Your browser doesn't support javascript.
loading
Wait or Pass? Promoting intersection's cooperation via identifying vehicle's social behavior.
Xie, Yubin; Liu, Yue; Zhou, Ronggang; Zhi, Xuezun; H S Chan, Alan.
Afiliación
  • Xie Y; School of Economics and Management, Beihang University, Beijing, China; Department of Systems Engineering, City University of Hong Kong, Hong Kong, China.
  • Liu Y; School of Economics and Management, Beihang University, Beijing, China.
  • Zhou R; School of Economics and Management, Beihang University, Beijing, China; Laboratory for Low-carbon Intelligent Governance, Beihang University, Beijing, China. Electronic address: zhrg@buaa.edu.cn.
  • Zhi X; School of Economics and Management, Beihang University, Beijing, China.
  • H S Chan A; Department of Systems Engineering, City University of Hong Kong, Hong Kong, China.
Accid Anal Prev ; 206: 107724, 2024 Jul 29.
Article en En | MEDLINE | ID: mdl-39079441
ABSTRACT
Lack of communication between road users can reduce traffic efficiency and cause safety issues like traffic accidents. Researchers are exploring how intelligent vehicles should communicate with the environment, other vehicles, and road users. This study explores the impact of social information communication on traffic safety and efficiency at intersections through vehicle-to-vehicle (V2V) communication. The research examines how these factors influence drivers' decision-making and cooperative behavior by incorporating social value orientation (SVO) and driving agent identity into V2V systems and automated vehicle (AV) decision-support systems. An experimental platform simulating intersection conflict scenarios was developed, and three studies involving 334 participants were conducted. The findings reveal that providing drivers with social information about opposing vehicles significantly promotes cooperative behavior and safer driving strategies. Specifically, the waiting rate for people facing proself vehicles (Mean = 0.22) is significantly higher than when facing prosocial vehicles (Mean = 0.79). When SVO is unknown, the waiting rate is around 0.5. Participants behaved more waiting when confronted with an AV than human-driven vehicles. With AV recommendations based on SVO, participants' final waiting rate increases as the recommended waiting rate increases. The optimal recommended waiting rate for AV is most acceptable when it matches the average waiting rate of the other vehicle. This research underscores the importance of integrating social information into V2V communication to improve road safety, aiding in designing automated decision-making strategies for AV and enhancing user satisfaction.
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Accid Anal Prev Año: 2024 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Accid Anal Prev Año: 2024 Tipo del documento: Article País de afiliación: China