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://github.com/gmarkkula/HFES2014ModelsAndFigs A MATLAB implementation of the looming accumulation intermittent braking control model described in (Markkula, 2014). This implementation is very similar to what has later been presented in more complete detail in (Svärd et al., 2017; Svärd et al., 2021) and as a more task-general model of sustained intermittent control in (Markkula et al., 2018; code linked here). Markkula, G. (2014). Modeling driver control behavior in both routine and near-accident driving.

Link: https://doi.org/10.17605/OSF.IO/647SY MATLAB implementation of a brake response time model based on accumulation of visual looming information, as initially proposed in (Markkula, 2014). The code also shows how to run a simple grid search to perform a maximum likelihood fitting of the model to observed brake response data, as done for example in (Engström et al, 2018; Xue et al., 2018; Piccinini et al., 2019). Engström, J., Markkula, G., Xue, Q.