Research

Lead Researcher(s)

Clive D’Souza
Assistant Professor of Industrial and Operations Engineering, College of Engineering

Project Abstract

The objective of this project is summarize the existing research and identify knowledge gaps in matching major disability types with potential barriers when performing tasks associated with using a low-speed autonomous shuttle. Designing autonomous vehicles for universal accessibility is challenged by a lack of standards and best practices. Further, the needs and challenges for each disability type are different and vehicle designs should consider integrating solutions to address those different needs and challenges. As recently as 2017, a breakout session on accessibility at the Autonomous Vehicle Symposium recognized that the need for a collaborative effort to review, analyze, and develop or make recommendations for updated sets of standards, policies, and regulatory frameworks for universal accessibility.
This project will develop a matrix to match major disability types with potential barriers when performing tasks associated with using a low-speed autonomous shuttle. We emphasize fundamental areas of potential mismatch between task goals and person capabilities (i.e., the “what” and “why,” e.g., step-less vehicle ingress for a wheeled mobility user), even though the technology solutions (i.e., the “how”) might evolve rapidly.
The matrix will be populated by conducting a review of the pertinent research literature (journal manuscripts, conference proceedings), white papers, and technology briefs available in the public domain. We limit our scope to low-speed autonomous shuttles operating as a fixed-route public transit vehicle. We envision this matrix serving as a conceptual framework to inventory, analyze, and develop requirements to address the safe, independent, and efficient use of autonomous shuttles by people with disabilities.

Project Outcome

A set of documents that summarize findings from a systematic literature review, highlighting evidence-based guidelines for accessibility and usability (by task and disability type) when available, and identifying areas where evidence is lacking.