Link: https://doi.org/10.17605/OSF.IO/ZBFXU Data from the real-world data collection described in (Kalantari et al., 2025), using a setup of two stereo camera sensors to record road user trajectories at two locations in Leeds, UK, with frequent driver-pedestrian interactions, one week per location. Kalantari, A. H., Lin, Y. S., Mohammadi, A., Merat, N., & Markkula, G. (2025). Testing the validity of multi-participant distributed simulation for understanding and modeling road user interaction. PsyArXiv preprint.

Link: https://zenodo.org/records/8321136 Data from the experiment described in (Ma et al., 2024) and (Lin et al., 2024), on pedestrian road-crossing decisions in a controlled environment. Participants were seated in front of a computer screen, with each trial showing an approaching car at one of four times to arrival, and the participants responded with button presses to indicate when they wanted to initiate road-crossing. Both behavioural data (the button press responses) and 64-channel EEG was recorded.

Link: https://doi.org/10.17605/OSF.IO/EAZG5 Data from the experiment described in (Schumann et al., 2023) and (Srinivasan et al., 2024), where participants drove the University of Leeds moving base driving simulator in a simulated urban environment, including both non-critical and safety-critical interactions with other, computer-controlled cars. Schumann, J. F., Srinivasan, A. R., Kober, J., Markkula, G., & Zgonnikov, A. (2023). Using models based on cognitive theory to predict human behavior in traffic: A case study.

Link: https://osf.io/ku3h4 Data from the experiment described in (Markkula et al., 2021), measuring human detection limits for visually looming (optically expanding) collision threats, as a function of collision threat kinematics. Participants watched a visual representation of the back of a car on a computer screen, and were instructed to respond with a button press as soon as they saw the car “coming closer”, i.e., growing on the screen. The dataset includes both behavioural responses (the button presses) as well as concurrently recorded 1024 Hz EEG data from a 64 electrode 10-20 international cap BioSemi system.

Link: https://doi.org/10.17605/OSF.IO/VA5KR Data collected with professional Jaguar Land Rover test drivers in three different low-friction vehicle testing manoeuvres, both in a real vehicle on a frozen lake, and in a high-fidelity reconstruction of the same tests in the University of Leeds moving-base driving simulator. Described and analysed in (Markkula et al., 2018) and (Romano et al., 2019). The simulator data for one of the tests is also available in the dataset linked here.

Link: https://doi.org/10.17605/OSF.IO/DF9PW Data from two experiments in the University of Leeds moving-base car driving simulator, as described in (Markkula et al., 2018): (1) simple lane-keeping on a straight road, (2) at-limit vehicle control on a circular curve track on ice. The latter data is also included in the dataset linked here. Markkula, G., Boer, E., Romano, R., & Merat, N. (2018). Sustained sensorimotor control as intermittent decisions about prediction errors: Computational framework and application to ground vehicle steering.