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Sensors 2016, 16(1), 102;

Drivers’ Visual Behavior-Guided RRT Motion Planner for Autonomous On-Road Driving

Department of Automation, University of Science and Technology of China, Hefei 230026, China
Institute of Applied Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230026, China
Author to whom correspondence should be addressed.
Academic Editor: Felipe Jimenez
Received: 22 November 2015 / Revised: 28 December 2015 / Accepted: 8 January 2016 / Published: 15 January 2016
(This article belongs to the Special Issue Sensors for Autonomous Road Vehicles)
Full-Text   |   PDF [6261 KB, uploaded 15 January 2016]   |  


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. View Full-Text
Keywords: motion planning; autonomous vehicle; drivers’ visual behavior; RRT (rapidly-exploring random tree); on-road driving motion planning; autonomous vehicle; drivers’ visual behavior; RRT (rapidly-exploring random tree); on-road driving

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Du, M.; Mei, T.; Liang, H.; Chen, J.; Huang, R.; Zhao, P. Drivers’ Visual Behavior-Guided RRT Motion Planner for Autonomous On-Road Driving. Sensors 2016, 16, 102.

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