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Formalizing Human-Machine Communication in the Context of Autonomous Vehicles
There are many situations where tacit communication between drivers and pedestrians governs and enhances safety. The goal of this study was to formalize this communication and apply it to the driving strategy of an autonomous vehicle. Toward this, a field study of the interaction between drivers and pedestrians was performed. Vehicles were instrumented to capture behavioral information on a driver as well as passengers and the traffic scenario in general. The data captured were reduced by data analysts to provide insights into the communication and driving patterns. The categorical reduction on driver, pedestrian, and environmental variables was captured. A domain-specific language (DSL) was developed to precisely describe the driver-pedestrian behavior, toward the development of a behavioral model for generating autonomous vehicle controls. Specifically, interaction was formalized through a probabilistic model, namely a partially observable Markov decision process (POMDP). This enabled the study of what-if scenarios with different risk averseness characteristics. One particular strategy was implemented on an autonomous vehicle and experimental observations were made.
Read the full study here.
Study performed by Safe-D National UTC, Texas A&M University, and Virginia Tech Transportation Institute. Sponsored by the Office of the Secretary of Transportation (OST) and the U.S. Department of Transportation (US DOT) State of Texas.