Andi Zang Wins Best Paper Award at AutonomousGIS 2017
The 1st ACM SIGSPATIAL Workshop on High-Precision Maps and Intelligent Applications for Autonomous Vehicles was held Tuesday, November 7th in Redondo Beach, CA.
EECS PhD Student Andi Zang has won the Best Paper Award at the 1st ACM SIGSPATIAL Workshop on High-Precision Maps and Intelligent Applications for Autonomous Vehicles (AutonomousGIS 2017), held Tuesday, November 7th in Redondo Beach, CA. His winning paper is entitled, “Lane Boundary Extraction from Satellite Imagery”, co-authored with EECS Masters Student Runsheng Xu (Northwestern University), Zichen Li (New York University) and David Doria (HERE North America).
In addition to his PhD studies (Advised by Prof. Goce Trajcevski, Iowa State and Prof. Xin Chen, EECS) Andi Zang is currently a Researcher for HERE North America. His research focuses on LiDAR point cloud processing and computer vision for understanding/modeling High Definition Map (HD Map), autonomous driving and other related applications.
AutonomousGIS 2017: Autonomous vehicles (aka self-driving cars) and their equipped intelligent systems are among the most exciting and innovative technologies in transportation. In order to operate safely, autonomous vehicles require high-precision geospatial datasets and maps, which contain significantly more detailed road information and the true ground (absolute accuracy) than those found in current conventional geospatial resources for driving. With the integration with artificial intelligence, machine learning, spatial data mining, image processing and other technologies, many innovative intelligent applications are fast growing. This international workshop brought both academic researchers and geospatial industry engineers & research scientists together to present, discuss, and share the state-of-the-art scientific research and technology advancement on related fields for autonomous vehicles. The AutonomousGIS participants had an opportunity to share the state-of-arts technology and brainstorm their visions of high-precision maps and development on future intelligent applications for autonomous vehicles.