Wear Helmets Research Articles

DIKOTOMI PENEGAKAN HUKUM PELANGGARAN LALULINTAS TERHADAP PENGENDARA YANG TIDAK MENGGUNAKAN HELM DI KOTA KENDARI

This research is based on the phenomenon of the people of Kendari City who think that riding on the road using a motorbike does not require wearing a helmet, especially at certain moments. This momentum such as the delivery of a corpse or even to the celebration of a democratic party, like a campaign in public areas. Some people of Kendari City only wear hats and peci (songko) at certain seasons, and some choose not to wear any head protection at all. Due to societal realities, the purpose of this research is to ascertain how law enforcement responds to drivers who don’t wear helmets in accordance with sociocultural circumstances and to understand Kendari City’s traffic law system from the perspective of Lawrence M. Friedman’s theory. The research method used is a normative-empirical method. The research results findings demonstrate that when law enforcement personnel’s roles are in conflict with the sociocultural context of Kendari City society, the application of Article 291 of the Traffic Law fails to work as best it might. At some times, including during campaigns or when delivering corpses, law enforcement officials in the traffic sector appear to find it difficult or impossible to enforce the law against drivers who don’t wear helmets. Therefore, the situation indicates that Article 291 of the Traffic Law’s traffic system doesn’t work as expected.

Prevalence of Ocular Injuries Following Road Traffic Accidents Among Two-Wheeler Riders/Pillions and the Factors Influencing Them Reported in a Tertiary Care Center in Chengalpattu in Tamil Nadu, India.

Introduction Road traffic accident-related ocular damage is a serious public health issue that can result in unilateral blindness. Several individual risk factors lead to ocular injury and various levels of visual impairment. Objectives The objective is to assess the demography and pattern of ocular, orbital, and eyelid injuries among motorcycle accident cases attending a medical college and hospital in Chengalpattu district Tamil Nadu India. Methodology A cross-sectional study was conducted among 67 victims of road traffic accidents who had sustained ocular injuries from October 2023 to June 2024. The selected participants were subjected to a questionnaire regarding thedetails of the history of events leading to the road traffic accident, and complete ophthalmic examination and radiological investigationswere carried out. Informed consent was obtained from all the participants, andin cases where the participant was unable to give consent, informed consent was obtained from their family members. The collected data were analyzed using SPSS version 21 (IBM Corp., Armonk, NY). Result The majority (76.1%) were male between the agegroup of31-40 years, and they were allmotor vehicle/cycle riders seated in the front. Most accidents happened in rural (68.7%), highways (52.2%), and the driver being under the influence of alcohol (64.2%). Most of the participants (80.6%) did not use helmets, and 19.4%were using mobile phoneswhile driving. Visual acuity wasaffected in 57% and 41.8%suffered from orbital floorfracture. About 92.5% of the participants sustained subconjunctival hemorrhagewith ecchymosis and 31.34% had lacerations of the upper eyelid with periorbital edema. Among the participants, 9% had a loss of vision due to vitreous hemorrhage and9%due to Berlin's edema. Conclusion Middle-aged males driving two-wheelers on rural highways, those who did not wear helmets, and thosewho wereunder theinfluence of alcohol werethe vulnerable group. It is advised thatboth front and back-seated persons use safety precautions, such as helmets, and avoid placing the child in front of the rider while driving. Stringentregulations designed to improve conformity to traffic rules are essential in preventing ocular injuries.

Parental non-compliance to childhood safety practices: A cross-sectional study.

Background & objectives To assess the pattern of non-compliance to childhood safety practices among parents of children between one and five yr of age and to determine the reasons behind the same. Methods A descriptive, cross-sectional study was conducted on 120 children between one and five yr of age through a questionnaire-based interview of the primary carer. Poor compliance was defined as compliance to <85 per cent of the recommended practices. Results Of the total 48 injuries, 32 were serious (66.6%). Falls and burns/scalds were the two common types of injuries. Nearly three-fourths (72.5%) of families showed overall poor compliance. Poorest compliance was observed towards four safety practices namely, helmet wearing, restricting number of pillion riders to two on a two-wheeler, seatbelt wearing in a car and avoiding food items with a high risk of choking in children <3 yr. Parental perception of 'unnecessary' and 'lack of knowledge' were the main reasons behind non-compliance. Interpretation & conclusions Low compliance among families occurred with respect to safety on motor vehicles and avoidance of choking hazards. Change of parental perception and improved awareness is necessary for the prevention of unintentional childhood injuries.

Assessing bicycle safety risks using emerging mobile sensing data

The surge in global electric bicycle ownership has exerted immense pressure on bicycle infrastructure. Theoretically, there’s a need to reassess the risk factors associated with multiple bike lane users. Based on this, there’s a practical need to re-evaluate the safety and quality of outdated infrastructure. This paper aims to reconsider risk factors related to bicycle infrastructure safety in the context of electric bicycles sharing lanes with traditional bicycles. Moreover, many countries lack precise spatial data concerning bicycle infrastructure. This study introduces a mobile sensing method based on bicycles, aiming to acquire daytime and nighttime bike lane datasets in a cost-effective, efficient, and large-scale manner. A computer vision-based bicycle risk factor assessment model was established, and the distribution of bicycle safety risk factors was visually analyzed. Research data was collected from a representative 59.5-kilometer bicycle lane area in Beijing. The results confirm the significant impact of the surge in electric bicycles, with electric bike users accounting for 72.1% of cyclists, 32.3% wearing helmets, and 8.4% riding against traffic. During the day, the highest-ranking risk factors include the type of bicycle lanes (half lacking dedicated lanes or being shared), roadside parking, and subpar road conditions. At night, insufficient street lighting are notable concerns. The research methodology is easily replicable and can be extended to new multi-user coexistence cycling environments or countries without bicycle spatial data, offering insights for bicycle safety policies and road design.

HELMET DETECTION AND LICENSE PLATE RECOGNITION USING CNN

Right now, we're facing a lot of issues with traffic rules in India that might use some fresh perspectives to fix. There has been a rise in the number of accidents and fatalities in India caused by the traffic offense of riding motorcycles or mopeds without a helmet. The current system mostly relies on CCTV records to track traffic offenses. In such cases, the traffic police have to zoom in on the license plate to identify the offending rider if they aren't wearing a helmet. The traffic offenses are common, and the number of persons riding motorbikes is growing daily, so this takes a lot of time and effort. Imagine a system that could detect whether a motorcyclist or moped rider isn't wearing a helmet and, if found, immediately get the license plate number. Recent studies have effectively accomplished this task using various characteristics such as CNN, R-CNN, LBP, HoG, HaaR, etc. In terms of speed, accuracy, and efficiency, however, these works have their limitations when it comes to object recognition and categorization. To try to automate the process of finding drivers who don't wear helmets and getting their license plate numbers, this study developed a system called Non-Helmet Rider detection. The core idea is based on three-level deep learning for object detection. Using YOLOv2, the first level detects a person and a motorbike or moped; the second level uses YOLOv3, and the third level uses YOLOv2. The items recognized are a helmet and a license plate. After then, Optical Character Recognition is used to obtain the license plate registration number. There are some limitations and requirements placed on all of these methods, particularly the one that extracts license plates. The efficiency of the process is crucial since video is used as an input in this task. We have developed a comprehensive system that can recognize helmets and retrieve license plate numbers using the aforementioned approaches.

Helmet Net: An Improved YOLOv8 Algorithm for Helmet Wearing Detection

It is of profound significance to detect whether cyclists wear helmets to protect their personal safety and maintain road traffic safety. Due to the limitations of space, distance and cyclist movement, it is challenging to detect helmet-wearing accurately and quickly. In view of this, a novel You Only Look Once (YOLOv8) algorithm for helmet-wearing detection is suggested in this paper. Firstly, YOLOv8n with the best performance test results is selected as the baseline model from several advanced object detection algorithms. Secondly, improvement measures are taken for YOLOv8n. The squeeze-and-excitation networks (SENet) is integrated at the C2f of the neck to improve the network representation ability. Part of conv modules in the backbone is replaced with the lightweight convolution (LConv) to improve the computational efficiency and the generalization ability of the model. The loss function is changed with the Wise-IoU (WIoU), to enhance the overall performance of the model. Additionally, the reasoning method is replaced by the slicing aided hyper inference (SAHI), which aims to lower the rate of missed detections for smaller objects and strengthen the accuracy of their detection. Through the above improvement methods, a new helmet-wearing detection algorithm is formed, called helmet net. Furthermore, in comparison to the YOLOv8n, the proposed algorism demonstrated an increase in precision, recall, and mean average precision (mAP) by 5, 7.3, and 6.4%, respectively, for helmet-wearing detection. At the same time, the speed reaches 111.1 fps, which can contribute to the real-time detection of helmet-wearing. After adding SAHI, the detection results show that the model can detect more small objects, which further enhances the competence of model for helmet-wearing detection.

Observational Study of the Clinical Impact of E-scooter Injuries at a Major Trauma Centre.

Background Due to the increased use of e-scooters in the UK, associated injuries have increased, and its cost has increased as well. Worldwide data on injuries related to e-scooter use are relatively limited, owing to the short duration since their first introduction to the public. Aim There has been an increase in using e-scooters as a method of transport recently. It was noticed that the severity and frequency of its injuries are increasing as well. This study focuses on the frequency of e-scooter-associated injuries, especially open fractures, and to evaluate its burden on the health system. Methods Data on e-scooter injuries were extracted from our trauma database from November 2017 to July 2022. Patients' notes and images were reviewed. Outcome measures were the type of injuries: site of bony fractures, closed vs. open fracture (using Anderson and Gustilo classification), number of operations, complications, and length of hospital stay. Results The number of patients enrolled was 104. The mean age was 34.3 years, and 78.8% (n = 82) were male. The main mechanism of injury was riding (91%, n = 95) vs. hitting by an e-scooter (9%, n = 9). Injured patients were more likely not to wear helmets (82% of total injured patients). Patients with bony injuries were 65.4% (n = 68), with 22.1% (n = 15) of them being open fractures. The most common bony injuries were lower limb-only fractures at 45.6% (n = 31), then upper limb-only injuries at 39.7% (n = 27). Combined upper and lower limb cases were 8.8% (n = 6), and pelvic injuries were 5.9% (n = 4). Head and neck injuries composed 23.1% (n = 24) of the reported injuries, including intracranial haemorrhages (9.6%, n = 10), extensive traumatic brain injury (3.8%, n = 4), haematoma/lacerations (3.8%, n = 4), cervical spine fractures (1.9%, n = 2), and skull fractures (1.9%, n = 2). The mean duration of hospital stay was 8.6 days, and 9.6% (n = 10) of patients needed intensive therapy unit(ITU) admission. The number of patients presented as trauma calls was 55.8% (n = 58). Patients who needed surgical intervention either under orthopaedics or other specialties were 52.9% (n = 55) and 21.2% (n = 22) of total patients had complications either due to surgical intervention or a long hospital stay. Conclusion E-scooter riding can lead to serious injuries that can end with limb- or life-threatening conditions. The most common demographic characteristics were adults in their early 30s. There should be more emphasis on wearing protective gear like wrist and elbow guards in addition to helmets. Future prospective studies with larger cohorts across multiple regions and hospitals are necessary to truly characterize the nature and cost of e-scooter injuries.

PRE-YOLO: A Lightweight Model for Detecting Helmet-Wearing of Electric Vehicle Riders on Complex Traffic Roads

Electric vehicle accidents on the road occur frequently, and head injuries are often the cause of serious casualties. However, most electric vehicle riders seldom wear helmets. Therefore, combining target detection algorithms with road cameras to intelligently monitor helmet-wearing has extremely important research significance. Therefore, a helmet-wearing detection algorithm based on the improved YOLOv8n model, PRE-YOLO, is proposed. First, we add small target detection layers and prune large target detection layers. The sophisticated algorithm considerably boosts the effectiveness of data manipulation while significantly reducing model parameters and size. Secondly, we introduce a convolutional module that integrates receptive field attention convolution and CA mechanisms into the backbone network, enhancing feature extraction capabilities by enhancing attention weights within both channel and spatial aspects. Lastly, we incorporate an EMA mechanism into the C2f module, which strengthens feature perception and captures more characteristic information while maintaining the same model parameter size. The experimental outcomes indicate that in comparison to the original model, the proposed PRE-YOLO model in this paper has improved by 1.3%, 1.7%, 2.2%, and 2.6% in terms of precision P, recall R, mAP@0.5, and mAP@0.5:0.95, respectively. At the same time, the number of model parameters has been reduced by 33.3%, and the model size has been reduced by 1.8 MB. Generalization experiments are conducted on the TWHD and EBHD datasets to further verify the versatility of the model. The research findings provide solutions for further improving the accuracy and efficiency of helmet-wearing detection on complex traffic roads, offering references for enhancing safety and intelligence in traffic.

EVALUATION OF NECK FATIGUE CAUSED BY WEARING HELMET IN MOTION: AN ELECTROMYOGRAPHY STUDY

The increased weight of multifunction and intelligent helmet systems can cause an exacerbation of the neck workload. Further, increased weight of the helmet can reduce the wearing comfort, and prolonged use may cause neck fatigue and even neck injury. This study evaluates the fatigue of neck muscles caused by wearing a helmet in motion using electromyography (EMG) measurement. Additionally, a comprehensive evaluation model for neck fatigue was established effectively. Twenty healthy subjects were selected to run for 10[Formula: see text]min continuously in each task by wearing two different helmets, respectively, during which the EMG signals of different neck muscles and subjective scores were recorded and analyzed. Two-way repeated measures analysis of variance (ANOVA) and Pearson correlation analysis were used to obtain indicators of neck fatigue, based on which the support vector machine (SVM) was used to construct a classification model of neck fatigue. By using integral EMG (IEMG) and root mean square (RMS) for assessing the neck injury in sternocleidomastoideus and trapezius muscles, it was observed that the mean power frequency (MPF) and median frequency (MF) in trapezius and splenius capitis could be used as effective evaluation indicators of neck fatigue. The EMG activity in trapezius was the highly sensitivity to fatigue when wearing helmets in motion. The classifier for distinguishing three different neck fatigue levels was 91.67% accurate. This study demonstrates the feasibility of an EMG-based method to evaluate the neck fatigue caused by wearing a helmet in motion, and also provides an evaluation method for the optimal design of helmets in the future.

SDCB-YOLO: A High-Precision Model for Detecting Safety Helmets and Reflective Clothing in Complex Environments

The correct wearing of safety helmets and reflective vests is of great significance in construction sites, offices, and civil engineering sites. Aiming to address the issues of low detection accuracy and high algorithm complexity caused by complex background environments in the small target detection of safety helmets and reflective clothing using existing algorithms, an improved algorithm based on YOLOv8n is proposed. Firstly, the SE module is utilized to reduce interference in complex environments. Next, the IOU function is modified to speed up calculations. Then, a lightweight universal upsampling operator (CARAFE) is employed to obtain a larger receptive field. Finally, the Bidirectional Feature Pyramid Network is used to replace the Concat module of the original head layer. Based on these four modifications made to the model, this article names the new model SDCB-YOLO, derived from the initial letters of the four respective modules. The experimental results show that the mAP of the SDCB-YOLO model on the test set reached 97.1%, which is 4.6% higher than YOLOv5s and 3.5% higher than YOLOv8n. Additionally, the model boasts a parameter count of 3,094,304, a computational load of 8.4 GFLOPs, and a model size of 6.13 MB. Compared to YOLOv5s, with a parameter count of 7,030,417, a computational cost of 16.0 GFLOPs, and a model size of 13.79 MB, the SDCB-YOLO model is significantly smaller. When compared to YOLOv8n, with a parameter count of 3,011,628, a computational complexity of 8.2 GFLOPs, and a model size of 6.11 MB, the SDCB-YOLO model’s parameters and model size are only slightly increased, while maintaining a comparable computational load. Therefore, the improved detection algorithm presented in this article not only ensures the lightweight nature of the model but also significantly enhances its detection accuracy.

Sex differences in cervical disc height and neck muscle activation during manipulation of external load from helmets.

Neck pain associated with helmet-wear is an occupational health problem often observed in helicopter pilots and aircrew. Whether aircrew helmet wearing is associated with physiological and biomechanical differences between sexes is currently unknown. This study investigated neuromuscular activation patterns during different helmet-wearing conditions. The helmet load was manipulated through a novel Helmet Balancing System (HBS) in healthy, non-pilot male and female participants (n=10 each, age 19-45years) in two phases. Phase A assessed the acute effects of helmet-wear on neck muscles activation during head movements. Phase B examined changes in muscle activity and cervical disc height after wearing a helmet for 45 min. In Phase A, muscle activity was similar between sexes in many movements, but it was higher in female participants when wearing a helmet than in males. The HBS reduced muscle activity in both sexes. In Phase B, female participants exhibited a greater level of muscular fatigue, and male participants' cervical disc height was significantly decreased [5.7 (1.4) vs. 4.4 (1.5)mm, P<0.001] after continuous wearing. Both sexes showed no significant change in muscle fatigue and disc height [male: 5.0 (1.3) vs. 5.2 (1.4)mm, P=0.604] after applying HBS. These findings demonstrate sex-specific physiological and biomechanical responses to wearing a helmet. They may indicate different postural and motor control strategies, associated with different neck pain aetiologies in male and female aircrew, the knowledge of which is important to reduce or prevent musculoskeletal injuries associated with helmet wearing.

An improved YOLOv8 safety helmet wearing detection network

In the field of industrial safety, wearing helmets plays a vital role in ensuring workers’ health. Aiming at addressing the complex background in the industrial environment, caused by differences in distance, the helmet small target wearing detection methods for misdetection and omission detection problems are needed. An improved YOLOv8 safety helmet wearing detection network is proposed to enhance the capture of details, improve multiscale feature processing and improve the accuracy of small target detection by introducing Dilation-wise residual attention module, atrous spatial pyramid pooling and normalized Wasserstein distance loss function. Experiments were conducted on the SHWD dataset, and the results showed that the mAP of the improved network improved to 92.0%, which exceeded that of the traditional target detection network in terms of accuracy, recall, and other key metrics. These findings further improved the detection of helmet wearing in complex environments and greatly enhanced the accuracy of detection.

Helmet wearing and related factors among electric personal mobility device users in Korea.

Using data from the 2022 Korea Community Health Survey (n = 13 320), this study investigated helmet use and related factors among Korean adults using personal mobility devices, without distinguishing between private and hired users. Among mobility device users, 32.1% responded that they always wore a helmet. The proportion of helmet use was 35.2% among men, 25.8% among women, 29.2% among those aged 19-44 years, 42.3% among those aged 45-64 years and 26.6% among those aged 65 years or older. Furthermore, those who drank less frequently and were physically active were more likely to wear helmets. Moreover, people who always wore a seat belt when driving a car or sitting in the rear seat and people who always wore a helmet when riding a motorcycle were more likely to wear a helmet while using electric personal mobility devices. Approximately one-third of users always wore a helmet. The helmet-wearing rate was related to general characteristics such as gender and education level, and to safety behaviors such as wearing a seat belt when driving a car, sitting in the rear seat of a car, or when riding a motorcycle. In addition to considering personal characteristics investigated in this study, the helmet-wearing rate should be improved through policies or systems at the national or regional levels.

Impact of a Helmet Distribution Program in the Pediatric Emergency Department.

Bicycle helmet use has repeatedly been shown to protect riders from serious injury. Despite this, a majority of children and adolescents do not regularly wear helmets. Our primary objective was to determine if an emergency department (ED)-based helmet distribution program could increase the amount of time children report wearing helmets. This was a prospective cohort study of children aged 3 to 17 years presenting to the ED of an urban, tertiary care hospital. Participants were surveyed on their helmet use habits and perceptions regarding the efficacy and importance of helmets. Participants then received a bicycle helmet along with safety counseling in the ED and bicycle safety handouts provided by the American Academy of Pediatrics. Participants were contacted for follow-up 8 weeks after enrollment. We enrolled a total of 94 patients. Post-intervention surveys were obtained from 47% of participants. Our helmet program resulted in a statistically significant increase in the amount of time children reported wearing bicycle helmets. Prior to ED intervention, 48% of participants reported wearing a helmet "most of the time" or "all of the time." After participating, 86% of participants indicated that they wore a helmet "most of the time" or "all of the time." Participants indicated a variety of reasons for not wearing helmets. Of all patients contacted for follow-up, 16% indicated that they were involved in an accident after participating where the helmet they were given prevented a head injury. Helmet distribution programs based in the pediatric ED increase rates of bicycle helmet usage among pediatric patients. Additional study is needed to determine effect on head injury prevention.

TRAFFIC VIOLATION PREDICTION USING DEEP LEARNING BASED ON HELMETS WITH NUMBER PLATE RECOGNITION

At the moment, two-wheelers are the most widely used kind of transportation. It is highly advised that both bike riders and soldiers wear helmets. Many academics are interested in object tracking in video surveillance, which is an important application and burgeoning field of study in image processing and machine learning. Finding objects in an image with a bounding box and different categories or shapes of the objects placed is called object detection. This study reviews tracking techniques, classifies them into several categories, and concentrates on significant and practical tracking approaches. After reviewing broad strategies under a scan of the literature on various methodologies, we analyze potential research areas. This study uses the YOLOv8 algorithm, an image processing technique, to identify motorcycle riders who do not wear helmets. Additionally, put the Optical Character Recognition method into practice to identify the license plate in a picture and retrieve user information. Next, determine the fine amount. Eventually, SMS services will be able to notify consumers in order to prevent motorbike accidents. We evaluate the framework in terms of speed and accuracy.

God, Hero, King? Imagery of ‘Pearson Wearing Helmet’ on the Coins of Seleukos I

God, Hero, King? Imagery of ‘Pearson Wearing Helmet’ on the Coins of Seleukos I

Efficient and Lightweight Neural Network for Hard Hat Detection

Electric power operation, as one of the key fields in the world, faces particularly prominent safety issues. Ensuring the safety of operators has become the most fundamental requirement in power operation. However, there are some safety hazards in power construction. These hazards are mainly due to weak safety awareness among staff and the failure to standardize the wearing of safety helmets. In order to effectively address this situation, technical means such as video surveillance technology and computer vision technology can be utilized to monitor whether staff are wearing helmets and provide timely feedback. Such measures will greatly enhance the safety level of power operation. This paper proposes an improved lightweight helmet detection algorithm named YOLO-M3C. The algorithm first replaces the YOLOv5s backbone network with MobileNetV3, successfully reducing the model size from 13.7 MB to 10.2 MB, thereby increasing the model’s detection speed from 42.0 frames per second to 55.6 frames per second. Then, the CA attention mechanism is introduced into the backbone network to enhance the feature extraction capability of the model. Finally, in order to further improve the detection recall rate and accuracy of the model, a knowledge distillation of the model was carried out. The experimental results show that, compared with the original YOLOv5s algorithm, the average accuracy of the improved YOLO-M3C algorithm is improved by 0.123, and the recall rate is the same. These results verify that the algorithm YOLO-M3C has excellent performance in target detection and recognition, which can improve accuracy and confidence, while reducing false detection and missing detection, and effectively meet the needs of helmet-wearing detection.

HR-YOLO: A Multi-Branch Network Model for Helmet Detection Combined with High-Resolution Network and YOLOv5

Automatic detection of safety helmet wearing is significant in ensuring safe production. However, the accuracy of safety helmet detection can be challenged by various factors, such as complex environments, poor lighting conditions and small-sized targets. This paper presents a novel and efficient deep learning framework named High-Resolution You Only Look Once (HR-YOLO) for safety helmet wearing detection. The proposed framework synthesizes safety helmet wearing information from the features of helmet objects and human pose. HR-YOLO can use features from two branches to make the bounding box of suppression predictions more accurate for small targets. Then, to further improve the iterative efficiency and accuracy of the model, we design an optimized residual network structure by using Optimized Powered Stochastic Gradient Descent (OP-SGD). Moreover, a Laplace-Aware Attention Model (LAAM) is designed to make the YOLOv5 decoder pay more attention to the feature information from human pose and suppress interference from irrelevant features, which enhances network representation. Finally, non-maximum suppression voting (PA-NMS voting) is proposed to improve detection accuracy for occluded targets, using pose information to constrain the confidence of bounding boxes and select optimal bounding boxes through a modified voting process. Experimental results demonstrate that the presented safety helmet detection network outperforms other approaches and has practical value in application scenarios. Compared with the other algorithms, the proposed algorithm improves the precision, recall and mAP by 7.27%, 5.46% and 7.3%, on average, respectively.

The safety effect of increased pedestrian protection, autonomous emergency braking for pedestrians and bicyclists on passenger cars, and speed management

This was the first retrospective study to estimate the effect of increased pedestrian protection, autonomous emergency braking, and speed management to reduce serious injuries among pedestrians and bicyclists. More specifically, the aim was to estimate the injury mitigating effects of the following interventions: AEB with pedestrian and bicyclist detection, Euro NCAP pedestrian test score, active bonnet, traffic calming at pedestrian and bicycle crossings, and additionally, the combined effect of the above-mentioned treatments. The main source of data was the Swedish traffic data acquisition system (Strada), where information of road traffic crashes between passenger cars and pedestrians or bicyclists for the period 1 January 2003–31 December 2022 was obtained. Cars with optional fitment of AEB systems were identified, and the license registration number was used to access individual car equipment lists to identify whether the vehicle was equipped with AEB with pedestrian and/or cyclist detection. Information about traffic calming at pedestrian and bicycle crossings was obtained from the Swedish Transport Administration. The injury metric used was risk of permanent medical impairment (RPMI) of at least one percent and ten percent. RPMI captures the risk of long-term medical impairment based on a diagnosed injury location and Abbreviated Injury Severity (AIS) score. The relative difference between the mean values of RPMI (mRPMI1%+ and mRPMI10%+) was calculated and tested using an independent two sample t-test which was conducted for unequal sample sizes and variance. Although many results were found to be statistically non-significant, the following results were found to be significant at least at 90% level. Pedestrian mRPMI10%+ was reduced by 44% in speed zones ≤ 50 km/h comparing the group struck by cars equipped with AEB with pedestrian detection compared to the group struck by cars without the system. For cyclists, the mRPMI10%+ was reduced by 35% in speed zones ≤ 50 km/h. For crashes within ± 20 meters from a pedestrian or bicycle crossing, the AEB system reduced 60% of pedestrians mRPMI10%+ at crossings with good safety standard compared to crossings of poor safety standard. The comparison of cars with poor performance (1–9 points) in the NCAP pedestrian test and cars with a high score (28–36 points) showed that pedestrian mRPMI10%+ was reduced by 48% across all speed limits, and by 64% including only those aged ≤ 64 years. For bicyclists, a significant reduction of cyclist mRPMI10%+ was found comparing low scoring cars to high scoring cars in ≤ 30 km/h speed limit (-73%) and across all speed limits (-36%). Including only those aged ≤ 64 years, the reduction was 49%. For the active bonnet, a significant reduction of mRPMI1%+ by 24% was observed but given that the rate of helmet wearing was higher in the group struck by cars with active bonnet, this difference cannot be attributed to an effect of an active bonnet. The STA safety rating of pedestrian and bicycle crossings showed that overall pedestrian mRPMI1%+ was reduced by 15%, while cyclists mRPMI10%+ was reduced by 32% comparing crossings of high safety level to crossings of poor safety level. The analysis of combined interventions showed that the total reduction of pedestrians and cyclists mRPMI10%+ together was 69%, from 6.4% to 2%. This paper demonstrates that a road environment with adapted infrastructure and speed, combined with passenger car technologies that improve the safety for vulnerable road users, can create significant reductions of serious (long-term) injuries among pedestrians and bicyclists.

Intelligent Helmet System for Prevention of Accidents

Abstract: India, like, has a huge number of road accidents almost every year. The accidents may be due to like many reasons like by drinking and driving, driving like rashly, exceeding the speed limit, etc., you know. Sometimes, the person who like gets injured might not be like responsible for the Accident, you know what I mean. It might be the fault of some other vehicle rider, maybe. But overall, both riders will get like affected, kind of. Due to a lack, you know, of first aid and emergency medical services on time, the riders may like die, which is kind of sad, you know. Some deaths are occurred due to the ambulance not reaching the desired location on time, which is, like, bad, you know. In case of an accident, to save time and inform the concerned person, a system is proposed like which can made sure that the rider gets the required attention in a short time, you know. In, like, India, many people use two-wheeler vehicles as compared to four-wheeler vehicles because of its, you know, low which is kind, like, interesting, you know what I mean. In many accidents, the rider gets injured mainly on the head, kind of, you know. A helmet kind of plays a very important role in saving the life of the ridden, you know what I mean. So to encourage people to wear helmets and to avoid accidents, a design is proposed that synchronizes the, you know, module present in the bike, which is kind of cool.