Longitudinal Driving Skills Transfer from Driver to Smart Vehicle

Autonomous Vehicles (AVs) research has attracted much attention in recent years. In fact, some functionality of AV, such as cruise control, lane keeping and automatic parking have already been employed in commercial vehicles. In a fully AV system, the vehicle drives like a human being and might even outperform humans in some aspects, e.g., shorter reaction time when facing dangerous situations. To have AVs drive like human beings, discovering how to transfer human driving skills to smart vehicles has become a key issue. In this study, we have developed a human-to-vehicle skills transfer system that is able to control the smart vehicle to drive safely in the longitudinal direction under a virtual environment constructed within the Carla simulator. A deep Q-learning algorithm has been employed to build the learning network, which will obtain its control policy during the interaction between the agent and the environment. To achieve human-to-vehicle skills transfer, human data collected with the simulator is employed as the learning target for the learning network. Various experimental results show that longitudinal human driving skills can successfully transfer to a smart vehicle through the developed learning network.