Risk-Aware Warning and Control for Interactive Traffic Safety

This project aims to pioneer a risk-aware control methodology tailored for ego vehicles operating in freeway driving scenarios, such as ramp merging and lane changing. The dynamic nature of these situations, characterized by intricate interactions, demands a meticulous approach due to uncertainties stemming from various sources—human factors, sensor-based stochasticity, and contextual information such as road geometry and vehicle types. For instance, in the context of an ego vehicle performing a ramp merge, it is vital to consider not only the distance between vehicles on the main road but also their speed, intent to yield, and overall traffic observations. This understanding is crucial, as attempting to merge into a gap within a truck platoon might be riskier than with non-truck vehicles. In this highly interactive and uncertain environment, the safety of human drivers heavily relies on Advanced Driver Assistance Systems (ADAS) for accurate risk perception, even when an immediate collision is not imminent. The primary objective is to develop a comprehensive risk assessment tool capable of quantifying the diverse risk factors influencing ego vehicles within multi-vehicle interactions. This tool can significantly enhance overall safety, whether a human driver is utilizing active ADAS or opting for full automation by the controller. Seamlessly integrating with existing ADAS, this tool can alert drivers to potential dangers when risk assessment exceeds a certain threshold and can take necessary preventive actions to avert collisions. Building upon prior research in safety-critical autonomous driving, the goal is to seamlessly integrate the proposed novel risk management toolbox with ADAS/safety controller development, culminating in a distinctive interaction-aware framework for ego vehicles. The research team proposes the application of Conditional Value at Risk (CVaR) to quantify the cumulative risk that autonomous vehicles face during interactions with surrounding vehicles. This approach accounts for stochasticity arising from human drivers, uncertainty from sensor inputs, and contextual information such as road geometry and vehicle types. CVaR, a robust risk management tool, provides a quantitative measure of potential losses beyond a specified confidence level, helping identify tail risks, compare strategies, and facilitate informed decisions in risk management and analysis. For human-driven vehicles, this risk management tool acts as a verification layer for existing ADAS, mitigating possible unsafe actions by reckless or distracted drivers. For fully autonomous vehicles, this tool can be seamlessly integrated with safety-critical controllers, such as pre-existing Control Barrier Function (CBF)-based safe controllers, to obtain formally provable safety guarantees. Notably, these safe controllers have demonstrated remarkable performance in initial collision avoidance scenarios, and subsequent efforts will focus on developing risk-aware CBF-based controllers, followed by validation through simulation and real-world vehicle testing. To validate their approach, the team will conduct a rigorous testing phase in simulation and the real world by using a 1/10th scale autonomous race car. The significance of this approach is twofold: first, its implementation in existing ADAS can convey to drivers the comprehensive risk associated with desired actions within the current interactive environment; second, for fully autonomous vehicles, it offers interpretable risk-aware behavior with formally proven safety guarantees, enhancing overall operational safety.


  • English


  • Status: Active
  • Funding: $195372
  • Contract Numbers:


  • 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:

    Dolan, John

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

Subject/Index Terms

Filing Info

  • Accession Number: 01900239
  • Record Type: Research project
  • Source Agency: Safety21
  • Contract Numbers: 69A3552344811
  • Files: UTC, RIP
  • Created Date: Nov 20 2023 8:25PM