Li, Shihao (Southeast university), Chen, Jingxu (Southeast University), Zhou, Bojian (Southeast University), Zhang, Yong (southeast university, school of transportation)

Towards Analyzing the Platoon Stability of Connected and Autonomous Vehicles with Uncertain Information Deviations

Scheduled for presentation during the Poster Session "Automated vehicle platoons" (ThBT16), Thursday, September 26, 2024, 14:30−16:30, Foyer

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

Connected and autonomous vehicles (CAVs) can operate by perceiving status information through sensing and communication technologies, thereby forming platoon with higher stability. Nonetheless, under the influence of diverse stochastic influencing factors within real-world traffic, the essential information that supports the normal movements of CAVs may randomly deviate from the true information, triggering uncertain information deviations. However, existing studies has not revealed the relationship between CAVs platoon stability and uncertain information deviations. Hence, this study seeks to bridge this critical gap. First, we formulate a generic model incorporating the information with deviations that originate from both the sensor and communication layers. In particular, by introducing the probability of CAVs being free from information deviations (i.e., probabilistic parameter), this model amalgamates the normal and compromised models, so as to precisely capture the dynamics of CAVs with uncertain information deviations. Subsequently, we define the concept of pseudo string stability and derive the generalized platoon stability criteria. Then, we conduct the numerical experiments to analyze the sensitivity of relevant parameters on CAVs platoon stability. Further, we also show how to guarantee the platoon stability of CAVs with uncertain information deviations under different cases.