  In response to the evolving challenges posed by small unmanned aerial
vehicles (UAVs), which possess the potential to transport harmful payloads or
independently cause damage, we introduce MMAUD: a comprehensive Multi-Modal
Anti-UAV Dataset. MMAUD addresses a critical gap in contemporary threat
detection methodologies by focusing on drone detection, UAV-type
classification, and trajectory estimation. MMAUD stands out by combining
diverse sensory inputs, including stereo vision, various Lidars, Radars, and
audio arrays. It offers a unique overhead aerial detection vital for addressing
real-world scenarios with higher fidelity than datasets captured on specific
vantage points using thermal and RGB. Additionally, MMAUD provides accurate
Leica-generated ground truth data, enhancing credibility and enabling confident
refinement of algorithms and models, which has never been seen in other
datasets. Most existing works do not disclose their datasets, making MMAUD an
invaluable resource for developing accurate and efficient solutions. Our
proposed modalities are cost-effective and highly adaptable, allowing users to
experiment and implement new UAV threat detection tools. Our dataset closely
simulates real-world scenarios by incorporating ambient heavy machinery sounds.
This approach enhances the dataset's applicability, capturing the exact
challenges faced during proximate vehicular operations. It is expected that
MMAUD can play a pivotal role in advancing UAV threat detection,
classification, trajectory estimation capabilities, and beyond. Our dataset,
codes, and designs will be available in https://github.com/ntu-aris/MMAUD.
