themselves. The sound signal warning distance specifically refers to a mechanism where, when pedestrians or cyclists enter a predefined range of the vehicle, it automatically emits a warning signal. This signal is to alert the approaching individuals that the vehicle is in a ready state and may proceed to the next action, thereby enhancing safety interaction and awareness on the road.

The data collected from this experiment will be divided into two parts: first, the participants' reaction times, speed adjustments, and distances obtained from VR devices; second, their perceptions of the trust, perceived safety, and comfort of interactions with autonomous electric vehicles gathered through post-experiment surveys.

The results of this experiment will assist policymakers in refining relevant laws and regulations, as the existing legislation concerning electric vehicles and AVAS do not take autonomous vehicles into account. It will also provide theoretical support for car manufacturers in the design of autonomous electric vehicles.

This research delves into the transformative potential of self-driving vehicles by investigating their impact on passengers’ happiness. As autonomous transportation technology rapidly evolves, understanding the user experience within these vehicles becomes essential. To investigate the happiness of the self-driving vehicles’ passengers, which is defined as positive emotions and cognitive well-being during self-driving rides, real-traffic test rides were conducted among 31 participants, companied with two before-ride and after-ride surveys. The study employs a comprehensive approach, combining self-reported survey data and biometric measurements, which includes the participants’ heart rate and the eye movement, to investigate passengers' happiness. The results present an overall positive emotions and positive attitudes towards the self-driving vehicle. Moreover, the findings present a notable shift in passengers' attitudes, with originally neutral sentiments transitioning to positive perceptions following the test ride. Participants exhibited various activities during the ride, enhanced comfort with the concept, and an improved satisfaction with self-driving technology. Remarkably, statistical trends suggest that self-driving vehicles hold the potential to alleviate stress and optimize time management, positively impacting passengers' overall well-being. Furthermore, biometric data of the participants presented participants’ different physical reaction on different traffic scenarios and indicated the happiness and well-being of the participant. The research emphasizes the broad implications of self-driving technology on individual happiness, concerning both emotions and attitudes, extending beyond functional enhancements to encompass passenger happiness and societal integration.