Personalized Human-Machine Cooperative Lane-Changing Based on Machine Learning

To reduce the interference and conflict of human-machine cooperative control, lighten the operation workload of drivers, and improve the friendliness and acceptability of intelligent vehicles, a personalized human-machine cooperative lane-change trajectory tracking control method was proposed. First, a lane-changing driving data acquisition test was carried out to collect different driving behaviors of different drivers and form the data pool for the machine learning method. Two typical driving behaviors from an aggressive driver and a moderate driver are selected to be studied. Then, a control structure combined by feedforward and feedback control based on Long Short Term Memory (LSTM) and model-based optimum control was introduced. LSTM is a machine learning method that has the ability of memory. It is used to capture the lane-changing behaviors of each driver to achieve personalization. For each driver, a specific personalized controller is trained using his driving data. Finally, a driver-in-the-loop simulation test was carried out to verify the effect of the proposed method. The results showed that when matching the driver with a proper personalized controller, the proposed method could reduce the steering wheel conflict between the driver and the machine system during lane-changing and lighten the operation workload.