Browse Topic: Safety regulations and standards
ABSTRACT Today we have autonomous vehicles already on select road-ways and regions of this country operating in and around humans and human operated vehicles. The companies developing and testing these systems have experienced varied degrees of success and failure with regard to safe operations within this public space. There have been safety incidents that have made national headlines (when human fatalities have occurred) and their also exist a litany of other physical incidents, usually with human operated systems, that have not grabbed the headlines. Some of the select communities where these autonomous systems have been operationally tested have revoked access to their roadways (kicked out) some of these companies. As a result of these incidents recent data suggests that the public trust in autonomous vehicles is eroding [1]. This situation is couponed by the fact that there are no established safety standards, measures or technological methods to help local, state or national
ABSTRACT The application of advanced FEV Automotive Smart Vehicle© methods and technologies while maintaining functional safety compliance and how it applies to similar features, requirements and capabilities across the fleet of DoD combat and tactical vehicles will be discussed. The requirement of technologies for DoD autonomous ground vehicle including leader follower, automated convoy operations, and intelligent applique kit’ are common to those specified in the automotive industries. Intelligent vehicles can be advanced and implemented in an expeditious manner through FEV Smart Vehicle technologies, techniques and methodologies while maintain compliance to required functional safety. The application and impact of ISO 26262 (2011) as well as Mil-Std. 882(E) to the implementation of the advanced technologies and techniques in support of full operational vehicle autonomy can hinder development. Leveraging the FEV Automotive Smart Vehicle reduces the time and cost for safety compliant
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
The automotive industry has seen accelerating demand for electrified transportation. While the complexity of conventional ICE vehicles has increased, the powertrain still largely consists of a mechanical system. In contrast, vehicle architectures in electrified transportation are a complex integration of power electronics, batteries, control units, and software. This shift in system architecture impacts the entire organization during new product development, with increased focus on high power electronic components, energy management strategies, and complex algorithm development. Additionally, product development impact extends beyond the vehicle and impacts charging networks, electrical infrastructure, and communication protocols. The complex interaction between systems has a significant impact on vehicle safety, development timeline, scope, and cost. A systems engineering approach, with emphasis on requirements definition and traceability, helps ensure decomposition of top level
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
This SAE Aerospace Information Report (AIR) identifies the risks and dangers associated with the carriage and use of pyrotechnic signaling devices in transport category aircraft life rafts and slide/rafts, and provides a rationale for allowing the use of alternative non-pyrotechnic devices authorized by FAA/TSO-C168. These devices offer an equivalent level of safety while eliminating flight safety risks, enhancing survivability of aircraft ditching survivors, reducing costs, eliminating dangerous goods transportation and handling issues, and reducing environmental impact of dangerous goods disposal
This SAE Recommended Practice presents a method and example results for determining the Automotive Safety Integrity Level (ASIL) for automotive motion control electrical and/or electronic (E/E) systems. The ASIL determination activity is required by ISO 26262-3, and it is intended that the process and results herein are consistent with ISO 26262. The technical focus of this document is on vehicle motion control systems. The scope of this SAE Recommended Practice is limited to collision-related hazards associated with motion control systems. This SAE Recommended Practice focuses on motion control systems since the hazards they can create generally have higher ASIL ratings, as compared to the hazards non-motion control systems can create. Because of this, the Functional Safety Committee decided to give motion control systems a higher priority and focus exclusively on them in this SAE Recommended Practice. ISO 26262 has a wider scope than SAE J2980, covering other functions and accidents
This report presents several challenges that the U.S. Army will face in the transition to autonomous vehicles, challenges that are only magnified in the current acquisition environment with limited testing. Artificial intelligence algorithms introduce additional complexity, resulting in systems with a complex combination of human, machine, and autonomous controllers. Army DEVCOM Analysis Center, Aberdeen Proving Ground, MD Artificial intelligence (AI) has become prevalent in many fields in the modern world, ranging from vacuum cleaners to lawn mowers and commercial automobiles. These capabilities are continuing to evolve and become a part of more products and systems every day, with numerous potential benefits to humans. AI is of particular interest in autonomous vehicles (AVs), where the benefits include reduced cognitive workload, increased efficiency, and improved safety for human operators. Numerous investments from academia and industry have been made recently with the intent of
This SAE Recommended Practice defines key terms used in the description and analysis of video based driver eye glance behavior, as well as guidance in the analysis of that data. The information provided in this practiced is intended to provide consistency for terms, definitions, and analysis techniques. This practice is to be used in laboratory, driving simulator, and on-road evaluations of how people drive, with particular emphasis on evaluating Driver Vehicle Interfaces (DVIs; e.g., in-vehicle multimedia systems, controls and displays). In terms of how such data are reduced, this version only concerns manual video-based techniques. However, even in its current form, the practice should be useful for describing the performance of automated sensors (eye trackers) and automated reduction (computer vision
This SAE Recommended Practice establishes uniform requirements and guidelines for the display of capacity information of personal watercraft
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