Universal Design, Technology, and Transportation Systems Aaron Steinfeld

Universal Design, Technology, and Transportation Systems Aaron Steinfeld

Universal Design, Technology, and Transportation Systems Aaron Steinfeld Robotics Institute, Carnegie Mellon University Rehabilitation Engineering Research Center on Accessible Public Transportation (RERC-APT) Co-Director Quality of Life Technologies Engineering Research Center (QoLT ERC) Lead, Safe Driving QoLT Systems Accessing the Future 2009 Cast of Many RERC-APT Carnegie Mellon

Anthony Tomasic John Zimmerman Ellen Ayoob Tim Andrianoff Steve Gardiner Rafae Aziz Jiseon (Daisy) Yoo Lauren Von Dehsen Alumni: Sun Young Park Priyanka Shetye Jean-Pierre (JP) Arsenault

University at Buffalo Ed Steinfeld Jordana Maisel Victor Paquet James Lenker Heamchand Subryan Danise Levine Clive DSouza United Spinal Association Gillig Corporation

Grimshaw-Architects Safe Driving QoLT Carnegie Mellon Drew Bagnell Anind Dey Arne Suppe Brian Ziebart University of Pittsburgh Linda van Roosmalen

Nahom Beyene Amy Lane Person & Society team Industry partners ... Accessible Transportation is Critical Employment, independence, & social engagement More than half a million people with disabilities cannot leave their homes because of transportation difficulties [1] One-third of people with disabilities have inadequate access to transportation [2]

46% of people with disabilities, compared to 23% of people without disabilities, reported feeling isolated from their communities [3] Lack of transportation (29%) was only second to a lack of appropriate jobs being available (53%), as the most frequently cited reason for being discouraged from looking for work [4] 1-month delay in nursing home admissions could save $1.12 B annually (US) [5] Apply IT advances in methods and technology to transportation Focus on need & capability, not diagnosis

Core Principles for Affecting Change Technology, as appropriate for end users Leverage existing advances in Intelligent Transportation Systems (ITS) End user input early and often Universal Design Primary: Any older or disabled user Secondary: Any user who could benefit from the technology Successful examples: Real-time arrival data from automated vehicle location (AVL)

VPG MV-1 Transit Status Quo Funding, relationships, & complexity Size and complexity of transit systems Consistent funding challenges Difficult to foster collaborative consumer participation Paratransit is very expensive More integration of accessibility into mainline components needed Transit: Travel Chain Use IT at the system level: best practices, accountability,

etc Citizen Science (Paolos, 2008) Rich media evidence for large-scale impact End user data collection Personal value/interest Opt-in Stationary works well Citizen weather stations Mobile now possible Air pollution

Access barriers www.neighborhood-networks.net www.zexe.net/GENEVE/map.php Collaboration is Attainable ParkScan.org Users provide pros & cons In 2007 alone: 425 registered users 1,531 observations 68% of contributed issues were addressed by the City City is an active participant

Transit Making it Useful Part I: Citizen science website Part II: Mobile, tailored, realtime information access Universal design features Open Services (e.g., BART) Database Backend Machine Learning Human-Computer Interaction Personal Vehicles Status Quo Reactive, infrequent, & incomplete Driving assessment: Doctor screening via interviews and

surveys, simulation/simulated driving assessments May not detect changes rapidly, occur infrequently By and large, discount importance of vehicle and environment familiarity Only ~400 Driver rehabilitation specialists (CDRS) in the US Vehicle changes are not cheap or easy to implement Hand controls (with training) start at $2k Conversion vans for wheelchairs start at $20k and rise quickly Driving: Increasing Safety Intervene above an extreme threshold (intervene before crash) Compress the distribution instead (reduce tailgating habits) Unsafe

Frequency Safe Risk Knipling, et al 2004 Behavior & Decisions are Personal Attempting to alter driver-specific outcomes through IT Research shows there are many different driving styles E.g., following behaviors: hunter, flow conformist, etc

Familiarity with own vehicle Controls Vehicle dynamics Vehicle size Familiarity with where typically drive Intersection handling Visual distractions Road type comfort/compensation

Limited cognitive demand for navigation Safe Driving Projects DriveCap: Low-cost aftermarket system that can measure capability on common denominator driving tasks in a wide range of vehicles DriveCap Advisor: Extend DriveCap to provide acceptable driver advice DriveCap Navigator: In-vehicle navigation that learns and supports robust application of rules related to driver capability and vehicle limits Vehicle Transformation: Basic research that enables safer driving through key design and vehicle control improvements Vehicle Enhancement: Basic development that enables safer driving through semi-autonomous vehicle control Policy, payment, acceptance, and related issues throughout

Driver Capability Assessment Autonomous vehicles (1990s - present) Use technology from autonomous vehicle research Augment & supplement capabilities of CDRS Appropriate: Trusted advisor Universal Design: Focus on driver capability, not diagnosis What Does the Data Look Like? Aggressiveness Time Gap to

Leading Vehicle (sec) Turn Rate Increases in tailgating and taking both wide and sharp turns faster Privacy: Context by Recipient Data collected QoLT P&S team Thank You Funding provided by the U.S. Dept. of Education, National Institute on

Disability and Rehabilitation Research, grant number H133E080019. Project Website: http://www.rercapt.org Part of this work is based on work supported by the National Science Foundation under Grant No. EEEC-540865. Project Website: http://www.qolt.org Robotics Institute School of Computer Science Carnegie Mellon University Email: [email protected]

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