title: Drivers' Visual Behavior-Guided RRT Motion Planner for Autonomous On-Road Driving.
abstract: This paper describes a real-time motion planner based on the drivers' visual behavior-guided rapidly exploring random tree (RRT) approach, which is applicable to on-road driving of autonomous vehicles. The primary novelty is in the use of the guidance of drivers' visual search behavior in the framework of RRT motion planner. RRT is an incremental sampling-based method that is widely used to solve the robotic motion planning problems. However, RRT is often unreliable in a number of practical applications such as autonomous vehicles used for on-road driving because of the unnatural trajectory, useless sampling, and slow exploration. To address these problems, we present an interesting RRT algorithm that introduces an effective guided sampling strategy based on the drivers' visual search behavior on road and a continuous-curvature smooth method based on B-spline. The proposed algorithm is implemented on a real autonomous vehicle and verified against several different traffic scenarios. A large number of the experimental results demonstrate that our algorithm is feasible and efficient for on-road autonomous driving. Furthermore, the comparative test and statistical analyses illustrate that its excellent performance is superior to other previous algorithms.
authors.affiliation: Department of Automation, University of Science and Technology of China, Hefei 230026, China. dumingbo@mail.ustc.edu.cn.|Institute of Applied Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230026, China. dumingbo@mail.ustc.edu.cn.
authors.forename: Mingbo
authors.initials: M
authors.lastname: Du
authors.affiliation: Institute of Applied Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230026, China. tmei@iim.ac.cn.
authors.forename: Tao
authors.initials: T
authors.lastname: Mei
authors.affiliation: Institute of Applied Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230026, China. hwliang@iim.ac.cn.
authors.forename: Huawei
authors.initials: H
authors.lastname: Liang
authors.affiliation: Institute of Applied Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230026, China. jjchen@hfcas.ac.cn.
authors.forename: Jiajia
authors.initials: J
authors.lastname: Chen
authors.affiliation: Department of Automation, University of Science and Technology of China, Hefei 230026, China. ruling@mail.ustc.edu.cn.|Institute of Applied Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230026, China. ruling@mail.ustc.edu.cn.
authors.forename: Rulin
authors.initials: R
authors.lastname: Huang
authors.affiliation: Institute of Applied Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230026, China. qiushui@mail.ustc.edu.cn.
authors.forename: Pan
authors.initials: P
authors.lastname: Zhao
keywords: RRT (rapidly-exploring random tree); autonomous vehicle; drivers’ visual behavior; motion planning; on-road driving