Browse Topic: Impact tests
Electric vehicles (EVs) face unique safety challenges under pole side impact conditions, largely due to the presence of floor-mounted battery packs. Existing regulatory test procedures, such as FMVSS 214, primarily address occupant injury using full-height cylindrical obstacles. These procedures were originally developed for internal combustion vehicles (ICVs). However, real-world roadside crashes frequently involve obstacles of varying heights, such as guardrails, curbs, and median bases. While these obstacles pose limited risk to the passenger compartment, they can intrude into the battery pack and trigger thermal runaway. This study investigates the influence of obstacle height on EV pole side impacts. Finite element simulations of a commercially available sedan were conducted against rigid obstacles of different heights. Results reveal a non-monotonic trend of battery intrusion governed by the interplay between rollover dynamics and structural stiffness. Theoretical analyses were
Automotive OEMs can derive significant cost savings by reducing the quantity of physical crash tests and thereby accelerate product development, when they follow the Euro NCAP Virtual Testing procedure. It helps in optimizing the overall vehicle development process via more efficient simulations, as well as facilitates in early adoption of new safety regulations. In this pursuit, companies must comply with strict Euro NCAP requirements, which includes transparency and traceability of virtual tests. A major challenge therein is model validation – which requires highly precise detailing and extensive use of data for accurately replicating real physics of the problem. Deploying these workflows into an existing simulation process can be a complicated and time-consuming task, particularly when integrating various simulation and testing methods. A powerful simulation process and data management system (SPDM) can thereby assist companies to automate their entire simulation process, ensures
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
Rear-facing infant seats that are positioned behind front outboard vehicle seats are at risk of being compromised by the rearward yielding of occupied front seat seatbacks during rear-impact collisions. This movement can cause the plastic shell of the infant seat to collapse and deform, increasing the risk of head injuries to the infant. Current designs of rear-facing infant seats typically do not consider the loading effects from the front seatback during rear-impact situations, which results in weak and collapsible shell structures. Moreover, regulatory compliance tests, such as FMVSS 213, do not include assessments of rear-facing infant seats under realistic rear-impact conditions. as the bench used for the regulatory test lacks realistic vehicle interior components. This study emphasizes the need for revised testing methodologies that employ sled tests with realistic seatback intrusion conditions to facilitate the development of improved infant seat designs. Research shows that
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
This study introduces a novel in-cabin health monitoring system leveraging Ultra-Wideband (UWB) radar technology for real-time, contactless detection of occupants' vital signs within automotive environments. By capturing micro-movements associated with cardiac and respiratory activities, the system enables continuous monitoring without physical contact, addressing the need for unobtrusive vehicle health assessment. The system architecture integrates edge computing capabilities within the vehicle's head unit, facilitating immediate data processing and reducing latency. Processed data is securely transmitted via HTTPS to a cloud-based backend through an API Gateway, which orchestrates data validation and routing to a machine learning pipeline. This pipeline employs supervised classifiers, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF) to analyze features such as temporal heartbeat variability, respiration rate stability, and heart rate. Empirical
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
Severe rear-impact collisions can cause significant intrusion into the occupant compartment when the structural integrity of the rear survival space is insufficient. Intrusion patterns are influenced by impact configuration—underride, in-line, or override—with underride collisions channeling forces below the beltline through the rear wheels as a primary load path. This force concentration rapidly propels the rear seat-pan forward, contacting the rearward-rotating front seatback. The resulting bottoming-out phenomenon produces a forward impulse that amplifies loading on the front occupant’s upper torso, increasing the risk of thoracic injury even when the head is properly supported by the head restraint. This study analyzes a real-world rear-impact collision that resulted in fatal thoracic injuries to the driver, attributed to the interaction between the driver’s seatback and the forward-moving rear seat pan. A vehicle-to-vehicle crash test was conducted to replicate similar intrusion
Perception radar company Arbe was at IAA Mobility in Munich this year to press the case that customers can and should trust automated vehicles. One reason is the global trend of stricter regulations from the NHTSA, Euro NCAP, and in China, which now require automated vehicles to safely meet demanding use cases that are not covered by current sensors, according to Arbe co-founder and CTO Noam Arkind. Arkind told SAE Media that one such category is detecting vulnerable road users (VRU) in poor weather and lighting conditions. “We know from recent tests that a lot of Chinese cars, for example, failed VRU detections in the dark,” he said. “Camera alone doesn't really have reliable pedestrian detection in a dark situation. Radar is a great sensor. It's very sensitive. It's not dependent on weather conditions or lighting conditions, but it's noisy, it's low resolution, and it's hard to use.”
In the demanding field of automotive crash testing, imaging systems face a dual challenge: They must survive extreme forces while delivering precise, distortion-free footage for post-test analysis. High-speed cameras, often priced in the tens of thousands of dollars, are essential for documenting the dynamics of impact. But the performance of these systems depends heavily on the optics in front of them.
This SAE Recommended Practice defines the minimum performance specifications for sensors used within anthropomorphic test devices (ATDs) when performing impact tests per SAE J211. It is intended that any agency proposing to conduct tests in accordance with SAE J211 shall be able to demonstrate that the transducers they use would meet the performance requirements specified in this document.
This study presents an analysis of 364 motorcycle helmet impact tests, including standard certified full-face, open-face, and half-helmets, as well as non-certified (novelty) helmet designs. Two advanced motorcycle helmet designs that incorporate technologies intended to mitigate the risk of rotational brain injuries (rTBI) were included in this study. Results were compared to 80 unprotected tests using an instrumented 50th percentile Hybrid III head form and neck at impact speeds ranging from 6 to 18 m/s (13 to 40 mph). Results show that, on average, the Head Injury Criterion (HIC) was reduced by 92 percent across certified helmets, compared to the unhelmeted condition, indicating substantial protection against focal head and brain injuries. However, findings indicate that standard motorcycle helmets increase the risk of AIS 2 to 5 rotational brain injuries (rTBI) by an average of 30 percent compared to the unprotected condition, due to the increased rotational inertia generated by
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