Browse Topic: Event data recorders
Proprietary, black box, and other hard-to-model subsystems are a leading source of schedule and labor cost across simulation supported analysis and lifecycle management. Using AI/ML technologies to rapidly develop and deploy digital twins of Hardware in the Loop (HWIL) and software systems reduces the Non-Recurring Engineering (NRE) in Modeling and Simulation (M&S) and supports validation of existing software digital twins. This approach also allows for portability of obsolete or proprietary components into a broader range of simulations or applications without exposing critical technologies. We present results of multiple case studies applying AI to black box components of interest to the ground vehicle community
Every digital engineering framework and modeling approach will include benefits and concerns. It is important to customize the response, within reason and based on the available resources, to the needs of the project and contract. For this case, the consideration of a large, singular model was overturned for a distributed model. The potential for a cyclic usage, which can be catastrophic in both performance issues and data loss, was mitigated by an innovative approach that allowed for two (2) systems models – one (1) Black Box and one (1) White Box – using a novel model federation strategy. The concerns of having two (2) system models were mitigated via acceptance and understanding that each system model would play its part appropriately based on model function, system development, and contract deliverables
Machine learning is used for the research and development of ITS services and the rider assistance for on-road motorcycle racing. Meanwhile, rider assistance systems for off-road motorcycles have yet to be developed, partly due to the complexity of the measurement conditions, as described in the previous paper. This research aims to create a reliable AI which is capable of classifying typical jump behaviors in off-road riding by machine learning to create a rider assistance system for off-road motorcycles. Motorcycle manufacturers and certain research institutes use motion sensors to collect data, but the data is obtained from a limited number of vehicles and riders. The creation of a rider assistance system requires a large amount of validation data. Furthermore, it is desirable to achieve the target with data that can be measured in mass-produced vehicles, which will make it possible to collect data even from general users. In addition, recent machine learning models are black boxes
A common scenario in engineering design is the evaluation of expensive black-box functions: simulation codes or physical experiments that require long evaluation times and/or significant resources, which results in lengthy and costly design cycles. In the last years, Bayesian optimization has emerged as an efficient alternative to solve expensive black-box function design problems. Bayesian optimization has two main components: a probabilistic surrogate model of the black-box function and an acquisition functions that drives the design process. Successful Bayesian optimization strategies are characterized by accurate surrogate models and well-balanced acquisition functions. The Gaussian process (GP) regression model is arguably the most popular surrogate model in Bayesian optimization due to its flexibility and mathematical tractability. GP regression models are defined by two elements: the mean and covariance functions. In some modeling scenarios, the prescription of proper mean and
This SAE Recommended Practice describes common definitions and operational elements of Event Data Recorders. The SAE J1698 series of documents consists of the following: SAE J1698-1 - Event Data Recorder - Output Data Definition: Provides common data output formats and definitions for a variety of data elements that may be useful for analyzing vehicle crash and crash-like events that meet specified trigger criteria. SAE J1698-2 - Event Data Recorder - Retrieval Tool Protocol: Utilizes existing industry standards to identify a common physical interface and define the protocols necessary to retrieve records stored by light duty vehicle Event Data Recorders (EDRs). SAE J1698-3 - Event Data Recorder - Compliance Assessment: Defines procedures that may be used to validate that relevant EDR output records conform with the reporting requirements specified in Part 563, Table 1 during the course of FMVSS-208, FMVSS-214, and other applicable vehicle level crash testing
This test method specifies the exposure racks, black boxes, and instrumentation, which shall be used for the outdoor weathering of materials for automotive exterior application
There’s no doubt the complexity of aerospace design systems is constantly increasing, driven by new demands on architecture and next-generation technologies. As a result, the costs and time associated with the creation, certification and deployment of mission-critical electronics hugely heighten if the systems are not managed in a new way
Around the turn of this century, the automotive industry introduced a new type of technology to drive the gauges on a vehicle’s instrument cluster. The change was unannounced to the collision reconstruction world, but soon after, investigators observed a marked increase in crashed vehicles displaying frozen gauges at what often appeared to be correct readings. The new technology was the use of stepper motors which require power to return to the zero position. Hence if electrical power is lost, the gauges stop in position. There have been a number of previous papers covering the operation of the instruments and crash testing of cars and motorcycles to establish the ability of the instruments to withstand the forces on the instrument during a collision. This paper aims to compare the frozen instrument readings from real world collisions with the available EDR data from the crashed vehicles. With the assistance of the collision reconstruction community, a large dataset of 236 vehicles
This SAE Recommended Practice provides common data output formats and definitions for a variety of data elements that may be useful for analyzing the performance of automated driving system (ADS) during an event that meets the trigger threshold criteria specified in this document. The document is intended to govern data element definitions, to provide a minimum data element set, and to specify a common ADS data logger record format as applicable for motor vehicle applications. Automated driving systems (ADSs) perform the complete dynamic driving task (DDT) while engaged. In the absence of a human “driver,” the ADS itself could be the only witness of a collision event. As such, a definition of the ADS data recording is necessary in order to standardize information available to the accident reconstructionist. For this purpose, the data elements defined herein supplement the SAE J1698-1 defined EDR in order to facilitate the determination of the background and events leading up to a
Automotive Event Data Recorders (EDRs) are often utilized to determine or validate the severity of vehicle collisions. Several studies have been conducted to determine the accuracy of the longitudinal change in velocity (ΔV) reported by vehicle EDRs. However, little has been published regarding the measurement of EDRs that are capable of reporting lateral ΔVs in low-speed collisions. In this study, two 2007 Toyota Camrys with 04EDR ECU Generation modules (GEN2) were each subjected to several vehicle-to-vehicle lateral impacts. The impact angles ranged from approximately 45 to 135 degrees and the stationary target vehicles were impacted at the frontal, central, and rear aspects of both the driver and passenger sides. The impact locations on the bullet vehicles were the front and rear bumpers and the impact speeds ranged from approximately 7.9 to 16.1 km/h. Instrumentation was mounted at the approximate center of gravity (CG) of the target vehicles, as well as on the front reinforcement
Reactivity controlled compression ignition (RCCI) engines are considered as a potent solution to realize near zero nitrogen oxides (NOx) and soot emission with higher thermal efficiency. However, operational control in RCCI engines is challenging, as events such as ignition and combustion phasing etc. are mostly decoupled from hardware induced start of injection. In modern control architecture, these real time data are internally computed using signals from cylinder pressure sensor (CPS). Lately, physics based control models or grey box models in RCCI engines were considered as a cost competitive and smart alternative to hardware signal source. In this work, an attempt was made to develop and compare physics based grey box model with data based neural networks, trained through supervised learning (or the black box models) to accurately predict dynamic combustion control parameters across five engine loads and incremental premix energy share not exceeding 60%. Chosen control parameters
Replacing a human driver is an extraordinarily complex task. While machine learning (ML) and its’ subset, deep learning (DL) are fueling breakthroughs in everything from consumer mobile applications to image and gesture recognition, significant challenges remain. The majority of artificial intelligence (AI) learning applications, particularly with respect to Highly Automated Vehicles (HAVs) and their ecosystem have remained opaque - genuine “black boxes.” Data is loaded into one side of the ML system and results come out the other, however, there is little to no understanding at how the decision was arrived at. To make these systems accurate, these AI systems require lots of data to crunch and the sheer computational complexity of building these DL based AI models also slows down the progress in accuracy and the practicality of deploying DL at scale. In addition, the training times and the forensic decision investigation — often measured in days, sometimes weeks and months — slows down
2012 Hyundai Genesis Coupes were manufactured with Airbag Control Modules (ACMs) with Event Data Recorder (EDR) functionality to record crash-related data. However, 2013 is the first model year supported by the download tool and software manufactured for Hyundai vehicles and distributed by Global Information Technologies (GIT) America, Inc. Prior published research has shown that EDR data can be collected from pre-2013 Hyundai vehicles using the GIT tool and some data elements from 2012 and earlier model year Hyundai vehicles are accurately translated - most notably, vehicle speed. To specifically examine the EDR data recorded by a 2012 Hyundai Genesis Coupe, two instrumented crash tests were conducted. Both tests involved broadside impacts into a second stationary vehicle and resulted in a non-deployment EDR recording. The Hyundai was human driven during both crash tests. EDR data was obtained from the Hyundai following each crash test by using a vehicle identification number (VIN
In the 2019 Boeing 737 Max crash, the recovered black box from the aftermath hinted that a failed pressure sensor may have caused the illfated aircraft to nosedive. This incident and others have fueled a larger debate on sensor selection, number, and placement to prevent the reoccurrence of such tragedies
This document provides nomenclature and references to related documents for heavy vehicle event data recorders (HVEDR) for heavy-duty (HD) ground wheeled vehicles. The SAE J2728 series of documents consists of the following
Automotive software is increasingly complex and critical to safe vehicle operation, and related embedded systems must remain up to date to ensure long-term system performance. Update mechanisms and data modification tools introduce opportunities for malicious actors to compromise these cyber-physical systems, and for trusted actors to mistakenly install incompatible software versions. A distributed and stratified “black box” audit trail for automotive software and data provenance is proposed to assure users, service providers, and original equipment manufacturers (OEMs) of vehicular software integrity and reliability. The proposed black box architecture is both layered and diffuse, employing distributed hash tables (DHT), a parity system and a public blockchain to provide high resilience, assurance, scalability, and efficiency for automotive and other high-assurance systems
The reduction of vehicle exhaust particle emissions is a success story of European legislation. Various particle number (PN) counters and calibration procedures serve as tools to enforce PN emission limits during vehicle type approval (VTA) or periodical technical inspection (PTI) of in-use vehicles. Although all devices and procedures apply to the same PN-metric, they were developed for different purposes, by different stakeholder groups and for different target costs and technical scopes. Furthermore, their calibration procedures were independently defined by different stakeholder communities. This frequently leads to comparability and interpretation issues. Systematic differences of stationary and mobile PN counters (PN-PEMS) are well-documented. New, low-cost PTI PN counters will aggravate this problem. Today, tools to directly compare different instruments are scarce. This is complicated by a dominance of proprietary, undisclosed or only partially disclosed manufacturer
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