Yubin Xie , Yue Liu , Ronggang Zhou , Xuezun Zhi , Alan H.S. Chan
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引用次数: 0
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.
道路使用者之间缺乏沟通会降低交通效率,并引发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交流。本研究探讨了通过车对车(V2V)通信,社会信息通信对交叉路口交通安全和效率的影响。研究通过将社会价值取向(SVO)和驾驶员身份纳入 V2V 系统和自动驾驶汽车(AV)决策支持系统,探讨了这些因素如何影响驾驶员的决策和合作行为。研究人员开发了一个模拟交叉路口冲突场景的实验平台,并开展了三项研究,共有 334 人参与。研究结果表明,向驾驶员提供对向车辆的社交信息能显著促进合作行为和更安全的驾驶策略。具体来说,面对亲己车辆时的等待率(平均值 = 0.22)明显高于面对亲社会车辆时的等待率(平均值 = 0.79)。当 SVO 未知时,等待率约为 0.5。与人类驾驶的车辆相比,参与者在面对 AV 时表现出更多的等待。在基于 SVO 的 AV 推荐中,参与者的最终等待率随着推荐等待率的增加而增加。当 AV 的最佳推荐等待率与其他车辆的平均等待率相匹配时,最容易被接受。这项研究强调了将社会信息整合到 V2V 通信中以改善道路安全的重要性,有助于设计 AV 自动决策策略并提高用户满意度。
期刊介绍:
Accident Analysis & Prevention provides wide coverage of the general areas relating to accidental injury and damage, including the pre-injury and immediate post-injury phases. Published papers deal with medical, legal, economic, educational, behavioral, theoretical or empirical aspects of transportation accidents, as well as with accidents at other sites. Selected topics within the scope of the Journal may include: studies of human, environmental and vehicular factors influencing the occurrence, type and severity of accidents and injury; the design, implementation and evaluation of countermeasures; biomechanics of impact and human tolerance limits to injury; modelling and statistical analysis of accident data; policy, planning and decision-making in safety.