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.

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10.21227/0q64-5f96
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[1] Harshith Pallapothu, Gopi Krishna G, Digvijay Sunil, Teja Yadav S, Nagasundari S, "Autonomous Vehicle dataset for Anomaly detection", IEEE Dataport, 2025. [Online]. Available: http://dx.doi.org/10.21227/0q64-5f96. Accessed: Feb. 16, 2025.
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author = {Harshith Pallapothu; Gopi Krishna G; Digvijay Sunil; Teja Yadav S; Nagasundari S },
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title = {Autonomous Vehicle dataset for Anomaly detection},
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AU - Harshith Pallapothu; Gopi Krishna G; Digvijay Sunil; Teja Yadav S; Nagasundari S
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Harshith Pallapothu, Gopi Krishna G, Digvijay Sunil, Teja Yadav S, Nagasundari S. (2025). Autonomous Vehicle dataset for Anomaly detection. IEEE Dataport. http://dx.doi.org/10.21227/0q64-5f96
Harshith Pallapothu, Gopi Krishna G, Digvijay Sunil, Teja Yadav S, Nagasundari S, 2025. Autonomous Vehicle dataset for Anomaly detection. Available at: http://dx.doi.org/10.21227/0q64-5f96.
Harshith Pallapothu, Gopi Krishna G, Digvijay Sunil, Teja Yadav S, Nagasundari S. (2025). "Autonomous Vehicle dataset for Anomaly detection." Web.
1. Harshith Pallapothu, Gopi Krishna G, Digvijay Sunil, Teja Yadav S, Nagasundari S. Autonomous Vehicle dataset for Anomaly detection [Internet]. IEEE Dataport; 2025. Available from : http://dx.doi.org/10.21227/0q64-5f96
Harshith Pallapothu, Gopi Krishna G, Digvijay Sunil, Teja Yadav S, Nagasundari S. "Autonomous Vehicle dataset for Anomaly detection." doi: 10.21227/0q64-5f96