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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's 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., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

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., (Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved.)