Optimal Automated Vehicle Piloting: Avoiding Perfection being the Enemy of the Good

Optimal timing for automated vehicle (AV) pilot programs is essential to balance early implementation benefits against the goal of perfected safety. Although AV technology is not accident-free, it shows potential in reducing crash rates. This work introduces a mathematical framework designed to maximize social welfare through AV pilots, integrating elements such as safety metrics, technology diffusion, and learning effects. The framework’s three models address scenarios that social planners may encounter, offering flexibility in balancing initial risks with anticipated safety improvements over time. A case study illustrates these models, emphasizing the framework’s utility in navigating trade-offs like managing higher early failure rates while leveraging learning-based enhancements. By adjusting input parameters, planners can customize pilot designs to meet community needs. Future improvements, such as variable weighting and iterative testing, are proposed to enhance the framework’s adaptability and its capacity to capture critical AV deployment considerations.