Connecting AV Behavior to Future Adoption: An On-Road Robotaxi Case Study

In technology development, measuring human interaction with that technology is often not done before a prototype is created. We show here a counter-example measuring human responses to simulated vehicle behavior in a real autonomous vehicle in public traffic, using Behavioral Intention to infer future repeat ridership from a specific in-car experience. This method allows a lightweight measure to compare the adoption or revenue impact of experiences before expensive development choices are made. Here, we discovered more patience with in-traffic AV stops than anticipated, with Behavioral Intention stable until stop duration was over 90 s. We used a private validation data set and corporate financial assumptions about ride value to project revenue based on these experiences, supporting decision-making around work prioritization and resources as well as vehicle behavior. We suggest that Behavioral Intention and its sibling measure, Behavioral Expectation, can be used to forecast technology usage behavior and guide system development.