Intelligent Connected Vehicle (ICV) is considered one of the most promising active safety technologies to address current transportation challenges. Human-Machine Interface (HMI) plays a vital role in enhancing user driving experience with ICV technology. However, in an ICV environment, drivers may exhibit excessive reliance on HMI, resulting in diminished proactive observation and analysis of the road environment, and subsequently leading to a potential decrease in drivers' situational awareness. This reduced situational awareness may consequently lead to a decline in their overall engagement in driving tasks. Therefore, to comprehensively investigate the impact of HMI on driver performance in various ICV environments, this study incorporates three distinct HMI systems: Control group, Warning group, and Guidance group. The Control group provides basic information, the Warning group adds front vehicle icon and real-time headway information, while the Guidance group further includes speed and voice guidance features. Additionally, the study considers three types of mainline vehicle gaps, namely, 30 m, 20 m, and 15 m. Through our self-developed ICV testing platform, we conducted driving simulation experiments on 43 participants in a freeway interchange merging area. The findings reveal that, drivers in the Guidance group exhibited explicit acceleration while driving on the ramp. Drivers in the Guidance and Warning groups demonstrated smoother speed change trends and lower mean longitudinal acceleration upon entering the acceleration lane compared to the Control group, indicating a preference for more cautious driving strategies. During the pre-merging section, drivers in the Warning group demonstrated a more cautious and smooth longitudinal acceleration. The Guidance group's HMI system assisted drivers in better speed control during the post-merging section. Differences in mainline vehicle gaps did not significantly impact the merging positions of participants across the three HMI groups. Drivers in the Guidance group merged closest to the left side of the taper section, while the Control group merged farthest. The research findings offer valuable insights for developing dynamic human–machine interfaces tailored to specific driving scenarios in the environment of ICVs. Future research should investigate the effects of various HMIs on driver safety, workload, energy efficiency, and overall driving experience. Conducting real-world tests will further validate the findings obtained from driving simulators.

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研究智能网联汽车环境中HMI对驾驶员并道性能的影响

智能网联汽车（ICV）被认为是解决当前交通挑战最有前途的主动安全技术之一。人机界面（HMI）在利用智能网联汽车技术增强用户驾驶体验方面发挥着至关重要的作用。然而，在智能网联汽车环境中，驾驶员可能会过度依赖HMI，导致对道路环境的主动观察和分析能力减弱，从而导致驾驶员态势感知能力潜在下降。情境意识的降低可能会导致他们对驾驶任务的整体参与度下降。因此，为了全面研究HMI对各种智能网联汽车环境中驾驶员表现的影响，本研究纳入了三个不同的HMI系统：控制组、警告组和指导组。控制组提供基本信息，警告组添加前车图标和实时车距信息，而指导组进一步包括速度和语音指导功能。此外，该研究还考虑了三种类型的主线车辆间距，即 30 m、20 m 和 15 m。通过自主研发的智能网联汽车测试平台，我们在高速公路立交合流区对43名参与者进行了驾驶模拟实验。研究结果显示，指导组的驾驶员在坡道上行驶时表现出明显的加速。与对照组相比，指导组和警告组的驾驶员在进入加速车道时表现出更平滑的速度变化趋势和更低的平均纵向加速度，表明他们倾向于更谨慎的驾驶策略。在合流前路段，警告组的驾驶员表现出更加谨慎和平稳的纵向加速。Guidance 小组的 HMI 系统帮助驾驶员在并道后路段更好地控制速度。主线车辆间隙的差异并未显着影响三个 HMI 组参与者的合并位置。指导组中的驱动器合并到最靠近锥形部分左侧的位置，而控制组中的驱动器合并到最远的位置。研究结果为开发针对智能网联汽车环境中特定驾驶场景的动态人机界面提供了宝贵的见解。未来的研究应该调查各种 HMI 对驾驶员安全、工作负载、能源效率和整体驾驶体验的影响。进行实际测试将进一步验证从驾驶模拟器获得的结果。