Autonomous vehicle

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.

The safe implementation and adoption of Autonomous Vehicle (AV) vision models on public roads requires not only an understanding of the natural environment comprising pedestrians and other vehicles but also the ability to reason about edge situations such as unpredictable maneuvers by other drivers, impending accidents, erratic movement of pedestrians, cyclists, and motorcyclists, animal crossings, and cyclists using hand signals.

This dataset is made for traditional, machine learning, and deep neural-network-based virtual sensor development and evaluation.

My dataset

The C3I Thermal Automotive Dataset provides > 35,000 distinct frames along with annotated thermal frames for the development of smart thermal perception system/ object detection system that will enable the automotive industry and researchers to develop safer and more efficient ADAS and self-driving car systems. The overall dataset is acquired, processed, and open-sourced in challenging weather and environmental scenarios. The dataset is recorded from a lost-cost yet effective 640x480 uncooled LWIR thermal camera.