Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/83106
Title: Autonomous Robot Driving Decision Strategy Following Road Sign And Traffic Rules: Simulation Validation
Authors: Chong, Zheng-Hao
Yee, Ling
Causo, Albert
Chen, I-Ming
Keywords: Autonomous Robot
Decision Strategy Following Road Signs
Issue Date: 2016
Source: Chong, Z. -H., Yee, L., Causo, A., & Chen, I. -M. (2016). Autonomous robot driving decision strategy following road signs and traffic rules: Simulation validation. 2016 16th International Conference on Control, Automation and Systems (ICCAS), 377-381.
Abstract: Autonomous robot driving decision strategy following road signs and traffic rules is described using simulation with a Turtlebot [1]. A fully autonomous robot driving strategy is presented, which follows human level logic in decision making. A vision-based autonomous vehicles navigation system for road vehicles includes three main parts: 1) road and traffic signs detection; 2) vehicle movement guidance system; and 3) decision making following human level logic. The first two modules have been studied independently for many years and obtained many good results using different solutions, but there is little research in fully integrated system with high level decision making to achieve fully autonomous robot navigation following road signs. It is valuable to study and apply this concept into a real system. A simulation world is built according to real environment scenario proving the concept of study.
URI: https://hdl.handle.net/10356/83106
http://hdl.handle.net/10220/42435
DOI: 10.1109/ICCAS.2016.7832347
Rights: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/ICCAS.2016.7832347].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Conference Papers

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