Browse Topic: Accident types
Side crashes are generally hazardous because there is no room for large deformation to protect an occupant from the crash forces. A crucial point in side impacts is the rapid intrusion of the side structure into the passenger compartment which need sufficient space between occupants and door trim to enable a proper unfolding of the side airbag. This problem can be alleviated by using the rising air pressure inside the door as an additional input for crash sensing. With improvements in the crash sensor technology, pressure sensors that detect pressure changes in door cavities have been developed recently for vehicle crash safety applications. The crash pulses recorded by the acceleration based crash sensors usually exhibit high frequency and noisy responses. The data obtained from the pressure sensors exhibit lower frequency and less noisy responses. Due to its ability to discriminate crash severities and allow the restraint devices to deploy earlier, the pressure sensor technology has
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
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
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
Vehicles with a high center of gravity (CG) and moderate wheel track, like compact Sport Utility Vehicles (SUVs), have a relatively low Static Stability Factor (SSF) and thus are inherently less stable and more susceptible to rollover crashes. Moreover, to be more maneuverable in highly populated urban areas, a smaller Turning Circle Diameter (TCD) is necessary. Here, Variable Gear Ratio (VGR) steering systems have major benefits over traditional Constant Gear Ratio (CGR) systems in terms of enhancing both roll stability and agility. To adapt VGR steering systems to a particular vehicle dynamic, Full Vehicle (FV) and Driver-in-the-Loop (DIL) simulations are utilized. Using this method, exact calibration is possible according to realistic driving conditions so that the VGR steering C-factor curve is properly tuned for optimal handling in on-center, off-centre, and transitional areas of the Steering Wheel Angle (SWA). Primary performance measures—e.g., SWA gradients at different lateral
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
Accidents during lane changes are increasingly becoming a problem due to various human based and environment-based factors. Reckless driving, fatigue, bad weather are just some of these factors. This research introduces an innovative algorithm for estimating crash risk during lane changes, including the Extended Lane Change Risk Index (ELCRI). Unlike existing studies and algorithms that mainly address rear-end collisions, this algorithm incorporates exposure time risk and anticipated crash severity risk using fault tree analysis (FTA). The risks are merged to find the ELCRI and used in real time applications for lane change assist to predict if lane change is safe or not. The algorithm defines zones of interest within the current and target lanes, monitored by sensors attached to the vehicle. These sensors dynamically detect relevant objects based on their trajectories, continuously and dynamically calculating the ELCRI to assess collision risk during lane changes. Additionally
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
Items per page:
50
1 – 50 of 2576