Browse Topic: Human Factors and Ergonomics
Every kid who has read a comic book or watched a Spider-Man movie has tried to imagine what it would be like to shoot a web from their wrist, fly over streets, and pin down villains. Researchers at Tufts University took those imaginary scenes seriously and created the first web-slinging technology in which a fluid material can shoot from a needle, immediately solidify as a string, and adhere to and lift objects.
With the increasing demand for material microimaging analysis, there is a growing need for advanced precision grinding and polishing equipment, especially for metals, ceramics, and composites. Existing automated systems struggle with handling complex material challenges. This paper presents a fully automated adaptive grinding and polishing machine based on an STM32 microcontroller that handles multi-material samples. The system includes modules for sample access, cleaning, pad replacement, human-computer interaction, and equipment communication. The STM32 microcontroller executes grinding and polishing tasks based on instructions from the host computer while dynamically adjusting PID control parameters using an improved weighted average optimization algorithm. This approach enhances control accuracy, stability, and overall surface treatment quality compared to traditional PID control methods.
This document is intended to establish a procedure to certify AD fallback test driver skill levels as an endorsement to SAE J3300 foundational level certification. The SAE J3300/3 endorsement can be used by the individual driver to qualify their skills as a test driver of vehicles with automated driving features. The SAE J3300/3 endorsement levels may also be used by test facilities or other organizations when seeking test or professional drivers with these skills. This document provides directions for obtaining the endorsement, including associated AD fallback test driving skill examination requirements, through SAE J3300-certified Examiners (refer to SAE J3300 for definition). Endorsement registration and associated records are administered through Probitas Authentication®. Probitas Authentication® is the current Independent Program Administrator for the SAE J3300 series. This document is a supplement to SAE J3300, providing information specific to the AD fallback test driver skill endorsement and clarifying the application of the rules set forth in SAE J3300 to the AD fallback test driver endorsement. While the references, definitions, rules, and guidelines presented in SAE J3300 Sections 1 through 5 apply to the AD fallback test driver endorsement, they are not repeated in this document.
ERRATUM
To explore the impact of guiding and warning visual combination factors at the entrance sections of highway tunnels on drivers’ visual characteristics and driving behavior, this study recruited 16 drivers to conduct on-road vehicle experiments at the entrance sections of the Yunling Tunnel’s left bore (with visual combination factors) and right bore (without visual combination factors). Seven visual characteristics and driving behavior indicators, including pupil diameter and vehicle speed, were collected and statistically analyzed. Representative indicators such as pupil diameter, standard deviation of fixation point position, and vehicle speed were selected to establish a trend surface model of visual characteristics and driving behavior. The results indicate that when driving at the entrance section of the left bore, drivers’ pupil diameter and fixation duration were significantly lower than those at the entrance section of the right bore. With the increase in the sweeping view angle, there was a more dispersed distribution of fixation points. Additionally, there were significant differences in the acceleration and lateral deviation of the driving vehicle, with the range of variation narrowing by 52.5% and 35.7%, respectively. The trend surface model results show that under the influence of visual combination factors, the reduction in drivers’ vehicle speed was smaller, and the impact of pupil diameter and standard deviation of fixation point position on vehicle speed was less pronounced. Overall, under the influence of visual combination factors, drivers’ visual characteristics showed significant changes, with improved speed control and manipulation levels, leading to more stable vehicle operation.
The UMV Peoplemover 2+2 is part of a modular vehicle family (Urban Modular Vehicle) that includes derivatives for passenger and cargo transport in urban environments. The platform supports automated movers as well as conventionally controlled vehicles with a human driver, ensuring high flexibility across applications. The modular platform enables the extensive use of common parts, allowing the efficient and cost-effective realization of multiple vehicle variants. The increased share of common parts also improves sustainability by reducing derivative-specific parts, material usage, and production complexity. A drivable demonstrator of the UMV Peoplemover 2+2 has already been realized. The vehicle is designed for the automated transport of up to four occupants in a 2+2 vis-à-vis seating arrangement and is targeted at demand-oriented shuttle services. While the drivable demonstrator validated the proof of concept, it lacked the core Level 4 hardware and software stack for automated driving functions. To address this limitation, we deployed a software-defined vehicle architecture to the concept. This paper introduces the novel e/e-architecture and software stack enabling the Peoplemover 2+2 to initiate its first shuttle service at the German Aerospace Center (DLR e.V.) in Stuttgart. We further detail the deployed multi-modal sensor suite, comprising modern solid-state LiDARs and a 4D imaging radar, which were carefully selected to meet the operational design domain requirements while also serving as a versatile research platform for future advanced perception studies. Finally, we analyze the SDV-based modular software stack, which facilitates rapid application development through straightforward switching between commercial, open-source, and in-house software domains, and supports parallel execution of domain-specific functions across all three software sources.
In recent years, the automotive industry has faced increasing pressure to accelerate development cycles and reduce costs. Simultaneously, ride comfort standards have risen due to the ongoing integration of autonomous driving functionalities. Consequently, it has become essential to ensure that ride comfort attains a high degree of maturity at the very early stages of the automotive development process. This necessitates the establishment of objective criteria that enable the reliable estimation of subjective ride comfort, utilizing simulation-based assessment methods. This study introduces a methodological framework designed to systematically translate the manufacturer specific subjective perception and assessment of ride comfort into objective descriptions using a dynamic driving simulator. The framework is conceived as a generic approach, enabling the comprehensive application to a wide spectrum of subjective ride comfort phenomena, while being specifically optimized for the challenges of the automotive industry. Employing this framework facilitates the derivation of highly detailed, objective descriptions of subjective ride comfort evaluations, which promotes the achievement of advanced ride comfort maturity for new vehicles in early development phases and supports the overall enhancement of ride comfort. The exemplary application of the framework to a transient, one-dimensional ride comfort phenomenon demonstrates its capability to derive robust objective models from subjective evaluations conducted with professional test drivers in a dynamic driving simulator environment.
Rigorous validation of SAE Levels 3 and 4 autonomous systems increasingly relies on simulation. However, the simulation-reality gap remains a challenge for human-in-the-loop assessments. This study empirically quantifies the behavioral fidelity of the Car-Learning-to-Act (CARLA) simulator by recreating specific real-world traffic scenarios using the high-precision exiD drone dataset. Twenty-five participants performed a series of maneuvers, including lane changes and time-critical cut-ins. Their performance was analyzed using Dynamic Time Warping (DTW), driver profiling, and Time-to-Collision (TTC) metrics. The findings reveal a clear distinction between relative and absolute behavioral validity. In strategic decision-making tasks, the simulation demonstrated remarkably high temporal fidelity. DTW analysis explained 94% of the trajectory variance. Participants initiated lane changes with an average lag of -9 frames (0.36 s) compared to naturalistic references. These results indicate that, despite the absence of peripheral optical flow, the simulator successfully elicits temporally correlated decision-making patterns suitable for assessing strategic driver intent. However, physical execution in reactive scenarios revealed significant absolute discrepancies. Although the high Pearson correlation (r ≈ 0.89) in velocity profiles proves that drivers recognize and react to hazards with realistic timing, their physical inputs were exaggerated. Participants displayed digital, over-modulated braking responses and maintained a negative safety bias of -11.26 m, a deviation attributed to the lack of vestibular g-force feedback and geometric minification. Furthermore, distinct driver profiles emerged. Risk-oriented participants exhibited a gaming effect by neglecting safety margins. In conclusion, while CARLA is highly valid for testing the temporal logic of driver interactions, absolute dynamics require calibration functions, such as force-feedback (pedal) tuning and visual deceleration cues like camera shake, to compensate for sensory limitations before it can be used for safety-critical validation.
Spinoff is NASA’s annual publication featuring successfully commercialized NASA technology. This commercialization has contributed to the development of products and services in the fields of health and medicine, consumer goods, transportation, public safety, computer technology, and environmental resources.
Passive fatigue can cause accidents with automated and regular vehicles. A proof-of-concept prototype [made with light-emitting diode (LED) matrices and white LED (WLED)] and a preliminary comparative usability test (N = 7) are used to study whether the active manipulation of simulated weather cues can be a potential countermeasure to passive fatigue. Participants rated system suitability, system impression, and their fatigue level similarly when they viewed a weather windshield heads-up display (HUD) versus a speedometer windshield HUD [no significant differences found and relatively small 95% confidence interval (CI) ranges around 0]. Qualitative analysis of interviews found that participants saw the potential value of the weather display and that display placement, dynamic graphics, and user activation were commonly mentioned themes. These results suggest the concept is theoretically possible, though further work is needed to prove the concept in practice.
Robotic manipulation remains one of the harder unsolved problems in automation engineering. Vision-based systems have matured considerably — object localization, pose estimation, and grasp planning from RGB-D data are now reliable enough for structured industrial environments. What vision cannot provide is contact information: whether a grasp is stable, whether a surface is beginning to slip, or how force is distributed across a fingertip during a hold. These signals are what close the control loop during manipulation, and without them, systems compensate through excessive grip force, conservative motion profiles, and large training datasets designed to paper over sensing uncertainty.
Items per page:
50
1 – 50 of 20962