General behavior and motion model for automated lane change

Lane change maneuver is a cause for many severe highway accidents and automatic lane change has great potentials to reduce the impact of human error and number of accidents. Previous researches mostly tried to find an optimal trajectory and ignore the behavior model. Presented methods can be applied for simple lane change scenario and generally fail for complicated cases or in the presence of time/distance constraints. Through analysis and inspiring of human driver lane change data, we propose a multi segments lane change model to mimic the human driver for challenging scenarios. We also propose a method to convert behavior/motion selection to a time-based pattern recognition problem. We developed a simulation platform in PreScan and evaluated proposed automatic lane change method for challenging scenarios.