Modeling and field experiments on autonomous vehicle lane changing with surrounding human‐driven vehicles

AbstractAbstractAutonomous vehicle (AV) technology is widely studied in both industrial and academic communities since it is regarded as a promising means for improving transportation safety and efficiency. Lane changing is a critical link for higher‐level AV operations. However, few studies on AV lane changing consider the dynamics of surrounding vehicles, particularly in a mixed traffic environment including human‐driven vehicles (HVs). Therefore, this article presents a dynamic lane‐changing model for AV incorporating human driver behavior in mixed traffic. The proposed model includes four key components: car following (and lane keeping), lane‐changing decision, dynamic trajectory generation, and model predictive control (MPC)‐based trajectory tracking. AV longitudinal control algorithm is also depicted in detail in this article. Field experiments are conducted on a large‐scale test track to test and validate the proposed model. An AV and three HVs are used in the lane‐changing experiments. Different human driver behaviors are considered in the experiment settings. Experimental results show that the proposed lane‐changing model can complete lane‐changing maneuvers efficiently when HVs are cooperative and can also robustly abort them when HVs are uncooperative. Compared with the measured human lane‐changing maneuvers, AV lane‐changing maneuvers from the proposed model are more comfortable and safer.