Numerical Reproducibility of Human Body Model Crash Simulations

The numerical reproducibility of a Finite Element (FE) Human Body Model (HBM) was evaluated by quantifying the variation in model predictions for diverse computer systems at different sites and settings. Repeated simulations, with varying number of Central Processing Unit (CPU) cores and model decomposition, of four high severity load cases – a full frontal, near-side frontal oblique and side impact with a full set of driver restraints, as well as a full frontal with a seat belt only restraint – was carried out on five computer systems. HBM responses were found to vary randomly with the Number of CPU cores (NCPU), but not due to different hardware or message parsing interface software at each computer system used. Implemented HBM updates reduced the variation in the near-side frontal oblique load case. When the NCPU used was fixed, identical results were obtained from all computer systems. This means the variation of HBM responses is due to the model decomposition. It is possible to quantify the numerical reproducibility of an FE HBM by repeated simulations, varying the NCPU and analyzing the coefficient of variation of the responses

The influence of car passengers’ sitting postures in intersection crashes

Car passengers are frequently sitting in non-nominal postures and are able to perform a wide range of activities since they are not limited by tasks related to vehicle control, contrary to drivers. The anticipated introduction of Autonomous Driven vehicles could allow “drivers” to adopt similar postures and being involved in the same activities as passengers, allowing them a similar set of non-nominal postures. Therefore, the need to investigate the effects of non-nominal occupant sitting postures during relevant car crash events is becoming increasingly important. This study aims to investigate the effect of different postures of passengers in the front seat of a car on kinematic and kinetic responses during intersection crashes. A Human Body Model (HBM) was positioned in a numerical model of the front passenger seat of a midsize Sports Utility Vehicle (SUV) in a total of 35 postures, including variations to the lower and upper extremities, torso, and head postures. Three crash configurations, representative of predicted urban intersection crashes, were assessed in a simulation study; two side impacts, a near-side and a far-side, respectively, and a frontal impact. The occupant kinematics and internal loads were analyzed, and any deviation between the nominal and altered posture responses were quantified using cross-correlation of signals to highlight the most notable variations. Posture changes to the lower extremities had the largest overall influence on the lower extremities, pelvis, and whole-body responses for all crash configurations. In the frontal impact, crossing the legs allowed for the highest pelvis excursions and rotations, which affected the whole-body response the most. In the two side-impacts, leaning the torso in the coronal plane affected the torso and head kinematics by changing the interaction with the vehicle\u27s interior. Additionally, in far-side impacts supporting the upper extremity on the center console resulted in increased torso excursions. Moreover, the response of the upper extremities was consistently sensitive to posture variations of all body regions

A method for predicting crash configurations using counterfactual simulations and real-world data

Traffic safety technologies revolve around two principle ideas; crash avoidance and injury mitigation for inevitable crashes. The development of relevant vehicle injury mitigating technologies should consider the interaction of those two technologies, ensuring that the inevitable crashes can be adequately managed by the occupant and vulnerable road user (VRU) protection systems. A step towards that is the accurate description of the expected crashes remaining when crash-avoiding technologies are available in vehicles. With the overall objective of facilitating the assessment of future traffic safety, this study develops a method for predicting crash configurations when introducing crash-avoiding countermeasures. The predicted crash configurations are one important factor for prioritizing the evaluation and development of future occupant and VRU protection systems. By using real-world traffic accident data to form the baseline and performing counterfactual model-in-the-loop (MIL) pre-crash simulations, the change in traffic situations (vehicle crashes) provided by vehicles with crash-avoiding technologies can be predicted. The method is built on a novel crash configuration definition, which supports further analysis of the in-crash phase. By clustering and grouping the remaining crashes, a limited number of crash configurations can be identified, still representing and covering the real-world variation. The developed method was applied using Swedish national- and in-depth accident data related to urban intersections and highway driving, and a conceptual Autonomous Emergency Braking system (AEB) computational model. Based on national crash data analysis, the conflict situations Same-Direction rear-end frontal (SD-ref) representing 53 % of highway vehicle-to-vehicle (v2v) crashes, and Straight Crossing Path (SCP) with 21 % of urban v2v intersection crashes were selected for this study. Pre-crash baselines, for SD-ref (n = 1010) and SCP (n = 4814), were prepared based on in-depth accident data and variations of these. Pre-crash simulations identified the crashes not avoided by the conceptual AEB, and the clustering of these revealed 5 and 52 representative crash configurations for the highway SD-ref and urban intersection SCP conflict situations, respectively, to be used in future crashworthiness studies. The results demonstrated a feasible way of identifying, in a predictive way, relevant crash configurations for in-crash testing of injury prevention capabilities

Validation of the SAFER Human Body Model Kinematics in Far-Side Impacts

Human Body Models are essential for real-world occupant protection assessment. With the overall purpose to create a robust human body model which is biofidelic in a variety of crash situations, this study aims to evaluate the biofidelity of the SAFER human body model in far-side impacts. The pelvis, torso and the upper and lower extremities of the SAFER human body model were updated. In addition, the shoulder area was updated for improved shoulder belt interaction in far-side impacts. The model was validated using kinematic corridors based on published human subject test data from two far-side impact set-ups, one simplified and one vehicle-based. The simplified far-side set-up included six configurations with different parameter settings, and the vehicle-based included two configurations: with and without far-side airbag, respectively. The updated SAFER HBM was robust and in general the model predicted the published human subject responses (kinematic CORA score > 0.65) for all configurations in both test set-ups. An exception was a 90 degree far-side impact with the D-ring in the forward position, in the simplified set-up. Here the model could not predict the shoulder belt retention, resulting in a low CORA score. Based on the overall results, the model is considered valid to be used for assessment of far-side impact countermeasures

Muscle Responses of Car Occupants: Numerical Modeling and Volunteer Experiments under Pre-Crash Braking Conditions

Over 30 000 fatalities related to the road transport system are reported anually in Europe. Of these fatalities, the largest share is car occupants, even though significant improvements in vehicle safety have been achieved by the implementation of in-crash restraints and pre-crash driver support systems. Integration of pre-crash and in-crash safety systems has a potential to further reduce car occupant fatalities and to mitigate injuries. The aims of this thesis are to study the muscle responses of car occupants subjected to integrated safety interventions, and to model them in a numerical human model with active muscles. More specifically, pre-crash braking with standard and reversible pre-tensioned restraints is investigated.A method to model car occupant muscle responses in a finite element (FE) human body model (HBM) was developed, utilizing feedback control of Hill-type muscle elements. It was found that the car occupant response to autonomous braking can be modeled with feedback control, by which stabilizing muscle activations are generated in response to external perturbations. However, modeling driver initiated braking requires the inclusion of a hypothesized anticipatory feed-forward response. Volunteer tests to provide validation data for the HBM were conducted, analyzed, and utilized for model validation. It was found that, in some car occupants, seat belt pre-tension can cause a startle response in the form of a bilateral, simultaneous, short peak contraction of all upper body muscles. Car occupant muscle activation levels during normal driving and in braking events were also quantified in percent of maximum voluntary efforts. The HBM developed with active muscles was able to capture the kinematic response of the volunteers in these events, with muscle activation levels of magnitude similar to that of the volunteers. The method to model muscle responses with feedback control in an FE HBM has the potential to improve the model response in all pre-crash and in-crash scenarios in which muscle contraction can influence occupant kinematics, for instance multiple events and roll-over accidents. It provides a means for the virtual development of advanced integrated restraints that can lead to improved vehicle safety and a reduced number of fatalities and injuries in the road traffic environment

Active Muscle Responses in a Finite Element Human Body Model

The development of automotive safety systems is moving towards an integration of systems thatare active before and during an impact. Consequently, there is a need to make a combinedanalysis of both the pre-crash and the in-crash phases, which leads to new requirements forHuman Body Models (HBMs) that today are used for crash simulations. In the pre-crash phasethe extended duration makes the active muscle response a factor that must be taken into accountin the HBM to correctly simulate the human kinematics.In this thesis, the active muscle response is modeled using a feedback control strategy with Hilltypeline muscle elements implemented in a Finite Element (FE) HBM. A musculoskeletalmodeling and feedback control method was developed and evaluated, with simulations of thehuman response to low level impact loading of the arm in flexion-extension motion. Then, themethod was implemented to control trunk and neck musculature in an FE HBM, to simulate theoccupant response to autonomous braking. Results show that the method is successful incapturing active human responses and that a variety of responses in volunteer tests can becaptured by changing of control parameters.The proposed method, to model active muscle responses in an FE HBM using feedback control,makes it possible to conduct a pre-crash simulation in order to determine the initial conditions foran in-crash simulation with an FE HBM. It also has a large potential to extend the use of FEHBMs to the simulation of combined pre-crash and in-crash scenarios, crash scenarios of longerduration such as roll-over accidents and, eventually, multiple events

Active Muscle Responses in a Finite Element Human Body Model

The development of automotive safety systems is moving towards an integration of systems thatare active before and during an impact. Consequently, there is a need to make a combinedanalysis of both the pre-crash and the in-crash phases, which leads to new requirements forHuman Body Models (HBMs) that today are used for crash simulations. In the pre-crash phasethe extended duration makes the active muscle response a factor that must be taken into accountin the HBM to correctly simulate the human kinematics.In this thesis, the active muscle response is modeled using a feedback control strategy with Hilltypeline muscle elements implemented in a Finite Element (FE) HBM. A musculoskeletalmodeling and feedback control method was developed and evaluated, with simulations of thehuman response to low level impact loading of the arm in flexion-extension motion. Then, themethod was implemented to control trunk and neck musculature in an FE HBM, to simulate theoccupant response to autonomous braking. Results show that the method is successful incapturing active human responses and that a variety of responses in volunteer tests can becaptured by changing of control parameters.The proposed method, to model active muscle responses in an FE HBM using feedback control,makes it possible to conduct a pre-crash simulation in order to determine the initial conditions foran in-crash simulation with an FE HBM. It also has a large potential to extend the use of FEHBMs to the simulation of combined pre-crash and in-crash scenarios, crash scenarios of longerduration such as roll-over accidents and, eventually, multiple events