Generation of a Precise Roadway Map for Autonomous Cars

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Abstract
This paper proposes a map generation algorithm for a precise roadway map designed for autonomous cars. The roadway map generation algorithm is composed of three steps, namely, data acquisition, data processing, and road modeling. In the data acquisition step, raw trajectory and motion data for map generation are acquired through exploration using a probe vehicle equipped with GPS and on-board sensors. The data processing step then processes the acquired trajectory and motion data into roadway geometry data. GPS trajectory data are unsuitable for direct roadway map use by autonomous cars due to signal interruptions and multipath; therefore, motion information from the on-board sensors is applied to refine the GPS trajectory data. A fixed-interval optimal smoothing theory is used for a refinement algorithm that can improve the accuracy, continuity, and reliability of road geometry data. Refined road geometry data are represented into the B-spline road model. A gradual correction algorithm is proposed to accurately represent road geometry with a reduced amount of control parameters. The developed map generation algorithm is verified and evaluated through experimental studies under various road geometry conditions. The results show that the generated roadway map is sufficiently accurate and reliable to utilize for autonomous driving.
Funding Information
  • National Research Foundation
  • Korean Government (Ministry of Education, Science and Technology) (2011-0017495)
  • Industrial Strategy Technology Development Program of Ministry of Knowledge Economy (MKE) (10039673)
  • Energy Resource R&D Program (2006ETR11P091C)
  • Ministry of Knowledge Economy, Science and Technology through the BK21 Program (201000000000173)
  • MKE and Korea Institute for Advancement in Technology (KIAT) through the Workforce Development Program in Strategic Technology

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