Evaluation of Cybersecurity Risks in Smart City Environments
Even though transportation systems introduce novel techniques to improve the safety of Vulnerable Road Users (VRUs), there were more than 40,000 fatalities and 2.3 million people injured in the transportation system. Especially, 95% of fatalities were on the streets, roads, and highways.[1]. For the upcoming Cooperative-Automated Driving Systems (C-ADS) era, challenges for ensuring VRUs safety and avoiding collisions on the road become urgent topics, especially intersections or merge and exit systems on the signalized highways. For the signalized intersection problem, the complexity comes from different VRUs, the trajectories of VRUs are crisscrossed, and there are potential conflict points where vehicles enter and leave [2]. U.S. DOT’s safety RD&T efforts aim to contribute to a future transportation system where transportation-related serious injuries and fatalities are eliminated using Cooperative Navigation System (CNS). The research team's objective is to provide a cyber-resilient framework for CAVs using learning-based control and mitigation strategies to improve the safety of C-ADS in CAVs operating on smart city roads and avoid collisions using Cooperative Collision Avoidance (CCA). The research team's work will ensure safety by identifying existing and emerging cybersecurity challenges and threats to Highly Automated Transportation Systems (HATS) (CAV + smart interchange) emerging from various sources of information (e.g.,V2Xdevices). Providing a safe and efficient method is the primary target for VRU at signalized intersections (four legs). C-ADS must avoid collision in a mixed environment and drive appropriately to pass the signalized intersection using CNS safely. Following methods can be used to achieve the research objectives: (1) CNS can integrate information from Vehicle to Vehicle (V2V), Vehicle to Infrastructures (V2I), and Vehicle to Pedestrian (V2P) communication and receive dynamic parameters from its onboard unit (OBU) to make path planning and navigation control for CAVs. From the cooperative information, C-ADS can enhance safety in the signalized smart intersection. (2) In the smart intersection, Smart Traffic System (STS), Road Side Unit (RSU) and Autonomous Intersection Management (AIM) are playing critical roles in CNS. They provide ways to exchange information and can be controlled to manage the intersection. The CCA can use this information to create a real-time implementable collision avoidance algorithm along with decision-making for a CAV that communicates with CNS. (3) The CCA strategy along with the fusion of sensors, and information exchange between V2V and V2I can be used to develop a learning-based control mechanism to mitigate collision of C-ADS at smart signalized intersection. (4) To ensure the dynamic environment safety, decentralized cooperation algorithm would be implemented in the system architecture. Each CAV will have its own decision-making process, even if there are other CAVs failed, the CAV will keep its own safety objectives. The decentralized design can also benefit the computation load for the entire system and avoid a single failure point compared to the centralized design. (5) Once the information is affected by threats, C-ADS can use the learning algorithm to mitigate the impact of doubtful information. Through analysis of threats, sensor fusion could compensate for doubtful information to maintain the performance of PNT systems and the safety of VRUs.
Language
- English
Project
- Status: Active
- Funding: $109605
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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:
Ohio State University Center for Automotive Research
930 Kinnear Road
Columbus, OH United States 43212 -
Principal Investigators:
Ahmed, Qadeer
- 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; Crash avoidance systems; Mobile communication systems; Risk assessment; Signalized intersections; Vulnerable road users
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Pedestrians and Bicyclists; Safety and Human Factors; Security and Emergencies; Vehicles and Equipment;
Filing Info
- Accession Number: 01901375
- 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 5:07PM