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When designing the structure of a new vehicle, car manufacturers need to ensure the compliance with strict safety requirements. Aiming to support the engineers in the early phase of this process, we propose a transfer learning framework for crashworthiness. This work explores the possibility to infer knowledge on future situations by exploiting data coming from past development processes. During the early phases of automotive development, assessing the crash safety implies dealing with the challenge of low data availability. Here, the engineers have no hardware test to rely on and can access only few finite element simulations. Under these circumstances, an attractive concept to investigate is the development of a machine learning approach able to learn from the past designs and to transfer the acquired knowledge to the new ones. Transfer learning can serve to this aim. With it, one learns the basic knowledge from a source domain A, and transfers it to a target domain B, characterized by low data availability. Here, we propose a transfer learning framework and apply it to an explicatory industrial crash example. The components produced in the past constitute the source domain; the new component design is the target domain. The proposed methodology can serve as an innovative solution to support car manufacturers in the early phase of vehicle development and thus improve the performance in crashworthiness scenarios. [ABSTRACT FROM AUTHOR]

To enable the timely adjustment of the control strategy of automobile active safety systems, enhance their capacity to adapt to complex working conditions, and improve driving safety, this paper introduces a new method for predicting road surface state information and recognizing road adhesion coefficients using an enhanced version of the MobileNet V3 model. On one hand, the Squeeze-and-Excitation (SE) is replaced by the Convolutional Block Attention Module (CBAM). It can enhance the extraction of features effectively by considering both spatial and channel dimensions. On the other hand, the cross-entropy loss function is replaced by the Bias Loss function. It can reduce the random prediction problem occurring in the optimization process to improve identification accuracy. Finally, the proposed method is evaluated in an experiment with a four-wheel-drive ROS robot platform. Results indicate that a classification precision of 95.53% is achieved, which is higher than existing road adhesion coefficient identification methods. [ABSTRACT FROM AUTHOR]

To explore the individual differences in visual distraction characteristics when drivers perform secondary tasks, a real-car experimental platform was constructed, and visual distraction data were collected from 30 participants. Four indicators, including total duration of visual distraction, number of visual distractions, average duration of a single visual distraction, and maximum duration of a single visual distraction, were selected for characteristic analysis of the collected data. The spectral clustering algorithm was employed to cluster drivers' distraction styles into cautious, normal, and aggressive types. The results indicate that increased complexity of secondary tasks significantly increases total duration, number of visual distractions, and maximum duration of a single visual distraction. Increased vehicle speed significantly decreases total duration of visual distraction, average duration of a single visual distraction, and maximum duration of a single visual distraction, with the impact on total duration of visual distraction and average duration of a single visual distraction becoming more pronounced with increased task complexity. Significant differences are found in visual distraction characteristics among the three distraction styles of drivers, with cautious and aggressive drivers tending towards multiple short-duration and fewer long-duration operation modes, respectively, during secondary task execution. [ABSTRACT FROM AUTHOR]

In order to reduce the amount of EEG collected by the fatigue driving monitoring system, this paper proposes a new EEG monitoring method based on compressed sensing. According to the driver's driving duration, the new proposed method adopts different sampling rate to compressive sample the EEG of the driver in different driving stages, and then realizes the accurate reconstruction of the compressed EEG data in the computer of vehicle. The experiment verifies effectiveness of the proposed method by collecting EEG signals from multiple subjects during simulated driving. The experimental results show that, compared with traditional sampling, the new method reduces the amount of sampled data by 3 orders of magnitude. And when the compression ratio is not higher than 70%, the reconstruction error is less than 0.16. [ABSTRACT FROM AUTHOR]

The welding quality during welding body-in-white (BIW) determines the safety of automobiles. Due to the limitations of testing cost and cycle time, the prediction of welding quality has become an essential safety issue in the process of automobile production. Conventional prediction methods mainly consider the welding process parameters and ignore the material parameters, causing their results to be unrealistic. Upon identifying significant correlations between vehicle body materials, we utilize principal component analysis (PCA) to perform dimensionality reduction and extract the underlying principal components. Thereafter, we employ a greedy feature selection strategy to identify the most salient features. In this study, a welding quality prediction model integrating process parameters and material characteristics is proposed, following which the influence of material properties is analyzed. The model is verified based on actual production data, and the results show that the accuracy of the model is improved through integrating the production process characteristics and material characteristics. Moreover, the overfitting phenomenon can be effectively avoided in the prediction process. [ABSTRACT FROM AUTHOR]

• Investigated mental workload and fatigue of the drivers using EEG. • Conducted experiments in authentic driving environments involving 39 participants. • Revealed higher mental workload on local street with heavy traffic and highway. • Mental fatigue index was correlated with the mental workload indexes. • Established that drivers with more experience exhibited lower mental workload. The safety and performance of automobile drivers depend on many factors. The mental status of the drivers is the foremost factor in ensuring driving safety, in addition to physical elements. Studies with drivers are generally conducted in driving simulators or with scenarios close to actual driving. This study investigated the mental workload of drivers by analyzing electroencephalography data recorded in totally spontaneous real driving tasks, to determine the effect of different road conditions and driving experience. Two mental workload indexes (Frontal Theta/Parietal Alpha and Frontal Midline Theta) and a mental fatigue index (Alpha + Theta/Beta) were calculated using the band powers. Drivers were found to experience a higher mental workload in road sections with heavy traffic and variable road parameters by analyzing EEG data in real traffic. The correlation coefficients between the fatigue index and the two workload indexes were found to be 0.577 and 0.678, respectively. The workload decreased with increasing driving experience. Therefore, having experienced drivers perform commercial driving tasks can ensure safer driving. By employing novel methods to handle real-world EEG data, autonomous driving systems can also benefit. [ABSTRACT FROM AUTHOR]

The autonomous vehicle (AV) technology has the potential to significantly improve safety and efficiency of the transportation and logistics industry. Full-scale AV testing is limited by time, space, and cost, while simulation-based testing often lacks the necessary accuracy of AV and environmentalmodeling. In recent years, several initiatives haveemerged to test autonomous software and hardware on scaled vehicles. This systematic literature review provides an overview of the literature surrounding small-scale self-driving cars, summarizing the current autonomous platforms deployed and focusing on the software and hardware developments in this field. The studies published in English-language journals or conference papers that present small-scale testing of self-driving cars were included. Web of Science, Scopus, Springer Link, Wiley, ACM Digital Library, and TRID databases were used for the literature search. The systematic literature search found 38 eligible studies. Research gaps in the reviewed papers were identified to provide guidance for future research. Some key takeaway emerging from thismanuscript are: (i) there is a need to improve themodels and neural network architectures used in autonomous driving systems, as most papers present only preliminary results; (ii) increasing datasets and sharing databases can help in developing more reliable control policies and reducing biasand variance inthe trainingprocess; (iii) small-scaledvehicles to ensure safety is a major benefit, and incorporating data about unsafe driving behaviors and infrastructure problems can improve the accuracy of predictive models. [ABSTRACT FROM AUTHOR]

Detecting the factors affecting drivers' safe driving and taking early warning measures can effectively reduce the probability of automobile safety accidents and improve vehicle driving safety. Considering the two factors of driver fatigue and distraction state, their influences on driver behavior are elaborated from both experimental data and an accident library analysis. Starting from three modes and six types, intelligent detection methods for driver fatigue and distraction detection from the past five years are reviewed in detail. Considering its wide range of applications, the research on machine vision detection based on facial features in the past five years is analyzed, and the methods are carefully classified and compared according to their innovation points. Further, three safety warning and response schemes are proposed in light of the development of autonomous driving and intelligent cockpit technology. Finally, the paper summarizes the current state of research in the field, presents five conclusions, and discusses future trends. [ABSTRACT FROM AUTHOR]

This manuscript presents a novel hybrid approach aimed at enhancing Vehicle-to-Vehicle (V2V) communication to bolster road safety and diminish collisions. The primary objective is to augment V2V communication to foster safer automobiles and diminish collision occurrences by developing and implementing a system capable of actively detecting, evaluating, and transmitting collision risks in real time. Given the escalating number of vehicles on roads, reliable communication among vehicles is imperative to ensure safety and avert collisions. The proposed system leverages the optimization of Arduino UNO controller parameters, employed to anticipate, and fine-tune the controller settings. The outcomes underscore the efficacy of the approach in fortifying V2V communication for safer automobiles and fewer collisions. The experimental findings showcase a notable reduction in errors compared to alternative methodologies. [ABSTRACT FROM AUTHOR]

Understanding the characteristics of time and distance gaps between the primary (PC) and secondary crashes (SC) is crucial for preventing SC ccurrences and improving road safety. Although previous studies have tried to analyse the variation of gaps, there is limited evidence in quantifying the relationships between different gaps and various influential factors. This study proposed a two-layer stacking framework to discuss the time and distance gaps. Specifically, the framework took random forests (RF), gradient boosting decision tree (GBDT) and eXtreme gradient boosting as the base classifiers in the first layer and applied logistic regression (LR) as a combiner in the second layer. On this basis, the local interpretable model-agnostic explanations (LIME) technology was used to interpret the output of the stacking model from both local and global perspectives. Through SC dentification and feature selection, 346 SCs and 22 crash-related factors were collected from California interstate freeways. The results showed that the stacking model outperformed base models evaluated by accuracy, precision, and recall indicators. The explanations based on LIME suggest that collision type, distance, speed and volume are the critical features that affect the time and distance gaps. Higher volume can prolong queue length and increase the distance gap from the SCs to PCs. And collision types, peak periods, workday, truck involved and tow away likely induce a long-distance gap. Conversely, there is a shorter distance gap when secondary roads run in the same direction and are close to the primary roads. Lower speed is a significant factor resulting in a long-time gap, while the higher speed is correlated with a short-time gap. These results are expected to provide insights into how contributory features affect the time and distance gaps and help decision-makers develop accurate decisions to prevent SCs. [ABSTRACT FROM AUTHOR]

Over forty thousand people die in motor vehicle crashes in the United States each year, and over two million are injured. The careful deployment Of driving automation systems could prevent many of these deaths and injuries, but only if it is accompanied by effective regulation. Conventional vehicle safety standards are inadequate because they can only test how technology performs in a controlled environment. To assess the safety of a driving automation system, regulators must observe how it performs in a range of unpredictable, real world edge cases. The National Highway Traffic Safety Administration (NHTSA) is trying to adapt by experimenting with a novel regulatory strategy. Instead of setting standards, the agency is using its statutory powers in unprecedented ways-ordering automation developers to report crashes daily and directing rapid recalls that require changes to defective software. NHTSA is betting that intense monitoring and the credible threat of recalls will push developers to prioritize safety. This Article argues that NHTSA's experimental strategy could be transformed into effective safety regulation. Regulators should (1) require that all new vehicles be equipped with telematics that can send safety data and receive software updates over the air: (2) mandate universal crash reporting; and (3) use recalls to force developers of driving automationsystems that create unreasonable risks to restrict where their systems can operate until they can develop safer code. [ABSTRACT FROM AUTHOR]

As the technology for high-performance automobiles is developing rapidly together with the significant growth of the automobile industry in modern society, customer demand for automobile safety is increasing accordingly, and thus the development of safety products is gaining momentum. The brake system is indispensable for safe driving of the automobile, and malfunction or disorder of the brake system can lead to serious accident, resulting in injury or death. Meanwhile, if the brake system can be monitored on a real-time basis, such monitoring system can notify drivers of dangerous circumstance, like excessive wear of brake pad, or overheating of brake disc. However, in the case of high-performance/oversize automobiles, frequent or sudden braking during summer causes disc temperature to rise above 600 °C, which affects the useful life of the sensor. In contrast, there has not yet been a breakthrough for the sensor that can be used in a condition that exceeds 600 °C. Based on the heat transfer theory, we have developed the sensor in this study that normally works under the temperature exceeding 600 °C and verified that the sensor works normally under the temperature of ≥825 °C. [ABSTRACT FROM AUTHOR]

Automated driving safety evaluation predominantly relies on scenario-based approaches. In this study, the authors adopt a functional scenario catalogue initially conceived by JAMA to evaluate automated driving safety on limited access highways. The potential of this catalogue to cover real-world crashes was investigated by comparing each scenario in the catalogue with crash patterns from two international data sources: the 2007 NHTSA’s pre-crash scenario typology for crash avoidance research report, and the 2020 IGLAD’s codebook. The results indicate the potential of the scenario catalogue to comprehensively cover both the NHTSA and the IGLAD crash scenario typologies. [ABSTRACT FROM AUTHOR]

Vehicle active safety system is the hotspot of current research and development, and the road adhesion coefficient is one of the essential parameters in the development of automobile active safety system, which plays a pivotal role in automobile safety. To improve the real-time performance and accuracy of the road adhesion coefficient, this paper proposes different control methods and compares and analyzes them. To accurately estimate the road adhesion coefficient in real-time, an eight-degree-of-freedom vehicle model was adopted in this paper. According to the tire torque and wheel speed, the adhesion coefficient was estimated based on the second-order nonlinear extended state observer. The experimental data, such as slip rate, using the improved tire model, is based on the recursive least squares method with forgetting factor and fuzzy logic algorithm real-time of tire-road friction coefficient estimation. The results show that the observer can effectively observe utilisation adhesion coefficient, and both control algorithms can estimate the road adhesion coefficient in real-time, effectively and quickly. On different roads, the use of the recursive least squares algorithm resulted in higher estimation accuracy, better robustness, and better control effect than using a fuzzy logic algorithm. [ABSTRACT FROM AUTHOR]

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The use of augmented reality head-up displays (AR-HUD) in automobile safety driving has drawn more and more interest in recent years. An AR-HUD display system should be developed to fit the vehicle and the complicated traffic environment in order to increase the driver's driving concentration and improve the man–vehicle synchronization. In this article, we suggest an AR-HUD display system with dual-layer virtual-image displays for the near field and far field, as well as further research and design of the adjustment system for multi-depth displays of far-field images. It also examines the EYEBOX horizontal adjustment margin of the dual light path. The analysis results show that the scale of EYEBOX is 120 × 60 mm2, the modulation transfer function (MTF) of near-field light path > 0.2 @ 6.7 lp/mm, and the MTF of far-field optical path > 0.4 @ 6.7 lp/mm. The distortion of the near-field optical path is less than 0.86%, and that of the far-field optical path is less than 2.2%. By modifying the folding mirror, the far-field optical path creates an 8 m to 24 m multi-depth virtual picture display. Image quality can be maintained when the near-field and far-field optical paths are moved horizontally by 25 mm and 100 mm, respectively. This study offers guidelines for the multi-depth display, EYEBOX horizontal adjustment, and optical layout of augmented reality head-up displays. [ABSTRACT FROM AUTHOR]

The research presented in this paper refers to the possibility to understand the information presented by symbols, which indicate safety status and possible troubles regarding automobiles and driving. The testing included sixteen symbols, six of which were ISO-verified and recommended symbols. The study included 204 youth respondents. The study used a multidisciplinary, ergonomic approach, based on research of the usability of symbols. The basic task of subjects was to recognize the symbols and to rate their confidence on the five-point scale that they gave exact answers. For each symbol, hypotheses about the proportion of correct answers at the population level were tested, applying the inferential statistics technique. The ISO criterion of 67% successful symbol recognition was adopted as the limit value for the justification of the use of symbols. The test results showed that eight out of sixteen symbols did not meet this criterion. The results of this research indicate that it is necessary to take certain measures in order to improve the understanding of the function of symbols on vehicle dashboards. One of the proposed measures consists in the improvement of training courses in driving schools, which from a theoretical but also a practical aspect should include education about symbols on car dashboards, primarily those responsible for informing about the safety status. In addition, a redesign of the symbols that had the lowest recognition rate can be recommended. [ABSTRACT FROM AUTHOR]

• This study aims to increase the attention of drivers to ensure the safety of pedestrians through a three-dimensional (3D) crosswalk design and to provide the zones of error identified by the lane deviation system as a reference for development. • Compared to two-dimensional (2D) signs, three-dimensional (3D) crosswalks are more likely to catch commuters' attention. • 3D crosswalks with the same color temperature have a positive significant effect on females. • The best result was obtained from the combination of yellow light-facing and red-shaded sides for the 3D crosswalk. Distracted driving tests the design of traffic signs and promotes a wide range of safety developments for automobiles, making the visual effects of driving extremely important. Therefore, this study aims to increase the attention of drivers to ensure the safety of pedestrians through a three-dimensional (3D) crosswalk design and to provide the zones of error identified by the lane deviation system as a reference for development. Crosswalk design will be tested with regards to two factors: light and shadow (light-facing vs. shaded) and color (monochrome vs. two-color), for the purpose of creating 3D crosswalk samples. Through a driving environment that simulates the outdoor color temperature, a visual psychological experiment was conducted on 30 subjects to test their visual attention and inclination to brake. The results of multivariate analysis of variance show that within the same color temperature, subjects with driving experience had a significant reaction to the 3D crosswalk, with this response being stronger among females when analyzed in terms of gender. Among the test samples used in the study, the best result was obtained from the combination of yellow light-facing and red-shaded sides for the 3D crosswalk as in addition to increasing drivers' attention, it can reduce the incorrect identification of the lane deviation system caused by the color white. [ABSTRACT FROM AUTHOR]

Estimating the state of surrounding vehicles is crucial to either prevent or avoid collisions with other road users. However, due to insufficient historical data and the unpredictability of future driving tactics, estimating the safety status is a difficult undertaking. To address this problem, an intelligent and autonomous traffic management system based on V2V technology is proposed. The main contribution of this work is to design a new system that uses a real-time control system and a fuzzy logic algorithm to estimate safety. The robot operating system (ROS) is the foundation of the control architechture, which connects all the various system nodes and generates the decision in the form of a speech and graphical message. The safe path is determined by a safety evaluation system that combines sensor data with a fuzzy classifier. Moreover, the suitable information processed by each vehicle unit is shared in the group to avoid unexpected problems related to speed, sudden braking, unplanned deviation, street holes, road bumps, and any kind of street issues. The connection is provided through a network based on the ZigBee protocol. The results of vehicle tests show that the proposed method provides a more reliable estimate of safety as compared to other methods. [ABSTRACT FROM AUTHOR]

With the development of autonomous driving technology, the ESP (Electronic stability program) has been widely used in the vehicle field. The safety and stability of vehicle driving have always been a particularly important issue. The complexity of electronic control systems makes it impractical to conduct online electrical performance testing on real vehicles in research and development. In this paper, a new signal simulation method for the ESP dynamic electrical performance testing is proposed by comparing the HIL (Hardware-in-the-loop) simulation methods for vehicles and analyzing the impact of the ESP on vehicle safety and stability. To begin with, a comprehensive structural design scheme for the HIL simulation platform which is proposed by analyzing the ESP structure and control principle. Then, the principle of lateral acceleration and yaw rate that is generated by vehicle which is analyzed, and a three-dimensional model of the combined simulation device was built. Subsequently, the physical development of the simulation device was completed. Finally, the feasibility of the simulation device was verified. The experimental results indicate that the simulation device can generate continuous lateral acceleration and yaw rate signals. With the motor rotation changes, the generated signal error is different. But the generated overall error is relatively small. Therefore, the simulation device can meet the usage requirements. [ABSTRACT FROM AUTHOR]

With the increasing adoption of digital technologies in the automotive industry, the revolution of vehicles opens new doors for many advanced applications to improve driver safety and comfort. Thanks to Advanced Driver Assistance Systems (ADAS), the future driving experience will undoubtedly be safer than today. But, despite the emergence of new trends, road accidents caused by aggressive driving are still a significant problem in many countries. This study presents an edge AI-assisted aggressive driver monitoring system based on the Internet of Vehicles (IoV) model. In the proposed system, the kNN algorithm and dynamic time warping method are used to recognize the signal patterns of aggressive drivers. The hardware platform is built on the RP2040 microcontroller-based Raspberry Pi Pico board, and the Waveshare Quad Expander used for sensor extensions. The MPU-9250 9-axis motion tracking sensor is used as an inertial measurement unit (IMU) to identify the patterns of drivers who make sudden lane changes, heavy acceleration, and harsh braking on the roads. Besides, the required software is created using the MicroPy-thon scripting language via Thonny IDE. The proposed method is tested on public transport vehicles to determine the drivers engaging in dangerous driving behavior for passengers. The obtained results show that the proposed method can provide satisfactory success in supporting the recognition of the aggressive behavior of drivers. [ABSTRACT FROM AUTHOR]

Enhancement in vehicle technology system is getting increased research popularity and adding a vehicle theft security system in order to avoid getting vehicle theft in the parking and sometimes driving in unsecured places. The proposed system provided security and better theft control by using facial recognition. When the unauthorized person try to start the ignition it will be notified by the IOT application. If owner want to allow user to start the vehicle then by text msg link of webpage is given. The system uses Microprocessor raspberry pi along with a pie cam and a WIFI controller installed in the vehicle the implemented system is very simple with greater security for vehicle anti-theft protection and low cost technique compared to others. [ABSTRACT FROM AUTHOR]

In automotive vehicles, the brake are playing an important role and it acts as control system which is used to keep the vehicle stationary as well as to retard the speed of vehicle. In recent developments in automobile sector. Many safety features are developed to improve the braking efficiency. Such as Ant-locking braking system, electronic brake distribution, brake detection system using sensors and many more. This travel movement is used to push brake over travel switch and push type direction control valve which turn on the buzzer. As well as supply the pressure to the pneumatic cylinder connected to the secondary braking system and brake is applied. [ABSTRACT FROM AUTHOR]

We use data on fatal crashes to quantify the risk of distracted driving. We repurpose, extend, and improve a methodology used to estimate the riskiness of drinking drivers (Levitt & Porter, 2001). Our analysis suggests that distracted drivers are three times more likely to cause a fatal crash than focused drivers. We also estimate that distracted drivers represent three to four percent of drivers on the road at any given time. Further, we find that distractions associated with cellphone use are less likely to cause a fatal crash than are distractions from other sources. The externality costs between $0.02 and $0.05 per mile driven. The insurance surcharge for a distracted driving citation that could internalize the avoidable insurance losses is approximately $577 per year. Our work extends the literature on distracted driving and traffic fatalities. We believe our results can inform policymakers on the traffic-safety and economic consequences of distracted driving. [ABSTRACT FROM AUTHOR]

The speed of test vehicles on the high-speed car track of the automobile test field is very high. Reducing the construction error of asphalt pavement is very important to ensure the safety of the test vehicle. In order to realize the paving of asphalt concrete pavement with super-large lateral inclination in the curve section of the high-speed car track in the automobile test field, a special paving control device and control method for the construction on the curve section with super-large lateral inclination were developed. Use the direction of the hanging hammer under the GPS device of paver to adjust the position of GPS device in real time, so that the geometric centre line of GPS device is always perpendicular to the horizontal plane. The reference control line is preset in the paver operation control device, and the lateral displacement deviation of the paver is adjusted to synchronize the data of the paver control device with the travel route. The precise control of the paver's forward route is realized, the construction precision of the super-large inclined asphalt pavement on the high-speed car track of the automobile test field is achieved, and the construction efficiency is greatly improved. It has important reference value for similar projects such as automobile testing field and racing track. [ABSTRACT FROM AUTHOR]

In order to reduce the probability of automobile safety incidents, the in-vehicle occupant monitoring is indispensable. However, occupant monitoring using frequency-modulated continuous wave (FMCW) radar can be challenging due to the interference from passengers' posture, movement, and the presence of multiple people. This paper proposes an improved method for generating point clouds using FMCW radar. The approach involves point cloud clustering, post-processing operations such as segmentation, merging, and filtering of the clustered point cloud to match the actual in-vehicle environment, and a state machine combination step. Experimental results show that the proposed method can achieve high recognition accuracy in scenarios with multiple passengers who are moving and sitting in a relaxed manner. [ABSTRACT FROM AUTHOR]

Under low-speed collision conditions, the thin-walled structures located at the forepart of passenger car act as a significant energy absorber, which can reduce the injury degree of passengers in collision accidents. Therefore, the design of thin-walled structures is particularly important in automobile body development. High stiffness and strength contribute to structural integrity, but the resulting large collision force will be detrimental to occupant safety. To address this issue, the paper proposed a novel negative Poisson ratio thin-walled tube (NPR-TWT). In addition, the second-order sensitivity-based response surface models (SB-RSMs) were established to improve the efficiency of design optimization. Furthermore, the NPR-TWT was optimized by using the non-dominated sorting genetic algorithm (NSGA-II) to enhance its comprehensive performance. The results showed that the optimized NPR-TWT could absorb collision energy by sufficient displacement deformation and reduce the peak impact force. It can provide theoretical and practical guidance for the application of thin-walled structures in automobile body safety. [ABSTRACT FROM AUTHOR]

Many traffic accidents occur in parking lots. One of the serious safety risks is vehicle-pedestrian conflict. Moreover, with the increasing development of automatic driving and parking technology, parking safety has received significant attention from vehicle safety analysts. However, pedestrian protection in parking lots still faces many challenges. For example, the physical structure of a parking lot may be complex, and dead corners would occur when the vehicle density is high. These lead to pedestrians' sudden appearance in the vehicle's path from an unexpected position, resulting in collision accidents in the parking lot. We advocate that besides vehicular sensing data, high-precision digital map of the parking lot, pedestrians' smart device's sensing data, and attribute information of pedestrians can be used to detect the position of pedestrians in the parking lot. However, this subject has not been studied and explored in existing studies. To fill this void, this paper proposes a pedestrian tracking framework integrating multiple information sources to provide pedestrian position and status information for vehicles and protect pedestrians in parking spaces. We also evaluate the proposed method through real-world experiments. The experimental results show that the proposed framework has its advantage in pedestrian attribute information extraction and positioning accuracy. It can also be used for pedestrian tracking in parking spaces. [ABSTRACT FROM AUTHOR]

We propose a miniaturized tripleband printed monopole antenna for 5G, WLAN, WiMAX, and X-band applications. Slots are etched in a printed rectangular monopole to design the antenna. A slot etched in a rectangular monopole increases the capacitance and therefore, decreases the resonant frequency or miniaturizes the antenna. Slots in a rectangular monopole antenna also create different current path lengths which resonates at different frequencies. Three slots are etched, and parameters are optimized to achieve triple bands to operate over 5G (3.3 to 3.6 GHz), WiMAX (3.4 to 3.6 GHz), WLAN (5.725-5.875 GHz), WLAN 5.9 GHz band (5.850-5.925 GHz), and X-band (7.3-7.9). The lower 45MHz (5.850-5.925 GHz) and upper 30MHz (5.895-5.925 GHz) of WLAN band also find applications for automobile safety in Cellular Vehicle-to-Everything (C-V2X) technology. The radiation patterns are nearly omnidirectional. The antenna is fabricated on a 0:154λ0 x 0:143λ0 board area, where λ0 is the free-space wavelength at 3.3 GHz. The measured results are in close agreement with the simulation ones. [ABSTRACT FROM AUTHOR]

Monitoring tire condition plays a deterministic role in the overall safety and economy of an automobile. The tire condition monitoring system (TCMS) alerts the driver of the vehicle if the inflation pressure of a particular tire decreases below a specific value. Owing to the high costs involved in realizing this system, most vehicles do not feature this technology as a standard. With highly robust and accurate sensors making their way into an increasing number of applications, obtaining signals of varied types (especially vibration signals) is becoming easier and more modularized. In addition, feature-based machine learning techniques that enable accurate responses to varied input conditions have sought greater scientific attention. However, deep learning is gradually finding greater applications pertaining to condition monitoring. One approach of deep learning is presented in this paper, which instantaneously monitors the vehicle tire condition. For this purpose, vibration signals were obtained through the rotation of the tire under different inflation pressure conditions using a low-cost microelectromechanical system (MEMS) accelerometer. [ABSTRACT FROM AUTHOR]

Prospective customers are becoming more concerned about safety and comfort as the automobile industry swings toward automated vehicles (AVs). A comprehensive evaluation of recent AVs collision data indicates that modern automated driving systems are prone to rear-end collisions, usually leading to multiple-vehicle collisions. Moreover, most investigations into severe traffic conditions are confined to single-vehicle collisions. This work reviewed diverse techniques of existing literature to provide planning procedures for multiple vehicle cooperation and collision avoidance (MVCCA) strategies in AVs while also considering their performance and social impact viewpoints. Firstly, we investigate and tabulate the existing MVCCA techniques associated with single-vehicle collision avoidance perspectives. Then, current achievements are extensively evaluated, challenges and flows are identified, and remedies are intelligently formed to exploit a taxonomy. This paper also aims to give readers an AI-enabled conceptual framework and a decision-making model with a concrete structure of the training network settings to bridge the gaps between current investigations. These findings are intended to shed insight into the benefits of the greater efficiency of AVs set-up for academics and policymakers. Lastly, the open research issues discussed in this survey will pave the way for the actual implementation of driverless automated traffic systems. [ABSTRACT FROM AUTHOR]

Machine learning systems have become integrated into some of the most vital decision-making aspects of humanity, including hiring decisions, loan applications, and automobile safety, to name just a few. As applications increase in both gravity and complexity, the data quality and algorithmic interpretability of the systems must rise to meet those challenges. This is especially vital for navigating the nuances of health care, particularly among the high stakes of surgical operations. In addition to inherent ethical challenges of enabling a "black box" system to influence decision-making in patient care, the creation of biased datasets leads to biased algorithms with the power to perpetuate discrimination and reinforce disparities. Transparency and responsibility are paramount to the implementation of artificial intelligence in surgical decision-making and autonomous robotic surgery. Machine learning has been permeating health care across diverse clinical and surgical contexts but continues to face sizable obstacles, including apprehension from patients and providers alike. To integrate the technology fully while upholding standard of care and patient-provider trust, one must acknowledge and address the ethical, financial, and legal implications of using artificial intelligence for patient care. [ABSTRACT FROM AUTHOR]

Automobile booster seats are intended to improve belt fit for children that are too large for a harness-style child restraint, but not yet big enough to fit properly in an adult seat belt. Our objective was to prospectively study the relationship between booster seat design and interaction with the seat belt (specifically, submarining risk) for a child occupant using computer simulation of automobile crash events. Frontal-impact simulations were performed with a 6-year-old child human body model. Simplified models of booster seats were developed using an automated process designed to capture key characteristics of booster geometry, stiffness, belt guide construction, and attachment to the vehicle seat. The child model was positioned in a range of postures from upright to slouched. Our main interest was submarining, where the child's pelvis slips under the lap belt and the belt loads into the abdomen (defined based on the motion of the lower lap belt edge relative to the ASIS). Among the parameters studied, the factors that had the greatest effect on submarining risk were the booster's stiffness and the child's posture. Booster models of a low-stiffness construction (similar to an inflatable booster) nearly always resulted in submarining, regardless of the other design characteristics of the booster. A slouched posture also substantially increased the likelihood of submarining (even for high-stiffness boosters). These results suggest that booster seats of a stiffer construction, and booster seats that promote an upright posture may provide a protective benefit compared to softer boosters and boosters that are more likely to result in slouching of the child. [ABSTRACT FROM AUTHOR]

Automobile sector is growing day by day due to the globalization and the urbanization. Further, the luxury life of human beings leads to the new development in the production of automobiles. Consumers are looking for fuel economy with safety features in automobiles. Hence, the weight reduction and strength performance of components play major role in manufacturing of automobiles. In this current investigation, we tried to develop new composite materials with light weight and better thermal and mechanical characteristics using natural and synthetic fibers. Totally, six types of composites were manufactured, namely, Epoxy/Banyan, Epoxy/Glass, Epoxy/Flax, Epoxy/Banyan/Flax, Epoxy/Banyan/Glass, and Epoxy/Flax/Glass by conventional hand layup method by keeping matrix-to-fiber ratio as 60:40. Mechanical and thermal characteristics of the composites were examined. Based on the thermal behavior of composites, Epoxy/Banyan composite was selected as the suitable candidate for thermal insulation applications in automobile industries as exhibited least thermal conductivity of 0.112 W/mK. Further, the mechanical characteristics were tested for Epoxy/Banyan composite. Results displayed the properties as: tensile strength − 75 MPa; flexural strength − 113 MPa; impact strength − 12.8 kJ/m2. [ABSTRACT FROM AUTHOR]

Self-driving, 'autonomous' vehicles (AVs) promise to change the world in profound ways. The suggested benefits include safety, efficiency and accessibility. However, researchers and others have been quick to raise questions about wider implications for mobility and urban environments and responsible development of the technology. In a discussion that has been dominated by science, engineering and narrow questions of ethics, there is a need to draw attention to the old questions of politics: Who wins? Who loses? Who decides? Who pays? AVs will not be defined by their supposed autonomy; they will be defined by a set of social relationships. The special collection that this paper accompanies brings together research from a range of disciplines to explore the politics of autonomous vehicles and provide a foundation for ongoing investigation. [ABSTRACT FROM AUTHOR]

This paper provides an observational before/after evaluation of the safety effects of edge-lane road (ELR) (also known as advisory bike lanes or advisory shoulders) installations in the United States. An ELR is a class of roadway that supports two-way automobile traffic within a single center lane and vulnerable road users (VRUs), i.e., bicyclists or pedestrians, in the edge lanes on either side. The use of a single lane by automobile users traveling in both directions is often a cause of potential safety concerns among the general public. This study employs a project-level empirical Bayes (EB) approach to before/after safety analysis for all US ELR sites where requisite crash data and other relevant characteristics were available. The analysis at 11 sites was performed with 8 years of crash data and more than approximately 60 million motor vehicle trips. Project-level EB analysis based on safety performance functions (SPFs) showed 8 of 11 ELR sites experienced a reduction in crash experience since installation. There was a ∼44% reduction in crashes among all sites compared to the expected crashes on the traditional two-lane two-way design that existed before ELR installation. This estimation assumed the calibration factor for all SPFs used in the EB analysis was 1.0. Because of the geographical spread of the ELRs being analyzed in this study, estimation of specific calibration factors for all 11 sites was beyond the scope of this work. To address this limitation, we conservatively assumed the SPF calibration factors to be 0.50 for locations for which calibration factors were not available. This assumption was made for 7 of the 11 sites. Even with this conservative assumption, the ELRs were estimated to have a ∼36% crash reduction. The results from this comprehensive evaluation of existing US ELRs should alleviate concerns the general public often has about the safety of ELRs for automobile users. [ABSTRACT FROM AUTHOR]

New energy vehicles have made considerable achievements after nearly a decade of development, but the range and safety of automobile battery packs are still a difficult problem for the industry to overcome. The charge state of the battery can reflect the charging and discharging situation and battery health, so it is significant to construct an accurate prediction model of the battery charge state. In view of this, an RBF neural network is utilized to predict the battery state of charge. Considering that the battery charge state is a nonlinear time series, the study introduces an integrated learning algorithm to further optimize the prediction model. Experimentally, the loss rate of the raised model is almost stable around 150 steps and the loss rate tends to be close to 0. The accuracy of the raised model also reached 81.60%, which is 2.84% and 1.80% higher than the control model, respectively; The recall rate of the raised model reached 77.92%, which was 4.73% and 25.31% higher than the control model, respectively. From the effectiveness test results, the raised model has higher accuracy and faster convergence rate than the control model, and the recall rate of the raised model is also higher, so the raised model has a certain progressiveness. [ABSTRACT FROM AUTHOR]

Although pure electric vehicles have the advantages of environmental protection and energy conservation, due to the constraints of power lithium-ion battery technology, they have a relatively short driving range and face problems such as high battery costs and low charging efficiency. Automotive electrical equipment affects the performance and safety of automobiles. In order to improve the efficiency and reliability of automotive electrical equipment, structural optimization is necessary. This article aims to improve the search ability and convergence speed of the algorithm by optimizing its parameters and introducing new genetic operations, in order to obtain a more optimal structure of automotive electrical equipment. Firstly, the basic principle and process of multi-population genetic algorithm were studied, and its problems in optimizing the structure of automotive electrical equipment were analyzed. Then, based on the characteristics of the problem, improvements were made to the multi-population genetic algorithm to improve its search ability. Through experimental verification, the improved multi-population genetic algorithm has achieved significant results in optimizing the structure of automotive electrical equipment. Compared with traditional algorithms, the improved algorithm has significantly improved its search ability and convergence speed. Through the optimized algorithm, a better structure of automotive electrical equipment was obtained, with higher efficiency and reliability. [ABSTRACT FROM AUTHOR]

The research presented in this paper refers to the possibility to understand the information presented by symbols, which indicate safety status and possible troubles regarding automobiles and driving. The testing included sixteen symbols, six of which were ISO-verified and recommended symbols. The study included 204 youth respondents. The study used a multidisciplinary, ergonomic approach, based on research of the usability of symbols. The basic task of subjects was to recognize the symbols and to rate their confidence on the five-point scale that they gave exact answers. For each symbol, hypotheses about the proportion of correct answers at the population level were tested, applying the inferential statistics technique. The ISO criterion of 67% successful symbol recognition was adopted as the limit value for the justification of the use of symbols. The test results showed that eight out of sixteen symbols did not meet this criterion. The results of this research indicate that it is necessary to take certain measures in order to improve the understanding of the function of symbols on vehicle dashboards. One of the proposed measures consists in the improvement of training courses in driving schools, which from a theoretical but also a practical aspect should include education about symbols on car dashboards, primarily those responsible for informing about the safety status. In addition, a redesign of the symbols that had the lowest recognition rate can be recommended.

In this article, I provide a brief historical perspective on how state-level policy was an integral part of the rise of school physical education in the first half of the twentieth century, as well as the evolution of sport pedagogy as a subdiscipline within the field of kinesiology. The role and importance of policy in changing behavior are presented using reductions in tobacco use and improvements in automobile safety as examples. “Impact” of best available research evidence is presented as a multilayered concept, from surface level impact at the personal level to “deep impact” that would be reflected in improved professional practice. The influence of the changing landscapes in K-12 and higher education present both external and internal barriers that, coupled with a lack of awareness (or neglect?) of the role and impact of policy, have hamstrung any appreciable progress in professional practice. In the final section, I argue that unless kinesiology becomes proactive in advocacy toward state-level policy development, school physical education will retain its marginalized place. Initial suggestions are provided to help initiate greater involvement in such policy development. [ABSTRACT FROM AUTHOR]

Axial folding of metal tubes has been renowned over ages as a superb energy-absorbing method. High-volume industrial items like automobiles, trains, and other sectors where energy must be absorbed in a controlled manner during a crush situation are using components based on this concept. From the perspective of passenger automobile safety design, it is crucial and anticipated to investigate crushing energy absorption. These are thoroughly investigated experimentally and computationally how aluminium foam-filled sections behave when compressed axially. To represent quasi-static test circumstances, nonlinear dynamic finite element studies are conducted. It is discovered that the predicted crushing force and fold formation are rather similar to the experimental facts. The mean crushing force of the foam-filled sections is calculated using straightforward closed-form methods based on the computational models. It is shown that the increase in the mean crushing force of a full column increases linearly with the cross-sectional area and foam compressive resistance. The proposal, for a variety of column designs, materials, and foam strengths, has been within 8% of the experimental data. Ultimately, the results highlight the advantages of using concentric cylindrical tubes to absorb impact energy in circumstances with axial loads. It only understands well how control the absorbed energy by using the geometrical features of such structures. The purpose of this study is to suggest design solutions on how to use concentric cylindrical tubes in energy absorption applications like crash-worthiness. [ABSTRACT FROM AUTHOR]

A hood-catch is a car part that fixes the vehicle's hood to its body. This serves as a mechanism for opening and closing the engine bay of an automobile and prevents safety accidents in which the hood is opened while driving due to vibration generated from off-road conditions by firmly latching. Research regarding the overall improvement of products by injection molding, with analysis based on computer-aided engineering, is being actively conducted. However, research on the shrinkage rate considering the characteristics of parts' shapes, such as length-to-volume ratio, is insufficient. Therefore, in this study, our research is concerned with (1) process improvement compared to the existing process using the design of an experimental method, (2) suggestion of a shrinkage-rate constant, considering both PA6 shrinkage ratio, which is a material characteristic, and length-to-volume ratio, which is a part shape characteristic, and (3) the effect of the proposed shrinkage rate on the accuracy of key dimensions of the hood-catch joint. The results of this study demonstrated that the shrinkage-rate constant needs to be considered simultaneously for both material characteristics and part shape characteristics. In particular, the shrinkage rates of the hood-catch's pivot sub-assembly and open lever with reinforced PA6, with length-to-volume ratios of 1.24 × 10−3 and 3.10 × 10−3, respectively, are suggested as 0.2% and 0.5%. [ABSTRACT FROM AUTHOR]