Search Results (20)

With the development of intelligent cockpits and in-vehicle human–machine interaction, in-vehicle agents have gradually become an important interaction medium connecting users with vehicle systems. As the most direct external representation of an in-vehicle agent, visual appearance may influence users’ evaluations of trust, satisfaction, and visual attractiveness. However, existing studies have mainly focused on in-vehicle voice interaction, trust in automation, or anthropomorphic characteristics of agents, while the adaptation of different visual appearance stimuli to users of different ages and task contexts remains insufficiently explored. Therefore, this study investigates age-adaptive visual appearance design for in-vehicle agents. A 3 × 2 × 2 mixed experimental design was adopted, with visual appearance stimulus, age group, and gender as independent variables. Forty-eight participants completed interaction tasks in simulated driving and non-driving contexts and evaluated the agents in terms of trust, satisfaction, and visual attractiveness. The results indicated that, within the present sample and the selected representative stimuli, age-related differences were observed in users’ evaluations of in-vehicle agent visual appearances. In driving tasks, visual appearance stimulus and age showed significant interaction effects on trust and satisfaction. In both driving and non-driving tasks, visual appearance stimulus and age showed significant interaction effects on visual attractiveness. Younger users tended to give higher evaluations to the selected animal-like and abstract stimuli, whereas relatively older users showed higher acceptance of the selected anthropomorphic stimulus. Open-ended feedback further indicated that the selected animal-like appearance had a relatively high selection proportion in both driving and non-driving tasks, especially in non-driving contexts, where it showed stronger emotional appeal. These preliminary findings suggest that the visual appearance design of in-vehicle agents may benefit from considering users’ age differences and task contexts. However, because each appearance category was represented by only one visual stimulus, the findings should be interpreted as exploratory evidence based on specific representative stimuli rather than as general effects of abstract, animal-like, or anthropomorphic appearance categories. This study provides empirical references and preliminary design implications for user-centered visual design of in-vehicle agents in intelligent cockpits. Full article

The hardware and software integration of intelligent vehicles is a complex system engineering with full-stack and full-domain integration, which exerts a significant impact on the cost of intelligentization of complete vehicles. This study firstly constructs a vehicle intelligence grading system covering four major functional domains, namely, intelligent driving, intelligent cockpit, intelligent vehicle control, and intelligent connectivity. It sorts out more than 120 functional subsystems and realizes the accurate mapping of hardware and software technical elements. Secondly, a full-time-scale-based hierarchical functional deployment framework is established, along with a system integration optimization model oriented to the computing–communication integrated network. Finally, a total cost of ownership evaluation model for the intelligentization of complete vehicles is constructed, and a systematic comparison is conducted on the integration schemes and cost differences between the distributed architecture and the “central computing + zonal control” architecture. The research framework established in this study is highly compatible with the vehicle-forward development process and can provide a solid theoretical basis and quantitative decision support for automotive manufacturers in terms of architecture selection, function planning, integrated design, and cost control. Full article

With the development of autonomous driving and intelligent connected vehicle technologies, the vehicle cabin is shifting from a simple transportation space to an intelligent mobile space integrating infotainment, interaction, and rest, and passenger comfort has gradually become an important factor affecting user experience, system trust, and perceived safety. Focusing on three categories of cabin environmental factors, namely the acoustic, optical, and thermal environments, this study develops an experimental design and comprehensive modeling method for passenger comfort evaluation. First, controlled single-factor experiments were conducted to establish quantitative mapping relationships between physical environmental parameters and subjective comfort ratings. The analytic hierarchy process (AHP) was then used to determine the weights of each indicator, and a penalty-based aggregation mechanism was introduced to construct a comprehensive comfort evaluation model. Finally, external validation was performed on an independent vehicle platform to examine the model’s applicability and consistency. The results show that acoustic comfort decreases as the sound pressure level increases, whereas optical and thermal comfort exhibit nonlinear behavior with optimal intervals. AHP weight results show that the thermal environment has the highest weight (0.4280), followed by the acoustic environment (0.3305) and the optical environment (0.2415). The external validation results indicate that the proposed model exhibits good predictive consistency across three steady-state operating conditions, with a mean absolute error of 0.122, a root-mean-square error of 0.150, and a Pearson correlation coefficient of 0.960. The findings show that the penalty-based aggregation model can effectively characterize the limiting-factor effect under the joint action of multiple environmental factors, providing a computable and interpretable evaluation framework for intelligent cockpit environmental control and automotive engineering experimental teaching. The conclusions of this study are mainly applicable to the current experimental platform and steady-state operating conditions, and further validation is still required with more vehicle models, dynamic road scenarios, and complex multi-environment factor disturbances. Full article

A novel facial landmark localization method, termed TSDM, is proposed by integrating T-shaped features with the Supervised Descent Method (SDM). Facial landmark localization is critical for driver fatigue and attention detection in intelligent cockpits. Traditional methods lack accuracy and robustness in complex in-cabin environments such as varying illumination and head pose changes, while deep learning approaches are computationally expensive on resource-constrained vehicle platforms. The T-shaped feature well matches facial geometry and enhances feature representation. T-shaped features are selected via AdaBoost for robust face detection, and SDM is then used to locate 68 facial landmarks. Experiments show that TSDM achieves higher accuracy, lower false-positive rates, and better efficiency than traditional methods, including Haar and LBPH. It also exhibits stronger robustness and better real-time performance than several lightweight deep learning models (such as 3D-aware methods and SAN) on CPU-only platforms, while achieving comparable or higher localization accuracy. Experimental results show that TSDM achieves a face detection rate of 97.43% and a normalized mean error (NME) of 3.4% on standard datasets. The proposed method provides a practical solution for driver state monitoring in resource-limited vehicular environments. Full article

Driver emotion recognition constitutes a fundamental pillar of intelligent cockpit systems, playing a pivotal role in enhancing driving safety and optimizing human–machine interaction. Despite the integration of vehicle sensor data in recent multimodal approaches, conventional fusion paradigms frequently encounter performance degradation due to the inherent noise and weak semantic correlation between vehicle telemetry and emotional states. To address these challenges, this study introduces a Dimensional Emotion-Guided Multi-task (DEGM) framework, a novel architecture designed to explicitly formalize the asymmetric roles of visual and vehicular modalities. Rather than employing simplistic feature concatenation, the proposed method maps multivariate vehicle data into a continuous Valence–Arousal–Dominance (VAD) space to characterize latent emotional tendencies within specific driving contexts. These predicted dimensions subsequently serve as semantic priors to conditionally modulate global facial representations through a Feature-wise Linear Modulation (FiLM) mechanism, facilitating robust and interpretable cross-modal interaction. Furthermore, the framework adopts a multi-task learning strategy that jointly optimizes discrete emotion classification and continuous dimension regression, leveraging the latter as a structural regularizer to refine the latent feature space. Comprehensive evaluations on the public PPB driving emotion dataset demonstrate that the proposed DEGM achieves a competitive accuracy of 87.50% and a weighted F1-score of 0.8727. The results validate that our framework provides a lightweight and robust paradigm for context-aware affect sensing, demonstrating strong potential for practical deployment in intelligent transportation systems. Full article

Under the trend of AI-defined vehicles, multi-agent collaboration has become the core feature for intelligent vehicles to deliver superior user experience (UX). Traditional linear and independent evaluation methods can no longer adapt to the new technical characteristics and logic. Taking the agents of four functional domains—intelligent driving, intelligent cockpit, intelligent vehicle control, and intelligent connectivity—and their cross-domain collaborative relationships as research objects, this study constructs a UX evaluation index system consisting of five primary indicators and 14 secondary indicators. Innovatively, the analytic network process is adopted for indicator weight allocation, which effectively characterizes the interdependencies among indicators caused by multi-agent collaboration. Meanwhile, the coupling coordination theory is introduced to construct a comprehensive UX index, enabling quantitative evaluation of the balanced development level across the five dimensions. The results show that in intelligent vehicle UX, excellence in a single dimension does not equal excellent overall UX. Only through the collaborative upgrading of multiple agents and balanced development of the five dimensions can the comprehensive UX be maximized. This study further reveals the UX mechanism of multi-agent collaboration in intelligent vehicles and determines the optimal collaborative evolution path based on the dynamic programming algorithm, providing theoretical support and practical guidance for automakers in rational product development planning. Full article

We propose a Dual-Channel assisted Music Emotion Perception and Visualization (DC-MEPV) framework designed for ambient lighting in intelligent vehicle cockpits, addressing the increasing demand for advanced human–machine interaction in the automotive industry. This framework consists of three main components: the Multi-Scale Feature Extraction Block (MSFEB), the Global Sequence Modeling Block (GSMB), and the Emotional Color Visualization Algorithm (ECV-Algo). The MSFEB extracts valence and arousal (V-A) features from dual channels at multiple temporal scales, with each channel employing a hybrid neural network architecture to capture multi-scale emotional representations. The GSMB integrates positional encoding, bidirectional long short-term memory (BiLSTM) networks, and multi-head self-attention mechanisms to dynamically model global emotional sequences. The ECV algorithm utilizes personalized emotion–color association rules to achieve expressive emotion-driven lighting visualization based on a continuous mapping from emotion space to color space. We conducted comprehensive comparison and ablation experiments to evaluate the model’s emotion perception performance, and designed three metrics to evaluate the quality of the generated visualizations. The model outperformed other networks in both comparative and ablation experiments. Additionally, the generated lights demonstrated strong performance in terms of CIEDE2000 variation rates, unique color ratios, and joint histogram entropy. DC-MEPV achieved excellent performance in emotion perception and visualizations on the DEAM and PMEmo datasets. Full article

Negative driving emotions constitute a significant factor compromising road safety. Current intelligent vehicle human machine interaction (HMI) systems predominantly focus on functional implementation, lacking the capability to perceive and adapt to the driver’s psychological state. To address this issue, this study investigates the intrinsic relationship between driving emotions and HMI through multimodal experiments. Experiment One reveals the distribution patterns of drivers’ visual attentional scope under different emotional states. Experiment Two establishes a color preference model for HMI interfaces corresponding to specific emotions. Experiment Three quantitatively analyzes the impact of emotional variations on the perceptual efficiency of auditory warnings. Based on the experimental data, an interaction design principle matching “Emotion-Scene-Modality” is formulated, guiding the design of a data-driven, emotion-adaptive HMI prototype system. This system can perceive the driver’s emotional state in real time via multimodal sensors and dynamically adjust interface color themes, information layout, warning sound effects, and voice interaction style according to predefined interaction strategies. Usability testing demonstrates that, compared to traditional static HMI, this affective adaptive system effectively mitigates the driver’s negative emotional load and provides alerts that are more perceptible and less likely to cause irritation during critical moments. Consequently, it offers a significant theoretical foundation and practical reference for constructing a safer and more comfortable next-generation intelligent vehicle cockpit interaction paradigm. Full article

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

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

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

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