Search Results (6)

With the rapid development of electric vehicles, the issue of wiper–windshield friction noise has become more prominent. However, limitations in the hardware and software configurations of existing experimental systems restrict in-depth studies of frictional vibration and noise mechanisms. This study develops an experimental system with functions for working condition adjustment, data acquisition, and analysis of wiper–windshield frictional vibration and noise. First, the overall design of the wiper–windshield experimental system is described. The system allows adjustment of the motion gear and friction coefficient and facilitates data collection and analysis of pressure, vibration, and noise. The design includes the mechanical structure, electronic and electrical components, and software system of the experimental setup. A PLC control program (lower computer) and human–computer interaction software (upper computer) based on LabVIEW are developed to drive and control the mechanical structure, enabling working condition adjustment, data acquisition, and analysis. Finally, an experimental scheme is implemented to verify the feasibility of the wiper–windshield experimental system. Mechanical property, vibration, and noise data from the wiper are collected by simulating the operating conditions of a real vehicle. The experimental results demonstrate that the designed wiper–windshield experimental system can adjust various working conditions and support the collection and analysis of diverse data, facilitating theoretical research on the generation mechanism, influence rules, and control methods for wiper–windshield frictional vibration and noise. Full article

Human–Machine Interfaces (HMIs) in passenger cars have become more complex over the years, with touch screens replacing physical buttons and with layered menu-structures. This can lead to distractions. The purpose of this study is to investigate how often vehicle controls are used while driving and which underlying factors contribute to usage. Thirty drivers were observed during driving a familiar route twice, in their own car and in an unfamiliar car. In a 2 × 1 within-subject design, the experimenter drove along with each participant and used a predefined checklist to record how often participants interacted with specific functions of their vehicle while driving. The results showed that, in the familiar car, direction indicators are the most frequently used controls, followed by adjusting radio volume, moving the sun visor, adjusting temperature and changing wiper speed. Factors that influenced task frequencies included car familiarity, gender, age and weather conditions. The type of car also appears to impact task frequency. Participants interacted less with the unfamiliar car, compared to their own car, which may indicate drivers are regulating their mental load. These results are relevant for vehicle HMI designers to understand which functions should be easily and swiftly available while driving to reduce distraction by the HMI design. Full article

Real-time monitoring of rainwater is a critical issue in the development of autonomous vehicles and smart homes, while the corresponding sensors play a pivotal role in ensuring their sensitivity. Here, we study a self-powered intelligent water droplet monitoring sensor based on a solid–liquid triboelectric nanogenerator (SL-TENG). The sensor comprises a SL-TENG, a signal acquisition module, a central processing unit (CPU), and a wireless transmission module, facilitating the real-time monitoring of water droplet signals. It is worth noting that the SL-TENG has self-powering characteristics and can convert the kinetic energy of water droplets into electrical energy. The excellent output performance, with open-circuit voltage of 9 V and short-circuit current of 2 μA without any treatment of the SL-TENG, can provide an effective solution to the problem that traditional sensor need battery replacement. In addition, the SL-TENG can generate stable amplitude electrical signals through water droplets, exemplified by the absence of decay in a short-circuit current within 7 days. More importantly, the sensor is equipped with intelligent analytical capabilities, allowing it to assess rainfall based on variables such as amplitude and frequency. Due to its excellent stability and intelligent analysis, this sensor can be used for roof rainwater monitoring, intravenous administration monitoring, and especially in automobile automatic wipers and other fields. Full article

Many design principles for rain gauges that have the capacity to record rainfall intensity have been proposed or developed. These are here grouped into 15 categories, and the abilities and limitations of each are discussed. No standard or optimum method has emerged, despite more than 80 years of effort in the last two centuries, together with prior work from the 17th C onwards. Indeed, new methods continue to be explored for both point-based and area-wide collections of intensity data. Examples include the use of signal attenuation by rain along the tower-to-tower links of cellular phone networks, monitoring the speed of vehicle windscreen wipers, and exploiting the sound or vision from security and traffic-monitoring cameras. Many of these approaches have the potential to provide vastly more observation sites than conventional meteorological stations equipped with rain gauges. Some of these contemporary approaches seek to harness the potential of crowdsourced or citizen-science data. It is hoped that the present overview of methods will provide a guide for those wishing to collect or analyses rainfall intensity data for application in areas such as soil erosion processes, ecohydrology, agrochemical washoff, or urban flash flooding. Because rainfall intensity is one of the key aspects of the hydrologic cycle likely to respond as climate change and variability proceed, the choice of appropriate data collection methods has additional contemporary importance for the monitoring of regional and global precipitation changes. Full article

We consider an optimization problem on the maximal magnitude of angular acceleration of the output-links of a commercially available center-driven linkage system (CDLS) for vehicle wipers on windshield. The purpose of this optimization is to improve the steadiness of a linkage system without weakening its normal function. Thus this optimization problem is considered under the assumptions that the frame of the fixed links of linkage system is unchanged and that the input-link rotates at the same constant angular speed with its length unchanged. To meet the usual requirements for vehicle wipers on windshield, this optimization problem must be solved subject to 10 specific constraints. We expect that optimizing the maximal magnitude of angular acceleration of the output-links of a linkage system would also be helpful for reducing the amplitudes of sound waves of wiper noise. We establish the motion model of CDLS and then justify this model with ADAMS. We use a “Differential Evolution” type method to search for the minimum of an objective function subject to 10 constraints for this optimization problem. Our optimization computation shows that the maximal magnitude of angular acceleration of both output-links of this linkage system can be reduced by more than 10%. Full article

Driving in an adverse rain environment is a crucial challenge for vision-based advanced driver assistance systems (ADAS) in the automotive industry. The vehicle windshield wiper removes adherent raindrops that cause distorted images from in-vehicle frontal view cameras, but, additionally, it causes an occlusion that can hinder visibility at the same time. The wiper-occlusion causes erroneous judgments by vision-based applications and endangers safety. This study proposes behind-the-scenes (BTS) that detects and removes wiper-occlusion in real-time image inputs under rainy weather conditions. The pixel-wise wiper masks are detected by high-pass filtering to predict the optical flow of a sequential image pair. We fine-tuned a deep learning-based optical flow model with a synthesized dataset, which was generated with pseudo-ground truth wiper masks and flows using auto-labeling with acquired real rainy images. A typical optical flow dataset with static synthetic objects is synthesized with real fast-moving objects to enhance data diversity. We annotated wiper masks and scenes as detection ground truths from the collected real images for evaluation. BTS outperforms by achieving a 0.962 SSIM and 91.6% F1 score in wiper mask detection and 88.3% F1 score in wiper image detection. Consequently, BTS enhanced the performance of vision-based image restoration and object detection applications by canceling occlusions and demonstrated it potential role in improving ADAS under rainy weather conditions. Full article