Generating Safety-Critical Driving Scenarios for the Design of the CAV Proving-Ground - Using Domain Knowledge, Causality, and Large Language Models

Connected Autonomous Vehicles (CAVs) have witnessed significant advancements in recent years, largely due to the progress in machine learning-enabled sensing and decision-making algorithms. A paramount challenge for their widespread deployment in the real world, however, is safety evaluation. While most existing driving systems are trained and evaluated using naturalistic scenarios from daily life or heuristically-generated adversarial ones, safety-critical scenarios are extremely rare considering the sheer number of cars on the road. This leads to very imbalanced data and high cost for data collection. Consequently, methods that can generate realistic risky scenarios become essential for safety assessment and cost reduction. This research aims to enhance the testing procedures for Connected and Autonomous Vehicles by generating critical driving scenarios. These scenarios play a pivotal role in ensuring the safety and reliability of CAVs before they are deployed on roads. The research team plans to incorporate domain-specific knowledge about driving and road conditions, draw on causality inferences to comprehend the sequences of events leading to critical situations, and utilize the reasoning abilities of large language models to produce realistic and diverse driving scenarios. By incorporating these components, the research team aims to develop a holistic testing framework that presents a more accurate depiction of real-world driving challenges for CAVs. This will not only boost the robustness of CAV testing but also guide the design of proving grounds. Specifically, the research team will collaborate with PennSTART to implement the team's scenario generation approach in designing a proving ground for CAVs in Pennsylvania. Additionally, we'll harness augmented reality technologies to amplify the capabilities of the physical infrastructure by integrating virtual road users, including wheelchair users and the visually impaired. The research team's ultimate objective is to make a tangible real-world impact, potentially through technology transfer or the launch of a startup.

• Record URL:
  https://ppms.cit.cmu.edu/projects/detail/469

Language

• English

Project

• Status: Active
• Funding: $98000
• Contract Numbers:

  69A3552344811

• Sponsor Organizations:

  Carnegie Mellon University

  Safety21 National UTC for Promoting Safety
  Pittsburgh, PA  United States  15213

  Office of the Assistant Secretary for Research and Technology

  University Transportation Center Program
  ,    
• Managing Organizations:

  Carnegie Mellon University

  Safety21 National UTC for Promoting Safety
  Pittsburgh, PA  United States  15213
• Project Managers:

  Stearns, Amy

• Performing Organizations:

  Carnegie Mellon University

  ,    
• Principal Investigators:

  Zhao, Ding

• Start Date: 20230701
• Expected Completion Date: 20240630
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Artificial intelligence; Autonomous vehicles; Connected vehicles; Forecasting; Machine learning; Safety; Test procedures; Virtual reality
• Geographic Terms: Pennsylvania
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01900366
• Record Type: Research project
• Source Agency: Safety21
• Contract Numbers: 69A3552344811
• Files: UTC, RIP
• Created Date: Nov 21 2023 6:42PM