Lifetime Driver Learning Initiative

Crow, Ed; Sussman, E. D. · 1997 · ROSA P / John A. Volpe National Transportation Systems Center (U.S.)

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Summary

This document outlines the planning activities, program framework, and action plan for the Lifetime Driver Learning Initiative, a proposed federal research and development effort aimed at improving highway safety through human-centered transportation systems. The initiative was motivated by the recognition that while infrastructure and vehicle design improvements have contributed to safety, human operator behavior remains the largest untapped area for reducing fatalities and injuries. With over 40,000 annual deaths and significant economic costs attributed to traffic crashes, the document argues for a shift from traditional, novice-focused driver education to a comprehensive, lifelong learning strategy. This approach seeks to leverage advances in computer technology and simulation, drawing parallels to effective military training methods used for pilots and troops, to address the needs of novice, aggressive, and aging drivers. The methodology involved a planning forum held in July 1997 in State College, Pennsylvania, organized by the Pennsylvania State University Center for Intelligent Transportation in cooperation with federal agencies including the Department of Transportation, NASA, and the Department of Defense. Approximately 50 experts from public and private sectors participated in discussions to identify key issues regarding judgment, decision-making, instructional design, and economic thresholds for training. The resulting framework proposes a multi-agency, public-private partnership to conduct long-term research. The immediate action plan, designated as Phase One (12–18 months), includes four primary tasks: conducting systems-level analysis to prioritize high-risk scenarios and driver cohorts; designing a model public-private partnership with defined incentive structures; developing public awareness models to frame accidents as a public health issue; and initiating preliminary data collection through both in-vehicle and in-simulator studies. The findings and proposed outcomes emphasize the need to reinvent driver education by utilizing simulation-based training to expose drivers to risky roadway situations in a safe context, thereby developing automatic responses. The document highlights specific demographic concerns, noting that teens have crash rates four to five times the average, aggressive driving is increasing, and the aging population faces frailty-related risks. It suggests that current driver education may be ineffective or even counterproductive, necessitating rigorous study of the science of driving. The proposed program aims to baseline in-vehicle behavior and determine the necessary fidelity of simulation for effective knowledge transfer. The significance of this initiative lies in its potential to integrate advanced information technology and simulation into driver training, creating a sustainable model for maintaining driver proficiency across the lifespan. By establishing a cooperative framework involving federal agencies, state departments, and private industry, the initiative seeks to secure federal funding and private sector incentives to deploy affordable training programs. The document concludes that addressing human performance through such an integrated, long-term R&D plan is essential for realizing sizeable gains in transportation efficiency and safety in the 21st century.

Key finding

The document proposes a multi-year, interagency research program to develop simulation-based driver training for novice, aggressive, and aging drivers, modeled after effective military training techniques, to address the inadequacy of traditional driver education in preventing crashes.

Methodology

other

Provenance

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Topics

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• older driver retraining
• learner drivers
• driver education effectiveness
• public messaging
• novice drivers
• simulator training transfer

Information type

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• Applied Guidance: countermeasure evaluation
• Methodological Resource: dataset resource
• Theoretical Contribution: computational model