Retrieving Common Discretionary Lane Changing Characteristics From Trajectories

Conventional lane change methods directly collected steering angle data via onboard sensors to accurately capture the actions of individual drivers. We can hardly use such methods to collect massive data from examinees, because of time and financial costs. In order to retrieve common steering behaviors for lots of drivers, we propose a method to retrieve common discretionary lane change (DLC) steering characteristics from trajectory data. The key technique of this new method is solving an inverse problem that converts the measured trajectory into the unmeasured steering maneuvers under the assumed vehicle movement dynamics. We find that most normal DLC trajectories in the next generation simulation datasets could be well reproduced by a simple target heading angle preview control model. This finding sheds important light into driver behavior study and better explains how human control vehicles. Based on these findings, we can nonintrusively evaluate driving performance or physiological states of drivers based on online roadside monitoring data (e.g., the data collected from roadside video cameras). This opens a promising field of applications for enhancing driving safety.