Autonomous Vehicle dataset for Anomaly detection

This dataset was developed using the MOBATSim simulator in MATLAB 2020b, designed to mimic real-world autonomous vehicle (AV) environments. It focuses on providing high-quality data for research in anomaly detection and cybersecurity, particularly addressing False Data Injection Attacks (FDIA). The dataset includes comprehensive sensor information, such as speed, rotational movements, positional coordinates, and labelled attack data, enabling supervised learning. With diverse driving scenarios—urban, highway, and obstacle-laden environments—the dataset is tailored for various AV applications, including navigation algorithms, cybersecurity model validation, and feature engineering. Its modular and scalable structure, formatted in CSV, ensures easy integration into machine learning pipelines and serves as a valuable resource for advancing intelligent transportation systems and AV cybersecurity research.