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自动驾驶汽车与行人交互中的沟通界面设计:基于行人过街决策模型的评估
引用本文:蒋倩妮,庄想灵,马国杰. 自动驾驶汽车与行人交互中的沟通界面设计:基于行人过街决策模型的评估[J]. 心理科学进展, 2021, 29(11): 1979-1992. DOI: 10.3724/SP.J.1042.2021.01979
作者姓名:蒋倩妮  庄想灵  马国杰
作者单位:陕西省行为与认知心理学重点实验室暨陕西师范大学心理学院, 西安 710062
基金项目:国家自然科学基金项目(31970998)
摘    要:自动驾驶汽车要进入人车混行的普通道路, 需确保与过街行人之间的交互安全和效率。为解决这一问题, 高等级自动驾驶汽车往往在车辆外部装置显示设备, 即外部人机界面(eHMIs)以和行人沟通信息。在具体设计上, 已有研究主要采用文字、图形、投影等视觉沟通形式, 传达车辆状态(是否在自动驾驶模式)、意图和对行人的过街建议等沟通信息, 并在真实路段实验、虚拟场景及实验室实验等情境中评估了界面的使用对行人过街意向、速度和准确性等指标的影响。然而, 以行人为中心的外部界面设计需系统地支持行人过街决策前各阶段的信息加工需求。因此, 我们结合行人过街决策过程和情境意识理论, 提出行人与自动驾驶汽车交互中的动态过街决策模型, 从行人认知加工视角评估各种界面的沟通效果。评估的结果启示, eHMIs应促进行人对车辆信息的感知、理解和预测。在感知阶段, 应采用多种类型界面、多呈现载体相结合, 增强信息的可识别性。在理解阶段, 需结合文字说明、合理选择沟通视角、信号标准化和培训提高可理解性。在预测阶段, 应结合车辆内隐运动信息, 帮助行人快速准确获取车辆未来行动意图。更重要的是, 未来研究应关注在多行人、多车辆混行情境下的信息沟通设计及其对行人的影响。理论方面, 未来研究也需要关注外部界面如何通过自下而上的通路影响情境意识和心智模型的形成。

关 键 词:自动驾驶汽车  外部人机交互界面  eHMIs  行人过街决策模型  行人安全  
收稿时间:2020-12-04

Evaluation of external HMI in autonomous vehicles based on pedestrian road crossing decision-making model
JIANG Qianni,ZHUANG Xiangling,MA Guojie. Evaluation of external HMI in autonomous vehicles based on pedestrian road crossing decision-making model[J]. Advances In Psychological Science, 2021, 29(11): 1979-1992. DOI: 10.3724/SP.J.1042.2021.01979
Authors:JIANG Qianni  ZHUANG Xiangling  MA Guojie
Affiliation:Shaanxi Key Laboratory of Behavior and Cognitive Neuroscience, School of Psychology, Shaanxi Normal University, Xi’an 710062, China
Abstract:For autonomous vehicles driven in road context with pedestrians, it is essential to ensure safe and efficient interaction with pedestrians. Compared with the interaction between traditional vehicles and pedestrians, the interaction between autonomous vehicles and pedestrians brings new risks. On the one hand, the driver’s attention may be distracted from the driving task, which resulted in lower reliability of their non-verbal cues. On the other hand, pedestrians are not familiar with autonomous vehicles, which may lead to false expectations of vehicle behavior that led to a higher risk of conflicts. To solve the problem, autonomous vehicles of high level (e.g. above L3) are usually equipped with an external human-machine interface (eHMIs) to communicate with pedestrians. An overview of current studies shows that the current external eHMIs mainly conveyed vehicle status (whether it is in auto mode), intentions, and road-crossing advice to pedestrians in visual modalities such as text, icon, projection, etc. These eHMIs have been evaluated to determine their effect on pedestrian crossing intention, speed, and accuracy in real as well as simulated contexts. However, a user-centered design of eHMIs should systematically support pedestrian information processing needs during road crossing decision making. To fill the gap, a conceptual model was proposed to capture pedestrian’s dynamic road crossing decision-making when interacting with autonomous vehicles. The model integrated pedestrian’s road crossing decision-making process and the situation awareness theory. Based on the model, eHMIs should promote pedestrians’ perception of the traffic elements related with the vehicle, comprehension of their meaning, and the projection of the vehicle’s future behaviors. Design of eHMIs should support pedestrian information processing needs for each of the three phases. The first phase is to perceive the status and dynamics of vehicles in the traffic environment. To support the perception of the displayed information, the recognizability of information is the key to improve the effectiveness of interfaces. Researchers should apply multiple modalities’ interfaces to convey the vehicle’s information, for example, conveying information by the combination of projection, dynamic light, and icon interface can improve the recognizability. And they should reside interfaces in conjunction with the vehicle, street infrastructure, and the pedestrian to cope with the more complex traffic situation. The second phase is to comprehend the situation based on information collected in the perception stage. To improve comprehensibility, text interfaces should present concise phrases, and non-text interfaces should be standardized and explained with texts, otherwise, they should be trained to pedestrians to improve comprehensibility. Besides, the perspective of the message also affects the clarity of the displayed information. For red light, it can be interpreted from the perspective of the pedestrian as “Please stop” or from the perspective of the vehicle “I will stop”. An appropriate message perspective can improve pedestrians’ understanding and acceptance of the information so that they can make safe crossing decisions. In the projection phase, eHMIs need to help pedestrians predict crossing risks and assist them in making decisions quickly and accurately. Researchers should combine eHMIs with vehicle motion information to convey the vehicle’s future action intentions more intuitively. For example, pedestrians can predict vehicle intention more quickly and accurately by presenting the real-time vehicle speed on eHMIs. For future research, we suggest an extension of current findings to more complex contexts beyond the one-vehicle-one-pedestrian scenario and focus on how the design affects pedestrians in multi-pedestrian and multi-vehicle mixed traffic conditions. Efforts are also needed to understand how the communication interface affected the formation and update of pedestrian situation awareness, as well as the role of mental models in human-vehicle interactions. These mechanisms can facilitate the model-based evaluation of future interfaces and inform new theory-based designs in complex scenarios.
Keywords:autonomous vehicles  external human machine interfaces  eHMIs  pedestrian road crossing decision-making model  pedestrian safety  
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