NDLI: Collision avoidance in on-road environment for autonomous driving

Content Provider	IEEE Xplore Digital Library
Author	Noh, S. Woo-Yong Han
Copyright Year	2014
Description	Author affiliation: Electron. & Telecommun. Res. Inst., Daejeon, South Korea (Noh, S.; Woo-Yong Han)
Abstract	Collision avoidance is an essential part in autonomous navigation. This paper proposes a collision avoidance method in on-road environment for autonomous driving. The proposed method divides a road map into six lane-level regions, assigns risk observers to corresponding regions, evaluates collision risks of situations based on risk observer distribution, and determines behaviors to deal with various collision-risky situations. Risk observers, designed to recognize and analyze situations at the level of lanes with regards to safety, return collision risks of situations. Based on the results of each risk observer, the system determines collision-free maneuvers to follow a global path. The method was implemented in robot operating system (ROS) for a reusable and hardware-independent software platform and tested in a two-phase procedure; simulation tests through a visualization tool called RViz in ROS with logged data and in-vehicle tests on the closed road with other vehicles to verify that our system operates properly on the road for autonomous driving without collisions. It successfully performed in several scenarios which can be happened in real road environment. The demonstration videos for in-vehicle tests are linked at fhttp://youtu.be/cjLcUdGg1j0, http://youtu.be/tB2rC6gMhcM.
Starting Page	884
Ending Page	889
File Size	2156359
Page Count	6
File Format	PDF
ISSN	20937121
e-ISBN	9788993215076
DOI	10.1109/ICCAS.2014.6987906
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-22
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Control, Robotics and Systems (ICROS)
Subject Keyword	collision avoidance robot operating system decision-making Autonomous driving risk observer Global Positioning System
Content Type	Text
Resource Type	Article

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