A Computational Driver Model to Predict Driver Control at Unsignalised Intersections

The number of cyclists fatally struck when crossing a driver's travel path at an unsignalised intersection has been stable in recent years, indicating that more effort should be made to improve safety in this specific conflict scenario. The most recent safety systems help drivers avoid collisions with cyclists, but improving cyclist safety further requires resolving challenges unique to bicycles and cyclists. In this paper we propose a predictive computational model of driver behaviour in the intersection scenario. Although a handful of studies have focused on describing driver behaviour in this scenario, no computational model that can predict driver control can be found in the literature. The proposed model is based on a biofidelic human sensorimotor-control modelling framework. Two visual cues were used: 1) optical longitudinal looming and 2) projected post-encroachment time between the bicycle and the car. The model was optimised using data from a test-track study in which participants were asked to drive through an intersection where a cyclist would cross their travel path. The performances of the model were evaluated by comparing the simulated driver-control process with the observed control behaviour for each trial using a leave-one-out cross-validation process. The results show that the model performed rather well, reproducing braking controls and kinematics that were similar to the observations. The extent to which the model could be used by safety systems' threat-assessment algorithms is discussed. Future research to improve the model's performances is suggested