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., & Merat, N. (2018). Simulating the effect of cognitive load on braking responses in lead vehicle braking scenarios. IET Intelligent Transport Systems, 12(6), 427–433. https://doi.org/10.1049/iet-its.2017.0233

Markkula, G. (2014). Modeling driver control behavior in both routine and near-accident driving. Proceedings of the Human Factors and Ergonomics Society Annual Meeting*, 58(1), 879–883. https://doi.org/10.1177/1541931214581185

Piccinini, G. B., Lehtonen, E., Forcolin, F., Engström, J., Albers, D., Markkula, G., Lodin, J., & Sandin, J. (2019). How do drivers respond to silent automation failures? Driving simulator study and comparison of computational driver 2 braking models 3. Human Factors. https://doi.org/10.1177/0018720819875347

Xue, Q., Markkula, G., Yan, X., & Merat, N. (2018). Using perceptual cues for brake response to a lead vehicle: Comparing threshold and accumulator models of visual looming. Accident Analysis & Prevention, 118, 114–124. https://doi.org/10.1016/j.aap.2018.06.006