Development of a Generalized Integrity Monitoring Framework For CAV Application

CAV applications can be broadly classified into three major categories: safety, mobility, and environmental. Mobility and environmental applications require a coarse positioning accuracy (5-10 m) and lane-level positioning accuracy (< 1 m), while safety-critical applications require a where-in-lane level accuracy (< 0.2 m). Uncertainty in positioning information can sabotage driving functionalities and cause a safety concern. Position uncertainty of a vehicle can be attributed to the sensor suite available on the vehicle, along with any additional external sensor information that may aid the in-vehicle sensors. Furthermore, a fully connected and Automated vehicle (CAV) may turn itself into a degraded CAV, AV-only, CV-only, or Human-driven vehicle (HDV) while experiencing communication or control loss. The type, quantity, placement, and measurement uncertainty of sensors play a great role in determining the navigation performance of a vehicle. Additionally, in a mixed traffic scenario, there exist various types of positioning solutions, vehicles, sensor modalities, communications capabilities, and applications. It is unlikely that two vehicles having the same set of sensors and computation hardware will output similar navigation performance. Therefore, it is important to study and analyze the integrity of positioning systems under various conditions. Integrity monitoring (IM) methods have been extensively studied and developed for in-vehicle sensor systems primarily consisting of GNSS receivers (e.g. RAIM) often integrated with IMUs, vehicle odometry, and perception sensors such as radar, camera, and LiDAR. The localization performance and safety of a vehicle have mainly been studied from an ego vehicle's perspective focusing on enabling automated driving functions through onboard sensors and compute platform. Further, there is a surge in V2V/V2I/V2P/V2X research focusing on information sharing between road agents for improved positioning, navigation, and control. Given the number of sensor sources and the amount of data shared between road agents, it is imperative to develop integrity monitoring frameworks for cooperative scenarios. Based on the research team's initial literature survey, despite the growing interest in this area, there is a noticeable gap in the current literature that addresses the "cooperative-IM (integrity monitoring)" framework, indicating the need for further research to propose new Required Navigation Performance (RNP) parameters that may support CAV applications. As a part of the research team's smart intersection projects (City of Riverside & City of Rialto), it has conducted various Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) based cooperative positioning experiments, where perception data from both onboard and roadside sensors i.e. camera, LiDAR, is shared among the vehicles and infrastructure through C-V2X communication. The research team has successfully demonstrated improved vehicle detection and positioning accuracy (0.3 meters with Roadside LiDAR). Furthermore, the research team is currently experimenting with Cohda wireless MK6 modules to transmit basic safety messages (BSMs) and other information among vehicles in a V2V setting. However, the question remains whether the empirically determined Required Navigation Performance (RNP) values hold consistent across different driving conditions, positioning hardware, and communication topologies. Inconsistencies in RNP parameters would lead to the failure of CAV applications.

Language

  • English

Project

  • Status: Active
  • Funding: $Federal 118412, Cost-share 59206
  • Contract Numbers:

    69A3552348324

    Illinois Institute of Technology.

  • 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:

    Center for Assured and Resilient Navigation in Advanced Transportation Systems

    Illinois Institute of Technology
    Chicago, IL  United States  60616
  • Project Managers:

    Narang, Aashish

  • Performing Organizations:

    Center for Assured and Resilient Navigation in Advanced Transportation Systems

    Illinois Institute of Technology
    Chicago, IL  United States  60616
  • Principal Investigators:

    Qiu, Hang

    Barth, Matthew

  • Start Date: 20241001
  • Expected Completion Date: 20250930
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program
  • Subprogram: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01934810
  • Record Type: Research project
  • Source Agency: Center for Assured and Resilient Navigation in Advanced Transportation Systems
  • Contract Numbers: 69A3552348324, Illinois Institute of Technology.
  • Files: UTC, RIP
  • Created Date: Oct 22 2024 4:21PM