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Investigating the impact of HMI on drivers' merging performance in intelligent connected vehicle environment.
Wang, Yugang; Lyu, Nengchao; Wu, Chaozhong; Du, Zijun; Deng, Min; Wu, Haoran.
Afiliação
  • Wang Y; Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, Hubei, China.
  • Lyu N; Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, Hubei, China. Electronic address: lnc@whut.edu.cn.
  • Wu C; Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, Hubei, China; National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan 430063, Hubei, China.
  • Du Z; Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, Hubei, China.
  • Deng M; Wuhan Zhongjiao Traffic Engineering CO.,Ltd, Wuhan 430000, Hubei, China.
  • Wu H; College of Automotive Engineering, Hubei University of Automotive Technology, Shiyan 442002, Hubei, China.
Accid Anal Prev ; 198: 107448, 2024 Apr.
Article em En | MEDLINE | ID: mdl-38340472
ABSTRACT
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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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Condução de Veículo Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Condução de Veículo Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article