PROJECT TITLE

The Driver in the Driverless Car: Simulating Vehicle Automation for Evaluation of Driver Behavior and Performance

PRIMARY INVESTIGATOR

Anuj Pradhan, PhD
Assistant Research Scientist, Human Factors Group, U-M Transportation Research Institute

PROJECT DATES

May 2014 – April 2016

PROJECT TEAM

C. Raymond Bingham, PhD
Research Professor, Psychiatry, School of Medicine; Health Behavior and Health Education, School of Public Health; and head of the Young Driver Behavior and Injury Prevention Group, U-M Transportation Research Institute

Ryan Eustice, PhD
Associate Professor, Naval Architecture and Marine Engineering and Computer Science, and Mechanical Engineering, U-M Transportation Research Institute

John Sullivan, PhD
Associate Research Scientist, Head, Human Factors Group, U-M Transportation Research Institute

OBJECTIVE

The development of an experimental driving simulation platform to enable research studies to gather evidence and increase the scientific understanding of the relevant issues related to vehicle automation.  The studies will examine driver performance, attention, and emotional state at different levels of automation.

APPROACH

Build a driving simulation platform in which various safety-control functions will occur without driver input depending on the levels of automation.

Two Phases:

  1. Build a driving simulation platform with automated driving capabilities.
  2. Examine fundamental human factors research questions through a series of experiments conducted with the simulator.

OUTCOMES

A unique and important outcome of the experimental aspects of the study was the identification and development of specific and relevant scenarios that presented takeover situations with a high level of ecological validity for experimental studies.

The pilot study was instrumental in laying groundwork for conducting user studies for automated vehicles, and for establishing the protocols and IRB procedures.

A critical result related to examination of driver behavior during take-over was the pattern of eye movements in various conditions. In the baseline condition, there was a much greater dispersal of the driver’s glances during automated mode versus manual mode, indicating a wide scanning of the driving environment, as well as the driver’s surrounding environment, including the inside of the vehicle and off to the periphery.

Simulating both the strengths and limitations of current and future generations of vehicle technology will allow more rapid innovation in the driver interfaces and in-system performance specifications through safe, cost-effective, and fast investigation. Importantly, drivers with a wide range of backgrounds and capabilities can be tested before any physical testing is conducted. It will also play a role in testing and certification issues that are highly applicable to actual fleet deployment strategies.


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