Supplementary data for the paper 'Towards the detection of driver–pedestrian eye contact'

Non-verbal communication, such as eye contact between drivers and pedestrians, has been regarded as one way to reduce accident risk. So far, studies have assumed rather than objectively measured the occurrence of eye contact. We address this research gap by developing an eye contact detection method and testing it in an indoor experiment with scripted driver-pedestrian interactions at a pedestrian crossing. Thirty participants acted as a pedestrian either standing on an imaginary curb or crossing an imaginary one-lane road in front of a stationary vehicle with an experimenter in the driver’s seat. In half of the trials, pedestrians were instructed to make eye contact with the driver; in the other half, they were prohibited from doing so. Both parties’ gaze was recorded using eye trackers. An in-vehicle stereo camera recorded the car’s point of view, a head-mounted camera recorded the pedestrian’s point of view, and the location of the driver’s and pedestrian’s eyes was estimated using image recognition. We demonstrate that eye contact can be detected by measuring the angles between the vector joining the estimated location of the driver’s and pedestrian’s eyes, and the pedestrian’s and driver’s instantaneous gaze directions, respectively, and identifying whether these angles fall below a threshold of 4°. We achieved 100% correct classification of the trials involving eye contact and those without eye contact, based on measured eye contact duration. The proposed eye contact detection method may be useful for future research into eye contact.

诸如驾驶员与行人之间的眼神交流这类非言语沟通，已被视为降低事故风险的有效途径之一。迄今为止，相关研究多基于假设而非对眼神交流的发生情况进行客观测量。本研究针对这一研究空白，开发了一种眼神交流检测方法，并在室内场景下开展实验，模拟人行横道处驾驶员与行人的预设互动场景。30名参与者扮演行人，或站在虚拟路缘处，或在一辆由实验人员担任驾驶员的静止车辆前方穿越虚拟单车道道路。在半数实验试次中，要求行人与驾驶员进行眼神交流；剩余试次则禁止行人做出该行为。研究采用眼动仪（eye tracker）记录双方的注视轨迹；车载立体相机采集车辆第一视角画面，头戴式相机采集行人第一视角画面，并通过图像识别技术估算驾驶员与行人的眼部位置。本研究证明，可通过以下方式实现眼神交流检测：分别计算连接驾驶员与行人估算眼部位置的向量，与行人、驾驶员瞬时注视方向之间的夹角，并判断这些夹角是否低于4°的阈值。基于实测的眼神交流时长，本研究实现了对有眼神交流与无眼神交流两类试次的100%正确分类。本研究所提出的眼神交流检测方法，可为未来眼神交流相关研究提供有效支撑。