Yan Junlian, Zhang Daowen, Luo Qirui, Yang Jixiang, Xu Lei, Xu Hao, Zhang Yihong
School of Automobile and Transportation, Xihua University, Chengdu, China.
Sichuan New Energy Vehicle Intelligent Control and Simulation Test Technology Engineering Research Center, Chengdu, China.
PLoS One. 2025 Jun 23;20(6):e0325837. doi: 10.1371/journal.pone.0325837. eCollection 2025.
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, considers driving reliability through the fuzzy evaluation method, and comprehensively determines the node weights of the network model. Subsequently, by utilizing the node weights to enhance the information entropy model, a scientific evaluation of the CAVs' driving loop effectiveness is achieved. Through comparisons and validations across several scenarios, it has been demonstrated that this method can be effectively applied to the planning, modeling, evaluation, and optimization of CAVs network models.
作为汽车行业的一种新兴发展趋势，联网自动驾驶汽车（CAV）的网络模型构建及驾驶循环有效性评估具有重要意义。本文的目的是构建CAV的驾驶循环网络模型并评估该模型的有效性，从而优化系统性能，提高驾驶安全性和可靠性。在本研究中，通过整合CAV的驾驶过程并引入观察、定位、决策和行动（OODA）循环的概念，建立了CAV的驾驶循环网络模型，能够对复杂的驾驶过程进行有效建模。对于有效性评估，提出了一种方法。该方法使用解释结构模型（ISM）和复杂网络理论来衡量节点的重要性，通过模糊评估方法考虑驾驶可靠性，并综合确定网络模型的节点权重。随后，利用节点权重增强信息熵模型，实现了对CAV驾驶循环有效性的科学评估。通过在多个场景下的比较和验证，表明该方法可有效应用于CAV网络模型的规划、建模、评估和优化。
