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Texas A&M studies AI to improve transport for riders with mobility challenges

Posted on October 16, 2017

Texas A&M Transportation Institute worked with IBM and the Texas A&M University College of Engineering to explore ways of using IBM’s innovative AI system to improve campus transportation for  riders with mobility challenges.
Texas A&M Transportation Institute worked with IBM and the Texas A&M University College of Engineering to explore ways of using IBM’s innovative AI system to improve campus transportation for  riders with mobility challenges.
This summer, researchers, faculty and students at Texas A&M University explored innovative new ways to use artificial intelligence (AI) to improve campus transportation for mobility-challenged riders on campus.

As part of its Campus Transportation Technology Initiative, the Texas A&M Transportation Institute (TTI) worked with IBM and the Texas A&M University College of Engineering to explore ways of using IBM’s innovative AI system to improve campus transportation for such riders. The effort, a senior design capstone course project in Texas A&M’s Department of Industrial and Systems Engineering (ISEN), examined how to integrate Watson’s natural language interface with the Texas A&M Engineering Program’s autonomous shuttle. This was the first time IBM partnered with a major university to explore the use of Watson’s services in alternative transportation mobility solutions.

For their capstone class project, the students’ task was to research and plan for the integration of Watson into the autonomous shuttle to augment the operational and human interaction capabilities for mobility-challenged riders on campus.

The shuttle, developed by Srikanth Saripalli, Ph.D., of the Texas A&M Department of Mechanical Engineering, is an autonomous, electric utility vehicle that pairs a light detection and ranging (LIDAR) imaging system with a GPS-platted waypoint path to perform autonomous operations. This type of vehicle will undergo on-campus trials to determine if it can serve the needs of Texas A&M’s mobility-challenged population. While testing and refinement of the shuttle operations are ongoing, the team wishes to continue pursuing advancements in all areas of the shuttle by enhancing the user and passenger interface with advanced communication and control capabilities.

TTI Senior Research Scientist Bob Brydia, who leads the CTTI, explains: “We want to take the capabilities of the incredibly advanced IBM natural language and analytics platform [Watson] and take the first steps of incorporating it into the autonomous shuttle to be the interface between passengers and technology. The goal is to provide comfort, safety and ease of use to passengers, especially considering the target audience of a mobility-challenged population.”

“Collaborating on this project with TTI and Texas A&M provided IBM with the opportunity to explore how Watson’s cognitive services can be stretched to enable exciting new use cases, such as motorized mobility for the blind, alternative human-to-vehicle interaction, and predictive mobility service scheduling,” says Leigh Williamson, IBM distinguished engineer, adoption leader – Watson and cloud platform. “Working together at this early stage in development enabled IBM to learn new mechanisms for attaching cognitive computing features to an innovative new mobility platform.”

Williamson worked directly with the students to introduce them to the Watson platform and gave them a running start toward the objectives of the project and the technical support necessary for them to become familiar with the platform.

Through both operational deployments and classroom projects, the CTTI has been applying and evaluating private-sector innovations over the last year for their potential role in the campus environment, as well as examining the transportation efficiencies on campus. These efforts are also laying the groundwork for embracing transformative technologies on campus, such as the integration of autonomous vehicles.

The next phase of the project will include taking the use cases identified by the ISEN capstone team and drilling down to more technical levels. This further study will examine issues such as identifying data transfer needs, determining how much data is needed and how often the data should be examined, deciding what format to provide the data in and by what mechanism, etc. Brydia explains, “These are critical technical intelligence steps necessary before a full integration of Watson and the shuttle could be accomplished.”

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