Browse Topic: Frontal collisions
With the rapid development of automated driving and the increasing adoption of “zero-gravity” seats, the crash safety of highly reclined occupants has become a critical issue. The current THOR dummy, designed for frontal impacts in the standard upright posture, exhibits limitations when directly applied to reclined seating configurations, including insufficient spinal flexion capability and excessive posterior pelvic rotation. In this study, the thoracolumbar spine kinematics of the THUMS human body model, reconstructed against post-mortem human subject (PMHS) tests, were analyzed. A two-segment linear fitting was employed to characterize a “dummy-like” spinal flexion response, yielding a virtual rotational hinge located near the thoracolumbar joint of the original THOR model. The characteristic rotation angle obtained from THUMS showed a strong linear correlation with the flexion moment of the T12–L1 vertebrae. Based on this relationship, the rotational joint of the THOR dummy was
Indian passenger car accident data indicates that approximately 44% of crashes are frontal impacts (Refer fig 1). Among the injuries sustained in these crashes, lower leg injuries are notably critical, contributing to nearly 25% of driver occupant injuries (Refer fig 2). To evaluate such injuries, the Bharat New Car Assessment Program (BNCAP) includes lower leg injury metrics as part of the Frontal Offset Deformable Barrier (ODB64) test. While the overall injury performance is assessed at the vehicle level, BNCAP also monitors vehicle interior intrusions—particularly pedal intrusions—as key contributors to lower limb injury severity. A major challenge in frontal crashes is the intrusion of the vehicle's front-end structure into the occupant compartment. Rigid components, particularly the brake pedal assembly, can be displaced rearward during a crash, significantly increasing the risk of lower leg injuries. Therefore, minimizing pedal intrusions into the driver foot-well is critical for
A passenger vehicle's front-end structure's structural integrity and crashworthiness are crucial to ensure compliance with various frontal impact safety standards (such as those set by Euro NCAP & IIHS). For a new front-end architecture, design targets must be defined at a component level for crush cans, longitudinal, bumper beam, subframe, suspension tower and backup structure. The traditional process of defining these targets involves multiple sensitivity studies in CAE. This paper explores the implementation of Physics-Informed Neural Networks (PINNs) in component-level target setting. PINNs integrate the governing equations into neural network training, enabling data-driven models to adhere to fundamental mechanical principles. The underlying physics in our model is based upon a force scheme of a full-frontal impact. A force scheme is a one-dimensional representation of the front-end structure components that simplifies a crash event's complex physics. It uses the dimensional and
Commercial vehicle sector (especially trucks) has a major role in economic growth of a nation. With improving infrastructure, increasing number of trucks on roads, accidents are also increasing. As per RASSI (Road Accident Sampling System India) FY2016-23 database, commercial vehicles are involved in 42% of total accidents on Indian roads. Involvement of trucks (N2 & N3) is over 25% of total accidents. Amongst all accident scenarios of N2 &N3, frontal impacts are the most frequent (26%) and causing severe occupant injuries. Today, truck safety development for frontal impact is based on passive safety regulations (viz. front pendulum – AIS029) and basic safety features like seatbelts. In any truck accident, it is challenging rather impossible to manage comprehensive safety only with passive safety systems due to size and weight. Accident prevention becomes imperative in truck safety development due to extremely high energy involved in front impact scenarios. The paper presents a unique
Occupant Safety systems are usually developed using anthropomorphic test devices (ATDs), such as the Hybrid III, THOR-50M, ES-2, and WorldSID. However, in compliance with NCAP and regulatory guidelines, these ATDs are designed for specific crash scenarios, typically frontal and side impacts involving upright occupants. As vehicles evolve (e.g., autonomous layouts, diverse occupant populations), ATDs are proving increasingly inadequate for capturing real-world injury mechanisms. This has led to the adoption of computational Human Body Models (HBMs), such as the Global Human Body Models Consortium (GHBMC) and Total Human Model for Safety (THUMS), which offer superior anatomical fidelity, variable anthropometry, active muscle behaviour modelling, and improved postural flexibility. HBMs can predict internal injuries that ATDs cannot, making them valuable tools for future vehicle safety development. This study uses a sled CAE simulation environment to analyze the kinematics of the HBMs
The effect of seat belt misuse and/or misrouting is important to consider because it can influence occupant kinematics, reduce restraint effectiveness, and increase injury risk. As new seatbelt technologies are introduced, it is important to understand the prevalence of seatbelt misuse. This type of information is scarce due to limitations in available field data coding, such as in NASS-CDS and FARS. One explanation may be partially due to assessment complexity in identifying misuse and/or misrouting. An objective of this study was to first identify types of lap-shoulder belt misuse/misrouting and associated injury patterns from a literature review. Nine belt misuse/misrouting scenarios were identified including shoulder belt only, lap belt only, or shoulder belt under the arm, for example, while belt misrouting included lap belt on the abdomen, shoulder belt above the breasts, or shoulder belt on the neck. Next, the literature review identified various methods used to assess misuse
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