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Guo Y, Liu Y, Wang B, Huang P, Xu H, Bai Z. Trajectory planning framework for autonomous vehicles based on collision injury prediction for vulnerable road users. ACCIDENT; ANALYSIS AND PREVENTION 2024; 203:107610. [PMID: 38749269 DOI: 10.1016/j.aap.2024.107610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 04/23/2024] [Accepted: 04/29/2024] [Indexed: 06/03/2024]
Abstract
Due to the escalating occurrence and high casualty rates of accidents involving Electric Two-Wheelers (E2Ws), it has become a major safety concern on the roads. Additionally, with the widespread adoption of current autonomous driving technology, a greater challenge has arisen for the safety of vulnerable road participants. Most existing trajectory planning methods primarily focus on the safety, comfort, and dynamics of autonomous vehicles themselves, often overlooking the protection of vulnerable road users (VRUs), typically E2W riders. This paper aims to investigate the kinematic response of E2Ws in vehicle collisions, including the 15 ms Head Injury Criterion (HIC15). It analyzes the impact of key collision parameters on head injuries, establishes injury prediction models for anticipated scenarios, and proposes a trajectory planning framework for autonomous vehicles based on predicting head injuries of VRUs. Firstly, a multi-rigid-body model of two-wheeler-vehicle collision was established based on a real accident database, incorporating four critical collision parameters (initial collision velocity, initial collision position, and collision angle). The accuracy of the multi-rigid-body model was validated through verifications with real fatal accidents to parameterize the collision scenario. Secondly, a large-scale effective crash dataset has been established by the multi-parameterized crash simulation automation framework combined with Monte Carlo sampling algorithm. The training and testing of the injury prediction model were implemented based on the MLP + XGBoost regression algorithm on this dataset to explore the potential relationship between the head injuries of the E2W riders and the crash variables. Finally, based on the proposed injury prediction model, this paper generated a trajectory planning framework for autonomous vehicles based on head collision injury prediction for VRUs, aiming to achieve a fair distribution of collision risks among road users. The accident reconstruction results show that the maximum error in the final relative positions of the E2W, the car, and the E2W rider compared to the real accident scene is 11 %, demonstrating the reliability of the reconstructed model. The injury prediction results indicate that the MLP + XGBoost regression prediction model used in this article achieved an R2 of 0.92 on the test set. Additionally, the effectiveness and feasibility of the proposed trajectory planning algorithm were validated in a manually designed autonomous driving traffic flow scenario.
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Affiliation(s)
- Yage Guo
- State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, Hunan University, Changsha 410082, China
| | - Yu Liu
- State Key Laboratory of Vehicle NVH and Safety Technology, China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China
| | - Botao Wang
- State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, Hunan University, Changsha 410082, China
| | - Peifeng Huang
- State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, Hunan University, Changsha 410082, China
| | - Hailan Xu
- China Merchants Testing Vehicle Technology Research Institute Co., Ltd, Chongqing, 400041, China
| | - Zhonghao Bai
- State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, Hunan University, Changsha 410082, China.
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Henningsen MJ, Lindgren N, Kleiven S, Li X, Jacobsen C, Villa C. Subject-specific finite element head models for skull fracture evaluation-a new tool in forensic pathology. Int J Legal Med 2024; 138:1447-1458. [PMID: 38386034 PMCID: PMC11164801 DOI: 10.1007/s00414-024-03186-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 02/09/2024] [Indexed: 02/23/2024]
Abstract
Post-mortem computed tomography (PMCT) enables the creation of subject-specific 3D head models suitable for quantitative analysis such as finite element analysis (FEA). FEA of proposed traumatic events is an objective and repeatable numerical method for assessing whether an event could cause a skull fracture such as seen at autopsy. FEA of blunt force skull fracture in adults with subject-specific 3D models in forensic pathology remains uninvestigated. This study aimed to assess the feasibility of FEA for skull fracture analysis in routine forensic pathology. Five cases with blunt force skull fracture and sufficient information on the kinematics of the traumatic event to enable numerical reconstruction were chosen. Subject-specific finite element (FE) head models were constructed by mesh morphing based on PMCT 3D models and A Detailed and Personalizable Head Model with Axons for Injury Prediction (ADAPT) FE model. Morphing was successful in maintaining subject-specific 3D geometry and quality of the FE mesh in all cases. In three cases, the simulated fracture patterns were comparable in location and pattern to the fractures seen at autopsy/PMCT. In one case, the simulated fracture was in the parietal bone whereas the fracture seen at autopsy/PMCT was in the occipital bone. In another case, the simulated fracture was a spider-web fracture in the frontal bone, whereas a much smaller fracture was seen at autopsy/PMCT; however, the fracture in the early time steps of the simulation was comparable to autopsy/PMCT. FEA might be feasible in forensic pathology in cases with a single blunt force impact and well-described event circumstances.
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Affiliation(s)
- Mikkel Jon Henningsen
- Section of Forensic Pathology, Department of Forensic Medicine, University of Copenhagen, Copenhagen, Denmark.
| | - Natalia Lindgren
- Division of Neuronic Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Svein Kleiven
- Division of Neuronic Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Xiaogai Li
- Division of Neuronic Engineering, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Christina Jacobsen
- Section of Forensic Pathology, Department of Forensic Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Chiara Villa
- Section of Forensic Pathology, Department of Forensic Medicine, University of Copenhagen, Copenhagen, Denmark
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Pérez-Zuriaga AM, Dols J, Nespereira M, García A, Sajurjo-de-No A. Analysis of the consequences of car to micromobility user side impact crashes. JOURNAL OF SAFETY RESEARCH 2023; 87:168-175. [PMID: 38081692 DOI: 10.1016/j.jsr.2023.09.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 07/28/2023] [Accepted: 09/18/2023] [Indexed: 12/18/2023]
Abstract
INTRODUCTION The strong rise in modes of travel commonly referred to as micromobility has changed the mobility patterns and lifestyles in cities worldwide, especially after the COVID-19 pandemic. It has led to a significant increase in the number of crashes involving these types of vehicles, especially bicycles and stand-up e-scooters. The risk of crashes is higher at intersections where motor-vehicles perform a turning maneuver crossing a bike lane. METHOD The consequences of a passenger car-to-micromobility vehicle side-impact crashes, considering both bicycle and e-scooter, were studied based on the results of the simulation of several scenarios with PC-Crash software. Two injury criteria were applied: Head Injury Criterion (HIC15) and 3 ms chest acceleration criterion. RESULTS When motor-vehicle speed is lower than 50 km/h, the 3 ms chest acceleration never exceeds the 60 g threshold. However, at 50 km/h, it is close to 50 g in the case of e-scooter rides. At this speed, HIC15 is considerably greater than 1000, both for bicycles and for e-scooters, and the safety margin of 700 is exceeded at 45 km/h for e-scooters. CONCLUSIONS In case of motor vehicle-to-micromobility vehicle side-impact crash, riding a bicycle is safer than riding an e-scooter since the observed HIC15 experienced by the cyclists is lower than that experienced by the e-scooter rider when motor vehicle speed is greater than 30 km/h. PRACTICAL APPLICATIONS To reduce micromobility users injury risk at intersections, motor vehicle speed limit should be equal or lower than 40 km/h. At this impact speed, the activation of hood or bumper airbags could be justified.
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Affiliation(s)
- Ana María Pérez-Zuriaga
- Highway Engineering Research Group (HERG), Universitat Politècnica de València, Camino de Vera, s/n, 46022 Valencia, Spain
| | - Juan Dols
- Institute of Design and Manufacturing, Universitat Politècnica de València, Camino de Vera, s/n, 46022 Valencia, Spain
| | - Martín Nespereira
- Institute of Design and Manufacturing, Universitat Politècnica de València, Camino de Vera, s/n, 46022 Valencia, Spain
| | - Alfredo García
- Highway Engineering Research Group (HERG), Universitat Politècnica de València, Camino de Vera, s/n, 46022 Valencia, Spain
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Zou D, Fan Y, Liu N, Zhang J, Liu D, Liu Q, Li Z, Wang J, Huang J. Multiobjective optimization algorithm for accurate MADYMO reconstruction of vehicle-pedestrian accidents. Front Bioeng Biotechnol 2022; 10:1032621. [PMID: 36545682 PMCID: PMC9760744 DOI: 10.3389/fbioe.2022.1032621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 11/23/2022] [Indexed: 12/12/2022] Open
Abstract
In vehicle-pedestrian accidents, the preimpact conditions of pedestrians and vehicles are frequently uncertain. The incident data for a crash, such as vehicle deformation, injury of the victim, distance of initial position and rest position of accident participants, are useful for verification in MAthematical DYnamic MOdels (MADYMO) simulations. The purpose of this study is to explore the use of an improved optimization algorithm combined with MADYMO multibody simulations and crash data to conduct accurate reconstructions of vehicle-pedestrian accidents. The objective function of the optimization problem was defined as the Euclidean distance between the known vehicle, human and ground contact points, and multiobjective optimization algorithms were employed to obtain the local minima of the objective function. Three common multiobjective optimization algorithms-nondominated sorting genetic algorithm-II (NSGA-II), neighbourhood cultivation genetic algorithm (NCGA), and multiobjective particle swarm optimization (MOPSO)-were compared. The effect of the number of objective functions, the choice of different objective functions and the optimal number of iterations were also considered. The final reconstructed results were compared with the process of a real accident. Based on the results of the reconstruction of a real-world accident, the present study indicated that NSGA-II had better convergence and generated more noninferior solutions and better final solutions than NCGA and MOPSO. In addition, when all vehicle-pedestrian-ground contacts were considered, the results showed a better match in terms of kinematic response. NSGA-II converged within 100 generations. This study indicated that multibody simulations coupled with optimization algorithms can be used to accurately reconstruct vehicle-pedestrian collisions.
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Affiliation(s)
- Donghua Zou
- School of Forensic Medicine, Guizhou Medical University, Guiyang, China,Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, China
| | - Ying Fan
- School of Forensic Medicine, Guizhou Medical University, Guiyang, China,Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, China
| | - Ningguo Liu
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, China
| | - Jianhua Zhang
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, China
| | - Dikun Liu
- School of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Qingfeng Liu
- School of Forensic Medicine, Guizhou Medical University, Guiyang, China
| | - Zhengdong Li
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, China,*Correspondence: Zhengdong Li, ; Jiang Huang, ; Jinming Wang,
| | - Jinming Wang
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, China,*Correspondence: Zhengdong Li, ; Jiang Huang, ; Jinming Wang,
| | - Jiang Huang
- School of Forensic Medicine, Guizhou Medical University, Guiyang, China,*Correspondence: Zhengdong Li, ; Jiang Huang, ; Jinming Wang,
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Liu Y, Wan X, Xu W, Shi L, Bai Z, Wang F. A novel approach to investigate effects of front-end structures on injury response of e-bike riders: Combining Monte Carlo sampling, automatic operation, and data mining. ACCIDENT; ANALYSIS AND PREVENTION 2022; 168:106599. [PMID: 35219105 DOI: 10.1016/j.aap.2022.106599] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 01/17/2022] [Accepted: 02/09/2022] [Indexed: 06/14/2023]
Abstract
Transportation safety related to e-bikes is becoming more problematic with the growing popularity in recent decade years, however, rare studies focused on the protection for e-bike riders in traffic accidents. This paper aimed to investigate the relationship between vehicle front-end structures and rider's injury based on a novel approach including modeling, sampling, and analyzing. Firstly, a parametrized model for front-end structures of the vehicle was developed with nine parameters to realize the standardization of multi-body models of car to e-bike collision considering three stature riders and different impacting velocities. Secondly, a framework, combining Monte Carlo sampling for twelve initial variables and automatic operation for 1000 impact simulations, was built to obtain valid results automatically and then to construct a big dataset. Finally, according to the sensitive variables to riders' vulnerable regions, the decision tree algorithm was further adopted to develop the decision or prediction model on injuries. The novel approach achieved the stochastical generation of vehicle shapes and the automatic operation of multi-body models. The results showed that the rider's head, pelvis, and thighs were more vulnerable to being injured in the car to e-bike perpendicular accidents. The three decision tree models (HIC15, lateral force of pelvis, bending moment of upper leg) were validated to be accurate and reliable according to the confusion matrix with the precision of more than 80% and the receiver operating characteristic curves (ROC) with the under area more than 85%. Based on decision tree models, not only the effects of front-end structural parameters on the corresponding injury but also the interaction mechanism between various variables can be clearly interpreted. Each route from the same root node to hierarchical middle nodes then to various leaf nodes represented a decision-making process. And the different branches under the same decision node directly illustrated the correlation between variables, which is highly readable and comprehensible. During the safety performance design of front-end structures, the rational value of variables could be decided according to decision routes that resulted in lower injury levels; Even if the accident was inevitable, the collision parameters could be controlled within a certain range for the least injury according to the prediction rules. Based on the novel framework coupling Monte Carlo sampling and automatic operation, it's foreseeable to apply the parametric and standard car-to-e-bike collision models to develop the virtual test system and to optimize front-end shapes for rider's protection.
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Affiliation(s)
- Yu Liu
- State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China; State Key Laboratory of Vehicle NVH and Safety Technology, China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China
| | - Xinming Wan
- State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China; State Key Laboratory of Vehicle NVH and Safety Technology, China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China.
| | - Wei Xu
- State Key Laboratory of Vehicle NVH and Safety Technology, China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China
| | - Liangliang Shi
- State Key Laboratory of Vehicle NVH and Safety Technology, China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China
| | - Zhonghao Bai
- State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China
| | - Fang Wang
- School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410205, China
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Analysis of electric scooter user kinematics after a crash against SUV. PLoS One 2022; 17:e0262682. [PMID: 35061814 PMCID: PMC8782370 DOI: 10.1371/journal.pone.0262682] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 12/30/2021] [Indexed: 11/19/2022] Open
Abstract
The article presents the results of the analysis of electric scooter user kinematics after a crash against a vehicle. The share of electric scooters (e-scooters) in urban traffic has been growing in recent years. The number of road accidents involving e-scooters is also increasing. However, the safety situation of electric scooter users is insufficiently researched in terms of kinematics and injury outcomes. The article presents the importance of this problem based on an in-depth literature analysis of e-scooter-related types of accidents, injuries percentages, and helmet use. Subsequently, four accident scenarios were designed and simulated using two numerical codes–LS-DYNA for handling finite element (FE) code (the vehicle and scooter model) and MADYMO for multibody code (dummy model). Scenario one is a side bonnet crash that simulates an accident when the scooter drives into the side-front of the vehicle. The second and the third simulation is a side B-pillar crash, which was divided into two dummy’s positions: the squat and up-right. The fourth simulation is a frontal impact. For each scenario, subsequent frames describing the dummy movement are presented. The after-impact kinematics for various scenarios were analyzed and discussed. The plots of the dummy’s head linear acceleration and its magnitude for the analyzed scenarios were provided. As the study is devoted to increasing riders safety in this means of transportation, the potential directions for further research were indicated.
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Liu Y, Wan X, Xu W, Shi L, Deng G, Bai Z. An intelligent method for accident reconstruction involving car and e-bike coupling automatic simulation and multi-objective optimizations. ACCIDENT; ANALYSIS AND PREVENTION 2022; 164:106476. [PMID: 34844065 DOI: 10.1016/j.aap.2021.106476] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 11/02/2021] [Accepted: 11/03/2021] [Indexed: 06/13/2023]
Abstract
Car-electric bicycle (e-bike) accidents have been the subject of strong attention due to the widespread usage of e-bikes and a high casualty rate for their riders. Manually conducted accident reconstruction is based on the trial-and-error method with a limited number of parameter combinations, which makes it time-consuming and subjective. This paper aims to develop an intelligent method for accurate, high-efficient reconstruction of accidents involving cars and e-bikes. First, an automatic operation framework, which can drive the MADYMO program and perform results analysis automatically, was built with four multi-objective optimization algorithms available - NSGA-Ⅱ, NCGA, AMGA, and MOPS; The optimization condition was controlled with 12 design variables, 5 objective functions, and 3 constraints. Then, a real e-bike accident with surveillance video was reconstructed through the proposed framework to verify its validity using comparisons of simulated and actual rest positions, initial variables, kinematic response, and head injury. Lastly, the simulation data were used to study the effects of the initial variables on objectives with a multiple linear regression model. The results showed that it took only about 24 h in total for optimization with 480 automatic operations. Optimal conditions were searched at run numbers of 469, 430, 323, and 474 for NSGA-Ⅱ, NCGA, AMGA, and MOPS, respectively. NSGA-Ⅱ had the best performance for e-bike accident reconstruction with average errors of objectives below 5%; Good consistencies for the rider's kinematic response in three stages after collision were observed between simulations and screenshots from the surveillance video, as well as for velocities between the simulation and those estimated from the surveillance video and for head injury between the simulation and the medical report. In contrast to the subjective trial-and-error method that highly depends on the analyst's intuition and experience, this intelligent method is based on multi-objective optimization theory, with which results can be optimized in terms of the automatic change of initial variables. All the above comparisons demonstrate that the method is valid for effectively improving efficiency without simultaneously compromising accuracy. This intelligent method, coupling automatic simulation and multi-objective optimization, can also be applied to other accident reconstructions, and the significant order of initial variables' effects on objectives can provide recommendations for further reconstructions.
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Affiliation(s)
- Yu Liu
- State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China; State Key Laboratory of Vehicle NVH and Safety Technology, China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China
| | - Xinming Wan
- State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China; State Key Laboratory of Vehicle NVH and Safety Technology, China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China
| | - Wei Xu
- State Key Laboratory of Vehicle NVH and Safety Technology, China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China
| | - Liangliang Shi
- State Key Laboratory of Vehicle NVH and Safety Technology, China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China
| | - Gongxun Deng
- Key Laboratory of Traffic Safety on Track, Ministry of Education, School of Traffic & Transportation Engineering, Central South University, Changsha 410075, China
| | - Zhonghao Bai
- State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China.
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Gao W, Bai Z, Zhu F, Chou CC, Jiang B. A study on the cyclist head kinematic responses in electric-bicycle-to-car accidents using decision-tree model. ACCIDENT; ANALYSIS AND PREVENTION 2021; 160:106305. [PMID: 34332291 DOI: 10.1016/j.aap.2021.106305] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 10/12/2020] [Accepted: 07/12/2021] [Indexed: 06/13/2023]
Abstract
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.
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Affiliation(s)
- Wenrui Gao
- The State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Hunan 410082, China
| | - Zhonghao Bai
- The State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Hunan 410082, China
| | - Feng Zhu
- Hopkins Extreme Materials Institute, The Johns Hopkins University, USA
| | - Clifford C Chou
- Bioengineering Center, Wayne State University, MI 48201, USA
| | - Binhui Jiang
- The State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Hunan 410082, China.
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