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.

本数据集基于MATLAB 2020b环境中的MOBATSim模拟器开发，旨在模拟真实世界的自动驾驶汽车（Autonomous Vehicle，AV）运行场景。本数据集聚焦于为异常检测与网络安全领域的研究提供高质量数据，尤其针对虚假数据注入攻击（False Data Injection Attacks，FDIA）开展专项研究。该数据集包含全面的传感器数据，涵盖车速、旋转运动参数、位置坐标以及带标注的攻击样本，可支撑监督学习任务。该数据集涵盖城市道路、高速公路以及多障碍物环境等多样化驾驶场景，专为各类自动驾驶汽车应用量身打造，涵盖导航算法研发、网络安全模型验证以及特征工程等方向。其采用CSV格式存储，具备模块化与可扩展的结构特性，可轻松集成至机器学习流程中，是推动智能交通系统与自动驾驶汽车网络安全研究的宝贵资源。

搜集汇总

背景与挑战

背景概述

该数据集是用于异常检测的自动驾驶车辆数据集，通过MOBATSim模拟器在MATLAB中生成，模拟真实自动驾驶环境，包含速度、位置等传感器数据和标记的攻击信息，支持监督学习。数据集涵盖城市、高速公路等多种驾驶场景，适用于异常检测、网络安全研究和自动驾驶算法验证，以CSV格式提供便于机器学习集成。