Search Results (25)

Intelligent driving cabins operated by artificial intelligence technology are evolving into the third living space. They aim to integrate perception, analysis, decision making, and intervention. By using multimodal biosignal acquisition technologies (flexible sensors and non-contact sensing), it is possible to monitor the physiological indicators of heart rate and blood pressure in real time. Leveraging the benefits of domain controllers in the vehicle and edge computing helps the AI platform reduce data latency and enhance real-time processing capabilities, as well as integrate the cabin’s internal and external data through machine learning. Its aim is to build tailored health baselines and high-precision risk prediction models (e.g., CNN, LSTM). This system can initiate multi-level interventions such as adjustments to the environment, health recommendations, and ADAS-assisted emergency parking with telemedicine help. Current issues consist of sensor precision, AI model interpretation, security of data privacy, and whom to attribute legal liability to. Future development will mainly focus on cognitive digital twin construction, L4/L5 autonomous driving integration, new biomedical sensor applications, and smart city medical ecosystems. Full article

With the rapid advancement of aviation technology, modern aircraft cockpits are evolving toward high automation and intelligence, making pilot-cockpit interaction a critical factor influencing flight safety and efficiency. Pilot pose estimation and behavior recognition are critical for monitoring pilot state, preventing operational errors, and enabling adaptive human–machine interaction, thus playing an essential role in aviation safety assurance and intelligent cockpit development. However, existing methods face challenges in real-time performance, reliability, and computational complexity in practical applications. Traditional approaches, such as wearable sensors and image-processing-based algorithms, demonstrate certain effectiveness but still exhibit limitations in aviation environments. To address these issues, this paper proposes a lightweight pilot pose estimation and behavior recognition framework, integrating Vision Transformer with depth-wise separable convolution to optimize the accuracy and efficiency of keypoint detection. Additionally, a novel multimodal data fusion technique is introduced, along with a scientifically designed evaluation system, to enhance the robustness and security of the system in complex environments. Experimental results on a pilot keypoint detection dataset captured in a simulated cockpit environment show that the proposed method achieves 81.9 AP, while substantially reducing model parameters and notably improving inference efficiency compared with HRNet. This study provides new insights and methodologies for the design and evaluation of aviation human-machine interaction systems. Full article

The extensive integration of intelligent and bionic technologies in the automotive industry has significantly heightened interest in the advancement of smart vehicle cockpits. The growing demand for automobile cockpit functions makes the personalization of intelligent automobile bionic cockpits more challenging. In addition, the evaluation and selection for cockpits considering multiple attributes remains incomplete, which hinders the development of intelligent automobile bionic cockpits. Thus, this paper constructed a multiple criterion model considering the personalization needs of drivers and passengers, which include comfort, security, and spiritual entertainment needs. A novel decision-making approach that merges the entropy measure and the Elimination and Choice Expressing Reality (ELECTRE) method is introduced to address the selection challenges of smart vehicle cockpits. This methodology incorporates the Spherical Fuzzy Set (SFS) to accurately gather and interpret the data within the decision matrix. This study employs a practical application by examining three types of intelligent automobile cockpits to validate the effectiveness of the proposed decision-making method. Through sensitivity analysis and comprehensive validation, the findings substantiate that the research offers a potent instrument for addressing the selection challenges associated with intelligent automobile cockpits, providing valuable insights for designers. Full article

In the global automotive industry’s transition towards electrification and intelligence, the influence of key technology configurations of battery electric vehicles (BEVs) on consumer purchasing decisions and market sales has become increasingly prominent. This paper empirically investigates the impact of BEVs’ key technology features—specifically, driving range, Advanced Driver-Assistance Systems (ADASs), and intelligent cockpits—on sales, with a particular focus on the interaction effect between ADAS score and price. Employing panel data from the Chinese market spanning January 2023 to March 2025, this study analyzes 783 observations across 29 models and 13 brands using a multilevel mixed-effects model (MEM). The results indicate that driving range and intelligent cockpit score (ICS) are significantly and positively associated with sales growth, whereas price has a significant negative effect. More importantly, a significant interaction effect exists between the ADAS score and price, which implies that the impact of ADASs on sales varies across different price levels. Specifically, in lower-priced models, a high ADAS score corresponds to a decrease in sales, while its effect trends toward positive in higher-priced models. Furthermore, a high ADAS score significantly reduces consumers’ price sensitivity.Compared with prior macro-level studies, our contribution is jointly quantifying (i) the main effects of range and ICS and (ii) a price-contingent ADAS effect within a model-within-brand MEM, revealing that higher ADAS scores attenuate price sensitivity in premium segments. These findings offer actionable guidance for configuration bundling and pricing across market segments. Full article

Recent breakthroughs in artificial intelligence are accelerating the intelligent transformation of vehicles. Vehicle electronic and electrical architectures are converging toward centralized domain controllers. Deep learning, reinforcement learning, and deep reinforcement learning now form the core technologies of domain control. This review surveys advances in deep reinforcement learning in four vehicle domains: intelligent driving, powertrain, chassis, and cockpit. It identifies the main tasks and active research fronts in each domain. In intelligent driving, deep reinforcement learning handles object detection, object tracking, vehicle localization, trajectory prediction, and decision making. In the powertrain domain, it improves power regulation, energy management, and thermal management. In the chassis domain, it enables precise steering, braking, and suspension control. In the cockpit domain, it supports occupant monitoring, comfort regulation, and human–machine interaction. The review then synthesizes research on cross-domain fusion. It identifies transfer learning as a crucial method to address scarce training data and poor generalization. These limits still hinder large-scale deployment of deep reinforcement learning in intelligent electric vehicle domain control. The review closes with future directions: rigorous safety assurance, real-time implementation, and scalable on-board learning. It offers a roadmap for the continued evolution of deep-reinforcement-learning-based vehicle domain control technology. Full article

With the advancement of intelligent human–computer interaction (IHCI) technology, the accurate recognition of an operator’s intent has become essential for improving the collaborative efficiency in complex tasks. To address the challenges posed by stringent safety requirements and limited data availability in pilot intent recognition within the aviation domain, this paper presents a human intent recognition model based on operational sequence comparison. The model is built based on standard operational sequences and employs multi-dimensional scoring metrics, including operation matching degree, sequence matching degree, and coverage rate, to enable real-time dynamic analysis and intent recognition of flight operations. To evaluate the effectiveness of the model, an experimental platform was developed using Python 3.8 (64-bit) to simulate 46 key buttons in a flight cockpit. Additionally, five categories of typical flight tasks along with three operational test conditions were designed. Data were collected from 10 participants with flight simulation experience to assess the model’s performance in terms of recognition accuracy and robustness under various operational scenarios, including segmented operations, abnormal operations, and special sequence operations. The experimental results demonstrated that both the linear weighting model and the feature hierarchical recognition model enabled all three feature scoring metrics to achieve high intent recognition accuracy. This approach effectively overcomes the limitations of traditional methods in capturing complex temporal relationships while also addressing the challenge of limited availability of annotated data. This paper proposes a novel technical approach for intelligent human–computer interaction systems within the aviation domain, demonstrating substantial theoretical significance and promising application potential. Full article

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

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

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

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

Situation awareness (SA) discrimination is significant, allowing for the pilot to maintain task performance and ensure flight safety, especially during high-stress flight tasks. Although previous research has attempted to identify and classify SA, existing SA discrimination models are predominantly binary and rely on traditional machine learning methods with limited physiological modalities. The current study aimed to construct a triple-class SA discrimination model for pilots facing high-stress tasks. To achieve this, a flight simulation experiment under typical high-stress tasks was carried out and deep learning algorithms (multilayer perceptron (MLP) and the attention mechanism) were utilized. Specifically, eye-tracking (ET), heart rate variability (HRV), and electroencephalograph (EEG) modalities were chosen as the model’s input features. Comparing the unimodal models, the results indicate that EEG modality surpasses ET and HRV modalities, and the attention mechanism structure has advantageous implications for processing the EEG modalities. The most superior model fused the three modalities at the decision level, with two MLP backbones and an attention mechanism backbone, achieving an accuracy of 83.41% and proving that the model performance would benefit from multimodal fusion. Thus, the current research established a triple-class SA discrimination model for pilots, laying the foundation for the real-time evaluation of SA under high-stress aerial operating conditions and providing a reference for intelligent cockpit design and dynamic human–machine function allocation. Full article

The development of large language models (LLMs) has promoted a transformation of human–computer interaction (HCI) models and has attracted the attention of scholars to the evaluation of personality traits of LLMs. As an important interface for the HCI and human–machine interface (HMI) in the future, the intelligent cockpit has become one of LLM’s most important application scenarios. When in-vehicle intelligent systems based on in-vehicle LLMs begin to become human assistants or even partners, it has become important to study the “personality” of in-vehicle LLMs. Referring to the relevant research on personality traits of LLMs, this study selected the psychological scales Big Five Inventory-2 (BFI-2), Myers–Briggs Type Indicator (MBTI), and Short Dark Triad (SD-3) to establish a personality traits evaluation framework for in-vehicle LLMs. Then, we used this framework to evaluate the personality of three in-vehicle LLMs. The results showed that psychological scales can be used to measure the personality traits of in-vehicle LLMs. In-vehicle LLMs showed commonalities in extroversion, agreeableness, conscientiousness, and action patterns, yet differences in openness, perception, decision-making, information acquisition methods, and psychopathy. According to the results, we established anthropomorphic personality personas of different in-vehicle LLMs. This study represents a novel attempt to evaluate the personalities of in-vehicle LLMs. The experimental results deepen our understanding of in-vehicle LLMs and contribute to the further exploration of personalized fine-tuning of in-vehicle LLMs and the improvement in the user experience of the automobile in the future. Full article

Lane changing is a frequent traffic accident scenario. To improve the driving safety in lane changing scenarios, the blind spot display of lane changing is increased through human–machine interaction (HMI) interfaces in intelligent vehicles to improve the driver’s rate of risk perception with regard to the driving environment. However, blind spot information will increase the cognitive load of drivers and lead to driving distraction. To quantify the coupling relationship between blind spot display and drivers’ cognitive load, we proposed a method to quantify the cognitive load of the driver’s interaction by improving the AttenD algorithm, collecting feature data by carrying out a variety of real-vehicle road-testing experiments on three kinds of intelligent vehicles, and then establishing a model blind spot design and driver cognitive load correlation model using Bayesian Logistic Ordinal Regression (BLOR) and Categorical Boosting (CatBoost). The results show that the blind spot image display can reduce the driver’s cognitive load more effectively as it is closer to the driver, has a larger area, and occupies a higher proportion of the center control screen, especially when it is located in the middle and upper regions of the center control screen. The improved AttenD algorithm is able to quantify the cognitive load of the driver, which can be widely used in vehicle testing, HMI interface development and evaluation. In addition, the analytical framework constructed in this paper can help us to understand the complex impact of HMI in intelligent vehicles and provide optimization criteria for lane change blind spot design. Full article

Highly integrated information sharing among people, vehicles, roads, and cloud systems, along with the rapid development of autonomous driving technologies, has spurred the evolution of automobiles from simple “transportation tools” to interconnected “intelligent systems”. The intelligent cockpit is a comprehensive application space for various new technologies in intelligent vehicles, encompassing the domains of driving control, riding comfort, and infotainment. It provides drivers and passengers with safety, comfort, and pleasant driving experiences, serving as the gateway for traditional automobile manufacturing to upgrade towards an intelligent automotive industry ecosystem. This is the optimal convergence point for the intelligence, connectivity, electrification, and sharing of automobiles. Currently, the form, functions, and interaction methods of the intelligent cockpit are gradually changing, transitioning from the traditional “human adapts to the vehicle” viewpoint to the “vehicle adapts to human”, and evolving towards a future of natural interactive services where “humans and vehicles mutually adapt”. This article reviews the definitions, intelligence levels, functional domains, and technical frameworks of intelligent automotive cockpits. Additionally, combining the core mechanisms of human–machine interactions in intelligent cockpits, this article proposes an intelligent-cockpit human–machine interaction process and summarizes the current state of key technologies in intelligent-cockpit human–machine interactions. Lastly, this article analyzes the current challenges faced in the field of intelligent cockpits and forecasts future trends in intelligent cockpit technologies. Full article