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Safe Driving Across Domains

This project tackles the critical research question: How can pre-trained autonomous driving models be seamlessly and dynamically adapted across diverse operational design domains (ODDs) to achieve safe and robust deployment? ODDs define the specific conditions under which an autonomous driving system can operate safely—spanning factors like road types, traffic density, weather patterns, and geographic features. For instance, a model trained to navigate Pittsburgh’s urban intersections must also adapt to Pittsburgh’s rural routes with sparse signage and rolling hills, or even underdeveloped, unpaved, mixed-traffic roads. What is the right way for a robot to, in real time, smoothly switch between different planning trajectories to ensure safety and consistency amid changing driving conditions? By focusing on adapting pre-trained models rather than retraining them from scratch, this project offers a scalable approach to ensure safe deployment. This is particularly critical in data-limited settings—collecting training data for every possible scenario can cost millions and take years. For example, equipping autonomous systems to operate in rural areas avoids costly new data collection while preserving high safety standards. A single deployment failure in an untested environment could cost lives and erode public trust, emphasizing the importance of rigorous adaptation. With an estimated 1.35 million road traffic deaths annually worldwide in drastically different driving conditions, this project aims to ensure that autonomous driving technologies can be deployed safely. In other words, this research algorithmically and proactively catalyzes translation of new technologies to data-limited areas, instead of passively relying on commercial technology spillover – the latter would take years or even decades. The research team leverages diverse datasets to ensure robust testing. These include driving data from TIER IV in Japan, and over 10 million miles of publicly available driving data from Waymo, collected across 25 U.S. cities and capturing diverse urban environments. In Pittsburgh, the team has access to truck-mounted camera data that covers mixed urban and rural routes, while stationary cameras funded through a PITA partnership with RIDC capture real-time trajectory data in high-traffic industrial areas. Simulation-based fine-tuning complements these datasets, addressing data gaps in under-documented scenarios, such as rural roads with limited signage. This rich dataset ecosystem provides the foundation for testing and validating model adaptations in diverse operational design domains. The project employs open source, cutting-edge algorithms, including inverse reinforcement learning (RL), guided diffusion, blueprints, and fine-tuning. Inverse RL provides an overall framework to complement rule-based modules to directly and flexibly extract algorithm parameters (sometimes implicitly) from expert (human or simulated) driving. Guided diffusion allows for precise and smooth generation and adjustments of such parameters in constrained domains, such as adapting autonomous driving algorithms from Pittsburgh's dense urban streets to its hilly rural roads. Blueprinting ensures scalable and structured design of template models that are transferable across diverse contexts, while fine-tuning efficiently adapts pre-trained models to novel environments, particularly in low-data settings. Open source models already implement these algorithms. The team adapts these powerful emerging methods to address challenges in rural areas and infrastructure-limited regions.

Language

  • English

Project

  • Status: Completed
  • Funding: $196,094.00
  • Contract Numbers:

    69A3552344811

    69A3552348316

  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    Safety21 University Transportation Center

    Carnegie Mellon University
    Pittsburgh, PA  United States  15213

    Carnegie Mellon University

    Pittsburgh, PA  United States 
  • Performing Organizations:

    Carnegie Mellon University

    Pittsburgh, PA  United States 
  • Principal Investigators:

    Zhang, Peter

    Tang, Pingbo

  • Start Date: 20250701
  • Expected Completion Date: 20260831
  • Actual Completion Date: 20260831
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 02002855
  • Record Type: Research project
  • Source Agency: Safety21 University Transportation Center
  • Contract Numbers: 69A3552344811, 69A3552348316
  • Files: UTC, RIP
  • Created Date: Sep 17 2026 4:37PM