Connected Vehicles (CVs) are the key enabling technology for Intelligent Transportation Systems (ITSs) that offer great opportunities for improving traffic safety and efficiency. They provide several innovative safety-related applications such as traffic management and monitoring, which involve the transmission of messages from all vehicles on the road. Basic Safety Messages (BSMs) constitute an essential type of control message. However, several critical issues affect the BSM messages’ reliability. In this paper, a model-based approach for detecting discordant BSMs is proposed, which allows to avoid the vehicle disturbance. This approach consists of detecting incoherence in communication metric values, where the detection is formulated as an anomaly detection problem that is solved using the Gaussian distribution. The detection process allows the vehicles to cross their prediction to achieve more precision in deciding whether to accept or reject a message from a vehicle. The efficiency of our model for detecting an anomaly has been evaluated through simulations using our generated dataset. The obtained results indicate that the proposed model provides high performance in terms of detection rate. Moreover, we evaluate and validate the proposed approach through formal evaluation, where it demonstrates promising performances, as compare it with a concurrent approach through simulations considering important metrics.

联网汽车 (CV) 是智能交通系统 (ITS) 的关键支持技术，它为提高交通安全和效率提供了巨大的机会。它们提供了几种创新的安全相关应用，例如交通管理和监控，其中涉及来自道路上所有车辆的信息传输。基本安全消息 (BSM) 构成了一种基本类型的控制消息。但是，有几个关键问题会影响 BSM 消息的可靠性。在本文中，提出了一种基于模型的检测不一致 BSM 的方法，该方法可以避免车辆干扰。这种方法包括检测通信度量值的不连贯性，其中检测被表述为使用高斯分布解决的异常检测问题。检测过程允许车辆超越他们的预测，以更精确地决定是接受还是拒绝来自车辆的消息。我们的模型检测异常的效率已经通过使用我们生成的数据集的模拟进行了评估。获得的结果表明，所提出的模型在检测率方面具有很高的性能。此外，我们通过正式评估评估和验证所提出的方法，其中它展示了有希望的性能，并通过考虑重要指标的模拟将其与并发方法进行比较。获得的结果表明，所提出的模型在检测率方面具有很高的性能。此外，我们通过正式评估评估和验证所提出的方法，其中它展示了有希望的性能，并通过考虑重要指标的模拟将其与并发方法进行比较。获得的结果表明，所提出的模型在检测率方面具有很高的性能。此外，我们通过正式评估评估和验证所提出的方法，其中它展示了有希望的性能，并通过考虑重要指标的模拟将其与并发方法进行比较。