Browse Topic: Safety testing and procedures
The automotive industry is rapidly advancing towards autonomous vehicles, making sensors such as Cameras, LiDAR, and RADAR critical components for ensuring constant information exchange between the vehicle and its surrounding environment. However, these sensors are vulnerable to harsh environmental conditions like rain, dirt, snow, and bird droppings, which can impair their functionality and disrupt accurate vehicle maneuvers. To ensure all sensors operate effectively, dedicated cleaning is implemented, particularly for Level 3 and higher autonomous vehicles. It is important to test sensor cleaning mechanisms across different weather conditions and vehicle operating scenarios to ensure reliability and performance. One crucial aspect of testing is tracking the trajectory of the cleaning fluid to ensure it does not cause self-soiling of vehicles and affects the field of view or visibility zones of other components like the windshield. While wind tunnel tests are valuable, digitalizing
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
The growing environmental, economic, and social challenges have spurred a demand for cleaner mobility solutions. In response to the transformative changes in the automotive sector, manufacturers must prioritize digital validation of products, manufacturing processes, and tools prior to mass production. This ensures efficiency, accuracy, and cost-effectiveness. By utilizing 3D modelling of factory layouts, factory planners can digitally validate production line changes, substantially reducing costs when introducing new products. One key innovation involves creating 3D models using point cloud data from factory scans. Traditional factory scanning processes face limitations like blind spots and periodic scanning intervals. This research proposes using drones equipped with LiDAR (Light Detection and Ranging) technology for 3D scanning, enabling real-time mapping, autonomous operation, and efficient data collection. Drones can navigate complex areas, access small spaces, and optimize
Ambient light reflecting off internal components of the car, specifically the Head-Up Display (HUD), creates unwanted reflections on the Windshield. These reflections can obscure the driver's field of view, potentially compromising safety and reducing visual comfort. The extent of this obscuration is influenced by geometrical factors such as the angle of the HUD and the curvature of the Windshield, which need to be analyzed and managed. The primary motivation is to improve driver safety and visual comfort. This is driven by the need to address the negative impact of ambient light reflecting off Head-Up Displays (HUDs), which can impair visibility through the Windshield. There is a need for tools and methods to address this issue proactively during the vehicle design phase. This study employs a tool-based modeling method to trace the pathways of ambient light from its source, reflecting off the HUD, and onto the Windshield using a dimensional modeling tool. It focuses on: Geometrical
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
Traditionally, occupant safety research has centered on passive safety systems such as seatbelts, airbags, and energy-absorbing vehicle structures, all designed under the assumption of a nominal occupant posture at the moment of impact. However, with increasing deployment of active safety technologies such as Forward Collision Warning (FCW) and Autonomous Emergency Braking (AEB), vehicle occupants are exposed to pre-crash decelerations that alter their seated position before the crash. Although AEB mitigates the crash severity, the induced occupant movement leads to out-of-position behavior (OOP), compromising the available survival space phase and effectiveness of passive restraint systems during the crash. Despite these evolving real-world conditions, global regulatory bodies and NCAP programs continue to evaluate pre-crash and crash phases independently, with limited integration. Moreover, traditional Anthropomorphic Test Devices (ATDs) such as Hybrid III dummies, although highly
The objective of this study was to examine the effect of Correlated Colour Temperature (CCT) of automotive LED headlamps on driver’s visibility and comfort during night driving. The experiment was conducted on different headlamps having different correlated colour temperatures ranging from 5000K to 6500K in laboratory. Further study was conducted involving participants of different age group and genders for understanding their perception to identify objects when observed in light of different LED headlamps with different CCTs. Studies have shown that both Correlated Colour Temperature and illumination level affect driver’s alertness and performance. Further study required on headlamps with automatically varying CCT to get better solution on driver’s visibility and safety.
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