Project: #275 A Scenario-based Database for Connected and Autonomous Driving in A Smart City
Progress Report - Reporting Period Ending: March 30, 2019
Principal Investigator: DING ZHAO <dingzhao@cmu.edu>
Status: Active

Start Date: Jan. 1, 2019
End Date: Dec. 31, 2019

Research Type: Advanced
Grant Type: Research
Grant Program: FAST Act - Mobility National (2016 - 2022)
Grant Cycle: 2018 Traffic21


Progress Report  (Last Updated: March 25, 2019, 3:08 p.m.)
% Project Completed to Date: 25
% Grant Award Expended: 0
% Match Expended &amp; Document: 0

USDOT Requirements

Accomplishments
The researchers have integrated a data collection platform with multiple advanced sensors and have started to collect traffic data in March of 2019. A database and the corresponding website have been set up on a local server. 
The researcher and the CMU project manager will meet with Alex Pazuchanics to present the discuss the progress and findings of the research.
In conclusion, Task 1 “Integrate a data collection platform by installing multiple advanced sensors such as camera, radar, Lidar, and IMU. Collect the data around the city ” has been completed. 

Impacts
This research will benefit the City of Pittsburgh management as well as the intelligent transportation community by providing a scenario-based traffic dataset which reveals the traffic patterns in Pittsburgh. Also, this research is the application of a well-developed theory called “Traffic Primitives” which can extract the fundamental driving scenarios of traffic behaviors automatically.

Other
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Outcomes


New Partners
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Issues
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