The world is embracing the presence of connected autonomous vehicles which are expected to play a major role in the future of intelligent transport systems. Given such connectivity, vehicles in the networks are vulnerable to making incorrect decisions due to anomalous data. No sophisticated attacks are required; just a vehicle reporting anomalous speeds would be enough to disrupt the entire traffic flow. Detection of such anomalies is vital to ensure the security of a vehicular network. We propose the use of traffic flow theory for anomalous data detection in vehicular networks, by evaluating the consistency of microscopic parameters which are derived by traffic flow theory (i.e. speed and space-headway) with macroscopic views of traffic under different traffic conditions. Though a little attention has been given to using traffic flow properties to determine anomalous basic safety message (BSM) data, the fundamental nature of traffic flow properties makes it a robust assessment tool. Usually, traffic flow data are determined through roadside units (RSUs) such as cameras and loop detectors; they are financially impractical to roll out on an entire network. Therefore, the method proposed in this study establishes traffic flow data that are used as “ground truth” through RSUs if available, or by the vehicles' own sensor systems. The numerical results indicate that the proposed method provides extremely reliable and consistent predictions of anomalous BSM data. The more the road segment is congested, the higher the accuracy of the anomalous space-headway detection. The anomalous speed detection performs robustly well across all the traffic conditions. The study also finds that both global and local ground truths provide consistent results.

世界正在拥抱互联自动驾驶汽车的存在，预计它们将在智能交通系统的未来中扮演重要角色。在这种连通性的情况下，由于数据异常，网络中的车辆很容易做出错误的决定。不需要复杂的攻击；仅报告异常速度的车辆就足以破坏整个交通流。检测此类异常对于确保车载网络的安全至关重要。通过评估由交通流理论（即速度和时空距离）派生的微观参数与不同交通状况下交通的宏观视角的一致性，我们提出将交通流理论用于车辆网络中异常数据检测。尽管已经对使用交通流属性确定异常的基本安全消息（BSM）数据给予了少许关注，但是交通流属性的基本性质使其成为一种可靠的评估工具。通常，交通流量数据是通过路边单元（RSU）（例如摄像机和环路检测器）确定的；在整个网络上部署它们在财务上是不切实际的。因此，本研究中提出的方法建立了交通流数据，这些数据通过RSU（如果有）或由车辆自身的传感器系统用作“地面实况”。数值结果表明，所提出的方法为异常BSM数据提供了极其可靠和一致的预测。道路拥堵程度越高，空间车距异常检测的准确性越高。异常速度检测在所有交通状况下均表现出色。该研究还发现，全球和当地的地面事实都提供了一致的结果。