Unraveling the Platoon Dynamics of Connected Automated Vehicles With Information Errors

Errors are ubiquitous under the influence of multifarious uncertain factors in real traffic, causing information that used to regulate the mobility of connected automated vehicles (CAVs) to be inconsistent with true information, which provoke concerns on CAVs platoon performance. To date, due to the lack of appropriate model and analytical methodology, our understanding of influence mechanisms of various information errors on CAVs and the resulting anomalous platoon dynamics remains limited. Therefore, this study develops a comprehensive framework to fill these gaps. First, we expound the possible information errors in CAVs, upon which we categorize them into three types: source error, transmission error, and latency error. Second, we formulate a generic bi-layer architecture model with dynamical information flow topology, incorporating the information with errors from both of vehicular and communication layers, so as to describe the longitudinal dynamics of CAVs with information errors. Third, we derive the closed-loop and pseudo string stability conditions of CAVs platoon in the presence of information errors. Final, we perform extensive numerical simulations to exposit the effects of three types of information errors on CAVs platoon dynamics. The results show that information errors may cause deteriorated platoon stability, oscillations, reduced traffic efficiency, wasted road resources, and even collision accidents, which undoubtedly degrade the platoon performance. Overall, this study uncovers how different types of information errors influence the individual behaviors of CAVs and thus the longitudinal dynamics evolution of platoon, laying foundation to establish corresponding anomaly detection and counteracting control methods.

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Institutions

Publication Details

Journal
IEEE Transactions on Intelligent Transportation Systems
Published
2024-08-21
DOI
https://doi.org/10.1109/tits.2024.3440955
Citations
3
Primary Topic
Traffic control and management
Type
article
Field-Weighted Citation Impact
0.98

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article

Unraveling the Platoon Dynamics of Connected Automated Vehicles With Information Errors

Min Xu, Bojian Zhou, Shihao Li
3 citations
IEEE Transactions on Intelligent Transportation Systems
Traffic control and management
0.98
article

Unraveling the Platoon Dynamics of Connected Automated Vehicles With Information Errors

Min Xu, Bojian Zhou, Shihao Li
article en
3 citations

Abstract

Errors are ubiquitous under the influence of multifarious uncertain factors in real traffic, causing information that used to regulate the mobility of connected automated vehicles (CAVs) to be inconsistent with true information, which provoke concerns on CAVs platoon performance. To date, due to the lack of appropriate model and analytical methodology, our understanding of influence mechanisms of various information errors on CAVs and the resulting anomalous platoon dynamics remains limited. Therefore, this study develops a comprehensive framework to fill these gaps. First, we expound the possible information errors in CAVs, upon which we categorize them into three types: source error, transmission error, and latency error. Second, we formulate a generic bi-layer architecture model with dynamical information flow topology, incorporating the information with errors from both of vehicular and communication layers, so as to describe the longitudinal dynamics of CAVs with information errors. Third, we derive the closed-loop and pseudo string stability conditions of CAVs platoon in the presence of information errors. Final, we perform extensive numerical simulations to exposit the effects of three types of information errors on CAVs platoon dynamics. The results show that information errors may cause deteriorated platoon stability, oscillations, reduced traffic efficiency, wasted road resources, and even collision accidents, which undoubtedly degrade the platoon performance. Overall, this study uncovers how different types of information errors influence the individual behaviors of CAVs and thus the longitudinal dynamics evolution of platoon, laying foundation to establish corresponding anomaly detection and counteracting control methods.

IEEE Transactions on Intelligent Transportation SystemsVol. 25(11)
Hong Kong Polytechnic University (HK), Southeast University (CN)
National Natural Science Foundation of China, Hong Kong Polytechnic University
Openalex Percentile: Top 24%
Traffic control and management
0.98
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