Search Results (107)

Using personal smart devices such as mobile phones to perform precise distal pointing in intelligent cockpits is a developing trend. The present study investigated the effects of different control display gains (CD gains) and wrist movement modalities on performance and comfort for precise distal pointing interaction. Twenty healthy participants performed a precise distant pointing task with four constant CD gains (0.6, 0.8, 0.84, and 1.0), two dynamic CD gains, and two wrist movement modalities (wrist extension and rotation) by using a mobile phone as the input device. Physiological electromyographic data, task performance, and subjective questionnaire data were collected. Comparative results show that constant CD gain is superior to dynamic CD gain and that 0.8 to 1.0 is the optimum range of values. The data showed a clear and consistent trend in performance and comfort as the CD gain increased from 0.6 to 1.0, with performance and comfort becoming progressively better, reaching an optimum at 0.84. In terms of the wrist control method, the rotation mode had smaller task completion time than the extension mode. The results of this study provide a basis for the design of remote interaction using mobile phones in an intelligent cockpit. Full article

This paper presents a multi-participant driving simulation framework designed to support traffic experiments involving the simultaneous collection of vehicle telemetry and cognitive data. The system integrates motion-enabled driving cockpits, high-fidelity steering and pedal systems, immersive visual displays (monitor or virtual reality), and the Assetto Corsa simulation engine. To capture cognitive states, dry-electrode EEG headsets are used alongside a custom-built software tool that synchronizes EEG signals with vehicle telemetry across multiple drivers. The primary contribution of this work is the development of a modular, scalable, and customizable experimental platform with robust data synchronization, enabling the coordinated collection of neural and telemetry data in multi-driver scenarios. The synchronization software developed through this study is freely available to the research community. This architecture supports the study of human–human interactions by linking driver actions with corresponding neural activity across a range of driving contexts. It provides researchers with a powerful tool to investigate perception, decision-making, and coordination in dynamic, multi-participant traffic environments. Full article

Cockpit noise, as a critical environmental factor affecting flight safety, may impair pilots’ cognitive functions, leading to a decreased operational performance and decision-making errors, thereby posing potential threats to aviation safety. In order to reveal the relationship between the cockpit noise sound pressure level and pilot physiological indicators, and provide a scientific basis for cockpit noise airworthiness standards, this experiment takes pilot trainees as the research subject. Based on the principle of multimodal data synchronization, a sound field reconstruction system is used to reconstruct the cockpit sound field. Electroencephalogram (EEG), electrocardiogram (ECG), and electrodermal activity (EDA) measurements are carried out in different sound pressure level noise operating environments. The results show that with the increase in the sound pressure level, the significant suppression of α-wave activity in the occipital and parietal regions suggests that the cortical resting state is lifted and visual attention is enhanced; the enhancement of the β-wave in the frontal regions reflects the enhancement of alertness and prefrontal executive control, and the suppression of θ-wave activity in the frontal and temporal regions may indicate that cognitive tuning is suppressed, which reflects the brain’s rapid adaptive response to external noise stimuli in a high-noise environment; noise exposure triggers sustained sympathetic nerve hyperactivity, which is manifested by a significant acceleration of the heart rate and a significant increase in the mean value of skin conductance when the noise sound pressure level exceeds 70 dB(A). The correlation analysis between physiological indicators shows that cockpit noise has a multi-system synergistic effect on human physiological indicators. The experimental results indicate that noise has a significant impact on EEG, ECG, and EDA indicators. Full article

This paper evaluates a new interaction mode for object manipulation tasks in virtual reality (VR) utilizing an aircraft cockpit simulation. Building on prior research, this study examines the effectiveness and user experience of a mixed-interaction mode that involves the combination of handheld controllers with hand gestures. Qualitative interviews with participants provided detailed feedback on the combined input approach. The analysis highlights the strengths and challenges of the mixed-interaction mode, indicating a perceived increase in task completion efficacy and enhanced user experience. As an outcome of the research, design guidelines were developed based on participants’ insights, focusing on the optimal balance of naturalness and precision for mixed interaction in VR that can also be utilized more generally. This study offers practical implications for creating immersive virtual environments and informs future research in VR interaction modes and user experience. Full article

This study presents an analysis of flight crew communication during the crash of United Airlines Flight 232 at Sioux Gateway Airport in Iowa, USA. Conversation analysis (CA) techniques are used to identify five recurring phenomena in the crew communication and five critical interactions. These are combined to produce a description of the communication process during an unprecedented airline emergency. One of the findings is that communication was simplified and the pilots largely used plain language when speaking with air traffic control (ATC). This was an appropriate communication strategy for the context of the Flight 232 accident but would be problematic if applied to other situations. The analysis also identifies aspects of the crew’s performance that are relevant to contemporary crew resource management (CRM) programs: active participation in communication, updating the shared mental model, making problem solving a joint task, expanding the team boundary to accept an off-duty pilot, and managing the workload. Finally, the study highlights significant details of the Flight 232 accident that are often overlooked and may not generalize to other settings. Full article

With heavy trucks being more widely used in the logistics industry, more and more lorry drivers are frequently exposed to the acoustic-thermal dynamically coupled cockpit environment for a long time. The comfort in the cockpit directly affects driving safety and occupational health. However, the existing research lacks a multi-parameter fusion prediction method for occupant annoyance in this scenario. In this paper, we studied the effect of an acoustic-thermal composite environment on the annoyance level of truck occupants and predicted the annoyance level of the human body by combining environmental parameters and physiological parameters. A total of 20 adult males participated in the subjective annoyance evaluation test, and 60 sets of sample data were obtained under four working conditions by collecting environmental parameters and monitoring physiological parameters, and the effect of acoustic-thermal composite environments was explored using statistical analysis in combination with the subjects’ annoyance polls. The results showed that the human physiological parameters were significantly correlated with the thermal environment, and the correlation coefficient between PMV value and skin temperature was r₁ = 0.99, with p < 0.05. The subjective annoyance level was more sensitive to the thermal environment than noise. The correlation coefficient between PMV and annoyance level was r₂ = 0.931, and the correlation coefficient between the noise parameter roughness R and annoyance level was r₃ = 0.545. The results of this study were based on the screened predictor variables, the annoyance prediction model using the random forest algorithm showed high accuracy on the test set (R² = 0.941, root mean square error RMSE = 0.259, mean absolute error MAE = 0.201). The study showed that the annoyance prediction model incorporating environmental and physiological parameters could estimate subjects’ annoyance more accurately. Full article

Driver factors are the main cause of road traffic accidents. For the research of automotive active safety, an identification method for road hypnosis of a driver of a car with dynamic human–vehicle heterogeneous data fusion calculation is proposed. Road hypnosis is an unconscious driving state formed by the combination of external environmental factors and the psychological state of the car driver. When drivers fall into a state of road hypnosis, they cannot clearly perceive the surrounding environment and make various reactions in time to complete the driving task. The safety of humans and cars is greatly affected. Therefore, the study of the identification of drivers’ road hypnosis is of great significance. Vehicle and virtual driving experiments are designed and carried out to collect human and vehicle data. Eye movement data and EEG data of human data are collected with eye movement sensors and EEG sensors. Vehicle speed and acceleration data are collected by a mobile phone with AutoNavi navigation, which serves as an onboard sensor. In order to screen the characteristics of human and vehicles related to the road hypnosis state, the characteristic parameters of the road hypnosis in the preprocessed data are selected by the method of independent sample T-test, the hidden Markov model (HMM) is constructed, and the identification of the road hypnosis of the Ridge Regression model is combined. In order to evaluate the identification performance of the model, six evaluation indicators are used and compared with multiple regression models. The results show that the hidden Markov-Ridge Regression model is the most superior in the identification accuracy and effect of the road hypnosis state. A new technical scheme reference for the development of intelligent driving assistance systems is provided by the proposed comprehensive road hypnosis state identification model based on human–vehicle data can provide, which can effectively improve the life recognition ability of automobile intelligent cockpits, enhance the active safety performance of automobiles, and further improve traffic safety. Full article

This paper presents a comprehensive examination of eye-tracking technologies aimed at enhancing multimodal interactions within aviation environments. By reviewing and comparing various eye-tracking approaches, we provide valuable insights into their strengths, limitations, and prospective applications. Eye tracking synergizes with human–machine interfaces to optimize cockpit operations improve safety and elevate user experience. Beyond cockpit interactions, these technologies hold substantial promises in training and simulation environments, offering detailed insights into pilots’ visual attention and decision-making processes. This data-driven approach enhances pilot training programs, contributing to more effective and targeted instruction. The integration of eye-tracking data with other physiological and performance metrics fosters a deeper understanding of pilot behavior, paving the way for innovations in interface design and operational protocols. Finally, this paper discusses the diversity of current eye-tracking technologies in aviation, emphasizing their practical usability in gaze-tracking systems and multimodal interaction frameworks. Full article

The advent of Artificial Intelligence in the cockpit and the air traffic control centre in the coming decade could mark a step-change improvement in aviation safety, or else could usher in a flush of ‘AI-induced’ accidents. Given that contemporary AI has well-known weaknesses, from data biases and edge or corner effects, to outright ‘hallucinations’, in the mid-term AI will almost certainly be partnered with human expertise, its outputs monitored and tempered by human judgement. This is already enshrined in the EU Act on AI, with adherence to principles of human agency and oversight required in safety-critical domains such as aviation. However, such sound policies and principles are unlikely to be enough. Human interactions with current automation in the cockpit or air traffic control tower require extensive requirements, methods, and validations to ensure a robust (accident-free) partnership. Since AI will inevitably push the boundaries of traditional human-automation interaction, there is a need to revisit Human Factors to meet the challenges of future human-AI interaction design. This paper briefly reviews the types of AI and ‘Intelligent Agents’ along with their associated levels of AI autonomy being considered for future aviation applications. It then reviews the evolution of Human Factors to identify the critical areas where Human Factors can aid future human-AI teaming performance and safety, to generate a detailed requirements set organised for Human AI Teaming design. The resultant requirements set comprises eight Human Factors areas, from Human-Centred Design to Organisational Readiness, and 165 detailed requirements, and has been applied to three AI-based Intelligent Agent prototypes (two cockpit, one air traffic control tower). These early applications suggest that the new requirements set is scalable to different design maturity levels and different levels of AI autonomy, and acceptable as an approach to Human-AI Teaming design teams. Full article

Human factors are the most important factor in road traffic crashes. Human-caused traffic crashes can be reduced through the active safety system of vehicles. Road hypnosis is an unconscious driving state caused by the combination of external environmental factors and the driver’s psychological state. When drivers fall into a state of road hypnosis, they cannot clearly perceive the surrounding environment and make various reactions in time to complete the driving task, and driving safety is greatly affected. Therefore, road hypnosis identification is of great significance for the active safety of vehicles. A road hypnosis identification model based on XGBoost—Hidden Markov is proposed in this study. Driver data and vehicle data related to road hypnosis are collected through the design and conduct of vehicle driving experiments. Driver data, including eye movement data and EEG data, are collected with eye movement sensors and EEG sensors. A mobile phone with AutoNavi navigation is used as an on-board sensor to collect vehicle speed, acceleration, and other information. Power spectrum density analysis, the sliding window method, and the point-by-point calculation method are used to extract the dynamic characteristics of road hypnosis, respectively. Through normalization and standardization, the key features of the three types of data are integrated into unified feature vectors. Based on XGBoost and the Hidden Markov algorithm, a road hypnotic identification model is constructed. The model is verified and evaluated through visual analysis. The results show that the road hypnosis state can be effectively identified by the model. The extraction of road hypnosis-related features is realized in non-fixed driving routes in this study. A new research idea for road hypnosis and a technical scheme reference for the development of intelligent driving assistance systems are provided, and the life identification ability of the vehicle intelligent cockpit is also improved. It is of great significance for the active safety of vehicles. Full article

Improving the performance of human–computer interaction systems is an essential indicator of aircraft intelligence. To address the limitations of single-modal interaction methods, a multimodal interaction model based on gaze and EEG target selection is proposed using deep learning technology. This model consists of two parts: target classification and intention recognition. The target classification model based on long short-term memory networks is established and trained by combining the eye movement information of the operator. The intention recognition model based on transformers is constructed and trained by combining the operator’s EEG information. In the application scenario of the aircraft radar page system, the highest accuracy of the target classification model is 98%. The intention recognition rate obtained by training the 32-channel EEG information in the intention recognition model is 98.5%, which is higher than other compared models. In addition, we validated the model on a simulated flight platform, and the experimental results show that the proposed multimodal interaction framework outperforms the single gaze interaction in terms of performance. Full article

In this study, finite element numerical simulations were used to investigate the deformation of critical vehicle components and the damage characteristics of the retaining wall at the end of the emergency escape ramp after the impact of a Ford 800 truck on the retaining wall of the refuge lane. A finite element model of the reinforced concrete retaining wall of the truck was created using the LS-DYNA (R11.0) program and the correctness of the constructed finite element model was confirmed by tests. The parameters of the reinforced concrete retaining wall were determined using orthogonal tests. Finite element numerical simulations of vehicle impact on the retaining wall were carried out, and the results showed that two stages of deformation occurred at the front and rear sides of the cockpit during the impact process, and the damage of the retaining wall increased with the increase in the vehicle speed, the impact angle, and the bumper stiffness. Punching shear damage occurred in the impact region of the wall and shear damage occurred at the corners of the wall. Full article

The aim of this study was to assess the applicability of the Mechanical Systems Coordination Working Group’s (MSCWG) findings, based on FAR 25.831(g), to Chinese pilots through a human physiological experiment conducted in a high-temperature environment to investigate the effects of core temperature. Methods: A controlled experiment was carried out in a high-temperature environment simulation room involving a cohort of healthy males aged 18–50 years. Wireless physiological monitoring equipment and a neurobehavioral assessment system were utilized to track changes in physiological parameters and neurobehavioral responses at varying core temperatures and time intervals. Results: There was a significant increase in human core body temperature, skin temperature, and heart rate as the ambient temperature rose, all remaining within acceptable physiological limits. Although arterial and venous oxygen saturation decreased with increasing ambient temperature, the difference was not statistically significant. The neurobehavioral abilities of the subjects did not exhibit notable changes across different core temperature–time conditions. Conclusions: The core temperature limits set forth by the MSCWG have been shown to have a safe impact on the physiological and behavioral aspects of Chinese pilots, which can be used as an equivalent safety regulation for airworthiness compliance validation under CCAR 25.831(g). Limitation: The present study was constrained to a male sample, it did not thoroughly explore female responses, and it had a small sample size (10 per group). The latter two factors may have affected the statistical validity and generalizability of the results. Full article

The article reviews autogiros, concentrating on their flight history, development, application, flight principle, components, and advantages over other aircraft. Firstly, the history of autogiros is presented, focusing on breakthrough inventions and clarifying their significance for overall rotorcraft development. Then, contemporary scientific research on the autogiro is reviewed in detail, and the available research gap is determined. The flight principle and technical fundamentals of autogiros are also briefly discussed, and a comparison between autogiros, helicopters, and fixed-wing aircraft is performed. Autogiros’ applications for civil, military, and mixed purposes are pointed out and schematically presented. The main part of the article comprises an overview of the different components and systems in the structure of the reviewed aircraft, including the main rotor, propeller, engine, cockpit, and others. Additionally, a comprehensive classification mostly concerning contemporary and homologated autogiros is described and schematically presented. Experimental and compound gyroplane designs are also examined and marked in the classification. The aircraft are categorized depending on the main structure type, mast availability, number of seats, number of rotors and rotor blades, rotor and mast position, propeller and tail type and position, pre-rotator type, and power source. The idea of different autogiro variants presented in the classification is enhanced with visual examples. This work is an addition to the efforts of promoting autogiros and research on them. It offers complete information regarding the aircraft and could serve as a kind of starting point for engineers in the design process of such types of flying machines. Full article

The cockpit is evolving from passive, reactive interaction toward proactive, cognitive interaction, making precise predictions of driver intent a key factor in enhancing proactive interaction experiences. This paper introduces Cockpit-Llama, a novel language model specifically designed for predicting driver behavior intent. Cockpit-Llama predicts driver intent based on the relationship between current driver actions, historical interactions, and the states of the driver and cockpit environment, thereby supporting further proactive interaction decisions. To improve the accuracy and rationality of Cockpit-Llama’s predictions, we construct a new multi-attribute cockpit dataset that includes extensive historical interactions and multi-attribute states, such as driver emotional states, driving activity scenarios, vehicle motion states, body states and external environment, to support the fine-tuning of Cockpit-Llama. During fine-tuning, we adopt the Low-Rank Adaptation (LoRA) method to efficiently optimize the parameters of the Llama3-8b-Instruct model, significantly reducing training costs. Extensive experiments on the multi-attribute cockpit dataset demonstrate that Cockpit-Llama’s prediction performance surpasses other advanced methods, achieving BLEU-4, ROUGE-1, ROUGE-2, and ROUGE-L scores of 71.32, 80.01, 76.89, and 81.42, respectively, with relative improvements of 92.34%, 183.61%, 95.54%, and 201.27% compared to ChatGPT-4. This significantly enhances the reasoning and interpretative capabilities of intelligent cockpits. Full article