  In order to minimize the impact of lane change (LC) maneuver on surrounding
traffic environment, a hierarchical automatic LC algorithm that could realize
local optimum has been proposed. This algorithm consists of a tactical layer
planner and an operational layer controller. The former generates a
local-optimum trajectory. The comfort, efficiency, and safety of the LC vehicle
and its surrounding vehicles are simultaneously satisfied in the optimization
objective function. The later is designed based on vehicle kinematics model and
the Model Predictive Control (MPC), which aims to minimize the tracking error
and control increment. Combining macro-level and micro-level analysis, we
verify the effectiveness of the proposed algorithm. Our results demonstrate
that our proposed algorithm could greatly reduce the impact of LC maneuver on
traffic flow. This is reflected in the decrease of total loss for nearby
vehicles (such as discomfort and speed reduction), and the increase of traffic
speed and throughput within the LC area. In addition, in order to guide the
practical application of our algorithm, we employ the HighD dataset to validate
the algorithm. This research could also be regarded as a preliminary
foundational work to develop locally-optimal automatic LC algorithm. We
anticipate that this research could provide valuable insights into autonomous
driving technology.
