School of Economics and Management, Beihang University, Beijing, China; Department of Systems Engineering, City University of Hong Kong, Hong Kong, China.
School of Economics and Management, Beihang University, Beijing, China.
Accid Anal Prev. 2024 Oct;206:107724. doi: 10.1016/j.aap.2024.107724. Epub 2024 Jul 29.
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.
道路使用者之间缺乏沟通会降低交通效率，并导致交通事故等安全问题。研究人员正在探索智能车辆应如何与环境、其他车辆和道路使用者进行通信。本研究通过车对车（V2V）通信探讨了社会信息通信对交叉口交通安全和效率的影响。该研究通过将社会价值取向（SVO）和驾驶代理身份纳入 V2V 系统和自动驾驶（AV）决策支持系统，研究了这些因素如何影响驾驶员的决策和合作行为。开发了一个模拟交叉口冲突场景的实验平台，并进行了三项涉及 334 名参与者的研究。研究结果表明，向驾驶员提供有关对向车辆的社会信息可显著促进合作行为和更安全的驾驶策略。具体来说，面对自利车辆的等待率（Mean=0.22）明显高于面对亲社会车辆的等待率（Mean=0.79）。当 SVO 未知时，等待率约为 0.5。与人类驾驶车辆相比，参与者在面对 AV 时表现出更多的等待行为。随着基于 SVO 的 AV 建议，参与者的最终等待率随着建议等待率的增加而增加。当推荐的等待率与另一辆车的平均等待率相匹配时，AV 的最佳推荐等待率最被接受。本研究强调了将社会信息纳入 V2V 通信以提高道路安全的重要性，有助于设计自动驾驶决策策略并提高用户满意度。
