Browse Topic: Safety regulations and standards
With the surge in adoption of artificial intelligence (AI) in automotive systems, especially Advanced Driver Assistance Systems (ADAS) and autonomous vehicles (AV), comes an increase of AI-related incidents–several of which have ended in injuries and fatalities. These incidents all share a common deficiency: insufficient coverage towards safety, ethical, and/or legal requirements. Responsible AI (RAI) is an approach to developing AI-enabled systems that systematically take such requirements into account. Existing published international standards like ISO 21448:2022 (Safety of the Intended Functionality) and ISO 26262:2018 (Road Vehicles – Functional Safety) do offer some guidance in this regard but are far from being sufficient. Therefore, several technical standards are emerging concurrently to address various RAI-related challenges, including but not limited to ISO 8800 for the integration of AI in automotive systems, ISO/IEC TR 5469:2024 for the integration of AI in functional
With the trend of increasing technological complexity, software content and mechatronic implementation, there are increasing risks from systematic failures and random hardware failures, which is to be considered within the scope of functional safety. ISO 26262 series of standards provides guidance to mitigate these risks by providing appropriate requirements and processes. To develop a safe product with respect to above mentioned complexities, it is very critical to develop a safe system and hence a thorough and robust “Technical Safety Concept” is very important to ensure absence of unreasonable risk due to hazards caused by malfunctions of E/E systems. ISO26262-Part 4 provides guidelines for “Product development at the system level”, to design safety-related systems that include one or more electrical and/or electronic (E/E) systems and that are installed in series production road vehicles. Defining requirements at system level for each individual technology and systematically
Verification and validation (V&V) is the cornerstone of safety in the automotive industry. The V&V process ensures that every component in a vehicle functions according to its specifications. Automated driving functionality poses considerable challenges to the V&V process, especially when data-driven AI components are present in the system. The aim of this work is to outline a methodology for V&V of AI-based systems. The backbone of this methodology is bridging the semantic gap between the symbolic level at which the operational design domain and requirements are typically specified, and the sub-symbolic, statistical level at which data-driven AI components function. This is accomplished by combining a probabilistic model of the operational design domain and an FMEA of AI with a fitness-for-purpose model of the system itself. The fitness-for-purpose model allows for reasoning about the behavior of the system in its environment, which we argue is essential to determine whether the
This document derives from the Federal Motor Vehicle Safety Standards (FMVSS) 105 and 135 vehicle test protocols as single-ended inertia-dynamometer test procedures. The test sequences enable brake output measurement, friction material effectiveness, and corner performance in a controlled and repeatable environment. This SAE Document also includes optional sections for parking brake output performance for rear brakes with hydraulic or Electric Park Brakes (EPB). It applies to brake corners from vehicles covered by the FMVSS 105 and 135 when using the appropriate brake hardware and test parameters. The FMVSS 135 applies to all passenger cars and light trucks up to 3500 kg of gross vehicle weight (GVWR). The FMVSS 105 applies to all passenger cars, multi-purpose vehicles, buses, and trucks above 3500 kg of GVWR. This document does not include testing for school bus applications or vehicles equipped with hydraulic brakes with a GVWR above 4540 kg. This document does not evaluate or
With the current trend of including the evaluation of the risk of brain injuries in vehicle crashes due to rotational kinematics of the head, two injury criteria have been introduced since 2013 – BrIC and DAMAGE. BrIC was developed by NHTSA in 2013 and was suggested for inclusion in the US NCAP for frontal and side crashes. DAMAGE has been developed by UVa under the sponsorship of JAMA and JARI and has been accepted tentatively by the EuroNCAP. Although BrIC in US crash testing is known and reported, DAMAGE in tests of the US fleet is relatively unknown. The current paper will report on DAMAGE in NCAP-like tests and potential future frontal crash tests involving substantial rotation about the three axes of occupant heads. Distribution of DAMAGE of three-point belted occupants without airbags will also be discussed. Prediction of brain injury risks from the tests have been compared to the risks in the real world. Although DAMAGE correlates well with MPS in the human brain model across
To reduce the harm caused by the failure of electronic and electrical system, the application of ISO 26262 functional safety standard in the automotive industry is more and more widespread. As a critical safety-related electronic and electrical system in automobile, electric power steering is very important and necessary to meet the requirements of functional safety. This paper introduces the main development activities of functional safety at software level. In order to realize the purpose of freedom from interference in memory, the safety mechanism of memory protection is proposed in software safety analysis. The memory protection is realized in AUTOSAR architecture by configuration.
As model-based systems engineering is proliferating throughout the aerospace industry as a method to manage the development of complex cyber-physical systems, opportunities to leverage formal methods for verification and validation purposes are significant. As a system model described in SysML can contain the level of semantics required to define strict system requirements, it is possible to create a translation tool to generate SRL (SADL (Semantic Application Design Language) Requirements Language) to leverage ASSERT™ (Analysis of Semantic Specifications and Efficient generation of requirements-based Tests) for verification and validation of the system requirements. SADL [13] is a controlled English grammar that translates directly into OWL (Web Ontology Language) [14]. As part of the validation of the SRL requirements, ASSERT™ leverages a theorem prover to look for conflict and completeness errors. For verification, ASSERT™ uses a Satisfiability Modulo Theories (SMT) solver for the
Designing an effective AVAS system, not only to meet safety regulations, but also to create the expected perception for the vulnerable road user, relies on knowledge of the acoustic transfer function between the sound actuator and the receiver. It is preferable that the acoustic transfer function be as constant as possible to allow transferring the sound designed by the car OEM to ensure the safety of vulnerable road users while conveying the proper brand image. In this paper three different methodologies for the acoustic transfer function calculations are presented and compared in terms of accuracy and calculation time: classic Boundary Element method, H-Matrix BEM accelerated method and Ray tracing method. An example of binaural listening experience at different certification positions in the modeled simulated space is also presented.
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