Identifying drivers’ perception-reaction time (PRT) in car-following processes via two different methods using vehicle trajectory data

Abstract

To judge whether the connected and automated vehicles (CAVs) could perform better than human, this study proposes two methods to identify perception-reaction time (PRT) of human drivers using vehicle trajectory data - the calibrated-based and duration-based methods. The calibrated-based method utilises an intelligent driver model (IDM) to capture the longitudinal driving behaviours and delineate the PRT as a model parameter. While the duration-based approach collects PRT by manually extracting the PRT value based on the trajectory data. To investigate the factors influencing these types of PRTs, two regression models are proposed. Results reveal important contributing factors affecting the PRT values of human drivers, including the mean and standard deviation of the leading vehicle’s speed, and the speed and gap difference between leading and following vehicles, as well as the mean speed of the following vehicle. Besides, the same factor has different effects on the calibrated and selected PRTs. For instance, the std of the gap between leading and following vehicles manifests negative and positive impacts on the calibrated and selected PRTs, respectively. In summary, findings of this study can provide valuable insights into the identification of the PRT values of human drivers, potentially improving future design of CAVs.