Effectiveness evaluation of connected and automated vehicles’ driving loop: Node weights and driving reliability
Junlian Yan, Daowen Zhang, Qirui Luo, Jixiang Yang, Lei Xu, Hao Xu, Yihong Zhang, Zhengmao Li, Zhengmao Li, Zhengmao Li
TL;DR
This paper evaluates the driving loop of connected and automated vehicles using a network model to improve system performance and driving reliability.
Contribution
A novel network model and evaluation method for CAVs' driving loop using ISM, complex network theory, and fuzzy evaluation.
Findings
A network model of the driving loop for CAVs was established using the OODA framework.
Node weights were determined using ISM and complex network theory to evaluate effectiveness.
The method was validated across scenarios and shown to improve planning and optimization of CAV systems.
Abstract
As an emerging development trend in the automotive industry, the construction of the network model and the effectiveness evaluation of the driving loop for Connected and Automated Vehicles (CAVs) are of significant importance. The objective of this paper is to construct a network model of the driving loop for CAVs and evaluate the effectiveness of the model, thereby optimizing system performance and enhancing driving safety and reliability. In this study, by integrating the driving process of CAVs and introducing the concept of the Observation, Orientation, Decision, and Action (OODA) loop, a network model of the driving loop for CAVs was established, enabling effective modeling of the complex driving process. For effectiveness evaluation, a method is proposed. This method measures the importance of nodes using the Interpretive Structural Model (ISM) and complex network theory,…
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Taxonomy
TopicsAutonomous Vehicle Technology and Safety · Traffic Prediction and Management Techniques · Vehicle emissions and performance