Transportation Engineering and Science Program
For the purpose of fulfilling the project aims, the 4 directly involved graduate students (Wali, Kamrani, Graves, and Salimi) have
gained in-depth understanding of Markov Switching models, Markov Decision Processes, Inverse Reinforcement Learning,
Gossip Algorithms, and large-scale data processing and mining techniques. This was achieved by studying advanced texts
(e.g., Markov models applied to time-series data), related research articles, and by taking technical online courses.
During the past year, they have received one-on-one mentoring from faculty. To encourage graduate students achieve tangible
and meaningful progress, regular weekly or bi-weekly NSF "driving volatility" project meetings were held on Fridays by the 3
PIs (Khattak, Chakraborty, and Nambisan); the involved graduate students presented their research work and the progress achieved.
Several other graduate students (4) benefited from the presentations made by the directly involved graduate students, and some of
them are now involved in related research, e.g., Mr. A. Srinivasan. The meetings created a platform to evaluate individuals' work
on the project as well as an atmosphere for brainstorming and exchanging ideas in order to tackle research challenges. Two post-doctoral
researchers (Liu and Rios-Torres) have benefited from participation in project meetings and contributed in terms of research direction.
The activities also provided graduate students with invaluable opportunity to improve their academic writing skills
for preparing research papers.
An additional 8 graduate students benefited from learning about CAVs in the Fall 2015 semester, when Dr. Chakraborty lectured
about Markov Decision Processes and advanced Inverse Reinforcement Learning techniques in a course taught by Dr. Khattak on
Intelligent Transportation Systems. Graduate and undergraduate students benefited from the Speaker Series Webinars in Fall
2015 that focused on CAVs, with presentations by Dr. Mike Walton, Dr. Andreas Malikopoulos, and Dr. Qing Cao.
A total of 4 undergraduate students were directly involved in the project-they helped edit papers generated by this study and
also are helping with literature review and technology development tasks. About 30 undergraduates received information about
driverless emerging technologies, when Dr. Khattak lectured on CAVs in an undergraduate course (CE 310).
Team efforts have included K-12 students. Dr. Nambisan partnered with Dr. Jennifer Richards, a faculty colleague with
expertise in K-12 STEM Education, to develop and lead a "Summer Transportation Academy for Teachers (STAT)." This was
held from June 18 to 22, 2016. It included several field trips and site visits to transportation related sites and
centers in order to provide 10 K-12 teachers exposure to transportation system elements and highlight linkages to
grade-appropriate curricular topics so as to enable them to develop authentic learning experiences (course modules)
for their students. The experiences included a demonstration of the instrumented vehicle (Humvee) and its use for
field data collection for the NSF project. It also included a demonstration by Dr. Chakraborty's students of augmented
reality tools to visualize various driving and roadway scenarios. The teachers will implement and assess the curricular modules and then make them available for online publication.
In another educational effort, Dr. Nambisan partnered with Dr. Chien-Fei Chen, the Education and Outreach Director for NSF's CURENT
Engineering Research Center led by the University of Tennessee. Ms. Ali Boggs, a PhD student in Transportation Engineering led efforts
to mentor two high school students in a 4-week long summer Young Scholars Program. This was to provide them grade appropriate
experiential learning opportunities, and enhance their soft skills.
Additionally, Dr. Chakraborty partnered with Ms. Susan Bothman, a teacher at Bearden High School, to develop transportation
based lesson arcs to be included in the Garrett Morgan Technology and Transportation Education Program's repository. These are
tailored to the Algebra 1 and Algebra 2 courses.
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