Browse Topic: Human factors
Camera-based mirror systems (CBMS) are being adopted by commercial fleets based on the potential improvements to operational efficiency through improved aerodynamics, resulting in better fuel economy, improved maneuverability, and the potential improvement for overall safety. Until CBMS are widely adopted it will be expected that drivers will be required to adapt to both conventional glass mirrors and CBMS which could have potential impact on the safety and performance of the driver when moving between vehicles with and without CBMS. To understand the potential impact to driver perception and safety, along with other human factors related to CBMS, laboratory testing was performed to understand the impact of CBMS and conventional glass mirrors. Drivers were subjected to various, nominal driving scenarios using a truck equipped with conventional glass mirrors, CBMS, and both glass mirrors and CBMS, to observe the differences in metrics such as head and eye movement, reaction time, and
Headlight glare remains a persistent problem to the U.S. driving public. Over the past 30 years, vehicle forward lighting and signaling systems have evolved dramatically in terms of styling and lighting technologies used. Importantly, vehicles driven in the U.S. have increased in size during this time as the proportion of pickup trucks and sport-utility vehicles (SUVs) has increased relative to passenger sedans and other lower-height vehicles. Accordingly, estimates of typical driver eye height and the height of lighting and signaling equipment on vehicles from one or two decades ago are unlikely to represent the characteristics of current vehicles in the U.S. automotive market. In the present study we surveyed the most popular vehicles sold in the U.S. and carried out evaluations of the heights of lighting and signaling systems, as well as typical driver eye heights based on male and female drivers. These data may be of use to those interested in understanding how exposure to vehicle
The research activity aims at defining specific Operational Design Domains (ODDs) representative of Italian traffic environments. The paper focuses on the human-machine interaction in Automated Driving (AD), with a focus on take-over scenarios. The study, part of the European/Italian project “Interaction of Humans with Level 4 AVs in an Italian Environment - HL4IT”, describes suitable methods to investigate the effect of the Take-Over Request (TOR) on the human driver’s psychophysiological response. The DriSMI dynamic driving simulator at Politecnico di Milano has been used to analyse three different take-over situations. Participants are required to regain control of the vehicle, after a take-over request, and to navigate through a urban, suburban and highway scenario. The psychophysiological characterization of the drivers, through psychological questionnaires and physiological measures, allows for analyzing human factors in automated vehicles interactions and for contributing to
High-efficiency manufacturing involves the transmission of copious amounts of data, exemplified both by trends in the automotive industry and advances in technology. In the automotive industry, products have been growing increasingly complex, owing to multiple SKUs, global supply chains and the involvement of many tier 2 / Just-In Time (JIT) suppliers. On top of that, recalls and incidents in recent years have made it important for OEMs to be able to track down affected vehicles based on their components. All of this has increased the need for OEMs to be able to collect and analyze component data. The advent of Industry 4.0 and IoT has provided manufacturing with the ability to efficiently collect and store large amounts of data, lining up with the needs of manufacturing-based industries. However, while the needs to collect data have been met, corporations now find themselves facing the need to make sense of the data to provide the insights they need, and the data is often unstructured
This SAE Edge Research Report explores advancements in next-generation mobility, focusing on digitalized and smart cockpits and cabins. It offers literature review, examining current customer experiences with traditional vehicles and future mobility expectations. Key topics include integrating smart cockpit and cabin technologies, addressing challenges in customer and user experience (UX) in digital environments, and discussing strategies for transitioning from traditional vehicles to electric ones while educating customers. User Experience for Digitalized and Smart Cockpits and Cabins of Next-gen Mobility covers both on- and off-vehicle experiences, analyzing complexities in developing and deploying digital products and services with effective user interfaces. Emphasis is placed on meeting UX requirements, gaining user acceptance, and avoiding trust issues due to poor UX. Additionally, the report concludes with suggestions for improving UX in digital products and services for future
This Recommended Practice provides a procedure to locate driver seat tracks, establish seat track length, and define the SgRP in Class B vehicles (heavy trucks and buses). Three sets of equations that describe where drivers position horizontally adjustable seats are available for use in Class B vehicles depending on the percentages of males to females in the expected driver population (50:50, 75:25, and 90:10 to 95:5). The equations can also be used as a checking tool to estimate the level of accommodation provided by a given length of horizontally adjustable seat track. These procedures are applicable for both the SAE J826 HPM and the SAE J4002 HPM-II.
This SAE Recommended Practice establishes three alternate methods for describing and evaluating the truck driver's viewing environment: the Target Evaluation, the Polar Plot and the Horizontal Planar Projection. The Target Evaluation describes the field of view volume around a vehicle, allowing for ray projections, or other geometrically accurate simulations, that demonstrate areas visible or non-visible to the driver. The Target Evaluation method may also be conducted manually, with appropriate physical layouts, in lieu of CAD methods. The Polar Plot presents the entire available field of view in an angular format, onto which items of interest may be plotted, whereas the Horizontal Planar Projection presents the field of view at a given elevation chosen for evaluation. These methods are based on the Three Dimensional Reference System described in SAE J182a. This document relates to the driver's exterior visibility environment and was developed for the heavy truck industry (Class B
This SAE Recommended Practice describes two-dimensional 95th percentile truck driver side view, seated stomach contours for horizontally adjustable seats (see Figure 1). There is one contour and three locating lines to accommodate male-to-female ratios of 50:50, 75:25, and 90:10 to 95:5.
This SAE Recommended Practice describes two-dimensional, 95th percentile truck driver, side view, seated shin-knee contours for both the accelerator operating leg and the clutch operating leg for horizontally adjustable seats (see Figure 1). There is one contour for the clutch shin-knee and one contour for the accelerator shin-knee. There are three locating equations for each curve to accommodate male-to-female ratios of 50:50, 75:25, and 90:10 to 95:5.
This Recommended Practice provides procedures for defining the Accelerator Heel Point and the Accommodation Tool Reference Point, a point on the seat H-point travel path which is used for locating various driver workspace accommodation tools in Class B vehicles (heavy trucks and buses). Three accommodation tool reference points are available depending on the percentages of males and females in the expected driver population (50:50, 75:25, and 90:10 to 95:5). These procedures are applicable to both the SAE J826 HPM and the SAE J4002 HPM-II.
Innovators at NASA Johnson Space Center have developed an adjustable thermal control ball valve (TCBV) assembly which utilizes a unique geometric ball valve design to facilitate precise thermal control within a spacesuit. The technology meters the coolant flow going to the cooling and ventilation garment, worn by an astronaut in the next generation space suit, that expels waste heat during extra vehicular activities (EVAs) or spacewalks.
This recommended practice shall apply to all on-highway trucks and truck-tractors equipped with air brake systems and having a GVW rating of 26 000 lb or more.
At the InCabin USA vehicle technology expo in Detroit, Ford customer research lead Susan Shaw said that the sea of letters around ADAS features and control and indicator icons that vary between vehicles are often confusing to drivers. Shaw pointed out that the following all represent features related to driving lanes: LDW, LKA, LKS, LFA, LCA. These initialisms (groups of letters that form words) are not the only ways the industry refers to these technologies, as some OEMs have their own names for similar things. It all contributes to what can be dangerous assumptions on the part of a driver. “It's shocking how many people think their vehicle will apply the brakes in an emergency, when the car has no such system,” she said. As an overview to the subject of control and indicator iconography, Shaw began with an introduction to user experience research by talking about a classic example: Norman is the author of “The Design of Everyday Things.” A so-called Norman door is any door that is
This SAE Systems Management Standard specifies the Habitability processes throughout planning, design, development, test, production, use and disposal of a system. Depending on contract phase and/or complexity of the program, tailoring of this standard may be applied. Appendix C provides guidance on tailoring standard requirements to fit the various DoD acquisition pathways. The primary goals of a contractor Habitability program include: Ensuring that the system design complies with the customer Habitability requirements and that discrepancies are reported to management and the customer. Identifying, coordinating, tracking, prioritizing, and resolving Habitability risks and issues and ensuring that they are: ◦ Reflected in the contractor proposal, budgets, and plans. ◦ Raised at design, management, and program reviews. ◦ Debated in working group meetings. ◦ Coordinated with Training, logistics, and the other HSI disciplines. ◦ Included appropriately in documentation and deliverable
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
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.
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.
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
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
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.”
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
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