A Vehicle Abnormal Behavior Detection Model in Single Intelligent Vehicle Scenarios

Abstract

Connected and automated vehicles (CAVs) play a vital role in transforming human mobility, tackling road congestion and road safety. However, CAVs rely heavily on the security, accuracy, and stability of sensor readings and network data. When there are anomalies in the data, it is necessary to detect them in a timely manner and handle them. However, under single intelligent vehicle scenarios, existing detection methods often struggle to identify unknown types of anomalies and are difficult to deploy on computationally limited vehicle terminals. To address the aforementioned issues, this paper proposes a vehicle anomaly data detection method based on deep learning. First, we modify the discriminator based on the GAN network, so that the network can assign different weights to different sensors, thus improving the generalization performance of the model. Afterwards, we assign weights to each parameter of the model during the training process, and then prune the model according to the weights to improve its computational speed. We verify the reliability of our method on the Safe Pilot Model Deployment (SPMD) data set. It is shown that the proposed model has good detection performance for various anomaly data, especially when facing data that were not encountered during the training process, and the proposed model effectively reduces the computational time of the detection process.