A Personalized Behavior Learning System for Human-Like Longitudinal Speed Control of Autonomous Vehicles

Planner Smoothness Driving simulator
DOI: 10.3390/s19173672 Publication Date: 2019-08-26T08:38:23Z
ABSTRACT
As the main component of an autonomous driving system, motion planner plays essential role for safe and efficient driving. However, traditional planners cannot make full use on-board sensing information lack ability to efficiently adapt different scenes behaviors drivers. To overcome this limitation, a personalized behavior learning system (PBLS) is proposed in paper improve performance planner. This based on neural reinforcement (NRL) technique, which can learn from human drivers online realize human-like longitudinal speed control (LSC) through demonstration (LFD) paradigm. Under LFD framework, desired be learned by PBLS converted low-level commands proportion integration differentiation (PID) controller. Experiments using simulator real data show that reproducing their LSC scenes. Moreover, comparative experiment with adaptive cruise (ACC) demonstrates superior maintaining comfort smoothness.
SUPPLEMENTAL MATERIAL
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