Cooperative Sensing of Vulnerable Road Users and Real-time Response to Potential Collisions via Connected Vehicle and Infrastructure Communication

Intersection safety is critical for all traffic participants, but especially for vulnerable road users (VRU) such as pedestrians and cyclists. Recent autonomous driving advances allow mitigation of several human driver risk factors, such as fatigue and recklessness. However, state-of-the-art autonomous driving technology also has limitations. An individual vehicle’s perceptual field-of-view can be compromised by nearby occluding objects, greatly reducing detection accuracy, and distant objects can be difficult to detect. Limitations in detection accuracy introduce further challenges in the downstream tasks of object tracking, trajectory prediction, and motion planning. In this project, the research team develops techniques for cooperative sensing at intersections to address these challenges and enable more effective identification of potential collisions involving VRUs and then combine them with novel CAV collision-mitigating actions to improve VRU safety. Cooperative sensing: Building on recent research in cooperative object recognition by multiple connected autonomous vehicles (CAVs) near an intersection, the team will develop extended techniques for tracking and predicting the trajectories of travelers. The team will first evaluate different approaches to reconciling shared feature maps (specifically the use of distributed Kalman filtering methods versus newer transformer-based approaches) to determine a baseline object tracking procedure. Second, the team will consider the tracking performance benefit of either additionally incorporating information from fixed-position camera/lidar sensors at the intersection (e.g., as would be possible at signalized intersections that use such sensors to support adaptive traffic signal control systems) or substituting them for CAV sensing altogether. Finally, the team will adapt and apply these results, which traditionally assume vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication and focus on accurate sensing of vehicles, to the sensing of VRUs. The team will use available traffic data to evaluate their initial technology results and use their East Liberty Surtrac/PedPal testbed site to test live VRU detection. Collision mitigation: To enable safe response to projected collisions with sensed VRU, the project will also investigate CAV strategies for taking evasive action. Recently deep reinforcement learning (DRL) has successfully dealt with autonomous driving tasks. The team aims to use DRL to train an agent across various intersection scenarios with human-like reactive agents to learn to decode social maneuvers from the past observed trajectories of the surrounding road users and execute a safe strategy for crossing the intersection. The developed algorithms will be tested on CommonRoad scenarios with reactive agents trained on a wide range of human behaviors. The proposed work will use state-dropout-based curriculum RL with Control Barrier Functions (CBF) to react in cases wherein the ego vehicle comes close to collision with other road users. This allows the ego agent to receive the CBF’s initial help to learn how to react safely, and then as the agent learns, lift the CBF constraints in order to cross intersections faster without compromising on safety. The proposed approach is coupled with existing state-dropout-based curriculum RL where future states of the ego agent are initially available as privileged information to ease learning for the RL agent but are subsequently removed so the RL agent can efficiently cross the intersection.

Language

  • English

Project

  • Status: Active
  • Funding: $200000
  • Contract Numbers:

    69A3552344811

  • 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:

    Carnegie Mellon University

    Pittsburgh, PA  United States 

    Safety21 University Transportation Center

    Carnegie Mellon University
    Pittsburgh, PA  United States  15213
  • Project Managers:

    Stearns, Amy

  • Performing Organizations:

    Carnegie Mellon University

    Pittsburgh, PA  United States 

    Safety21 University Transportation Center

    Carnegie Mellon University
    Pittsburgh, PA  United States  15213
  • Principal Investigators:

    Smith, Stephen

  • Start Date: 20240701
  • Expected Completion Date: 20250630
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01933407
  • Record Type: Research project
  • Source Agency: Safety21 University Transportation Center
  • Contract Numbers: 69A3552344811
  • Files: UTC, RIP
  • Created Date: Oct 13 2024 10:46AM