A traffic sign recognition system is crucial for safely operating an autonomous driving car and efficiently managing road facilities. Recent studies on traffic sign recognition tasks show significant advances in terms of accuracy on several benchmarks. However, they lack performance evaluation in driving cars in diverse road environments. In this study, we develop a traffic sign recognition framework for a vehicle to evaluate and compare deep learning-based object detection and tracking models for practical validation. We collect a large-scale highway image set using a camera-installed vehicle for training models, and evaluate the model inference during a test drive in terms of accuracy and processing time. In addition, we propose a novel categorization method for urban road scenes with possible scenarios. The experimental results show that the YOLOv5 detector and strongSORT tracking model result in better performance than other models in terms of accuracy and processing time. Furthermore, we provide an extensive discussion on possible obstacles in traffic sign recognition tasks to facilitate future research through numerous experiments for each road condition.

This paper presents a dominant channel occupancy (DCO) mechanism for the Wi-Fi backscatter uplink in the industrial Internet of things (IIoT). The DCO provides high-priority channel access and reliable burst transmission to the Wi-Fi backscatter devices, thereby enabling the Wi-Fi backscatter tag to deliver its tag information to the Wi-Fi reader without interference from neighboring legacy Wi-Fi devices to guarantee the timeliness and reliability of the IIoT system. For the former, we consider three types of medium access control (MAC) configurations: “carrier sense multiple access with collision avoidance (CSMA/CA) starting with short inter-frame space (SIFS)”, “freezing of the backoff period”, and “reduced CW_min.” In addition, the DCO uses the SIFS between burst packets to guarantee reliable burst transmission. To verify the effectiveness of DCO and determine a proper value for MAC parameters, we conduct experimental simulations under IEEE 802.11n PHY/MAC environments. The simulation results show that the reduced CW_min has the most significant effect on the channel occupancy. The Wi-Fi backscatter devices achieve much higher throughput than the separate cases when two or more configurations are used simultaneously. Moreover, the results exhibit that the use of SIFS between consecutive packets supports reliable burst transmission regardless of the transmission of the legacy Wi-Fi devices in the vicinity.

Recently, wireless charging technologies for large moving objects, such as electric vehicles and robots, have been actively researched. The power transmitting and receiving coils in most large moving objects are structurally separated by a given distance, which exposes a high output power to the outside world. If a foreign metal object enters the area between these two coils during wireless power transfer, fire hazards or equipment damage may occur. Therefore, we propose a method for detecting foreign metal objects in the gap between the transmitting and receiving coils in a wireless high-power transfer system. A resonant detection coil set is used to exploit the change induced in electrical characteristics when a foreign metal object is inserted. The mutual inductance of the foreign metal object changes the impedance of the detection coil set. We developed a simple circuit to detect both the magnitude and phase change of the voltage signal according to the altered impedance. Additionally, we implemented a prototype of a wireless power transfer system with a detection system to verify that even small foreign metal objects can be detected effectively.

In a wireless high-power transfer system with a distance of several tens of centimeters apart between the transmitter and receiver coils, one of the most challenging issues is to detect metallic foreign objects between the transmitter and receiver coils. The metallic foreign objects must be detected and removed since these reduce the transmission efficiency and cause heat generation of the transmitter and receiver. This paper presents two-layer symmetric balanced coil array so that if there are metallic foreign objects, it can be detected through the change of the inductance of the balanced coils. Since the balanced coil is composed of coils that are in a symmetrical relationship in position, there is no need for a reference coil, and interference between coils is reduced by dividing the coil into two layers. In addition, a novel serial-resonance Maxwell bridge circuit to improve the inductance change detection performance is presented in this paper. The proposed metallic foreign object detection system is implemented using two-layer balanced coil array with a serial-resonance Maxwell bridge and the experimental results show that voltage changes of hundreds of mV to several V occur when a metallic foreign object is inserted, so that even small metals such as clips can be detected.

In complex urban road conditions, stationary or illegally parked vehicles present a considerable risk to the overall traffic system. In safety-critical applications like autonomous driving, the detection of stopped vehicles is of utmost importance. Previous methods for detecting stopped vehicles have been designed for stationary viewpoints, such as security cameras, which consistently monitor fixed locations. However, these methods for detecting stopped vehicles based on stationary views cannot address blind spots and are not applicable from driving vehicles. To address these limitations, we propose a novel deep learning-based framework for detecting stopped vehicles in dynamic environments, particularly those recorded by dashcams. The proposed framework integrates a deep learning-based object detector and tracker, along with movement estimation using the dense optical flow method. We also introduced additional centerline detection and inter-vehicle distance measurement. The experimental results demonstrate that the proposed framework can effectively identify stopped vehicles under real-world road conditions.