With the rapid development of artificial intelligence, in the open communication environment, interference and network attacks on intelligent terminals are more frequent. Therefore, communication security and system security issues in the intelligent platoons become hot topics. Motivated by these, the concept of zero trust was proposed. Under this communication framework, all clients are not trusted by default, and the principle of “never trust, always verify” is followed. In order to maintain communication, they need to be authenticated and authorized. This work discusses the zero‐trust framework and mechanism aiming at communication security and gives the structure and module function in detail. Moreover, the impact of malicious cyber‐attacks on the network is parameterized, and the safety coefficients of the full‐trust and zero‐trust frameworks are compared to show the effect of the zero‐trust framework on information security. After that, the research studies range from information security to system security, where the effects of the zero‐trust framework on terminal distribution, controller design, and system stability are studied, taking an intelligent connected vehicle platoon as an example. Finally, three examples verify the positive impact of the zero‐trust framework on network information security.

The use of intelligent connected vehicles (ICVs) is an emerging concept in transportation systems, where the platoon leader processes various types of information from followers and roadside units to make accurate decisions. However, in complex network environments, the leader election process can become complicated and inefficient due to potential network attacks and abnormal node behaviours. To address this challenge, this paper presents a leader vehicle election mechanism based on dynamic trust assessment within a zero-trust framework. The proposed mechanism comprises two key components: dynamic trust assessment and adaptive leader election. The dynamic trust assessment method involves a trust evaluation mechanism composed of direct trust assessment and recommendation trust assessment based on the state information of vehicle nodes, where the dynamic trust assessment results are adjusted according to node behaviour information. The leader election mechanism involves periodic elections among the vehicle nodes on the basis of the results of the dynamic trust assessment, in which the vehicle node with the most votes is selected as the leader. Through comparative experiments against a full trust scenario, the proposed method demonstrates superior performance in resisting network attacks and identifying abnormal nodes, thereby significantly improving the safety, stability, and operational efficiency of the vehicle platoon.

Most of the past research on platooning control has not considered the impact of changes in the level of trust between vehicles in the platoon on the platoon control, and the communication process between connected vehicles is often subjected to malicious attacks such as time delays or interruptions, as well as tampering with status information, and the level of trust between vehicles changes, which affects the cooperative behaviors such as driving styles and inter-vehicle interval strategies. Changes in the level of trust between vehicles can affect the control process strategy of the vehicle, such as where the spacing strategy changes, thereby affecting the inputs to the controller, which in turn affects the outputs of the controller, and ultimately affects changes in the driving style of the vehicle, so considering the level of trust between vehicles in vehicle platoon control is critical to the safe operation of the vehicle platoon. To address this challenge, this paper proposes a dynamic splitting and merging control strategy for vehicle platoons based on the trust evaluation of vehicle nodes. Firstly, trust is evaluated using the Certainty Factor (C-F) uncertain reasoning model, which is a process that starts from initial evidence of uncertainty and derives reasonable conclusions with a certain degree of uncertainty by utilizing the uncertainty of evidence. An autoregressive model predicts short-term vehicle trajectories, and multi-source information from communication and perception is compared to determine vehicle node trust. Utilizing Bayesian reasoning methods, the trust level of vehicle nodes is updated. Based on traditional platoon control and trust evaluation, a software-level dynamic splitting and merging strategy is proposed to enhance resilience against unknown disturbances in a zero-trust environment. Finally, the system’s internal and string stability are analyzed, and the scheme’s effectiveness is validated through simulations. Note to Practitioners—The motivation of this paper is to solve the problem of vehicle platoon security control for connected vehicles in default distrust scenarios. Previous approaches were designed under the premise of considering mutual trust between vehicle nodes, ignoring the problems of inaccurate information interaction between connected vehicles and attacked interaction processes in real scenarios. To resist the risks of various types of attacks faced by intelligent networked vehicles in real traffic scenarios, this paper designs a vehicle platoon control strategy under zero-trust scenarios. Firstly, using the C-F uncertainty reasoning method, we propose a data-based node trust evaluation algorithm, and utilize its node trust evaluation results to re-establish the vehicle state equation under the zero-trust scenario. To make the vehicle platoon control under the zero-trust scenario scalable and resilient, this paper further proposes a vehicle platoon dynamic splitting and merging strategy based on the vehicle node trust evaluation scheme without changing the original communication topology of the vehicle platoon. The preliminary experimental results show that compared with the previous vehicle platoon control method under default vehicle node trust, the security of vehicle platoon operation is effectively improved. In the future, to better match real traffic scenarios, we will study how to dynamically adjust the trust threshold of vehicle nodes based on the vehicle operation environment, spacing strategy, sensor parameters, and other factors.