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Direct Impact

For the purpose of fulfilling the project aims, four graduate students directly involved (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, 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 to achieve tangible and meaningful progress, regular weekly or bi-weekly NSF “driving volatility” project meetings were held on Fridays by the 3 Principal Investigators (Khattak, Chakraborty, and Nambisan); the involved graduate students presented their research work and the progress achieved. Several other graduate students benefited from the presentations made by the directly involved graduate students, and some of them are now involved in related research. 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.

Indirect Impact

An additional eight graduate students benefited from learning about CAVs in the Fall 2015 semester, when Subhadeep Chakraborty lectured about Markov Decision Processes and advanced Inverse Reinforcement Learning techniques in a course taught by Asad 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 Mike Walton, Andreas Malikopoulos, and Qing Cao.

A total of four 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 Asad Khattak lectured on CAVs in an undergraduate course (CE 310).

Team efforts have included K-12 students. Shashi Nambisan partnered with 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 Subhadeep 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, Shashi Nambisan partnered with Chien-Fei Chen, the Education and Outreach Director for NSF’s CURENT Engineering Research Center led by the University of Tennessee. Ali Boggs, a PhD student in Transportation Engineering led efforts to mentor two high school students in a four-week long summer Young Scholars Program. This was to provide them grade appropriate experiential learning opportunities, and enhance their soft skills.

Additionally, Subhadeep Chakraborty partnered with 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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