Combined Hierarchical Learning Framework for Personalized Automatic Lane-Changing

There have been explosive developments in automatic driving in recent years. Several kinds of self-driving vehicles have been introduced, but drivers are generally required to stay alert and put their hands on the steering wheel during automatic driving, which means the vehicle is in a state of human-machine cooperative control. Hence personalization is needed to reduce conflicts between driver and machine. To achieve personalized automatic driving, a combined hierarchy learning framework (CHLF) based on gated recurrent units (GRU) and safety field is developed in this paper. Learning-based methods are gaining attention in the development of automatic vehicles. However, automatic driving based on neural networks is risky due to poor interpretability. To overcome this limitation, we divided the network into three layers according to function to achieve a hierarchy. We combine data- and mechanism-oriented methods to make the CHLF reliable and stable. Lane-changing (LC) driving data collected from real-vehicle field experiments are used to train the CHLF, which learns from the data to capture human driver behavior in LC. To verify the performance of the CHLF, a group of driver-in-the-loop experiments is conducted based on a driving simulation test bench. The results show that the CHLF can reduce driver steering output torque compared to the conventional method, which means there is less conflict between driver and machine during automatic LC, and the driver is more relaxed.