SAfety vehicle(s) using adaptive Interface Technology (save-it) Program dtrs57-02-r-20003 Public Release a proposal Submitted to

Section 3: Management Plan 3.1 Program Team Structure

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Section 3: Management Plan

3.1 Program Team Structure

The SAVE-IT program team will be comprised of diverse and geographically dispersed team members. Consequently, In order to assure an effective and efficient coordination of program activities, the management process will include four key features: (i.) assurance of mutual clear understanding of the program mission, tasks, schedule and budget via written documentation; (ii.) measurement of performance and frequent status reporting through common program management instruments; (iii.) early identification and resolution of problems to assure conformance with program objectives and still maintain high quality technical performance within cost and schedule restrictions; and (iv.) communication throughout the team of changes in requirements, concepts and schedule. To accomplish the mission of the SAVE-IT Program, the team organization will be structured as shown in Figure 8 (team structure chart).

Delphi Delco Electronics Systems will coordinate the SAVE-IT team. As such the Program Manager will be an employee of DDE. The program manager will manage the day-to-day activities of the SAVE-IT program including the deliverables, schedule timing, and budget. The Program Manager will provide an overall technical lead of the program in addition to serving as the primary liaison between the team members and the appointed Government representative. Due to the significant effort required in both human factors research and systems technology development a team leader will be assigned to provide management focus in their respective task areas (see Figure 5). Each task will have a task lead who will be responsible for accomplishing the task objectives and completing the deliverables on schedule. The task leads will be assisted by team members and other technical assistants (e.g., graduate student research assistants).

Figure 5. Team structure chart


Appendix A: References

Boer, E. R. (2001). Behavioral entropy as a measure of driving performance. Proceedings of the first international driving symposium on human factors in driver assessment, training and vehicle designs. Aspen, CO. pp. 225-229.

Brown, T., Lee, J. D., & McGehee, D. V. (in press). An attention-based model of driver performance in rear-end collision situations. Transportation Research Record.

De Waard, D. (1996). The measurement of drivers’ mental workload. Unpublished doctoral dissertation.

Green, P. (1995). Measures and methods used to assess the safety and usability of driver information systems. FHWA-RD-94-088.

Green, P. (2000). Crashes induced by driver information systems and what can be done to reduce them. Proceedings of the 2000 international congress on transportation electronics. Warrendale, PA. pp. 27-36.

Harwood, D. W., Council, F. M., Hauer, E., Hughes, W. E., & Vogt, A. (2000). Prediction of the expected safety performance of rural two-lane highways. FHWA-RD-99-207.

Kantowitz, B. H., Levison, W. H., Hughes, W., Taori, S., Palmer, J., Dingus, T. A., Hanowski, R., Lee, J. D., Mears, B., & Williges, R. (1997). Development of prototype driver models for highway design: Task A final project I work plan. Seattle, WA: Battelle Human Factors Transportation Center.

Lee, J. D., Caven, B., Haake, S., & Brown, T. (2000). Are conversations with your car distracting? Understanding the promises and pitfalls of speech-based interfaces. Proceedings of the 2000 international congress on transportation electronics. Warrendale, PA. pp. 51-58.

Michon, J. A. (Ed). (1993). Generic Intelligent Driver Support. Washington, DC: Taylor & Francis.

Mykityshyn, M. & Hansman, R. J. (1993). Electronic instrument approach plates: The effect of selective decluttering on flight crew performance. Paper presented at the Seventh International Symposium on Aviation Psychology.

NHTSA DTNH22-99-H-07019 (2001). Automotive collision avoidance system field operational test.

Recartes, M. A., & Nunes, L. M. (2000). Effects of verbal and spatial-imagery tasks on eye fixations while driving. Journal of Experimental Psychology: Applied, 6, 31-43.

Senders, J. W., Krisofferson, A. B., Levison, W. H., Dietrich, C. W., & Ward, J. L. (1967). The attentional demand of automobile driving. Highway Research Record #195, Washington, DC: Highway Research Board, 15-32.

Tsimhoni, O. & Green, P. A. (1999). Visual demand of driving curves as determined by visual occlusion. Vision in vehicles.

Wang, J. S., Knipling, R. R., & Goodman, M. J. (1996). The role of driver inattention in crashes: New statistics from the 1995 crashworthiness data system. The 40th Annual Proceedings of the Association for the Advancement of Automotive Medicine, October 7-9, 1996, Vancouver, British Columbia.

Appendix B: List of Figures





The SAVE-IT system problem space.



SAVE-IT Conceptual model



Attention allocation to driving and non-driving tasks



SAVE-IT proposal task chart



Team structure chart


Appendix C: List of Tables





Distraction and impairment countermeasure requirements



Situation-specific countermeasure requirements (Safety warning countermeasures)



Summary of tasks, task leads, deliverables, and objectives


Appendix D: Acronyms Glossary

ACAS Automotive Collision Avoidance System

ACC Adaptive Cruise Control

ACM Adaptive CounterMeasures

AHS Autonomous Highway System

ANCOVA Analysis Of COVAriance

ANOVA Analysis Of VAriance

BAA Broad Agency Announcement

BRT Brake Reaction Time

BSW Blind Spot Warning

CD Compact Disk

DDE Delphi Delco Electronic Systems

DSM Driver State Monitor

DVD Digital Versatile Disk

ETS Eye Tracking System

FCW Forward Collision Warning

FOT Field Operational Test

GM General Motors

GPS Global Positioning System

HMI Human Machine Interface

HMIP HMI fusion Processor

HUD Head-Up Display

IESIM Industrial Engineering Hyperion Simulator at the University of Iowa

ISS Integrated Safety Systems

IVI Intelligent Vehicles Initiative

LDW Lane Departure Warning

NADS National Advanced Driving Simulator

NHTSA National Highway Transportation Safety Administration

OEM Original Equipment Manufacturer

RDCWS Road Departure Crash Warning System

RSA Rollover Stability Advisory

RT Reaction Time

RTC Reaction Time due to Cognitive distraction

RTD Reaction Time due to Drowsiness impairment

RTREQ Reaction Time REQuirement

RTS Reaction Time due to Substance impairment

RTT Reaction Time due to Telematics demand

RTV Reaction Time due to Visual distraction

SAE Society for Automotive Engineers

SAVE-IT SAfety VEhicles using adaptive Interface Technologies

SDLP Standard Deviation of Lane Position

SDM Simulator Development Module at Iowa’s NADS facility

SIREN Simulator for Interdisciplinary Research in Ergonomics and Neuroscience at the University of Iowa

STA Situational Threat Assessment

TLC Time-to-Lane Crossing

TRC Transportation Research Center

TTC Time To Collision

UMTRI University of Michigan Transportation Research Institute

VIRTTEX VIRtual Test Track EXperiment

1 The most important types of driver distraction are cognitive and visual distractions. It is well known that two hands can perform two independent tasks and usually only one hand on the steering wheel is required for lane control. It is likely that the most detrimental effect of manual interaction with an interface is the glance at buttons and the cognitive load of carrying out the function. These aspects will already be captured by the visual and cognitive distraction algorithms. Similarly, the most distracting component of auditory messages is the cognitive load of processing the auditory material. Therefore, no further experiments are required or planned for manual distraction and auditory distraction.


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