Browse Topic: Human factors
ABSTRACT BAE Systems Combat Simulation and Integration Labs (CSIL) are a culmination of more than 14 years of operational experience at our SIL facility in Santa Clara. The SIL provides primary integration and test functions over the entire life cycle of a combat vehicle’s development. The backbone of the SIL operation is the Simulation-Emulation-Stimulation (SES) process. The SES process has successfully supported BAE Systems US Combat Systems (USCS) SIL activities for many government vehicle development programs. The process enables SIL activities in vehicle design review, 3D virtual prototyping, human factor engineering, and system & subsystem integration and test. This paper describes how CSIL applies the models, software, and hardware components in a hardware-in-the-loop environment to support USCS combat vehicle development in the system integration lab
ABSTRACT The goal of the human factors engineer is to work within the systems engineering process to ensure that a Crew Centric Design approach is utilized throughout system design, development, fielding, sustainment, and retirement. To evaluate the human interface, human factors engineers must often start with a low fidelity mockup, or virtual model, of the intended design until a higher fidelity physical representation or the working hardware is available. Testing the Warrior-Machine Interface needs to begin early and continue throughout the Crew Centric Design process to ensure optimal soldier performance. This paper describes a Four Step Process to achieve this goal and how it has been applied to the ground combat vehicle programs. Using these four steps in the ground combat vehicle design process improved design decisions by including the user throughout the process either in virtual or real form, and applying the user’s operational requirements to drive the design
ABSTRACT The use and operation of unmanned systems are becoming more commonplace and as missions gain complexity, our warfighters are demanding increasing levels of system functionality. At the same time, decision making is becoming increasingly data driven and operators must process large amounts of data while also controlling unmanned assets. Factors impacting robotic/unmanned asset control include mission task complexity, line-of-sight/non-line-of-sight operations, simultaneous UxV control, and communication bandwidth availability. It is critical that any unmanned system requiring human interaction, is designed as a “human-in-the-loop” system from the beginning to ensure that operator cognitive load is minimized and operator effectiveness is optimized. Best practice human factors engineering in the form of human machine interfaces and user-centered design for robotic/unmanned control systems integrated early in platform concept and design phases can significantly impact platform
Crew Station design in the physical realm is complex and expensive due to the cost of fabrication and the time required to reconfigure necessary hardware to conduct studies for human factors and optimization of space claim. However, recent advances in Virtual Reality (VR) and hand tracking technologies have enabled a paradigm shift to the process. The Ground Vehicle System Center has developed an innovative approach using VR technologies to enable a trade space exploration capability which provides crews the ability to place touchscreens and switch panels as desired, then lock them into place to perform a fully recorded simulation of operating the vehicle through a virtual terrain, maneuvering through firing points and engaging moving and static targets during virtual night and day missions with simulated sensor effects for infrared and night vision. Human factors are explored and studied using hand tracking which enables operators to check reach by interacting with virtual components
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
Connected and autonomous vehicles (CAVs) and their productization are a major focus of the automotive and mobility industries as a whole. However, despite significant investments in this technology, CAVs are still at risk of collisions, particularly in unforeseen circumstances or “edge cases.” It is also critical to ensure that redundant environmental data are available to provide additional information for the autonomous driving software stack in case of emergencies. Additionally, vehicle-to-everything (V2X) technologies can be included in discussions on safer autonomous driving design. Recently, there has been a slight increase in interest in the use of responder-to-vehicle (R2V) technology for emergency vehicles, such as ambulances, fire trucks, and police cars. R2V technology allows for the exchange of information between different types of responder vehicles, including CAVs. It can be used in collision avoidance or emergency situations involving CAV responder vehicles. The
Walking around the SAE WCX conference in Detroit this April and reading through the topic listings for the hundreds of sessions and thousands of presentations, I remembered why I enjoyed this conference so much. I used to attend as a reporter for other outlets, but I haven't been back to WCX since before the pandemic. It was different to walk the halls as editor of this magazine. What happens at WCX - and at dozens of mobility and transportation conferences around the world - is fascinating. I would bet big money that our readers agree. Still, sometimes it's difficult to translate the deeply technical work that makes up our days into something that piques the interest of those who don't spend inordinate amounts of time thinking about the “future of mobility
A University of Cambridge team used machine learning algorithms to teach a robotic sensor to quickly slide over lines of braille text. The robot was able to read the braille at 315 words per minute at close to 90 percent accuracy
Given the rapid advancements in engineering and technology, it is anticipated that connected and automated vehicles (CAVs) will soon become prominent in our daily lives. This development has a vast potential to change the socio-technical perception of public, personal, and freight transportation. The potential benefits to society include reduced driving risks due to human errors, increased mobility, and overall productivity of autonomous vehicle consumers. On the other hand, the potential risks associated with CAV deployment related to technical vulnerabilities are safety and cybersecurity issues that may arise from flawed hardware and software. Cybersecurity and Digital Trust Issues in Connected and Automated Vehicles elaborates on these topics as unsettled cybersecurity and digital trust issues in CAVs and follows with recommendations to fill in the gaps in this evolving field. This report also highlights the importance of establishing robust cybersecurity protocols and fostering
Automated driving has become a very promising research direction with many successful deployments and the potential to reduce car accidents caused by human error. Automated driving requires automated path planning and tracking with the ability to avoid collisions as its fundamental requirement. Thus, plenty of research has been performed to achieve safe and time efficient path planning and to develop reliable collision avoidance algorithms. This paper uses a data-driven approach to solve the abovementioned fundamental requirement. Consequently, the aim of this paper is to develop Deep Reinforcement Learning (DRL) training pipelines which train end-to-end automated driving agents by utilizing raw sensor data. The raw sensor data is obtained from the Carla autonomous vehicle simulation environment here. The proposed automated driving agent learns how to follow a pre-defined path with reasonable speed automatically. First, the A* path searching algorithm is applied to generate an optimal
This SAE Information Report relates to a special class of automotive adaptive equipment which consists of modifications to the power steering system provided as original equipment on personally licensed vehicles. These modifications are generically called “modified effort steering” or “reduced effort power steering.” The purpose of the modification is to alter the amount of driver effort required to steer the vehicle. Retention of reliability, ease of use for physically disabled drivers and maintainability are of primary concern. As an Information Report, the numerical values for performance measurements presented in this report and in the test procedure in the appendices, while based upon the best knowledge available at the time, have not been validated
Air-Launched Effects (ALEs) are a concept for operating small, inexpensive, attritable, and highly autonomous unmanned aerial systems that can be tube launched from aircraft. Launch from ground vehicles is planned as well, although Ground-Launched Effects are not yet a requirement. ALEs are envisioned to provide “reconnaissance, surveillance, target acquisition (RSTA), and lethality with an advanced team of manned and unmanned aircraft as part of an ecosystem including Future Attack and Reconnaissance Aircraft (FARA) and ALE.” A primary purpose of ALEs is to extend “tactical and operational reach and lethality of manned assets, allowing them to remain outside of the range of enemy sensors and weapon systems while delivering kinetic and non-kinetic, lethal and non-lethal mission effects against multiple threats, as well as, providing battle damage assessment data
Over the past few decades, aircraft automation has progressively increased. Advances in digital computing during the 1980s eliminated the need for onboard flight engineers. Avionics systems, exemplified by FADEC for engine control and Fly-By-Wire, handle lower-level functions, reducing human error. This shift allows pilots to focus on higher-level tasks like navigation and decision-making, enhancing overall safety. Full automation and autonomous flight operations are a logical continuation of this trend. Thanks to aerospace pioneers, most functions for full autonomy are achievable with legacy technologies. Machine learning (ML), especially neural networks (NNs), will enable what Daedalean terms Situational Intelligence: the ability to understand and make sense of the current environment and situation but also anticipate and react to a future situation, including a future problem. By automating tasks traditionally limited to human pilots - like detecting airborne traffic and identifying
This report reviews human factors research on the supervision of multiple unmanned vehicles (UVs) as it affects human integration with Air-Launched Effects (ALE). U.S. Army Combat Capabilities Development Command Analysis Center, Fort Novosel, Alabama Air-Launched Effects (ALEs) are a concept for operating small, inexpensive, attritable, and highly autonomous unmanned aerial systems that can be tube launched from aircraft. Launch from ground vehicles is planned as well, although Ground-Launched Effects are not yet a requirement. ALEs are envisioned to provide “reconnaissance, surveillance, target acquisition (RSTA), and lethality with an advanced team of manned and unmanned aircraft as part of an ecosystem including Future Attack and Reconnaissance Aircraft (FARA) and ALE.” A primary purpose of ALEs is to extend “tactical and operational reach and lethality of manned assets, allowing them to remain outside of the range of enemy sensors and weapon systems while delivering kinetic and
In this study, a novel assessment approach of in-vehicle speech intelligibility is presented using psychometric curves. Speech recognition performance scores were modeled at an individual listener level for a set of speech recognition data previously collected under a variety of in-vehicle listening scenarios. The model coupled an objective metric of binaural speech intelligibility (i.e., the acoustic factors) with a psychometric curve indicating the listener’s speech recognition efficiency (i.e., the listener factors). In separate analyses, two objective metrics were used with one designed to capture spatial release from masking and the other designed to capture binaural loudness. The proposed approach is in contrast to the traditional approach of relying on the speech recognition threshold, the speech level at 50% recognition performance averaged across listeners, as the metric for in-vehicle speech intelligibility. Results from the presented analyses suggest the importance of
This standard covers Manpower and Personnel (M&P) processes throughout planning, design, development, test, production, use, and disposal of a system. Depending on contract phase and/or complexity of the program, tailoring can be applied. The scope of this standard includes Prime and Subcontractor M&P activities; it does not include Government M&P activities. The primary goals of a contractor M&P program typically include: Ensuring that the system design complies with the latest customer Manpower estimates (numbers and mix of personnel, plus availability) and that discrepancies are reported to management and the customer. Ensuring that the system design is regularly compared to the latest customer personnel estimates (capabilities and limitations) and that discrepancies are reported to management and the customer. Identifying, coordinating, tracking, and resolving M&P risks and issues and ensuring that they are: ○ Reflected in the contractor proposal, budgets, and plans. ○ Raised at
This SAE Standard identifies contractor activities for planning and conducting HSI as part of procurement activities on Department of Defense (DoD) system acquisition programs. This standard covers HSI processes throughout system design, development, test, production, use, and disposal. Depending on contract phase, type of the program and/or complexity of the program, tailoring of this standard should be applied. Appendix A lists the requrememts (“shall” statements) in this standard along with unique numbers to facilitate tailoring. In addition, Appendix D provides tailoring guidance to better match requirememts in this standard to the DoD’s Adaptive Acquisition Framework pathways. The scope of this standard includes prime and subcontractor HSI activities; it does not include Government HSI activities, which are covered by DoD and service-level regulations and guidelines. HSI programs should use the latest version of standards and handbooks listed below, unless a particular revision is
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 Standard describes head position contours and procedures for locating the contours in a vehicle. Head position contours are useful in establishing accommodation requirements for head space and are required for several measures defined in SAE J1100. Separate contours are defined depending on occupant seat location and the desired percentage (95 and 99) of occupant accommodation. This document is primarily focused on application to Class A vehicles (see SAE J1100), which include most personal-use vehicles (passenger cars, sport utility vehicles, pick-up trucks). A procedure for use in Class B vehicles can be found in Appendix B
The purpose of this document is to establish air-conditioning design guidelines that will apply to most systems rather than the specific design of any particular system. Operating conditions and characteristics of the equipment will determine the design of any successful system; since these characteristics and conditions vary greatly from one application to another, the designer shall determine the goals expected to be reached under the conditions encountered. To determine the capacity of such items as blowers, condenser fans, condenser coils, evaporator coils, filters, compressors, etc., will require the adherence to several guidelines, some of which are outlined in the following paragraphs
The analysis of lipid biomarkers has gained increasing importance within environmental and archaeological fields because biomarkers are representative of plant and animal sources. Proven gold standard laboratory techniques for lipid biomarker extraction are laborious, with many opportunities for human error. As a solution, NASA Ames Research Center has developed a novel technology that provides an autonomous, miniaturized fluidic system for lipid analysis. The technology, in a single instrument, can accept an unprocessed soil, rock, or ice sample, comminute the sample, extract lipids via sonication and blending, filter out mineral residue, concentrate the analyte, and deliver the aliquot to downstream analytical instruments for molecular characterization, without requiring intervention from a human operator
The development of the autonomous applications for dismounted Soldier systems is paramount to defeating our adversaries, such as China and Russia, in future combat. A comprehensive literature review is necessary to assist in defining the best path forward. Army Research Laboratory, Aberdeen Proving Ground, MD The development of the artificial intelligence/machine learning (AI/ML) applications for dismounted Soldier systems is paramount to defeating our adversaries, such as China and Russia, in future combat. A comprehensive AI/ML literature review is a first step toward defining what exists and what can be applied and researched for our nation's defense in future warfare. There is a clear need to use the latest AI/ML technologies in threat identification and elimination without U.S. lives lost. A comprehensive literature review is necessary to assist in defining the best path forward. In theory, networked unmanned aerial vehicles (UAVs) using onboard cameras may assist in successful
Electro-hydraulic actuators, a type of soft actuators, can provide soft-touch vibrations due to their structural characteristics, but some problems need to be improved to apply them to vehicles. That is, it is necessary to increase excitation force, expand frequency band, lower driving voltage, and increase durability. This research aims to design a new type based on electro-hydraulic actuator and improve problems with its performance to develop a product that generates emotional vibration in vehicles. First, a new mechanism and design of an electro-hydraulic actuator called a PVC-gel film actuator are proposed. This actuator uses PVC-gel as a film which covers a dielectric liquid and uses carbon nanotube as a cathode material. In addition, a method of manufacturing an actuator with improved performance has been proposed by creating and testing prototypes with different sizes and material properties. It has been verified that the proposed actuator improves excitation force, frequency
Simulation plays a central role in almost every aspect of automotive product development. And as this month's cover story explains, ‘sim’ is extending its reach in automated-driving R&D, bringing efficiency to human factors and critical but tedious component-verification work. Some argue that most AV development should - and thanks to contemporary sim technology, can - be conducted in the virtual world. It's hard for me to imagine getting to consumer-ready SAE Level 4 and 5 driving automation without eventual heavy reliance on simulation-based validation. That notion comes hard against what's played out with Tesla, however. The EV leader effectively has leveraged its customers' on-the-road experiences to incrementally “harden” its automated-driving software. It's not an entirely off-the-ranch idea; many AV developers have relied on some sort of crowdsourcing data acquisition to help their systems learn. The difference, however, is that Tesla consigned this role - and its genuine risks
Creating technologies that amplify human experience and endeavor to help solve society's biggest challenges is the mission of the Toyota Research Institute. Gill Pratt has a gift for explaining complex topics in simple terms. And as Toyota Motor Co.'s chief scientist and CEO of the Toyota Research Institute (TRI), he also speaks frankly about the promises and potential pitfalls of new technologies. Addressing a rare group of visitors - tech reporters and analysts, including SAE Media - recently at TRI's Silicon Valley headquarters, Pratt noted the heightened public discourse around artificial intelligence, a core area of focus for many of TRI's 200 scientists and engineers. “Everybody is worried ChatGPT is going to be writing term papers for college students,” Pratt said half-jokingly about the controversial “chat bot” introduced in late 2022 by OpenAI. “But even our humor reflects the anxiety we have about this technology and its dual nature of good and evil.” Society, he observed
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