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Task 9: Safety Warning Countermeasures Matthew Smith Aug 12, 2003

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SAfety VEhicles using adaptive Interface Technology Phase 1 Research Program Quarterly Program Review. Task 9: Safety Warning Countermeasures Matthew Smith Aug 12, 2003. Task description. Team Members: Delphi: Matthew Smith (Lead), Harry Zhang Ford: Ksenia Kozac, Jeff Greenberg

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SAfety VEhicles using adaptive Interface Technology Phase 1 Research ProgramQuarterly Program Review

Task 9: Safety Warning Countermeasures

Matthew Smith

Aug 12, 2003

task description
Task description
  • Team Members:
    • Delphi: Matthew Smith (Lead), Harry Zhang
    • Ford: Ksenia Kozac, Jeff Greenberg
  • Objective:
    • Enhance safety warning countermeasures to adaptively respond to distraction, gaze, demand, and intent information, e.g.,
      • Deliver earlier warnings for distracted drivers
      • Minimize nuisance alerts when the driver is attending on intending a maneuver
  • Purpose:
    • Improve system effectiveness and driver acceptance of safety warning countermeasures and support the evaluation of adaptive enhancements
    • Reducing false alarms may increase the credibility of the warnings so that they are less likely to be ignored
    • Earlier warnings for distracted drivers may reduce the number of collisions
deliverables and schedule
Deliverables and Schedule
  • Deliverables:
    • Task 9A: A report based on the literature review and updated task definition document
    • Task 9B: A report that specifies the adaptive enhancements to the countermeasures, describes the research conducted to reach the provided specification, and proposes preliminary guidelines and standards (to be reviewed in Task 12)
  • Schedule:
    • 9A: Literature Review
      • First draft of literature review is complete
      • First draft currently under internal Delphi review before external circulation
    • 9B: Identify Adaptive Countermeasures
      • Identified Countermeasure Systems
      • Identified Adaptive Enhancement Issues
      • Defined and Described Potential Adaptive Enhancements
      • Selected and coded FCW algorithm
      • Selected FCW DVI
      • Preliminary Design BRT Study complete
literature review task 9a
Literature Review: Task 9a
  • Research Areas of Interest / Literature Review Sections
    • Ford may be adding a short Curve Speed Warning (CSW) section
  • Key Source Material
    • Collision Types (Najm, Sen, Smith, & Campbell, 2003)
    • Rear-end crashes and countermeasures (Burgett, Carter, Miller, Najm, & Smith, 1998; Knipling, Hendricks, Koziol, Allen, Tijerina, & Wilson, 1992; ACAS FOT reports, 2002-2003)
    • Lane change crashes and countermeasures (Chovan, Tijerina, Alexander, Hendricks, 1994; Tijerina, & Hetrick, 1997)
    • Intersections crashes and countermeasures (Pierowicz, Jocoy, Lloyd, Bittner, Pirson, 2000)
    • SVRD crashes and countermeasures (Pomerleau, Jochem, Thorpe, Batavia, Pape, Hadden, McMillan, Brown, and Everson, 1999)
task 9a literature review major findings
Task 9a: Literature ReviewMajor Findings


Light Vehicle Crashes

(Najm, Sen, Smith, & Campbell, 2003)

Roadway Fatalities

(U.S. Department of Transportation, 1997)

task 9a literature review major findings cont
Task 9a: Literature ReviewMajor Findings (cont.)
  • Rear-end Crashes
    • Most prevalent category of crashes
    • Knipling et al. (1992) estimated that over ¾ of rear-end collisions involve driver inattention (including inattentive and following too closely)
    • Forward Collision Warning (FCW) systems are designed to prevent RE collisions but from the ACAS FOT program appear to have high nuisance alert rates
      • Many participants complained that the warnings are too late if they are not attentive and unnecessary when they are attentive
  • Road departure Crashes
    • Largest cause of roadway fatalities (36%)
    • Mironer and Hendricks (1994) estimated 9% of SVRD involve driver inattention to lane keeping (25% to driver impairment)
    • Lane Drift Warning (LDW) designed to prevent unintentional lane departure
    • Curve Speed Warning (CSW) designed to prevent roadway departures caused by excessive speed on curved road segments
task 9a literature review major findings cont1
Task 9a: Literature ReviewMajor Findings (cont.)
  • Intersection Crashes
    • Second most prevalent category of crashes
    • Different countermeasures for different types of accidents (Pierwowicz et al., 2000)
    • Most sub-categories of intersection crashes require infrastructure support
    • Stop-sign violation warning (SSVW) involves simple sensor requirements and can target 18% of all intersection accidents
  • Lane Change/ Merge Crashes
    • 9% of collisions and 1% of fatalities
    • Wang et al. (1996) estimated 5.6% attributed to driver distraction and 17.2% to Looked-but-did-not-see (LBDNS)
    • Many blind-spot warning systems have emerged on the market
    • Many researchers suggest activating higher-levels of warning using the turn signal or some other indication of driver intention
task 9a literature review countermeasure systems
Task 9a: Literature Review Countermeasure Systems
  • Forward Collision Warning (FCW)
    • Strong relationship to driver distraction
    • Problematic nuisance alerts
    • Task 9 (SWC) will investigate FCW
  • Lane Drift Warning (LDW)
    • Problematic nuisance alerts (e.g., intentional lane changes)
    • Task 9 (SWC) task likely to investigate LDW
    • If not feasible for SAVE-IT on-road testing/prototype vehicle it may still be feasible in driving simulator analyses
  • Curve Speed Warning (CSW)
    • Can develop relatively simple system with GPS/Map matching
    • Ford is currently considering supporting this activity for SAVE-IT
  • Stop Sign Violation Warning (SSVW)
    • Simple system
    • Task 9 (SWC) likely to investigate SSVW
  • Blind Spot Warning (BSW)
    • Difficult to evaluate in single-channel driving simulator in Phase I
task 9a literature review forward collision warning fcw
Task 9a: Literature Review Forward Collision Warning (FCW)
  • Algorithm Alternatives
    • Time-headway
    • Time-to-collision
    • Kinematic Constraints
      • Calculates minimum range to avoid collision by braking at specified rate after a specified reaction time
      • Most comprehensive algorithm
  • Driver Vehicle Interface
task 9a literature review lane drift warning ldw
Task 9a: Literature Review Lane Drift Warning (LDW)
  • Algorithm Alternatives
    • Zero-order Time-to-Line crossing (TLC)
      • Simple but doesn’t consider rate of drift
    • First-order TLC
      • Assumes lateral acceleration will remain constant
    • Second-order TLC
      • Use of acceleration amplifies measurement error
    • Kinematic TLC
      • Takes into account upcoming road geometry
      • More complex measurement requirements
  • Driver Vehicle Interface
    • Steering-wheel counterforce
    • Tijerina et al. (1996) recommended not using both auditory and haptic
task 9a literature review stop sign violation warning ssvw
Task 9a: Literature Review Stop Sign Violation Warning (SSVW)
  • Pierowicz et al. (2000) used required deceleration to prevent intersection entry as the criterion for the warning
    • If required deceleration (ap) exceeds 0.35 g warn the driver
  • System only requires GPS signal and a digital map to determine the distance to the intersection
  • Driver Vehicle Interface
    • Pierowicz et al. (2000) used a stop-sign symbol on a HUD
task 9a literature review blind spot warning bsw
Task 9a: Literature Review Blind Spot Warning (BSW)
  • Tijerina and Hetrick (1997) suggested three stages of warning
    • Stage 1: object in blind spot
      • Suggested using visual-only stimulus
    • Stage 2: object in blind spot and turn signal is activated
      • Suggested using “augmented” visual-only stimulus (e.g., flashing visual)
    • Stage 3: object in blind spot and host vehicle moving toward blind spot
      • Suggested using multi-modality (e.g., visual plus haptic or auditory)
  • Driver Vehicle Interface

Task 9a: Literature Review Adaptive Enhancement Issues

  • Provide appropriate level of adaptive enhancement
    • Not over-sensitive so that it changes too frequently or appears to be unstable
    • Not under-sensitive so that it is non-responsive, providing little added value
    • Avoid closed-loop oscillations or impressions of system inconsistency
      • e.g.,
  • Billings (1997) argued that the adaptation must be predictable so the user can from a clear mental model of the system’s behavior



research task 9b
Research: Task 9b
  • Research Objectives/Strategy
    • Countermeasures usually involve imminent alert levels and must predict how quickly the driver must react to the alert
    • Determine how the results from the other experiments can be mapped onto adaptive enhancements to the countermeasures
      • Experiment 1: Imminent Alerted Brake Reaction Time (BRT)
    • Determine the effectiveness of different adaptive enhancements
      • Experiment 2: Comparison of Different Adaptive Enhancements
  • Experiment 1 Method
    • Measure BRT to the FCW alert as a function of different levels of distraction
    • Manipulate distraction level in the driving simulator using conditions from Tasks 5 (Cognitive Distraction) and Task 7 (Visual Distraction)
    • Lead vehicle brakes suddenly at an imminent level (e.g., 0.5 g)
  • Experiment 2 Method
    • Expose subjects to different implementations of enhanced countermeasure systems during differing levels of imposed driver distraction
    • Assess the driver acceptance issues surrounding adaptive enhancements (e.g., should alerts be delayed or suppressed completely when the driver is attentive?)
research task 9b experiment 1 imminent alerted brt
Research: Task 9b Experiment 1: Imminent Alerted BRT
  • Variables
    • Independent Variable: Distraction Level
    • Dependent Variable Brake Reaction Time (BRT)
  • Facilities/Apparatus/Subjects
    • Delphi Driving Simulator
    • Between-subject study
      • Can only surprise subjects once
      • 10-12 subjects per condition (50-60 total)
      • Subjects in 35 – 55 age group
  • Distraction Level Conditions
    • No distraction with alert
    • Mid-level cognitive distraction (from Task 5) with alert
    • High-level cognitive distraction (from Task 5) with alert
    • Mid-level visual distraction (from Task 7) with alert
    • High-level visual distraction (from Task 7) with alert
research task 9b experiment 2 adaptive enhancements
Research: Task 9b Experiment 2: Adaptive Enhancements
  • Variables
    • Independent Variables
      • Countermeasure System / Adaptive enhancement concept alternatives
      • Distraction Level
    • Dependent Variables
      • Subjective responses regarding the enhancement alternatives (driver acceptance)
      • Observe different adaptive enhancements with different drivers
  • Facilities/Apparatus/Subjects
    • Delphi Driving Simulator
    • Use subset (10 – 24) of participants recruited from the reaction time experiment
      • Likely use the high-level visual and/or cognitive distraction group because they are likely to be familiar with the concept of driver distraction in relation to collision warnings
  • Design
    • Instruct participants to drive in the simulator while engaging in the distraction tasks and experiencing the adaptive enhancements
    • Lead vehicle will frequently brake to assess adaptively-enhanced FCW
    • Compare different types of adaptive countermeasures and non-adaptive countermeasures
research task 9b issues concerns
Research: Task 9b Issues/Concerns
  • IRB approval
    • IRB approval (August 29th) may be delayed to allow more accurate and detailed submission (changes less likely)
  • Iteratively test and refine countermeasure concepts
    • Deadline of August 29th not feasible because Delphi driving simulator will not be available for development until early September (Used in Task 7)
    • Estimated completion October 17th
  • Facility Preparation
    • Deadline of August 29th not feasible because Delphi driving simulator will not be available for preparation until early September
    • Estimated completion October 17th
  • Design Adaptive Concept test study
    • Initial deadline of July 31st not feasible because of driving simulator delay
    • Estimated completion September 30th
  • Data Collection
    • Deadline of November 3rd not feasible because of delay in facility preparation
    • Estimated completion November 28th
  • Data Analysis, Final Report, and Phase II plan
    • Respective deadlines of Jan 5th, Jan 30th, and Feb 27th should still be possible