Seondong Heo

10.5626/JOK.2026.53.2.172

unmanned ground vehicle Autonomous Driving Transformer Anomaly Detection

The Robot Operating System (ROS) is a fundamental framework for unmanned ground vehicles (UGVs), particularly in facilitating autonomous driving by enabling data exchange among various sensors and control topics. As the development and deployment of autonomous driving technologies advance, UGVs face increased risks from threats such as denial-of-service (DoS) and spoofing attacks. In this study, we propose a method to detect these threats by collecting topic data from the ROS running on a UGV and identifying abnormal patterns within this data. To capture the complex relationships present, we utilize a transformer-based autoencoder. This model is trained on normal operating data to learn both temporal and inter-topic relationships, and detects anomalies by analyzing reconstruction errors that arise when abnormal patterns occur. We validate the model’s effectiveness by training it with normal data collected from a multi-purpose UGV and testing it against attack data generated from cyberattacks on the same vehicle.