Infrastructure-Based Sensor Fusion for Tracking Connected and Autonomous Supply Chain Assets in Cyber-Compromised Environments
The ongoing growth and economic benefits of America’s largest container ports are threatened by negative externalities associated with port operations, particularly increasing congestion and air pollution caused by drayage truck and rail modes serving the ports and traveling to inland transloading, rail yard, warehouse and distribution center facilities. For example, the San Pedro Bay Ports (SPBP) of Los Angeles and Long Beach in Southern California, the largest container port complex in the US and one of the largest in the world, is critical to the nation’s future intermodal logistics system and vital to economic growth and standard of living. One proposed solution to these issues is to transition the heavy duty (HD) drayage fleet (as well as long haul fleets) to zero emission connected and autonomous vehicles (CAVs), in order to increase supply chain capacity, throughput, safety, resilience and sustainability. In this research the research team proposes to develop the ITS-Irvine Freight Mobility Living Laboratory (FML2) to serve as an open innovation ecosystem for exploring field deployment of innovative approaches and technologies for advancing the introduction of autonomous operations to the nation’s supply chains. FML2 utilizes advanced inductive signature technology with existing freeway infrastructure¹, supplemented by LiDAR sensors and artificial intelligence-based data analytics developed by ITS-Irvine researchers², and automated license plate recognition and weigh-in-motion technology, to provide unprecedented detail and data on real-time heavy-duty truck classification and activity. FML2 currently comprises about 30 sites, and is being expanded to over 100 sites along similar major freight corridors in the Inland Empire region of Southern California (San Bernardino and Riverside counties) where freight facilities are concentrated and drayage and long-haul trucking is prevalent. The team will build upon previous ITS-Irvine research that has pioneered and successfully demonstrated vehicle reidentification and tracking based on inductive signature technologies ³, to explore fusion of multiple FML2 infrastructure-based sensing systems to achieve reidentification and tracking of HD CAVs in cyber-compromised environments. The initial focus will be on drayage trucks, as a pilot study for more general reidentification and tracking of mobile supply chain assets. The team will use field data to investigate the case of total-cyber-compromise, where one or more HD CAVs either enact predetermined safety shut-downs, or continue under the cyber-control of rogue actors, which could, for example, be for criminal, or terrorist purposes. Scenarios of partial cyber-compromise will be modeled using microscopic traffic simulation of FML2 operations. The ability to identify and track HD CAVs will provide responders with location and trajectory information that might not otherwise be available. Concurrently, the ability to anonymously reidentify and track all HD CAVs with public sensing infrastructure will provide additional real-time data to support freight transportation operations, planning and modeling, particularly for mixed systems of autonomous and non-autonomous HD vehicles. The team will also explore the reconstruction of microscopic trajectories using advanced point-cloud-based sensors to detect non-autonomous HD vehicles that may be operating under the influence, which may pose a significant safety hazard to other road users.
Language
- English
Project
- Status: Active
- Funding: $150000
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Contract Numbers:
69A3552348327
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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 Automated Vehicle Research with Multimodal Assured Navigation
Ohio State University
Columbus, OH United States 43210 -
Project Managers:
Kline, Robin
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Performing Organizations:
University of California, Irvine
Institute of Transportation Studies
4000 Anteater Instruction and Research Building
Irvine, CA United States 92697 -
Principal Investigators:
Ritchie, Stephen
- Start Date: 20231030
- Expected Completion Date: 20240830
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers Program
Subject/Index Terms
- TRT Terms: Autonomous vehicles; Computer security; Connected vehicles; Data fusion; Drayage; Sensors; Supply chain management; Tracking systems; Truck traffic
- Subject Areas: Data and Information Technology; Freight Transportation; Motor Carriers; Security and Emergencies; Vehicles and Equipment;
Filing Info
- Accession Number: 01901378
- Record Type: Research project
- Source Agency: Center for Automated Vehicle Research with Multimodal Assured Navigation
- Contract Numbers: 69A3552348327
- Files: UTC, RIP
- Created Date: Dec 4 2023 5:21PM