User - Centered Auditory Warning Signals in Snowplows

Principal Investigator(s):

Kathleen Harder, Former Senior Research Associate, College of Design

Project summary:

Because the snowplow operator's tasks are predominately visual, warnings presented visually may interfere with critical tasks. Auditory warnings could reduce visual load if they are meaningful, effectively signal danger, and are not annoying. The researchers conducted a driving simulation experiment using a 210-degree forward field-of-view driving simulator and a field test to investigate using auditory icons as side and forward collision-avoidance warnings. Participants in the experiment drove on simulated snow-covered roads in 105-meter (344-foot) visibility conditions. Analysis of data from 28 participants showed the side collision-avoidance warnings were equally effective; lane change response times were approximately 1.1 seconds for both a single- and double-beep car horn warning (although participants said that the double-beep warning sounded more urgent). Analysis of the forward collision-avoidance warning data, obtained from 32 participants, showed that the mean response time with a warning consisting of two bursts of screeching-tire sounds was significantly faster than with a single-screech warning - with both warnings significantly faster than the mean time obtained when no warning was given. The poorest collision outcomes occurred with no warning; outcomes were better with the single-screech warning, and better still with the double-screech warning. In the field test, six of seven snowplow operators preferred the double-beep side-collision warning. As a result, the researchers recommend that an auditory icon sounding like the double-beep of a car horn be used as a side collision-avoidance warning, and an auditory icon sounding like two successive bursts of screeching tires be used as a forward collision-avoidance warning.

Sponsor(s):

Project details:

  • Project number: 2000038
  • Start date: 03/2000
  • Project status: Completed
  • Research area: Transportation Safety and Traffic Flow
  • Topics: Intelligent vehicles, Safety