Safety evaluation for roadside crashes by vehicle–object collision simulation

Long, Kejun; Gao, Zhibo; Yuan, Quan; Xiang, Wang; Hao, Wei

doi:10.1177/1687814018805581

Cited by 12 publications

(6 citation statements)

References 24 publications

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“…They also proposed a new index for evaluating the accuracy of accident severity classification. Relatively similar work has been reported in [10]. However, the proposed severity metrics' effectiveness was not further validated with real-world data.…”

Section: Related Workmentioning

confidence: 89%

“…Many related studies [1], [8]- [10] conclude that fixed roadside attributes such as trees, poles, walls etc., and moving attributes such as pedestrians [11] pose dangerous road conditions to the motorists when they are close to the road. In addition to the crash data and simulations, real-world visual data can be used to assess the severity of roadside objects.…”

Section: Related Workmentioning

confidence: 99%

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Road Severity Distance Calculation Technique Using Deep Learning Predictions in 3-D Space

Perera

Verma

2022

IEEE Access

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Some roadside objects pose a significant danger to pedestrians and vehicles when they are too close to the road. A few examples are trees, poles and fences. Their proximity to the road can change over time due to natural conditions or human activities. Early detection of severe roadside conditions can help avoid accidents and save lives. However, detecting the roadside severity objects requires many resources and new techniques due to the size and complexity of the road network. Deep learning and image processing techniques can be leveraged to address this requirement and build an automatic roadside severity detection system. In this work, we propose a novel roadside attribute and distance calculation technique that extends our previous work in this area (lane-line method). The past work depended on the detected lane-line widths to calculate the distances. This method made mistakes in the presence of challenging road conditions and misclassifications. Here, we propose to combine camera configuration data with a neural network detector to develop a distance vs pixel model for reliable road severity distance calculation. We use camera metadata to transform the 2D image data predicted by the deep neural network to a 3D space. The improved model was tested with a real-world dataset. Compared to the lane-line method, the new combined model reported 36% and 37.5% accuracy improvements in right and left-hand side distances, respectively.INDEX TERMS deep learning, fully convolutional neural networks, road safety attributes I. INTRODUCTION

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Section: Related Workmentioning

confidence: 89%

Section: Related Workmentioning

confidence: 99%

Road Severity Distance Calculation Technique Using Deep Learning Predictions in 3-D Space

Perera

Verma

2022

IEEE Access

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“…Moreover, such as, improving crash cushion systems that provide road safety [12], ASI value of cable barrier for different speeds [13], determining the danger levels of obstacles such as traffic lights, trees, lighting poles, rocks, ditches, etc. that threaten roadside safety [14], and comparing the ASI-THIV values and AIS-HIC values [15][16] [17], these studies were carried out with the help of real crash tests and FE analyses.…”

Section: Figure 1 -(A) Concrete Barriers (Rigid) (B) Steel Guardrails...mentioning

confidence: 99%

Finite Element Analysis and Investigation of Critical Impact Point of Steel Guardrails Affecting Safety and Structural Performance

Özcanan

2023

Turkish Journal of Civil Engineering

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After the guardrails are designed, the structural adequacy and safety criteria are determined by the relevant standards and full-scale crash tests. One of the widely used standards is European Norm 1317 (EN1317). Guardrail systems generally consist of rails and posts. The guardrails are more rigid around the posts, which are mounted on the ground or embedded in soil at certain intervals. Therefore, it is important for driver/passenger and roadside safety to determine the most critical point in terms of structural and safety performance and design according to the most unfavorable situation. With this motivation, in this study, the effect of different impact points on the structural and safety performance of the H1W4 guardrail was investigated by finite element (FE) analysis. For this purpose, first of all, the finite element models of the H1W4-A system were calibrated and validated with real crash test data. Then, with the help of the validated models, analyses were completed for different impact points as 0.5, 1.0, 1.5 and 2.0 meters with a half-meter difference for the standard 2-meter post spacing. In the light of the measured safety parameters such as Acceleration Severity Index (ASI), Theoretical Head Impact Velocity (THIV) and structural performance criteria such as working width (W) and exit angle (α), the critical impact point for the guardrail was determined. Contrary to what is generally known, crashing vehicles into flexible points (0.5 and 1.0 m) rather than impacting rigid points (1.5 and 2.0 m) creates a more negative situation in crash tests.

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“…Whereas, Sybilski and Malachowski [12] carried out numerical analysis a disabled driver with additional equipment designed for his safety and convenience.The occupant responses during the bus accidents have been also analysed in other research publications [13,14]. Analysis of head, cervical spine, thoracic, and leg trauma is a topic that is widely discussed in the scientific world not only in crash tests of cars [15,16] or intercity buses [17][18][19] but also e.g., in the military industry. Gzik and Burkacki et al [20,21] conducted simulation tests in which they determined the level of head and cervical spine injuries during an explosion under a military vehicle.…”

Section: Previous State Of Researchmentioning

confidence: 99%

Experimental and modelling research on coach passengers’ safety in frontal impacts

Jamroziak

Joszko

Wolański

et al. 2020

Archiv.Civ.Mech.Eng

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Road traffic accidents involving coaches do not happen very often, but they are very dangerous because they affect a large number of passengers. Coaches (or intercity buses) are not equipped with safety belt harnesses. Valid regulations do not impose any obligation on coach manufacturers to provide intercity buses with either two- or three-point safety belts. This fact may result from the unawareness of risks and injuries that might befall the passengers with no safety belts during accidents. That is the reason why this work aims to compare the aftermath of coach accidents with no safety belts and the ones with safety belts. A detailed aim of this research is to analyse the results of dynamic loads during a frontal impact exerted on coach passengers travelling with and without (two- and three-point) safety belts. This objective was achieved by performing experimental studies and modelling which focused on the process of dynamic load transfer on the human body during a traffic accident. The research was conducted parallel on an adult and a child. The equivalent of a 50th percentile male was a hybrid III dummy (M50), whereas a child at the age of about 10 was represented by a P10 dummy. A numerical model was generated and verified in experimental testing in the scope of kinematics. Also, the comparison of the recorded courses of forces, acceleration, and moments was conducted. The results obtained from the tests were analyzed regarding the injury criteria for head, neck, and thorax. It was observed that both for the two-point safety system and the lack of safety belts, there were high values of acceleration recorded in the centre of gravity of the head. On the basis of the investigations conducted, it was ascertained that only a three-point safety belt system ensures the satisfaction of all injury criteria within admissible standards both in the case of criteria defined in the rules no. 80 and the rules no. 94 determined by the United Nations Economic Commission for Europe. It is the three-point safety belt system which should be obligatory in all intercity buses.

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Safety evaluation for roadside crashes by vehicle–object collision simulation

Cited by 12 publications

References 24 publications

Road Severity Distance Calculation Technique Using Deep Learning Predictions in 3-D Space

Road Severity Distance Calculation Technique Using Deep Learning Predictions in 3-D Space

Finite Element Analysis and Investigation of Critical Impact Point of Steel Guardrails Affecting Safety and Structural Performance

Experimental and modelling research on coach passengers’ safety in frontal impacts

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