Breakout 2-2: Automated Vehicle Challenges: How can Human Factors Research Help Inform Designers, Ro

Breakout 2.2: Automated Vehicle Challenges: How can Human Factors Research Help Inform Designers, Road Users, and Policy Makers?

This break-out session is sponsored by the TRB Subcommittee on Human Factors in Road Vehicle Automation.

Wednesday, July 12, 1:30 PM – 5:30 PM

Yosemite A

Organizers:

  • Anuj K. Pradhan, University of Michigan Transportation Research Institute
  • Chris Schwarz, University of Iowa National Advanced Driving Simulator
  • Fred Feng, University of Michigan Transportation Research Institute
  • John Sullivan, University of Michigan Transportation Research Institute
  • Shan Bao, University of Michigan Transportation Research Institute

Session Description

The purpose of the session is to provoke a lively discussion among industry, government, and academic experts with broad perspectives of the likely consequences that various levels of vehicle automation will have for humans adapting to these new technologies. We have deliberately sought experts outside of the usual human factors research community in an effort to understand those indirect effects that extend beyond immediate issue of vehicle control and operation. Indeed, we expect automation may alter how people have traditionally thought about mobility. Such changes will have likely consequences on the behavior of all road users.

Partially automated vehicles (SAE L2/L3), currently already deployed on public roads, are forerunners to those with high levels of automation (L3+). While these deployments are incremental and tentative, they have the potential to induce disruptions in the way people have traditionally interacted with vehicles. These disruptions will likely affect all road users—drivers of advanced vehicles, drivers of ‘legacy’ vehicles, pedestrians, and bicyclists. Significant gaps in our knowledge about users’ expectations, their perceptions, their strategic and tactical use, and their understanding of these systems limit our ability to anticipate how adoption of these technologies will play out on real roads, in real time, with real limitations, and representative use cases.

Education, training, and effective HMI design can play critical roles in raising driver/road-user awareness to ensure clear understanding about appropriate use of and expectations about these and future vehicle capabilities. As long as a driver is responsible for the supervision of these systems, human factors research will be tasked with predicting their direct effects on driver behavior and decision-making. It is also essential to understand their indirect effects on associated stakeholders, including manufacturers, policy makers, educators, and local legislators.

To that end, this meeting convenes experts identified and invited from academia, government, and industry to learn about and discuss issues from different perspectives, with insights on various approaches to address them. This workshop builds on two successful workshops on related topics conducted at the TRB 2017 Annual Meeting. An end objective is to identify relevant and timely research needs based on discussions between experts and participants.

Speakers:

  • Alex Epstein, Senior Director, Digital Strategy and Content, National Safety Council
  • Emily Frascaroli, Counsel, Ford Motor Co.
  • Nidhi Kalra, Senior Information Scientist and Director San Francisco Bay Area, RAND Corp.
  • Alain Kornhauser, Professor, Director, Transportation Program, Princeton University
  • Bernard Soriano, Deputy Director, California Department of Motor Vehicles
  • Trent Victor, Senior Technical Leader Crash Avoidance, Volvo Car Corp.

Session Structure & Activities

Moderator: Alex Roy

  1. Background & Introductions: (5 mins)
  2. Frame: Brief presentations by the 6 expert panelists on critical issues
  3. Deconstruct: Structured, moderated panel discussion
  4. Catalyze: Interactive audience discussions/Q&A with panelists
  5. Build: Focused audience discussion to identify, select, and prepare research needs statements

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