Avoiding Collisions in Connected and Autonomous Driving Using Safe Deep Reinforcement Learning Exploration through Control Barrier/Lyapunov Functions

According to recent statistics in the U.S., close to 6 million motor vehicle accidents were reported in 2022 with more than 42,000 fatalities and more than 1.6 million injuries. In 2021, vulnerable road users accounted for about one-third of all traffic fatalities including 7,388 pedestrians, 966 cyclists, and 5,932 motorcyclists. In 2021, about 116,000 pedestrians, 105,000 cyclists, and 201,000 motorcyclists were treated in hospitals after collisions. Motor vehicle accidents are the second leading cause of deaths resulting from unintentional injury in the U.S. Connected and autonomous driving with carefully developed crash mitigation methods will help in the reduction of road traffic accidents and the fatalities and injuries they cause, including the ones with vulnerable road users. Based on these facts, this proposal focuses on developing data driven collision avoidance algorithms that learn to avoid collisions with nearby vehicles and vulnerable road users, building upon the research team's approach to pedestrian and bicyclist safety in their Year 1 and Year 2 Safety 21 projects. A deep reinforcement learning safety algorithm with safe exploration will be developed using realistic hardware-in-the-loop (HIL) and vehicle-in-virtual-environment (VVE) platforms. The current state-of-the-art crash mitigation methods use a catalog of possible collision interaction scenarios using model-in-the-loop simulations for development and evaluation. While this approach will be successful in mitigating collisions for the scenarios in the catalog being used, it is not generic in nature and will not necessarily mitigate crashes in the many other possible dangerous interactions between the road actors. The motivation for the use of the data driven approach in this proposal is this limitation in scope and the difficulty of accurately modeling the interaction dynamics between vehicles and between vehicles and vulnerable road users in dangerous encounters. The deep reinforcement learning based collision avoidance algorithm will learn from interactions in complex traffic environments, adapt to various traffic scenarios, and satisfy real-time performance requirements, making it a more robust and advanced solution. The team will use a hybrid approach in that the trained deep reinforcement learning algorithm will only be activated and work during dangerous and close interactions with other road users. Training will also be based on such interactions using traffic microsimulation to generate realistic training interactions automatically as compared to the fixed nature of the scenario driven approach. Since reinforcement learning algorithms suffer from the absence of safety guarantees during the learning process, the team will integrate their deep reinforcement learning algorithm with control Lyapunov and control barrier functions to limit its training to feasible areas of interaction with other road actors within the road barriers, with realistic vehicle dynamic model constraints as compared to the oversimplified ones used in the literature. Vehicle-to-Vehicle and Vehicle-to-VRU connectivity will also be used as low latency, and high speed near field and over the cloud cellular and other means of communication are available and as they help significantly in non-line-of-sight situations in aiding the perception sensors of autonomous vehicles. Efficacy of the proposed method will be demonstrated using HIL simulations and with a real vehicle using VVE.

• Record URL:
  • https://ppms.cit.cmu.edu/projects/detail/573

Language

• English

Project

• Status: Completed
• Funding: $187,647.00
• 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:

  Ohio State University

  Columbus, OH  United States 
• Principal Investigators:

  Aksun-Guvenc, Bilin

  Guvenc, Levent

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

Subject/Index Terms

• TRT Terms: Algorithms; Autonomous vehicles; Connected vehicles; Crash avoidance systems; Hardware in the loop simulation; Machine learning; Vulnerable road users
• Subject Areas: Data and Information Technology; Highways; Pedestrians and Bicyclists; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 02002843
• Record Type: Research project
• Source Agency: Safety21 University Transportation Center
• Contract Numbers: 69A3552344811, 69A3552348316
• Files: UTC, RIP
• Created Date: Sep 17 2026 1:56PM