Investigating the safety effect of different sensors in various conditions in a connected vehicle environment – a Digital Twin approach
The emerging connected vehicle (CV) technology has brought unprecedented opportunities to implement applications to enhance the driving experience. Through vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication, traffic light state, live road geometry, and surrounding vehicle information can be collected and sent to the cars. Advanced driving advice can be further provided to the drivers. Many safety benefits have been proven to be promising based on CV technology for multiple driving scenarios, including ramp merging and diverging, signalized and non-signalized intersections, and pedestrian-presence scenarios. However, these safety benefits cannot be guaranteed if real-time V2V or V2I communication is not achieved. Considering the CV market penetration rate will not reach a high percentage in the short-term perspective, it will be a great challenge to implement the CV applications as the real-time vehicle information for non-CV is not acquired. Hence, utilizing sensors that act as roadside units (RSU) to percept the live traffic condition is of great significance for the CV technology to gain a scale effect. Exploring the effect of different sensors in various traffic and environmental conditions helps us to understand and expect what specific safety benefits can be gained and how to develop algorithms and suggestions for the drivers in the CV environment. Hence, it is crucial to investigate this prior to the actual sensor implementation. The most commonly used approach to explore the sensor effect is utilizing virtual simulations, as they can simulate a highly realistic environment and sensor function and plenty of research and applications have been carried on through sensor simulation. However, merely using simulation is good for testing sensor usage, but not sufficient to investigate its role and benefits in CV environment for different sensors, as it lacks the capability to go through the whole process of data collection, algorithm implementation, driving service application, and dynamic driving feedback in a real-time manner. The emerging digital twin approach is expected to address this issue. Digital Twin is a digital replica of a living or non-living physical entity. Digital twin technology paves the way to real-time monitoring and synchronization of real-world activities with virtual counterparts. A digital twin that accommodates these modules at a high level of detail while allowing for interactions across these modules is widely assumed that the "Digital Twin" is a simulation process that fully utilizes physical models, sensors, and historical data of operation. And with the development of the digital twin, the application of underlying technologies such as virtual reality, artificial intelligence, blockchain, 5G communication, and wearable devices will provide a paradigm-altering tool for researchers, practitioners, and policymakers. By following its concept, a digital twin framework of data collection and digital twinning, virtual sensor implementation and simulation, cloud computing, and driving services will be established for a CV environment.
Language
- English
Project
- Status: Active
- Funding: $220000
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Contract Numbers:
69A3551747131
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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:
University of Iowa, Iowa City
National Advanced Driving Simulator, 2401 Oakdale Blvd
Iowa City, IA United States 52242-5003 -
Performing Organizations:
University of Central Florida, Orlando
Department of Civil, Environmental & Contruction Engineering
1280 Pegasus Drive, 442B Engineering II
Orlando, FL United States 32816 -
Principal Investigators:
Abdel-Aty, Mohamed
- Start Date: 20220601
- Expected Completion Date: 20230601
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers
Subject/Index Terms
- TRT Terms: Connected vehicles; Digital simulation; Machine learning; Sensors; Vehicle safety; Virtual reality
- Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01847966
- Record Type: Research project
- Source Agency: Safety Research Using Simulation University Transportation Center (SaferSim)
- Contract Numbers: 69A3551747131
- Files: UTC, RIP
- Created Date: Jun 4 2022 10:37AM