As a new round of technological revolution and industrial transformation accelerates, intelligent connected vehicles have emerged as carriers for advanced technology integration across industries and multiple fields. The intelligent cockpit serves as a comprehensive application space for various new technologies of intelligent connected vehicles, fostering integration among intelligent technology, products, and markets to form a virtuous circle. Developing intelligent cockpits helps to enhance the overall competitiveness and innovation capabilities of the industry, driving the growth of the intelligent automotive industry ecosystem. This paper proposes the definition of the intelligent cockpit for automobiles, classifying it into five levels from “functional cockpit” to “fully cognitive intelligent cockpit” based on the human-machine-environment perspective. The paper also provides a preliminary description of the “3-horizontal-3- vertical” technical architecture of the intelligent cockpit and outlines the development roadmap. Additionally, the paper proposes a comprehensive evaluation index system and method for assessing the intelligence of automotive cockpits. By addressing these key aspects, we hope to shed light on the potential of intelligent cockpits in advancing the field of intelligent connected vehicles.

V2X is used for communication between the surrounding pedestrians, vehicles, and roadside units. In the Forward Collision Warning (FCW) of Phase One scenarios in V2X, multimodal modalities and multiple warning stages are the two main warning strategies of FCW. In this study, three warning modalities were introduced, namely auditory warning, visual warning, and haptic warning. Moreover, a multimodal warning and a novel multi-staged HUD warning were established. Then, the above warning strategies were evaluated in objective utility, driving performance, visual workload, and subjective evaluation. As for the driving simulator of the experiment, SCANeR was adopted to develop the driving scenario and an open-cab simulator was built based on Fanatec hardware. Kinematic parameters, location-related data and eye-tracking data were then collected. The results of the Analysis of Variance (ANOVA) indicate that the multimodal warning is significantly better than that of every single modality in utility and longitudinal car-following performance, and there is no significant difference in visual workload between multimodal warning and the baseline. The utility and longitudinal driving performance of multi-staged warning are also better than those of single-stage warning. Finally, the results provide a reference for the warning strategy design of the FCW in Intelligent Connected Vehicles.

Automated vehicles (AVs) are recognized as one of the most effective measures to realize sustainable transport. These vehicles can reduce emissions and environmental pollution, enhance accessibility, improve safety, and produce economic benefits through congestion reduction and cost savings. However, the consumer acceptance of and trust in these vehicles are not ideal, which affects the diffusion speed of AVs on the market. Providing transparent explanations of AV behaviour is a method for building confidence and trust in AV technologies. In this study, we investigated the explainability of user interface information in an Automated Valet Parking (AVP) system—one of the first L4 automated driving systems with a large commercial landing. Specifically, we proposed a scenario-based explanation framework based on explainable AI and examined the effects of these explanations on drivers’ objective and subjective performance. The results of Experiment 1 indicated that the scenario-based explanations effectively improved drivers’ situational trust and user experience (UX), thereby enhancing the perception and understanding that drivers had of the system’s intelligence capabilities. These explanations significantly reduced the mental workload and elevated the user performance in objective evaluations. In Experiment 2, we uncovered distinct explainability preferences among new and frequent users. New users sought increased trust and transparency, benefiting from guided explanations. In contrast, frequent users emphasised efficiency and driving safety. The final experimental results confirmed that solutions customised for different segments of the population are significantly more effective, satisfying, and trustworthy than generic solutions. These findings demonstrate that the explanations for individual differences, based on our proposed scenario-based framework, have significant implications for the adoption and sustainability of AVs.

As the global traffic environment becomes increasingly complex, driving safety issues have become more prominent, making manual-response driving warning systems (DWSs) essential. Augmented reality head-up display (AR-HUD) technology can project information directly, enhancing driver attention; however, improper design may increase cognitive load and affect safety. Thus, the design of AR-HUD driving warning interfaces must focus on improving attention and reducing cognitive load. Currently, systematic research on AR-HUD DWS interfaces is relatively scarce. This paper proposes an ecological interface cognitive balance design strategy for AR-HUD DWS based on cognitive load theory and environmental interface design theory. The research includes developing design models, an integrative framework, and experimental validation suitable for warning scenarios. Research results indicate that the proposed design effectively reduces cognitive load and significantly decreases driver response and comprehension times, outperforming existing interfaces. This design strategy and framework possess promotional value, providing theoretical references and methodological guidance for AR-HUD warning interface research.