The increasing level of intelligence in automobiles is driving a shift in the human–machine relationship. Users are paying more attention to the intelligent cabin and showing a tendency toward customization. As culture is considered to be an important factor in guiding user behavior and preference, this study innovatively incorporates cultural and human factors into the model to understand how individual cultural orientation influences user preference for innovative human-machine interaction (HMI) features. Firstly, this study considered five Hofstede cultural dimensions as potential impact factors and constructed a prediction model through the random forest algorithm so as to analyze the influence mechanism of culture. Subsequently, K-means clustering was used to classify the sample into three user groups and then predict their preferences for the innovative features in the intelligent cabin. The results showed that users with a higher power distance index preferred a sense of ceremony and show-off-related features such as ambient lighting and welcome mode, whereas users with high individualism were keen on a more open and personalized in-vehicle information system. Long-term orientation was found to be associated with features that help to improve efficiency, and users with a lower level of uncertainty avoidance and restraint were more likely to be attracted to new features and were also more willing to use entertainment-related features. The methodology developed in this study can be widely applied to people in different countries, thus effectively exploring the personal requirements of different individuals, guiding further user experience design and localization when breaking into a new market.

With the increasing adoption of regenerative braking technology in electric vehicles (EVs), one-pedal driving (OPD) mode has become a prevalent feature. While OPD offers technical advantages in energy efficiency, its implications for driver behavior and traffic safety remain unclear. To address the lack of human factors research in this domain, this study utilized a driving simulator to systematically compare driving performance between OPD and two-pedal driving (TPD) modes. Twenty-six participants engaged in car-following tasks under varying traffic densities (uncongested vs. congested) and cognitive load levels (normal vs. 1-back). Driving performance and safety were quantified using the absolute speed difference, distance headway, braking frequency, and Time-to-Collision at brake onset (TTC_brake). The results revealed a significant trade-off: while OPD simplified operation, it led to compromised driving performance compared to TPD in specific contexts. Specifically, OPD resulted in larger speed variations and reduced safety margins during the approach stage. Conversely, under high cognitive load, OPD demonstrated a protective effect by mitigating performance degradation. These findings suggest that while OPD can benefit drivers under mental pressure, its deployment requires adaptive safety strategies, such as the integration of Headway Monitoring Warning (HMW) and Forward Collision Warning (FCW), to compensate for performance deficits in complex traffic environments.