CCAT Director Receives ITS Outstanding Paper Award from INFORMS Transportation Science and Logistics Society

CCAT Director Receives ITS Outstanding Paper Award from INFORMS Transportation Science and Logistics Society

Screenshot from the INFORMS Meeting Zoom call
Screenshot of the INFORMS Transportation Science and Logistics Society meeting Zoom call

On October 18th, the Transportation Science & Logistics (TSL) society business meeting took place in a virtual setting. The group received a record number of best paper submissions from 13 different journals. The special interest group Intelligent Transportation Systems (ITS) Outstanding Paper was awarded to CCAT Director, Dr. Henry Liu, and his team. Their paper ‘Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment‘ was published in Nature Communications in February 2021.

According to their website, the INFORMS Transportation Science & Logistics society provides INFORMS members and friends with a sustained, specialized focus on all topics of transportation science and logistics, including current and potential problems and contributions to their solution, and supports efforts to extend, unify, and integrate related branches of knowledge and practice. We encourage the education of students and the continuing education of practitioners. The Society was formed in 2004 with the merger of the Transportation Science and Logistics Sections. The Aviation Applications Section became an affiliate of the Society in 2008 and all members of the Aviation Applications Section are also members of TSL. The Society is the editorial home of one of INFORMS’ flagship journals, Transportation Science.

The team behind the award-winning paper includes Henry Liu, Shuo Feng, Yiheng Feng, Haowei Sun, and Xintao Yan. on the 2020 CCAT Student Poster competition with their poster ‘A Data-driven Simulation of Naturalistic Driving Environment for Autonomous Vehicle Testing‘. The work proposes a naturalistic and adversarial driving environment that will radically reduce the number of vehicle miles driven needed to confirm an AVs safety on roadways. Known as the Safe AI Framework for Trustworthy Edge Scenario Test, or SAFE Test, this methodology was recently deployed at the American Center for Mobility (ACM) at the historic Willow Run site. You can learn more about the SAFE Test by watching this research highlight video below.