Browse Topic: Regulations
The evolution of Autonomous off-highway vehicles (OHVs) has transformed mining, construction, and agriculture industries by significantly improving efficiency and safety. These vehicles operate in high dust, uneven terrain, and potential communication failures, where safety is challenged. To guarantee vehicle safety in such situations, a robust architecture that combines AI-driven perception, fail-safe mechanisms, and conformance to many ISO standards is required. In unstructured environments, AI-driven perception, decision-making, and fail-safe mechanisms are not fully addressed by traditional safety standards like ISO26262 (road vehicles), ISO19014 (earth-moving machinery and it is replacing withdrawn ISO 15998), ISO12100 (Safety of machinery) and ISO25119 (agriculture), ISO 18497 (safety of highly automated agricultural machinery), and ISO/CD 24882 (cybersecurity for machinery).These standards mainly concentrate on the reliability of mechanical and electric/electronic systems
As an important bridge connecting cities and rural areas, highway transportation has an irreplaceable role in regional economic development [1]. Accompanied by the booming development of long-distance transportation industry, strengthening highway transportation is of great significance to improve people's living standards [2], but because of the special characteristics of truck transportation, fuel theft is frequent, seriously endangering the driver's life and the safety of goods transportation, although the police in the severe crackdown, but fuel theft seems to be in addition to inexhaustible, truck drivers lose oil incidents still occur from time to time, due to the increasingly serious energy problems, the world's countries have Due to the increasingly serious energy problems, countries around the world have formulated strict automotive fuel consumption rate (hereinafter referred to as fuel consumption) regulations [3], in the transportation process to prevent fuel theft is of
PACCAR's Phil Stephenson previews SAE COMVEC 2025 and offers insights into powertrain diversification, the role of AI, a software-defined future and the importance of people. Advancing technology to solve challenges involving regulation, compliance, autonomy, electrification, combustion engines and other areas is an obvious focus of SAE International's flagship gathering for the commercial vehicle and off-highway industries, COMVEC 2025 (https://comvec.sae.org/). But advancing people, which is vital to navigating this challenging environment, is a particular focal point for this year's engineering event being held near Chicago in September. Workforce development is just as critical as technology development, stresses Phil Stephenson, general manager of PACCAR Technical Center, where he leads a team of engineers, technicians, mechanics, scientists and business leaders. Stephenson is serving as the executive chair of SAE COMVEC 2025, which carries the theme “Advancement, Empowerment
When it comes to technology adoption, the healthcare industry is historically risk averse. Despite strict regulations protecting patient data and concerns over medical outcomes, a new report from Mordor Intelligence reports that the global market for wireless portable medical devices is expected to exceed $31.4 billion this year. 1 The same report projects 12.14 percent compound annual growth through 2030 to meet the demands of a burgeoning geriatric population for wearable and implantable devices and in-home vital signs monitoring.
This specification covers bonded honeycomb core made of aluminum alloy and supplied in the form of blocks, slices, or other configurations as ordered (see 8.5).
There is an increasing effort to reduce noise pollution across different industries worldwide. From a transportation standpoint, pass-by regulations aim to achieve this and have been implementing increasingly stricter emissions limits. Testing according to these standards is a requirement for homologation, but does little to help manufacturers understand why their vehicles may be failing to meet limits. Using a developed methodology such as Pass-by Source Path Contribution (SPC, also known as TPA) allows for identification of dominant contributors to the pass-by receivers along with corresponding acoustic source strengths. This approach is commonly used for passenger vehicles, but can be impractical for off-highway applications, where vehicles are often too large for most pass-by-suitable chassis dynamometers. A hybrid approach is thereby needed, where the same techniques and instrumentation used in the indoor test are applied to scenarios in an outdoor environment. This allows for
Airworthiness certification of aircraft requires an Airworthiness Security Process (AWSP) to ensure safe operation under potential unauthorized interactions, particularly in the context of growing cyber threats. Regulatory authorities mandate the consideration of Intentional Unauthorized Electronic Interactions (IUEI) in the development of aircraft, airborne software, and equipment. As the industry increasingly adopts Model-Based Systems Engineering (MBSE) to accelerate development, we aim to enhance this effort by focusing on security scope definitions – a critical step within the AWSP for security risk assessment that establishes the boundaries and extent of security measures. However, our findings indicate that, despite the increasing use of model-based tools in development, these security scope definitions often remain either document-based or, when modeled, are presented at overly abstract levels, both of which limit their utility. Furthermore, we found that these definitions
Safety Management Systems (SMSs) have been used in many safety-critical industries and are now being developed and deployed in the automated driving system (ADS)-equipped vehicle (AV) sector. Industries with decades of SMS deployment have established frameworks tailored to their specific context. Several frameworks for an AV industry SMS have been proposed or are currently under development. These frameworks borrow heavily from the aviation industry although the AV and aviation industries differ in many significant ways. In this context, there is a need to review the approach to develop an SMS that is tailored to the AV industry, building on generalized lessons learned from other safety-sensitive industries. A harmonized AV-industry SMS framework would establish a single set of SMS practices to address management of broad safety risks in an integrated manner and advance the establishment of a more mature regulatory framework. This paper outlines a proposed SMS framework for the AV
Physical testing is required to assess multiple vehicles in different conditions, specially to validate those related to regulations. The acoustic evaluations have difficulties and limitations in physical test; cost and time represent important considerations every time. Additionally, the physical validation happens once a prototype has been built, this takes place in a later phase of the development. Sound pressure is measured to validate different requirements in a vehicle, horn sound is one of these and it is related to a regulation of united nations (ECE28). Currently the validation happens in physical test only and the results vary depending on the location of the horn inside the front end of every vehicle. [7] In this article, the work for approaching a virtual validation method through CAE is presented with the intention to get efficiency earlier in product development process.
This SAE Recommended Practice establishes recommended procedures for the issuance, assignment, and structure of Identification Numbers on a uniform basis by states or provinces for use in an Assigned Identification Number (AIN).
The inductance parameter is important for the flux regulation performance of the hybrid excitation motor, and the axial structure leads to the change in the inductance parameter of the axial-radial hybrid excitation motor (ARHEM). To clarify the inductance characteristic of the ARHEM with different winding construction and the mutual coupling effect between the axial excitation and permanent magnet excitation on the inductance. Firstly, the structure of the ARHEM is presented. Secondly, the self and mutual inductance characteristics of ARHEM are analyzed using the winding function method. Then, the influence of the axial excitation structure on the armature reaction field and saliency ratio of ARHEM. On this basis, the mechanism of the mutual coupling, between the axial excitation and permanent magnet field under different excitation currents on the main air gap magnetic field, and the inductance of ARHEM with fractional slot are revealed.
In India, Driver Drowsiness and Attention Warning (DDAW) system-based technologies are rising due to anticipation on mandatory regulation for DDAW. However, readiness of the system to introduce to Indian market requires validations to meet standard (Automotive Industry Standard 184) for the system are complex and sometimes subjective in nature. Furthermore, the evaluation procedure to map the system accuracy with the Karolinska sleepiness scale (KSS) requirement involves manual interpretation which can lead to false reading. In certain scenarios, KSS validation may entail to fatal risks also. Currently, there is no effective mechanism so far available to compare the performance of different DDAW systems which are coming up in Indian market. This lack of comparative investigation channel can be a concerning factor for the automotive manufactures as well as for the end-customers. In this paper, a robust validation setup using motion drive simulator with 3 degree of freedom (DOF) is
You've got regulations, cost and personal preferences all getting in the way of the next generation of automated vehicles. Oh, and those pesky legal issues about who's at fault should something happen. Under all these big issues lie the many small sensors that today's AVs and ADAS packages require. This big/small world is one topic we're investigating in this issue. I won't pretend I know exactly which combination of cameras and radar and lidar sensors works best for a given AV, or whether thermal cameras and new point cloud technologies should be part of the mix. But the world is clearly ready to spend a lot of money figuring these problems out.
Heavy-duty vehicle regulations from the European Union specify a 43% carbon emissions reduction by 2030. The EU's carbon emissions reduction mandate climbs to 64% by 2035 before soaring to 90% by 2040. “The hydrogen combustion engine has a role to play to reduce CO2 emissions,” said Vincent Giuffrida, CFD engineer for IFP Energies novellas (IFPEN), a Rueil-Malmaison, France-headquartered public research and innovation organization. Giuffrida and IFPEN colleague and research engineer Olivier Colin were the presenters for a webinar addressing the “Development of a Dedicated Hydrogen Combustion System for Heavy-Duty Applications” in July. The webinar was hosted by Madison, Wisconsin-headquartered Convergent Science, whose CONVERGE CFD software simulates three-dimensional fluid flows. Features of the CFD software include autonomous meshing, complex moving geometries, a detailed chemical kinetics solver, advanced physical models, conjugate heat transfer model, fluid structure interaction
In the increasingly connected and digital world, businesses are sprinting to integrate technological advancements into their corporate fabric. This is evident with the emerging concept of “digital twinning.” Digital twins are virtual representations of real-world objects or systems used to digitally model performance, identify inefficiencies, and design solutions. This helps improve the “real world” product, reduces costs, and increases efficiency. However, this replication of a physical entity in the digital space is not without its challenges. One of the challenges that will become increasingly prevalent is the processing, storing, and transmitting of Controlled Unclassified Information (CUI). If CUI is not protected properly, an idea to save time, money, and effort could result in the loss of critical data. The Department of Defense's (DoD) CUI Program website defines CUI as “government-created or owned unclassified information that allows for, or requires, safeguarding and
Artificial intelligence (AI)-based solutions are slowly making their way into mobile devices and other parts of our lives on a daily basis. By integrating AI into vehicles, many manufacturers are looking forward to developing autonomous cars. However, as of today, no existing autonomous vehicles (AVs) that are consumer ready have reached SAE Level 5 automation. To develop a consumer-ready AV, numerous problems need to be addressed. In this chapter we present a few of these unaddressed issues related to human-machine interaction design. They include interface implementation, speech interaction, emotion regulation, emotion detection, and driver trust. For each of these aspects, we present the subject in detail—including the area’s current state of research and development, its current challenges, and proposed solutions worth exploring.
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