The driving behavior of bus drivers is related to the safety of all passengers and regulation of urban traffic. In order to analyze the relevant characteristics of speed and acceleration, accurate bus trajectories and patterns are essential for driver behavior analysis and development of effective intelligent public transportation. Exploiting real-time vehicle tracking, this paper develops a platform with vehicle-mounted terminals using differential global navigation satellite system (DGNSS) modules for driver behavior analysis. The DGNSS traces were used to derive the vehicle trajectories, which were then linked to road information to produce speed and acceleration matrices. Comprehensive field tests were undertaken on multiple bus routes in urban environments. The spatiotemporal results indicate that the platform can automatically and accurately extract the driving behavior characteristics. Furthermore, the platform’s visual function can be used to effectively monitor driving risks, such as speeding and fierce acceleration, in multiple bus routes. The details of the platform’s features are provided for intelligent transport system (ITS) design and applications.

For the application of the autonomous guidance of a quadrotor from confined undulant ground, terrain-following is the major issue for flying at a low altitude. This study has modified the open-source autopilot based on the integration of a multi-sensor receiver (a Global Navigation Satellite System (GNSS)), a Lidar-lite (a laser-range-finder device), a barometer and a low-cost inertial navigation system (INS)). These automatically control the position, attitude and height (a constant clearance above the ground) to allow terrain-following and avoid obstacles based on multi-sensors that maintain a constant height above flat ground or with obstacles. The INS/Lidar-lite integration is applied for the attitude and the height stabilization, respectively. The height control is made by the combination of an extended Kalman filter (EKF) estimator and a cascade proportional-integral-derivative (PID) controller that is designed appropriately for the noise characteristics of low accuracy sensors. The proposed terrain-following is tested by both simulations and real-world experiments. The results indicate that the quadrotor can continuously navigate and avoid obstacles at a real-time response of reliable height control with the adjustment time of the cascade PID controller improving over 50% than that of the PID controller.

With the increasing application potential of indoor personnel positioning, ultra-wideband (UWB) positioning technology has attracted more and more attentions of scholars. In practice, an indoor positioning process often involves multipath and Non-Line-Of-Sight (NLOS) problems, and a particle filtering (PF) algorithm has been widely used in the indoor positioning research field because of its outstanding performance in nonlinear and non-Gaussian estimations. Aiming at mitigating the accuracy decreasing caused by the particle degradation and impoverishment in traditional Sequential Monte Carlo (SMC) positioning, we propose a method to integrate the firefly and particle algorithm for multistage optimization. The proposed algorithm not only enhances the searching ability of particles of initialization but also makes the particles propagate out of the local optimal condition in the sequential estimations. In addition, to prevent particles from falling into the oscillatory situation and find the global optimization faster, a decreasing function is designed to improve the reliability of the particle propagation. Real indoor experiments are carried out, and results demonstrate that the positioning accuracy can be improved up to 36%, and the number of needed particles is significantly reduced.

Robotics is widely used in nearly all sorts of manufacturing. Steady performance and accurate movement of robotics are vital in quality control. Along with the coming of the Industry 4.0 era, oceans of sensor data from robotics are available, within which the health condition and faults are enclosed. Considering the growing complexity of the manufacturing system, an automatic and intelligent health-monitoring system is required to detect abnormalities of robotics in real-time to promote quality and reduce safety risks. Therefore, in this study, we designed a novel semantic-based modeling method for multistage robotic systems. Experiments show that sole modeling is not sufficient for multiple stages. We propose a descriptor to conclude the stages of robotic systems by learning from operational data. The descriptors are akin to a vocabulary of the systems; hence, semantic checking can be carried out to monitor the correctness of operations. Furthermore, the stage classification and its semantics were used to apply various regression models to each stage to monitor the quality of each operation. The proposed method was applied to a photovoltaic manufacturing system. Benchmarks on production datasets from actual factories show the effectiveness of the proposed method to realize an AI-enabled real-time health-monitoring system of robotics.

Electroluminescence (EL) imaging is a widely adopted method in quality assurance of the photovoltaic (PV) manufacturing industry. With the growing demand for high-quality PV products, automatic inspection methods based on machine vision have become an emerging area concern to replace manual inspectors. Therefore, this paper presents an automatic defect-inspection method for multi-cell monocrystalline PV modules with EL images. A processing routine is designed to extract the defect features of the PV module, eliminating the influence of the intrinsic structural features. Spectrum domain analysis is applied to effectively reconstruct an improved PV layout from a defective one by spectrum filtering in a certain direction. The reconstructed image is used to segment the PV module into cells and slices. Based on the segmentation, defect detection is carried out on individual cells or slices to detect cracks, breaks, and speckles. Robust performance has been achieved from experiments on many samples with varying illumination conditions and defect shapes/sizes, which shows the proposed method can efficiently distinguish intrinsic structural features from the defect features, enabling precise and speedy defect detections on multi-cell PV modules.

With the development of urbanization, the environment is the key to the safety of residents’ life and health and the United Nations’ Sustainable Development Goals (SDGs). Urban environmental changes and microclimate problems have attracted widespread attention. For the SDGs, monitoring the urban microclimate more accurately and effectively and ensuring residents’ environmental health and safety is particularly important when designing applications that can replace the traditional fixed-point urban environment or pollution monitoring. Based on the BeiDou Navigation Satellite System platform, this paper proposes a fine-granularity urban microclimate monitoring method using wearable multi-source (PM2.5, PM10, and other air pollutants) sensors innovatively, which includes the satellite position function by adopting the satellite pseudo-range differential positioning technology, environmental data perception through the embedded system and wireless transmission, as well as the GIS data processing and analysis system. The wearable sensor acquires position and service information data through the satellite positioning system and acquires environmental parameters through integrated mobile multi-source sensors. The data are cached and wirelessly transmitted to the cloud server for digital processing. The urban microclimate is evaluated and visualized through algorithm and map API. Mobile monitoring can be flexibly applied to complex and diverse urban spaces, effectively realizing all-weather, all-directional, and accurate microclimate monitoring of urban environmental quality.

Device-free localization (DFL) is a promising technique which could provide localization information for a target without requiring an electronic device. With the development of the smart city and smart transportation, DFL could form part of a basic technique that could be used to track and localize roadside vehicles. In this paper, some algorithms for three-dimensional (3D) DFL for vehicle surveillance are developed, including statistical methods for data, a method for communication link selection, a novel method of communication link weight allocation and some other minor approaches to obtain the location and approximate size of a static vehicle, as a basic technique of moving vehicle detection. Then, an experimental system is designed. Through security monitoring wireless sensor networks (WSN), real-time vehicle characteristics (i.e., location and size) are calculated automatically and intuitively displayed through a heat map. Experiments are performed to validate the effect of the proposal and the accuracy of the localization and size estimation.

Noise, vibration and harshness (NVH) problems in vehicle engineering are always challenging in both traditional vehicles and intelligent vehicles. Although high accuracy manufacturing, modern structural roads and advanced suspension technology have already significantly reduced NVH problems and their impacts; off-road condition, obstacles and extreme operating condition could still trigger NVH problems unexpectedly. This paper proposes a vehicular electronic image stabilization (EIS) system to solve the vibration problem of the camera and ensure the environment perceptive function of vehicles. Firstly, feature point detection and matching based on an oriented FAST and rotated BRIEF (ORB) algorithm are implemented to match images in the process of EIS. Furthermore, a novel improved random sampling consensus algorithm (i-RANSAC) is proposed to eliminate mismatched feature points and increase the matching accuracy significantly. And an adaptive Kalman filter (AKF) is applied to improve the adaptability of the vehicular EIS. Finally, an experimental platform based on a gasoline model car was established to validate its performance. The experimental results show that the proposed EIS system can satisfy vehicular performance requirements even under off-road condition with obvious obstacles.