Browse Topic: Mental processes
This paper investigates the use of full-body vibrotactile cueing to augment operator perception during swarm teleoperation tasks. Piloted simulations are conducted in a virtual reality (VR) flight simulation environment using a quadcopter swarm model and a nonlinear dynamic inversion (NDI) flight control architecture. A scaled version of the ADS-33 slalom Mission Task Element (MTE) is implemented to evaluate swarm formation maintenance and obstacle avoidance under four experimental conditions: Good Visual Environment (GVE), Degraded Visual Environment (DVE), and each of these conditions augmented with haptic feedback. Haptic cues are delivered through vibrotactile vests and sleeves to convey information on formation deformation and gate proximity. Experimental results involving human participants indicate that haptic feedback improves formation maintenance and increases operators’ situational awareness of follower drone positions without increasing perceived mental workload. While haptic cues provided modest assistance in gate localization, visual conditions remained the dominant factor influencing obstacle avoidance performance. Overall, the results indicate that full-body haptic feedback provides an effective modality for augmenting operator perception and supporting swarm supervision tasks, particularly in visually degraded environments.
Rotorcraft pilots operating in degraded visual environments encounter significant challenges during hover flight, where the absence of critical visual cues increases the risk of spatial disorientation. At low altitudes and in obstacle-rich environments, even minor losses in situational awareness can have severe consequences. Understanding the visual cues that support stable hover in good visual environments, and how their absence impacts performance and cognitive workload, is essential for mitigating these risks. This study examined key human factors in hover flight, focusing on the role of peripheral vision and microtextures in supporting pilot performance. It evaluated whether naturally relied-upon visual cues in good visual environment conditions can be artificially replicated to restore visual dominance in simulated degraded visual environments. Analysis included flight performance metrics, control inputs, physiological workload indicators, subjective assessments, and pilot feedback. The findings contribute to improved understanding of visual cueing and pilot adaptation in degraded conditions.
With the advent of digital displays in driver cabins in commercial vehicles, drivers are being offered many features that convey some useful or critical information to drivers or prompt the driver to act. Due to the availability of a vast number of features, drivers face decision fatigue in choosing the appropriate features. Many are unaware of all available functionalities displayed in the Human Machine Interface (HMI) System, leading to a bare minimum usage or complete neglect of helpful features. This not only affects driving efficiency but also increases cognitive load, especially in complex driving scenarios. To alleviate the fatigue faced by drivers and to reduce the induced lethargy to choose appropriate features, we propose an AI driven recommendation agent/system that helps the driver choose the features. Instead of manually choosing between multiple settings, the driver can simply activate the recommendation mode, allowing the system to optimize selections dynamically. The novelty of this proposal focuses on introducing Intelligence in HMI Systems in such a way that it will maximize the operational usage and reduce decision fatigue in drivers. In this paper, we aim to propose a novel metric - “Decision fatigue index” to conceptualize both – the reduction in driver's cognitive load and AI models to capture, train based on the data from the driver preferences, road conditions, vehicle dynamics and user customizations. The most relevant mitigation/intervention strategies will be augmented in the HMI, which enhances ease of use, improves safety, and ensures that drivers receive the most relevant assistance.
Chronic stress can lead to increased blood pressure and cardiovascular disease, decreased immune function, depression, and anxiety. Unfortunately, the tools we use to monitor stress are often imprecise or expensive, relying on self-reporting questionnaires and psychiatric evaluations.
A study of mental workload and the resultant cognitive-motor behavior is essential to understanding the intrinsic limitations of the human information processing system, the results of which have impact on the design of safety-critical systems. While the effects of increased task demand on mental workload and the quality of cognitive-motor performance has been previously investigated, it remains unclear how system controllability (i.e., expected handling qualities) impacts perceptual workload and performance. Furthermore, traditional EEG spectral metrics lack the temporal specificity to capture dynamic workload. Consequently, the purpose of this experiment was to examine objective brain dynamics, task performance, and subjective ratings during piloting tracking tasks of varying complexity while also challenging participants with different expected levels of handling qualities. Our results revealed a trend suggestive of increasing mental workload related to increased task complexity and varying levels of expected handling qualities. To examine dynamic operator workload with increased temporal fidelity, we introduce a time-resolved cross-correlation based approach to assess synchronous dynamics between cortical activity and behavioral performance. The findings herein highlight the practical significance of including analyses of the time domain in workload assessment, in addition to the functional utility of a combination of metrics in the study of the temporally linked cognitive-motor output associated with increased mental workload.
This paper investigates the use of multi-modal cueing through full-body haptic feedback to enhance pilot-vehicle system (PVS) performance, reduce mental workload (MWL), and increase situational awareness (SA) in both good and degraded visual environments (GVE/DVE). Piloted simulations were conducted using an H-60-like flight dynamics model in a virtual reality (VR) motion-based simulator, evaluating two ADS-33-like mission task elements (MTEs) – precision hover and slalom – under visual-only and combined visual and haptic feedback conditions in both GVE and DVE. The H-60 flight dynamics were augmented with a dynamic inversion (DI)- based stability augmentation system (SAS), implementing rate-command/attitude hold (RCAH) response type on the roll, pitch, and yaw axes and altitude hold response type on the vertical axis. The SAS was designed to achieve Level 1 handling qualities per ADS-33 standards. The full-body haptic cueing strategy leveraged an outer-loop DI control law, which provided vibrotactile feedback to cue desired roll, pitch, and yaw attitudes to the pilot. Roll cues were delivered via tactors mounted on the upper arms, pitch cues via tactors on the chest and back, and yaw cues via tactors on the calves. Eight test subjects participated in the piloted simulations, including three U.S. Navy test pilots and five subjects with different flying experiences. Results indicated that haptic feedback significantly improved hover performance, reducing MWL and enhancing SA, particularly in DVE. However, in the slalom task, predefined haptic guidance misaligned with pilots’ individual control strategies, leading to performance degradation. This finding highlights the need for pilot-specific adaptive haptic feedback to mitigate inconsistencies in dynamic maneuvering tasks.
Advancements in sensor technologies have led to increased interest in detecting and diagnosing “driver states”—collections of internal driver factors generally associated with negative driving performance, such as alcohol intoxication, cognitive load, stress, and fatigue. This is accomplished using imperfect behavioral and physiological indicators that are associated with those states. An example is the use of elevated heart rate variability, detected by a steering wheel sensor, as an indicator of frustration. Advances in sensor technologies, coupled with improvements in machine learning, have led to an increase in this research. However, a limitation is that it often excludes naturalistic driving environments, which may have conditions that affect detection. For example, reductions in visual scanning are often associated with cognitive load [1]; however, these reductions can also be related to novice driver inexperience [2] and alcohol intoxication [3]. Through our analysis of the research, we discover that the tendency to explore these singular driver states with only a comparison to “normal” driving is common. Additionally, research on interventions for these driver states is relatively scarce (fewer than 10% of cognitive load-related papers we examined assessed or discussed intervention solutions) and narrowly tailored to specific states [e.g., 4, vis-à-vis cognitive load]. States that share common behavioral and physiological markers tend to be explored independently when a more universal and integrated approach may be warranted. In this paper, we identify the need for a driver state and intervention framework that addresses these limitations by exploring state indicators and their overlap, interventions for one or multiple states, and major research gaps. Our framework offers practical approaches for handling one or many driver states, including interventions that may be deployed at different timings during a trip.
Biofeedback training is a technology that enhances cognitive and emotional capabilities, empowering peak performance. What sets it apart is the Biocybernetics adaptation systems, which not only collect biofeedback data but also dynamically adjust your environment based on physiological signals. Imagine surroundings adapting — changing lighting, sounds, and more — in response to your biofeedback. Traditionally confined to clinical or training rooms, the real innovation is its integration into daily life. This system offers a new level of self-regulation. Users can navigate daily life venues with real-time insights into their physiological signals, providing continuous feedback and motivation for cognitive and emotional control. Efforts yield positive surroundings, fostering well-being and peak performance.
Meet CARMEN — short for Cognitively Assistive Robot for Motivation and Neurorehabilitation — a small, tabletop robot designed to help people with mild cognitive impairment (MCI) learn skills to improve memory, attention, and executive functioning at home.
Engineers at the University of California San Diego in collaboration with clinicians, people with MCI, and their care partners have developed CARMEN, short for Cognitively Assistive Robot for Motivation and Neurorehabilitation — a small, tabletop robot designed to help people with mild cognitive impairment (MCI) learn skills to improve memory, attention, and executive functioning at home.
Using electrical impedance tomography (EIT), researchers have developed a system using a flexible tactile sensor for objective evaluation of fine finger movements. Demonstrating high accuracy in classifying diverse pinching motions, with discrimination rates surpassing 90 percent, this innovation holds potential in cognitive development and automated medical research.
Piloted flight involves the participation of both an air vehicle and a pilot. Modeling humans is becoming more and more important due to the growing integration of automatic systems interacting with the pilot at an ever deeper cognitive level. This research wants to offer a systematic approach to analyzing pilot behavior during a mission. The development of the pilot behavior meta-model was preceded by a careful definition of the required terminology, starting from the important assumption that human behavior is inherently teleological and therefore directed towards either explicit or implicit goals. A custom test performed with two pilots showed how humans respond to an unexpected event with a perceptible variation in muscle activation; the mechanism falls within the pilot's voluntary behavior as it aims at achieving a goal that changed following the event itself. The proposed meta-model can be adapted and used to analyze complex pilot behavior, possibly leading to new paradigms in the design of training procedures or novel cockpit layouts.
Advances in healthcare and medical treatments have led to longer life expectancies in many parts of the world. As people receive better healthcare and management of other health conditions, they are more likely to reach an age where neurodegenerative diseases become a greater risk. Neurodegenerative diseases, such as Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and Huntington's disease (HD), are complex and can affect various aspects of a person's cognitive, motor, and sensory functions.
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 considering speech recognition accuracy across a range of signal-to-noise ratios rather than the speech recognition threshold alone, and the importance of considering individual differences among listeners when evaluating in-vehicle speech intelligibility.
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 improving the enabling technologies for AVs. Google and Tesla are two of the more well-known examples in industry, with Google developing a self-driving car and Tesla providing its Full Self-Driving (FSD) autopilot system. Ford and BMW are also working on their own AVs.
Prior investigations of swarm robot control focus on optimizing communication and coordination between agents, with at most one human control scheme, or with discrete (rather than continuous) human control schemes. In these studies, focus tends to be on human-robot interactions, including human-machine gesture interfaces, human-machine interaction during conversation, or evaluation of higher-level mental states like comfort, happiness and cognitive load. While there is early work in human control of Unmanned Arial Vehicles (UAVs) and interface design, there are few systematic studies of how human operators perceive fundamental properties of small swarms of ground-based semi-autonomous robots. Therefore, the goal of this study is to better understand how humans perceive swarms of semi-autonomous agents across a range of conditions.
Modern in-vehicle experiences are brimming with functionalities and convenience driven by automation, digitalization, and electrification. While automotive manufacturers are competing to provide the best systems to their customers, there is no common ground to evaluate these in-vehicle experiences as they become increasingly complex. Existing automotive guidelines do not offer thresholds for cognitive distraction, or—more appropriately—“disengagement.” What can researchers can do to change this? Evaluation of the In-vehicle Experience discusses acceptable levels of disengagement by evaluating the driving context and exploring how system reliability can translate to distraction and frustration. It also covers the need to test systems for their complexity and ease of use, and to prevent users from resorting to alternative systems while driving (e.g., smartphones). It highlights the value in naturalistic data generation using vehicles already sold to customers and the issues around privacy and trust concerning such methods. Lastly, it talks about the opportunities and challenges behind developing automated testing methods for in-vehicle experiences that simulate human behavior and how to shorten evaluation timelines to enabling a much larger scale of systems testing. Click here to access the full SAE EDGETM Research Report portfolio.
A team of Cornell University researchers has laid the foundation for developing a new class of untethered soft robots that can achieve more complex motions with less reliance on explicit computation. By taking advantage of viscosity — the very thing that previously stymied the movement of soft robots — the new approach offloads control of a soft robot’s cognitive capability from the “brain” onto the body using the robot’s mechanical reflexes and ability to leverage its environment.
Automated driving is considered a key technology for reducing traffic accidents, improving road utilization, and enhancing transportation economy and thus has received extensive attention from academia and industry in recent years. Although recent improvements in artificial intelligence are beginning to be integrated into vehicles, current AD technology is still far from matching or exceeding the level of human driving ability. The key technologies that need to be developed include achieving a deep understanding and cognition of traffic scenarios and highly intelligent decision-making. Automated Vehicles, the Driving Brain, and Artificial Intelligenceaddresses brain-inspired driving and learning from the human brain's cognitive, thinking, reasoning, and memory abilities. This report presents a few unaddressed issues related to brain-inspired driving, including the cognitive mechanism, architecture implementation, scenario cognition, policy learning, testing, and validation. Click here to access the full SAE EDGETM Research Report portfolio.
Reliably operating electromagnetic (EM) systems including radar, communications, and navigation, while deceiving or disrupting the adversary, is critical to success on the battlefield. As threats evolve, electronic warfare (EW) systems must remain flexible and adaptable, with performance upgrades driven by the constant game of cat and mouse between opposing systems. This drives EW researchers and systems engineers to develop novel techniques and capabilities, based on new waveforms and algorithms, multifunction RF systems, and cognitive and adaptive modes of operation.
How do different parts of the brain communicate with each other during learning and memory formation? A new study by researchers at the University of California San Diego takes a first step at answering this fundamental neuroscience question.
Today, as the spread of vehicles equipped with autonomous driving functions increases, accidents caused by autonomous vehicles are also increasing. Therefore, issues regarding safety and reliability of autonomous vehicles are emerging. Various studies have been conducted to secure the safety and reliability of autonomous vehicles, and the application of the International Organization for Standardization (ISO) 26262 standard for safety and reliability improvement and the importance of verifying the safety of autonomous vehicles are increasing. Recently, Mobileye proposed an RSS model called Responsibility Sensitive Safety, which is a mathematical model that presents the standardization of safety guarantees of the minimum requirements that all autonomous vehicles must meet. In this article, the RSS model that ensures safety and reliability was derived to be suitable for variable focus function cameras that can cover the cognitive regions of radar and lidar with a single camera. It is thought that by calculating the safety distance under various acceleration and speed conditions, it could contribute to considering the safety distance depending on the performance of an autonomous vehicle in the future.
Items per page:
50
1 – 50 of 305