  To enable autonomous vehicles to perform discretionary lane change amidst the
random traffic flow on highways, this paper introduces a decision-making and
control method for vehicle lane change based on Model Predictive Control (MPC).
This approach divides the driving control of vehicles on highways into two
parts: lane-change decision and lane-change control, both of which are solved
using the MPC method. In the lanechange decision module, the minimum driving
costs for each lane are computed and compared by solving the MPC problem to
make lane-change decisions. In the lane-change control module, a dynamic
bicycle model is incorporated, and a multi-objective cost function is designed
to obtain the optimal control inputs for the lane-change process. Additionally,
A long-short term memory (LSTM) model is used to predict the trajectories of
surrounding vehicles for both the MPC decision and control modules. The
proposed lane-change decision and control method is simulated and validated in
a driving simulator under random highway traffic conditions.
