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1.
Sensors (Basel) ; 21(17)2021 Aug 24.
Artigo em Inglês | MEDLINE | ID: mdl-34502584

RESUMO

Anticipating pedestrian crossing behavior in urban scenarios is a challenging task for autonomous vehicles. Early this year, a benchmark comprising JAAD and PIE datasets have been released. In the benchmark, several state-of-the-art methods have been ranked. However, most of the ranked temporal models rely on recurrent architectures. In our case, we propose, as far as we are concerned, the first self-attention alternative, based on transformer architecture, which has had enormous success in natural language processing (NLP) and recently in computer vision. Our architecture is composed of various branches which fuse video and kinematic data. The video branch is based on two possible architectures: RubiksNet and TimeSformer. The kinematic branch is based on different configurations of transformer encoder. Several experiments have been performed mainly focusing on pre-processing input data, highlighting problems with two kinematic data sources: pose keypoints and ego-vehicle speed. Our proposed model results are comparable to PCPA, the best performing model in the benchmark reaching an F1 Score of nearly 0.78 against 0.77. Furthermore, by using only bounding box coordinates and image data, our model surpasses PCPA by a larger margin (F1=0.75 vs. F1=0.72). Our model has proven to be a valid alternative to recurrent architectures, providing advantages such as parallelization and whole sequence processing, learning relationships between samples not possible with recurrent architectures.


Assuntos
Pedestres , Humanos , Processamento de Linguagem Natural
2.
Sensors (Basel) ; 21(17)2021 Aug 30.
Artigo em Inglês | MEDLINE | ID: mdl-34502731

RESUMO

As a sub-direction of image retrieval, person re-identification (Re-ID) is usually used to solve the security problem of cross camera tracking and monitoring. A growing number of shopping centers have recently attempted to apply Re-ID technology. One of the development trends of related algorithms is using an attention mechanism to capture global and local features. We notice that these algorithms have apparent limitations. They only focus on the most salient features without considering certain detailed features. People's clothes, bags and even shoes are of great help to distinguish pedestrians. We notice that global features usually cover these important local features. Therefore, we propose a dual branch network based on a multi-scale attention mechanism. This network can capture apparent global features and inconspicuous local features of pedestrian images. Specifically, we design a dual branch attention network (DBA-Net) for better performance. These two branches can optimize the extracted features of different depths at the same time. We also design an effective block (called channel, position and spatial-wise attention (CPSA)), which can capture key fine-grained information, such as bags and shoes. Furthermore, based on ID loss, we use complementary triplet loss and adaptive weighted rank list loss (WRLL) on each branch during the training process. DBA-Net can not only learn semantic context information of the channel, position, and spatial dimensions but can integrate detailed semantic information by learning the dependency relationships between features. Extensive experiments on three widely used open-source datasets proved that DBA-Net clearly yielded overall state-of-the-art performance. Particularly on the CUHK03 dataset, the mean average precision (mAP) of DBA-Net achieved 83.2%.


Assuntos
Processamento de Imagem Assistida por Computador , Pedestres , Algoritmos , Humanos , Pesquisa , Semântica
3.
Sensors (Basel) ; 21(17)2021 Aug 30.
Artigo em Inglês | MEDLINE | ID: mdl-34502738

RESUMO

In the field of computer vision, object detection consists of automatically finding objects in images by giving their positions. The most common fields of application are safety systems (pedestrian detection, identification of behavior) and control systems. Another important application is head/person detection, which is the primary material for road safety, rescue, surveillance, etc. In this study, we developed a new approach based on two parallel Deeplapv3+ to improve the performance of the person detection system. For the implementation of our semantic segmentation model, a working methodology with two types of ground truths extracted from the bounding boxes given by the original ground truths was established. The approach has been implemented in our two private datasets as well as in a public dataset. To show the performance of the proposed system, a comparative analysis was carried out on two deep learning semantic segmentation state-of-art models: SegNet and U-Net. By achieving 99.14% of global accuracy, the result demonstrated that the developed strategy could be an efficient way to build a deep neural network model for semantic segmentation. This strategy can be used, not only for the detection of the human head but also be applied in several semantic segmentation applications.


Assuntos
Pedestres , Semântica , Humanos , Processamento de Imagem Assistida por Computador , Redes Neurais de Computação
4.
Sensors (Basel) ; 21(17)2021 Aug 30.
Artigo em Inglês | MEDLINE | ID: mdl-34502741

RESUMO

Pedestrian detection has been widely used in applications such as video surveillance and intelligent robots. Recently, deep learning-based pedestrian detection engines have attracted lots of attention. However, the computational complexity of these engines is high, which makes them unsuitable for hardware- and power-constrained mobile applications, such as drones for surveillance. In this paper, we propose a lightweight pedestrian detection engine with a two-stage low-complexity detection network and adaptive region focusing technique, to reduce the computational complexity in pedestrian detection, while maintaining sufficient detection accuracy. The proposed pedestrian detection engine has significantly reduced the number of parameters (0.73 M) and operations (1.04 B), while achieving a comparable precision (85.18%) and miss rate (25.16%) to many existing designs. Moreover, the proposed engine, together with YOLOv3 and YOLOv3-Tiny, has been implemented on a Xilinx FPGA Zynq7020 for comparison. It is able to achieve 16.3 Fps while consuming 0.59 W, which outperforms the results of YOLOv3 (5.3 Fps, 2.43 W) and YOLOv3-Tiny (12.8 Fps, 0.95 W).


Assuntos
Pedestres , Humanos , Inteligência
5.
Sensors (Basel) ; 21(17)2021 Sep 02.
Artigo em Inglês | MEDLINE | ID: mdl-34502799

RESUMO

Social distancing protocols have been highly recommended by the World Health Organization (WHO) to curb the spread of COVID-19. However, one major challenge to enforcing social distancing in public areas is how to perceive people in three dimensions. This paper proposes an innovative pedestrian 3D localization method using monocular images combined with terrestrial point clouds. In the proposed approach, camera calibration is achieved based on the correspondences between 2D image points and 3D world points. The vertical coordinates of the ground plane where pedestrians stand are extracted from the point clouds. Then, using the assumption that the pedestrian is always perpendicular to the ground, the 3D coordinates of the pedestrian's feet and head are calculated iteratively using collinear equations. This allows the three-dimensional localization and height determination of pedestrians using monocular cameras, which are widely distributed in many major cities. The performance of the proposed method was evaluated using two different datasets. Experimental results show that the pedestrian localization error of the proposed approach was less than one meter within tens of meters and performed better than other localization techniques. The proposed approach uses simple and efficient calculations, obtains accurate location, and can be used to implement social distancing rules. Moreover, since the proposed approach also generates accurate height values, exclusionary schemes to social distancing protocols, particularly the parent-child exemption, can be introduced in the framework.


Assuntos
COVID-19 , Pedestres , Calibragem , , Humanos , SARS-CoV-2
6.
Accid Anal Prev ; 161: 106381, 2021 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-34479122

RESUMO

It is well known that pedestrians are vulnerable road users. Their risk of being injured or killed in road traffic crashes is even higher as vehicle drivers often violate traffic rules and do not slow down or yield in front of crosswalks. In order to reduce this risk, many countries have issued strict regulations requiring vehicles to yield to pedestrians in front of crosswalks. While extensive literature exists on the interaction between vehicles and pedestrians, the consideration of heterogeneity in the behavior of vehicles is vastly overlooked. Accordingly, this study analyzes the yielding behavior of three types of vehicles under the "pedestrian priority" policy by processing drone footage collected in Xi'an City (China) with a Machine Vision Intelligent Algorithm. Moreover, this study proposes four additional indicators to the widely used yielding rate and yielding delay with the aim of evaluating yielding behavior of three types of vehicles. The results show that buses have the best yielding behavior from the perspective of yielding rate, yielding delay, waiting time, yielding angle and waiting site. Buses perform well in observing pedestrian dynamics near crosswalk, and perform exceptionally well in considering the "blind area" of vision. The location of the waiting site in front of the stop line and the length of the waiting time contribute to the safe crossing of pedestrians. In contrast, private cars perform badly in yielding to pedestrians. However, serious polarization can be observed across private cars, as the performance varies across the board. The relaxation of the homogenization assumption of the behavior of vehicles in pedestrian-vehicle interaction, alongside the improvements in the analysis via Machine Vision Intelligent Algorithm of videos acquired via drone, shows the possibility of having a deeper understanding of the yielding behavior of vehicles at crosswalk. The extension of the use of artificial intelligence methods to analyze drone footage has immense potential in understanding road user behavior and hence providing knowledge for crash prevention.


Assuntos
Inteligência Artificial , Pedestres , Acidentes de Trânsito/prevenção & controle , Automóveis , Humanos , Segurança , Caminhada
7.
Accid Anal Prev ; 161: 106291, 2021 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-34543783

RESUMO

The number of vehicle accidents involving pedestrians in Korea has decreased gradually since the Pedestrian Safety and Convenience Enhancement Act was enacted in 2012, but the number of serious pedestrian-related crashes per capital remains near the top of a list of such rates for member countries of the Organization of Economic Cooperation and Development. Previous studies of pedestrian safety have been conducted based on various built environments. However, few have analyzed spatio-temporal changes and influential factors over more than 10 years, despite dramatic changes in the built environment during such time spans. Here, we examine big data on pedestrian-related crashes in Seoul from 2009 to 2018 using a space-time cube methodology and binary logistic regression analysis. The results show that the trend in pedestrians killed or severely injured is decreasing with pedestrian environment enhancement projects and pedestrian safety measures in Seoul. Also, the analysis reveals a need to pay more attention to pedestrian safety in areas with a large older population. Pedestrian safety measures should be reinforced in areas of concentrated wholesale and retail businesses. This study also indicates that illegal parking poses a threat to pedestrian safety. Lastly, this study confirms some positive impacts of redeveloped or newly developed areas and pedestrian environment enhancement projects on pedestrian safety.


Assuntos
Pedestres , Acidentes de Trânsito , Ambiente Construído , Humanos , Seul , Análise Espaço-Temporal
8.
Sensors (Basel) ; 21(18)2021 Sep 13.
Artigo em Inglês | MEDLINE | ID: mdl-34577344

RESUMO

Accurate smartphone-based outdoor localization systems in deep urban canyons are increasingly needed for various IoT applications. As smart cities have developed, building information modeling (BIM) has become widely available. This article, for the first time, presents a semantic Visual Positioning System (VPS) for accurate and robust position estimation in urban canyons where the global navigation satellite system (GNSS) tends to fail. In the offline stage, a material segmented BIM is used to generate segmented images. In the online stage, an image is taken with a smartphone camera that provides textual information about the surrounding environment. The approach utilizes computer vision algorithms to segment between the different types of material class identified in the smartphone image. A semantic VPS method is then used to match the segmented generated images with the segmented smartphone image. Each generated image contains position information in terms of latitude, longitude, altitude, yaw, pitch, and roll. The candidate with the maximum likelihood is regarded as the precise position of the user. The positioning result achieved an accuracy of 2.0 m among high-rise buildings on a street, 5.5 m in a dense foliage environment, and 15.7 m in an alleyway. This represents an improvement in positioning of 45% compared to the current state-of-the-art method. The estimation of yaw achieved accuracy of 2.3°, an eight-fold improvement compared to the smartphone IMU.


Assuntos
Pedestres , Smartphone , Algoritmos , Cidades , Humanos , Semântica
9.
Artigo em Inglês | MEDLINE | ID: mdl-34444563

RESUMO

Crosswalks are critical locations in the urban transport network that need to be designed carefully as pedestrians are directly exposed to vehicular traffic. Although various methods are available to evaluate the level of service (LOS) at pedestrian crossings, pedestrian crossing facilities are frequently ignored in assessing crosswalk conditions. This study attempts to provide a comprehensive framework for evaluating crosswalks based on several essential indicators adopted from different guidelines. A new pedestrian crossing level of service (PCLOS) method is introduced in this research, with an aimto promote safe and sustainable operations at such locations. The new PCLOS employs an analytical point system to compare existing street crossing conditions to the guidelines' standards, taking into account the scores and coefficients of the indicators. The quantitative scores and coefficients of indicators are assigned based on field observations and respondent opinions. The method was tested to evaluate four pedestrian crosswalks in the city of Putrajaya, Malaysia. A total of 17 indicators were selected for the study after a comprehensive literature review. Survey results show that the provision of a zebra crossing was the most critical indicator at the pedestrian crossings, while drainage near crosswalks was regarded as the least important. Four indicators had a coefficient value above 4, indicating that these are very critical pedestrian crossing facilities and significantly impact the calculation of LOS for pedestrian crossings. Four crosswalks were evaluated using the proposed method in Putrajaya, Malaysia. The crosswalk at the Ministry of Domestic Trade Putrajaya got the "PCLOS A". In contrast, the midblock crossing in front of the Putrajaya Corporation was graded "PCLOS C". While the remaining two crosswalks were graded as "PCLOS B" crosswalks. Based on the assigned PCLOS grade, the proposed method could also assist in identifying current design and operation issues in existing pedestrian crossings and providing sound policy recommendations for improvements to ensure pedestrian safety.


Assuntos
Neuropeptídeos , Pedestres , Acidentes de Trânsito/prevenção & controle , Cidades , Proteínas do Citoesqueleto , Humanos , Segurança , Caminhada
10.
Sensors (Basel) ; 21(16)2021 Aug 04.
Artigo em Inglês | MEDLINE | ID: mdl-34450714

RESUMO

This paper reports on the progress of a wearable assistive technology (AT) device designed to enhance the independent, safe, and efficient mobility of blind and visually impaired pedestrians in outdoor environments. Such device exploits the smartphone's positioning and computing capabilities to locate and guide users along urban settings. The necessary navigation instructions to reach a destination are encoded as vibrating patterns which are conveyed to the user via a foot-placed tactile interface. To determine the performance of the proposed AT device, two user experiments were conducted. The first one requested a group of 20 voluntary normally sighted subjects to recognize the feedback provided by the tactile-foot interface. The results showed recognition rates over 93%. The second experiment involved two blind voluntary subjects which were assisted to find target destinations along public urban pathways. Results show that the subjects successfully accomplished the task and suggest that blind and visually impaired pedestrians might find the AT device and its concept approach useful, friendly, fast to master, and easy to use.


Assuntos
Pedestres , Equipamentos de Autoajuda , Pessoas com Deficiência Visual , Dispositivos Eletrônicos Vestíveis , Humanos , Smartphone
11.
Accid Anal Prev ; 159: 106289, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34340136

RESUMO

Crashes involving cyclists and pedestrians in Europe cause the deaths of about 7600 persons every year. Both cyclists and pedestrians are especially exposed in crashes with motorized vehicles and collisions with trucks can lead to severe injury outcomes. The two most frequent crash scenarios between trucks and these vulnerable road users (VRU) are: a) when the truck wants to turn right at an intersection, with a cyclist riding parallel and planning to cross the intersection and b) when a pedestrian crosses in front of the truck in perpendicular direction to the movement of the truck. Advanced Driver Assistance Systems (ADAS)-that are expected to prevent or mitigate these crashes-benefit from detailed information about the behavior of truck drivers. This study is a first exploration of this research area, with the aim to assess how drivers negotiate the encounters with VRUs in the two scenarios described above. Thirteen participants drove an instrumented truck on a test-track. After some baseline recordings, the drivers experienced two laps where they encountered a cyclist target and a pedestrian target crossing their path. The results show that the truck drivers adapted their kinematic and visual behavior in the laps where the VRU targets were crossing the intersection, compared to the baseline laps. The speed profiles of the drivers diverged approximately 30 m from the intersection and glances were directed more often towards front right and right, during the scenario with the cyclist in comparison to baseline laps. For the scenario with the pedestrian crossing, the drivers changed their speed about 14 m from the intersection and glances were directed more often towards the front center, compared to baseline laps. As a result, both the speed and distance from the intersection at the end of the maneuver were significantly different between VRU and baseline laps. Overall, the findings provide valuable information for the design of ADAS that warn the drivers about the presence of a cyclist travelling in parallel direction or that intervene to avoid a collision with a cyclist or pedestrian.


Assuntos
Condução de Veículo , Pedestres , Acidentes de Trânsito , Fenômenos Biomecânicos , Humanos , Veículos Automotores
12.
Comput Intell Neurosci ; 2021: 5410049, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34335717

RESUMO

Pedestrian detection is a specific application of object detection. Compared with general object detection, it shows similarities and unique characteristics. In addition, it has important application value in the fields of intelligent driving and security monitoring. In recent years, with the rapid development of deep learning, pedestrian detection technology has also made great progress. However, there still exists a huge gap between it and human perception. Meanwhile, there are still a lot of problems, and there remains a lot of room for research. Regarding the application of pedestrian detection in intelligent driving technology, it is of necessity to ensure its real-time performance. Additionally, it is necessary to lighten the model while ensuring detection accuracy. This paper first briefly describes the development process of pedestrian detection and then concentrates on summarizing the research results of pedestrian detection technology in the deep learning stage. Subsequently, by summarizing the pedestrian detection dataset and evaluation criteria, the core issues of the current development of pedestrian detection are analyzed. Finally, the next possible development direction of pedestrian detection technology is explained at the end of the paper.


Assuntos
Condução de Veículo , Aprendizado Profundo , Pedestres , Humanos , Inteligência
13.
Accid Anal Prev ; 160: 106298, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34358750

RESUMO

Three-fourths of pedestrian fatalities in the U.S. occur in the dark (National Center for Statistics and Analysis, 2020). Adaptive Headlight Systems (AHS) offer the potential to address this problem by improving the visibility of pedestrians for drivers and alerting pedestrians to approaching vehicles. The goal of this study was to investigate how pedestrians respond to different types of AHS. We conducted a mixed factor experiment with 106 college-age adults using a large-screen pedestrian simulator. The task for participants was to cross a stream of continuous traffic without colliding with a vehicle. There were four AHS treatment conditions that differed in the color (white or red) and timing of an icon projected on the roadway in front the participant as an AHS vehicle approached. Participants in the treatment conditions encountered a mix of AHS and non-AHS vehicles. There was also a control condition in which participants encountered only non-AHS vehicles. We found that the color and the timing of the icon projected on the roadway influenced the size of the gaps crossed. Participants in the red icon with early onset condition chose the largest gaps for crossing. An unexpected outcome was that participants in the AHS treatment conditions chose larger gaps even when crossing in front of non-AHS vehicles, suggesting that experiences with AHS vehicles generalized to non-AHS vehicles. We conclude that AHS can have a significant, positive impact on pedestrian road-crossing safety.


Assuntos
Pedestres , Acidentes de Trânsito/prevenção & controle , Adulto , Humanos , Segurança , Caminhada
14.
Accid Anal Prev ; 160: 106323, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34380083

RESUMO

Travel by bus is an efficient, cost-effective, safe and preferred means of intercity transport in many advanced countries. On the contrary, there is huge public sentiment about the safety records of intercity buses in low- and middle-income countries given the increasing bus-involved road traffic crashes and high fatality rates. This study sought to model the injury severity of intercity bus transport in Ghana using the random parameters multinomial logit with heterogeneity in means and variances modelling technique to account for unobserved heterogeneity in the dataset. The dataset involves crash data from the 575 km long Accra-Kumasi-Sunyani-Gonokrom highway in Ghana. Four discrete crash outcome categories were considered in this study: fatal injury, hospitalized injury, minor injury, and no injury. The study observed that crashes involving pedestrians, unlicensed drivers, and drivers and passengers aged more than 60 years have a higher probability of sustaining fatal injuries. Also, speeding, wrong overtaking, careless driving and inexperienced drivers were associated with fatal injury outcomes on the highway. The incidence of intercity bus transport crashes involving larger buses and minibuses were also found to more likely result in fatalities. The probability of hospitalized injury increased for crashes that occurred in a village setting. Given these findings, the study proposed improvement of the road infrastructure, enforcing seatbelt availability and use in intercity buses, increased enforcement of the traffic rules and regulations to deter driver recklessness and speeding as well as improving the luminance of the highways. Additionally, apps that have features for customers to rate intercity bus operators, the quality of services provided, and also have the option to report reckless driving activities can be developed to promote safe and inclusive public transport in the country.


Assuntos
Condução de Veículo , Pedestres , Ferimentos e Lesões , Acidentes de Trânsito , Gana/epidemiologia , Humanos , Modelos Logísticos , Veículos Automotores , Ferimentos e Lesões/epidemiologia
15.
Accid Anal Prev ; 161: 106355, 2021 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-34461394

RESUMO

Using simulation models to conduct safety assessments can have several advantages as it enables the evaluation of the safety of various design and traffic management options before actually making changes. However, limited studies have developed microsimulation models for the safety evaluation of active road users such as pedestrians. This can be attributed to the limited ability of simulation models to capture the heterogeneity in pedestrian behavior and their complex collision avoidance mechanisms. Therefore, the objective of this study is to develop an agent-based framework to realistically model pedestrian behavior in near misses and to improve the understanding of pedestrian evasive action mechanisms in interactions with vehicles. Pedestrian-vehicle conflicts are modeled using the Markov Decision Process (MDP) framework. A continuous Gaussian Process Inverse Reinforcement Learning (GP-IRL) approach is implemented to retrieve pedestrians' reward functions and infer their collision avoidance mechanisms in conflict situations. Video data from a congested intersection in Shanghai, China is used as a case study. Trajectories of pedestrians and vehicles involved in traffic conflicts were extracted with computer vision algorithms. A Deep Reinforcement Learning (DRL) model is used to estimate optimal pedestrian policies in traffic conflicts. Results show that the developed model predicted pedestrian trajectories and their evasive action mechanisms (i.e., swerving maneuver and speed changing) in conflict situations with high accuracy. As well, the model provided predictions of the post encroachment time (PET) conflict indicator that strongly correlated with the corresponding values of the field-measured conflicts. This study is a crucial step in developing a safety-oriented microsimulation tool for pedestrians in mixed traffic conditions.


Assuntos
Near Miss , Pedestres , Acidentes de Trânsito/prevenção & controle , China , Humanos , Segurança , Caminhada
16.
Accid Anal Prev ; 161: 106351, 2021 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-34461395

RESUMO

Cyclists and pedestrians account for a disproportionate amount of the world's 1.3 million road deaths every year. This is a growing problem in the United Sates where bicyclist and pedestrian fatalities have increased steadily since 2009. A large body of research suggests vehicle speeds are a key contributing factor for crashes. However, few studies of bicycle or pedestrian crash probability incorporate detailed vehicle speed data. This study uses probe vehicle speed data to examine the impact of vehicle speeds on bicycle and pedestrian crashes on the state of Georgia's network of major arterial roadways. The analysis examines 7000 road segments throughout the state in 2017. A Negative Binomial model relates annual crash and speed data on each segment. Models using speed percentiles (85th, 50th and 15th) are contrasted with models using speed differences (85th-50th and 50th-15th percentile). A small set of covariates are included: segment length, number of lanes, Average Annual Daily Traffic, and urbanicity. Results indicate that larger differences in high-end speed percentiles are positively associated with bicycle and pedestrian crash frequency on Georgia arterials. Furthermore, the coefficients on the high end of the speed distribution, measured by the difference in 85th and 50th percentile speeds, have greater magnitude and statistical significance than the low end of the distribution. This research shows a negative relationship between speed and crashes may be flawed, as it does not account for the distributions of speed. The findings in this study suggest that planners and engineers should identify areas with large speed distributions, especially at the high vehicle speeds, and work to reduce the fastest speeds on these roadways. To do so, differences in speed percentiles measured using probe vehicle speeds can be used to determine where high risk areas are located.


Assuntos
Pedestres , Acidentes de Trânsito , Ciclismo , Georgia , Humanos , Modelos Estatísticos
17.
Accid Anal Prev ; 161: 106344, 2021 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-34416577

RESUMO

Legal intervention is a powerful tool to reduce road traffic injuries (RTIs). China amended the Road Traffic Safety Law in 2011, but the impact of amended law on traffic crash deaths is still unknown. In this study, we conducted an interrupted time series analysis and examined years of life lost (YLLs) per 100,000 population as the assessment indicator to evaluate the association of road traffic safety law and traffic crash mortality. Annual YLLs data due to traffic deaths from 2002 to 2019 in China were obtained from the Global Burden of Disease (GBD) 2019. After implementation of the revised law, the average level of total YLLs per 100,000 population due to traffic deaths decreased from 1133.14 to 848.87, and the slope of annual YLLs per 100,000 population decreased by 30.11 (95% CI: 22.46, 37.75), indicating a steeper downward trend. The revised traffic law was associated with YLLs reduction due to traffic deaths for males, females, all age groups, pedestrians, motor vehicle users, and other road users, as well as traffic deaths attributed to alcohol use and tobacco use. These findings suggested that the revised Road Traffic Safety Law improved road safety by decreasing YLLs due to traffic deaths in China. However, the burden of RTIs is still heavy and efforts to further improve traffic laws and the adoption of other interventions are urgently needed.


Assuntos
Pedestres , Ferimentos e Lesões , Acidentes de Trânsito/prevenção & controle , China/epidemiologia , Feminino , Carga Global da Doença , Humanos , Análise de Séries Temporais Interrompida , Masculino , Ferimentos e Lesões/epidemiologia , Ferimentos e Lesões/prevenção & controle
18.
Accid Anal Prev ; 160: 106306, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34303494

RESUMO

In 2018, about 6,677 pedestrians were killed on the US roadways. Around one-fourth of these crashes happened at intersections or near intersection locations. This high death toll requires careful investigation. The purpose of this study is to provide an overview of the characteristics and associated crash scenarios resulting in fatal pedestrian crashes in the US. The current study collected five years (2014-2018) of fatal crash data with additional details of pedestrian crash typing. This dataset provides specifics of scenarios associated with fatal pedestrian crashes. This study applied associated rules mining on four sub-groups, which were determined based on the highest frequencies of fatal crash scenarios. This study also developed the top 20 rules for all four sub-groups and used 'a priori' algorithm with 'lift' as a performance measure. Some of the key variable categories such as dark with lighting condition, vehicle going straight, vehicle turning, local municipality streets, pedestrian age range from 45 years and above are frequently presented in the developed rules. The patterns of the rules differ by the pedestrian's position within and outside of crosswalk area. If the pedestrian is outside the crosswalk area, no lighting at dark is associated with high number of crashes. As lift provides quantitative measures in the form of the likelihood, the rules can be transferred into data-driven decision making. The findings of the current study can be used by safety engineers and planners to improve pedestrian safety at intersections.


Assuntos
Pedestres , Acidentes de Trânsito , Algoritmos , Humanos , Iluminação , Pessoa de Meia-Idade , Tempo
19.
Accid Anal Prev ; 160: 106305, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34332291

RESUMO

Due to the high frequent traffic accidents involving electric bicycles (E-bike), it urgently needs improved protection of cyclists, especially their heads. In this study, by adjusting the initial impact velocities of E-bike and car, initial impact angle between E-bike and car, initial E-bike impact location, and body size of cyclist, 1512 different accident conditions were constructed and simulated using a verified E-bike-to-car impact multi-body model. The cyclist's head kinematic responses including the head relative impact velocity, WAD (Wrap around distance) of head impact location and HIC15 (15 ms Head Injury Criterion) were collected from simulation results to make up a dataset for data mining. The decision tree models of cyclist's head kinematic responses were then created from this dataset and verified accordingly. Based on simulated results obtained from decision tree models, it can be found as follows. 1. In the E-bike-to-car accidents, the average head impact relative velocity and WAD of head impact location are higher than those in the car-to-pedestrian accidents. 2. Increasing the initial impact velocity of car can increase the cyclist's head relative impact velocity, WAD of head impact location, and HIC15. 3. The WAD of cyclist's head impact location is also significantly affected by the initial impact angle between E-bike and car and body size of cyclist: the WAD of head impact location becomes higher with increasing initial impact angle between E-bike and car and body size of cyclist. 4. The effects of initial E-bike impact location on the WAD of cyclist's head impact location is not significant when initial E-bike impact location is concentrated in the region of 0.25 m around the centerline of the car.


Assuntos
Ciclismo , Pedestres , Acidentes de Trânsito , Automóveis , Fenômenos Biomecânicos , Humanos
20.
Sensors (Basel) ; 21(12)2021 Jun 17.
Artigo em Inglês | MEDLINE | ID: mdl-34204484

RESUMO

Pedestrian detection by a car is typically performed using camera, LIDAR, or RADAR-based systems. The first two systems, based on the propagation of light, do not work in foggy or poor visibility environments, and the latter are expensive and the probability associated with their ability to detect people is low. It is necessary to develop systems that are not based on light propagation, with reduced cost and with a high detection probability for pedestrians. This work presents a new sensor that satisfies these three requirements. An active sound system, with a sensor based on a 2D array of MEMS microphones, working in the 14 kHz to 21 kHz band, has been developed. The architecture of the system is based on an FPGA and a multicore processor that allow the system to operate in real time. The algorithms developed are based on a beamformer, range and lane filters, and a CFAR (Constant False Alarm Rate) detector. In this work, tests have been carried out with different people and in different ranges, calculating, in each case and globally, the Detection Probability and the False Alarm Probability of the system. The results obtained verify that the developed system allows the detection and estimation of the position of pedestrians, ensuring that a vehicle travelling at up to 50 km/h can stop and avoid a collision.


Assuntos
Sistemas Microeletromecânicos , Pedestres , Acidentes de Trânsito , Emergências , Estudos de Viabilidade , Humanos
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