Exploring Human-Vehicle Communication to Balance Transportation Safety and Efficiency: A Naturalistic Field Study of Pedestrian-Vehicle Interactions (02-014)

Project Description: Level five automated vehicles are quickly moving to the forefront of the automotive industry. They are poised to make unparalleled advances in safety and efficiency. Current level five automated vehicles can be programmed to stop for moving objects. However, they are not programmed to understand or react to human behavior in roadway crossing scenarios. To maximize the efficiency of the transportation system, it is critical for level five automated vehicles to be able to interact appropriately with pedestrians who intend to cross the roadway. Although driving behavior is generally governed by the nature and the driving objectives of the driver, there are many situations (typically in crowded traffic conditions) where tacit communication between vehicle drivers and pedestrians govern driving behavior, significantly influencing transportation safety. The study aimed to formalize the tacit communication between vehicle drivers and pedestrians, in order to inform an investigation on effective communication mechanisms between autonomous vehicle and humans. Current autonomous vehicles engage in decision making primarily controlled by on-board or external sensory information, and do not explicitly consider communication with pedestrians. The study was a within subject 2x2x2 factorial experimental design. The three independent variables were driving context (normal driving vs. autonomous vehicle placard), driving route (1 vs. 2), and narration (yes vs. no). The primary outcome variable was driver-yield behavior. Each of the ten drivers completed the factorial design, requiring eight total drives. Data were collected using a data acquisition system (DAS) designed and installed on the experimental vehicle by the Virginia Tech Transportation Institute. The DAS collected video, audio, and kinematic data. Videos were coded using a proprietary software program, Hawkeye, based on an a prior data directory. Data Scope: A total of 1,808 pedestrian interactions are included in the dataset. This dataset includes the annotations across 97 different variables. Data analysts’ annotations of the video epochs are included in the CommAutoExport dataset. Data Specification: The CommAutoExport dataset contains the data analysts’ annotations for the 1,808 pedestrian interactions. Detailed description of each variable in data set, including data type, range of values, coding for categorical variables, identifiers used to denote missing values or errant data. The Tacit Communication Video Reduction Directory includes the values for the annotations in the CommAutoExport dataset. The CommAuto Driver Dataset includes information on the driving context, driving route, narration, and participant demographics.