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.

Smart environments and security systems require automatic detection of human behaviors including approaching to or departing from an object. Existing human motion detection systems usually require human beings to carry special devices, which limits their applications. In this paper, we present a system called APID to detect hand approaching behaviors by analyzing backscatter communication signals from a passive RFID tag on the object. APID does not require human beings to carry any device. The idea is based on the influence of hand movements to the vibration of backscattered tag signals. APID is compatible with commodity off-the-shelf devices and the EPCglobal Class-1 Generation-2 protocol. In APID, a commercial RFID reader continuously queries tags through emitting RF signals and tags simply respond with their IDs. A USRP monitor passively analyzes the communication signals and reports the approach and departure behaviors. We have implemented the APID system for both single-object and multi-object scenarios. Extensive evaluations demonstrate that APID can achieve high detection accuracy in both scenarios.

We study a new problem, refined localization, in this paper. Refined localization calculates the location of an object in high precision, given that the object is in a relatively small region such as the surface of a table. Refined localization is useful in many cyber-physical systems such as industrial autonomous robots. Existing vision-based approaches suffer from several disadvantages, including good lighting conditions, line of sight, pre-learning process, and high computation overhead. Also vision-based approaches cannot differentiate objects with similar colors and shapes. This paper presents a new refined localization system, called Trio, which uses passive Radio Frequency Identification (RFID) tags for low cost and easy deployment. Trio provides a new angle to utilize RF interference for tag localization by modeling the equivalent circuits of coupled tags. We implement our prototype using commercial off-the-shelf RFID reader and tags. Extensive experiment results demonstrate that Trio effectively achieves high accuracy of refined localization, i.e., <; 1 cm errors for several types of main stream tags.

As a micro-engineered biomimetic system to replicate key functions of living organs, organ-on-a-chip (OC) technology provides a high-throughput model for investigating complex cell interactions with both high temporal and spatial resolutions in biological studies. Typically, microscopy and high-speed video cameras are used for data acquisition, which are expensive and bulky. Recently, compressed sensing (CS) has increasingly attracted attentions due to its extremely low-complexity structure and low sampling rate. However, there is no CS solution tailored for tempo-spatial information acquisition. In this paper, we propose tempo-spatial CS (TS-CS), a unified CS architecture for OC stream, which achieves significant cost reduction and truly combines sensing with compression along the temporal and spatial domains. We point out that TS-CS can consistently achieve better performance by exploiting tempo-spatial compressibility in OC data. To this end, we comprehensively evaluate the system performance by employing four different bases for CS. With comparison to the traditional way, we show that TS-CS always obtains better recovery result with a throughput bound and can achieve around throughput improvement under a reconstruction demand by applying discrete cosine transform matrix as the basis.