With the increasing accessibility of drones, they have been warmly embraced across various sectors, especially in low-altitude logistics transportation. However, during drone delivery, legal drones dispatched by logistics companies are susceptible to malicious attacks, resulting in package theft or substitution. To address this, existing works focus on designing drone authentication to secure drone delivery. However, most of these methods require expensive specialized equipment, such as high-quality microphones and professional recording devices, resulting in high real-world application costs. In this paper, we propose DroneAudioID, a lightweight acoustic fingerprint-based drone authentication system that relies solely on common mobile devices. The basic idea is to employ acoustic fingerprints to authenticate different drones of the same model based on differences in fundamental frequency and harmonic components of drone audio. Specifically, the drone audio is recorded by a mobile device instead of sophisticated equipment. We apply wavelet transform to remove high-frequency noise during data preprocessing. Then, specialized filter banks are designed for feature extraction, leveraging the frequency characteristics of drone audio. Finally, we construct a Bi-Long Short-Term Memory (Bi-LSTM) with an Open-Max model for open-set classification. Extensive experiments are conducted on eight crafts drones of$DJI Mini2$, showing an authentication accuracy of 99.6%. A series of comprehensive experiments further validate DroneAudioID’s capability to defend against various attacks.

Among the foremost contributors to compromised driving safety is the abrupt emergence of obstacles or pedestrians within drivers’ non-line-of-sight regions. Previous investigations into non-line-of-sight imaging have predominantly depended on costly apparatus or have been confined to controlled laboratory settings (e.g., extensive planar reflectors and regulated illumination). Consequently, these technological approaches prove impractical within intricate driving environments. In this paper, we introduce a shadow based non-line-of-sight moving obstacle detection system devised to augment Advanced Driver Assistance Systems (ADAS), ensuring adequate time for safe response and halting. Our approach incorporates a shadow signal discriminator tailored to evaluate faint shadows generated by moving obstacles, such as pedestrians within blind spots. Note that we merely use commercial onboard sensors and our system is robust to various lighting scenarios and planar reflectors. We comprehensively assess our methodology's adaptability by employing datasets acquired from real-world driving scenarios encompassing diverse road surfaces and lighting conditions. The results substantiate the system's efficacy in detecting pedestrians in motion within NLOS regions, showcasing an impressive detection range of 22 meters. This proficiency enables the system to pre-emptively forewarn the ADAS, facilitating the maintenance of a safe distance from the pedestrian.

One of the most serious factors compromising driving safety is when people in drivers' non-line-of-sight areas rush out suddenly. Existing studies on non-line-of-sight imaging rely on expensive equipment or are limited to severe laboratory conditions (e.g., massive planar reflectors and controlled illumination), rendering these technologies inapplicable in complex driving scenarios. In this paper, we propose a non-line-of-sight moving obstacle detection system Ghost-Probe, which can provide an advanced driver assistance system (ADAS) with sufficient time to respond and stop safely. We design a shadow signal discriminator to assess the weak shadows created by a moving obstacle, such as pedestrians in the blind area, while simultaneously filtering out the impacts of other complicated illumination. Note that we merely use commercial monocular cameras and our system is robust to a wide range of lighting scenarios and planar reflectors. We evaluate the generalizability of our approach using the datasets collected in real-world driving scenarios with a variety of road surface and lighting circumstances. The results indicate that our system can detect the moving pedestrian in the non-line-of-sight area at a distance of 20 meters and offer the ADAS system advance warning to keep a safe distance.