A personalized behavior learning system for human-like longitudinal speed control of autonomous vehicles

As the main component of an autonomous driving system, the motion planner plays an essential role for safe and efficient driving. However, traditional motion planners cannot make full use of the on-board sensing information and lack the ability to efficiently adapt to different driving scenes and behaviors of different drivers. To overcome this limitation, a personalized behavior learning system (PBLS) is proposed in this paper to improve the performance of the traditional motion planner. This system is based on the neural reinforcement learning (NRL) technique, which can learn from human drivers online based on the on-board sensing information and realize human-like longitudinal speed control (LSC) through the learning from demonstration (LFD) paradigm. Under the LFD framework, the desired speed of human drivers can be learned by PBLS and converted to the low-level control commands by a proportion integration differentiation (PID) controller. Experiments using driving simulator and real driving data show that PBLS can adapt to different drivers by reproducing their driving behaviors for LSC in different scenes. Moreover, through a comparative experiment with the traditional adaptive cruise control (ACC) system, the proposed PBLS demonstrates a superior performance in maintaining driving comfort and smoothness.