HD Live Maps for Automated Driving: An AI Approach

Xin Chen, Director of Engineering in the Highly Automated Driving organization, HERE Technologies
Wednesday, November 7

Abstract: HD Maps, one of the key components of automated driving and a life-saving safety feature, serve as the hub for sensing, perception and decision. Making and maintaining a near-real time HD map on a global scale is an extremely challenging task. I will present how we apply AI technologies to automate the creation of HD Live Maps using both industrial capture and crowd- sourced based data collection. Quality Index is introduced to provide automated driving customers with the confidence of HD map accuracy and reliability in a dynamic world. We implement low power and high throughput edge perception as a reference implementation to enable crowd-sourced based HD map maintenance. Finally I will share best practices to democratize AI in our engineering organization and transition research into production in the creation of a half million kilometers of HD maps in 2017.

Biography: Dr. Xin Chen is a Director of Engineering in the Highly Automated Driving organization at HERE Technologies whose team is completing pioneering work to achieve the automation of next generation map creation using computer vision and machine learning technologies. He has over 50 U.S. Patents in LIDAR and image analysis for mapping and he has served on an NSF (National Science Foundation) panel to evaluate and award funding to multi- million dollar projects advancing research in these areas. Xin has been awarded 2010 and 2011 IMPACT awards to recognize "employees making outstanding contributions", an award recognizing "Significant Intellectual Property Contributors" for 2011-2012, 2013 and 2014 company-wide Hack Week top awards, and 2015 Berkeley Office Hackathon top award. He has numerous publications at CVPR and CVIU. Xin is an adjunct professor and PhD advisor at Northwestern University and Illinois Institute of Technology teaching "Geospatial Vision and Visualization" and "Biometrics" courses. Xin obtained his Ph.D. in Computer Science and Engineering from the University of Notre Dame.

Route planning in transportation from research to practice

Daniel Delling, Research Engineer at Apple Maps
Thursday November 8

Abstract: The last 15 years have seen astonishing progress in the performance of shortest path algorithms for transportation networks. In particular, for road networks, modern algorithms can be up to seven orders of magnitude faster than standard solutions. Since these algorithms enable several new applications, many of them have found their way into navigation services of major technology companies serving hundreds of millions of users every day. This talk highlights key techniques, discusses their impact on the industry, and provides an outlook on upcoming challenges.

Biography: Daniel Delling is a researcher and architect at Apple Inc. in Cupertino, USA. His research focuses on the design, analysis, and implementation of efficient algorithms and data structures. Before joining Apple, he received his PhD from the Karlsruhe Institute of Technology and was a Researcher at Microsoft Research Silicon Valley.