Enhancing Safety in Mixed-Autonomy Traffic via Prediction-Based Connected Autonomous Vehicle Control

This project proposes a new framework for prediction-based connected autonomous vehicle (CAV) control to enhance safety in mixed-autonomy traffic where CAVs and human-driven vehicles (HVs) coexist. Specifically, by predicting future traffic conditions behind a target CAV, the vehicle can be proactively controlled to improve the safety and efficiency of the overall traffic stream. This approach is motivated by the fact that a controlled CAV directly influences the behavior, safety, and performance of following HVs through car-following interactions. Accordingly, the proposed method jointly considers a CAV and its following HVs in the design of a safety-aware driving strategy. Although HVs do not communicate with CAVs, traffic states related to HVs can be estimated using partial traffic measurements collected by CAVs. Leveraging these predictions, the proposed control strategy will be formulated within a model predictive control (MPC) framework to improve safety and traffic efficiency for HVs following a CAV. Extensive simulation studies will be conducted under a range of traffic scenarios and HV driving styles to demonstrate the effectiveness of the proposed approach. In addition, multiple CAV penetration rates will be evaluated to examine scalability and deployment potential. This project is highly aligned with the Mid-America Transportation Center's (MATC’s) mission to advance transportation safety through technology development, technology transfer, and deployment. It addresses a timely safety challenge: near-term traffic will be mixed-autonomy, where early-generation autonomous vehicles (e.g., ACC-equipped vehicles and emerging CAVs) operate alongside the majority of HVs. In this environment, safety risks arise not only from individual vehicle performance, but also from interactions between automated and human drivers—an issue that is often overlooked in existing CAV control design. While this project will leverage an existing dataset collected in Minnesota and high-fidelity simulation data generated in Simulation of Urban MObility (SUMO) for numerical investigation and validation, the team anticipates extending the proposed methodology in future work using connected-vehicle and SPaT data to be collected by Dr. Li Zhao’s team at UNL, in collaboration with the Nebraska DOT.

Language

• English

Project

• Status: Active
• Funding: $111,113.00
• Contract Numbers:

  69A3552348307

• 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:

  Mid-America Transportation Center

  University of Nebraska-Lincoln
  2200 Vine Street, PO Box 830851
  Lincoln, NE  United States  68583-0851
• Project Managers:

  Bruner, Britain

• Performing Organizations:

  University of Kansas, Lawrence

  Transportation Research Institute
  2117 Learned Hall, 1530 W 15th Street
  Lawrence, KS  United States  66045
• Principal Investigators:

  Wang, Shi'an

  Kondyli, Alexandra

  Schrock, Steven

• Start Date: 20260601
• Expected Completion Date: 20270531
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Car following; Connected vehicles; Intelligent transportation systems; Predictive control; Traffic safety; Traffic simulation; Vehicle mix
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01989950
• Record Type: Research project
• Source Agency: Mid-America Transportation Center
• Contract Numbers: 69A3552348307
• Files: UTC, RIP
• Created Date: May 21 2026 10:41PM