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A platform for research: civil engineering, architecture and urbanism
Connected Preceding Vehicle Identification for Enabling Cooperative Automated Driving in Mixed Traffic
https://orcid.org/0000-0002-2344-5892
https://orcid.org/0000-0003-4597-6368
To enable the safe and fast formation of connected automated vehicle (CAV) platoons in real-world traffic, a preceding vehicle identification system for mixed traffic (PVIS-mixed) is proposed. PVIS-mixed utilizes the vehicle’s radar measurements and global positioning system (GPS) measurements reported by surrounding connected vehicles to find the communication identity of the preceding vehicle. The design of PVIS-mixed is based on three goals: a low probability of making a wrong identification, a low probability of missing the connected preceding vehicle, and short time consumption of the identification procedure. The proposed PVIS-mixed is evaluated in highway traffic simulated by real vehicle trajectory data from the Next Generation Simulation (NGSIM) program. Evaluation results showed that the performance of PVIS-mixed is not related to the adoption rate of connected vehicles, and 1 m is found to be the required relative positioning accuracy to make 99th percentile time consumption <10 s. It was observed that the multipath bias of GPS positioning could affect the usability of CAV platooning. The possible solutions are then discussed as future work.
Connected Preceding Vehicle Identification for Enabling Cooperative Automated Driving in Mixed Traffic
https://orcid.org/0000-0002-2344-5892
https://orcid.org/0000-0003-4597-6368
To enable the safe and fast formation of connected automated vehicle (CAV) platoons in real-world traffic, a preceding vehicle identification system for mixed traffic (PVIS-mixed) is proposed. PVIS-mixed utilizes the vehicle’s radar measurements and global positioning system (GPS) measurements reported by surrounding connected vehicles to find the communication identity of the preceding vehicle. The design of PVIS-mixed is based on three goals: a low probability of making a wrong identification, a low probability of missing the connected preceding vehicle, and short time consumption of the identification procedure. The proposed PVIS-mixed is evaluated in highway traffic simulated by real vehicle trajectory data from the Next Generation Simulation (NGSIM) program. Evaluation results showed that the performance of PVIS-mixed is not related to the adoption rate of connected vehicles, and 1 m is found to be the required relative positioning accuracy to make 99th percentile time consumption <10 s. It was observed that the multipath bias of GPS positioning could affect the usability of CAV platooning. The possible solutions are then discussed as future work.
Connected Preceding Vehicle Identification for Enabling Cooperative Automated Driving in Mixed Traffic
https://orcid.org/0000-0002-2344-5892
https://orcid.org/0000-0003-4597-6368
To enable the safe and fast formation of connected automated vehicle (CAV) platoons in real-world traffic, a preceding vehicle identification system for mixed traffic (PVIS-mixed) is proposed. PVIS-mixed utilizes the vehicle’s radar measurements and global positioning system (GPS) measurements reported by surrounding connected vehicles to find the communication identity of the preceding vehicle. The design of PVIS-mixed is based on three goals: a low probability of making a wrong identification, a low probability of missing the connected preceding vehicle, and short time consumption of the identification procedure. The proposed PVIS-mixed is evaluated in highway traffic simulated by real vehicle trajectory data from the Next Generation Simulation (NGSIM) program. Evaluation results showed that the performance of PVIS-mixed is not related to the adoption rate of connected vehicles, and 1 m is found to be the required relative positioning accuracy to make 99th percentile time consumption <10 s. It was observed that the multipath bias of GPS positioning could affect the usability of CAV platooning. The possible solutions are then discussed as future work.
Connected Preceding Vehicle Identification for Enabling Cooperative Automated Driving in Mixed Traffic
J. Transp. Eng., Part A: Systems
Chen, Zheng (author) / Park, B. Brian (author)
2022-05-01
Article (Journal)
Electronic Resource
English
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