Utilizing the DRUID Impairment App to Assess and Enhance Senior Adults’ Driving
- White Paper
- Jan 1, 2024
- 4 min read
MassAITC study shows participants with higher DRUID impairment scores were less steady in their driving performance
Below is an overview of the study and summary of the driving activities performed by study participants. The study was performed through the Massachusetts AI & Technology Center (MassAITC) at UMass. There were 40 participants in the study with specific age and driving requirements. A link to the complete Technical Report follows.
Overview
The pilot project’s main goal was to validate the DRUID® impairment assessment app for use with senior adults, ages 64 to 85 years, a result that could open potential commercial markets for Impairment Science, Inc. (ISI), which developed and markets DRUID. Our study compared study participants’ performance on Realtime Technology’s full-scale cab driving simulator, which is housed at the Human Performance Lab (HPL), Riccio College of Engineering, University of Massachusetts-Amherst (UMass-Amherst), and their pre-simulator baseline performance on the DRUID impairment assessment app.
DRUID has users perform four tasks that measure cognitive and motor performance by assessing balance, decision-making accuracy, reaction time, hand-eye coordination, and time estimation under conditions of divided attention. The app collects and integrates hundreds of measurements to produce an impairment score that ranges from 25 to 75, with a higher score indicating greater impairment.
We pioneered a novel approach: 1) developing several mini scenarios, as opposed to one, long scenario, which in turn could be divided into segments based on the participants’ instructions and road conditions; and 2) analyzing dozens of outcome measures, including simulator data and a review of recorded videos.
Preliminary analyses showed that, on a few outcome measures, participants with higher baseline impairment scores were less steady in their driving performance, specifically with more changes in accelerator pressure and greater variability in their rate of deceleration. Other findings indicated that participants with higher impairment scores were driving more cautiously, with fewer lane deviations, more speedometer checks, lower maximum deceleration rates, and greater maximum braking pressure. One explanation is that, as senior adults, those who performed poorly on the DRUID app became anxious about doing well on the driving simulator and therefore became especially motivated during their 20-minute session to demonstrate exceptional driving skills.
Driving Simulator: Scenarios
We employed a series of short, self-contained mini scenarios rather than the single extended drive typically used in HPL’s driving simulator experiments. Each mini scenario presented participants with a single, well-defined driving event under controlled conditions, after which the simulator environment was reset before proceeding to the next scenario.
Participants completed a total of eleven mini scenarios, each designed to assess driver behaviors and perception under varying roadway and traffic conditions. The first, a two-minute introductory drive on a four-lane divided highway, served as a warm-up exercise which imposed minimal demands other than maintaining lane position and speed while traffic flowed in the opposite lane.
Traffic Conditions
For the subsequent mini scenarios, we sought to introduce a range of traffic conditions to test the participants’ driving abilities (see Figure 2). Together, these scenarios allowed us to evaluate the study participants’ visual scanning, hazard perception, and decision-making skills under controlled and repeatable conditions, while also reducing overall exposure time in the simulator to minimize discomfort for older drivers.
Below is a list and brief description of the ten experimental drives we developed and analyzed.
Scenario 1: Merging onto Highway. The driver is instructed to proceed down an on-ramp to enter a highway.
Scenario 2: Tailgating. On a rural road, the driver approaches a vehicle traveling 5-10 mph below the speed limit, which they are expected to follow at a safe distance without tailgating. Note: Unfortunately, for technical reasons, we were unable to download data from this scenario.
Scenario 3: Being Overtaken. On a divided four-lane highway, a large truck overtakes the driver’s vehicle on the left and remains adjacent for several seconds. The participants are expected to maintain stable lane position and, if needed, make speed adjustments.
Scenario 4: Occluding Truck. On a residential street, the driver is instructed to pass a delivery truck parked on the right side of the road that partially obscures the view of oncoming traffic, pedestrians, and potential hazards.
Scenario 5: Hidden Driveway. On a residential street, a vehicle emerges unexpectedly from a driveway obscured by vegetation, prompting the driver to brake or otherwise respond safely.
Scenario 6: Hedge at 3-Way. On a residential street, the driver proceeds to a three-way intersection with hedges obstructing the view on the right side, which requires them to approach cautiously, stop as indicated by traffic control devices, and visually check for hazards before making a right turn.
Scenario 7: Sudden Yellow. On an urban street, a traffic signal at a four-way intersection changes to yellow just seconds before the driver reaches the stop line.
Scenario 8: Straight Crossing Path. On an urban street, the driver proceeds toward an intersection where a car emerged from a street on the right side without stopping and turned left. They are expected to approach the intersection cautiously to check for other vehicles that might turn without stopping or yielding.
Scenario 9: Left Turn at Path (1.5 minutes). On an urban street, the driver approaches a signalized three-way intersection and are instructed to turn left across oncoming traffic when safe.
Scenario 10: Gap Acceptance (2 minutes). The driver is instructed to make a left turn at a signalized four-way intersection on urban street, with oncoming traffic spaced at increasingly long intervals.
Comments