Identifying Critical Contextual Design Cues Through a Machine Learning Approach
Given the rise of autonomous systems in transportation, medical, and manufacturing industries, there is an increasing need to understand how such systems should be designed to promote effective interactions between one or more humans working in and around these systems. Practitioners often have difficulties in conducting costly and time-consuming human-in-the-loop studies, so an analytical strategy that helps them determine whether their designs are capturing their planned intent is needed. A traditional top-down, hypothesis-driven experiment that examined whether external displays mounted on autonomous cars could effectively communicate with pedestrians led to the conclusion that the displays had no effect on safety. However, by first taking a bottom-up, data-driven machine learning approach, those segments of the population that were most affected by the external displays were identified. Then, a hypothesis-driven, within-subjects analysis of variance revealed that an external display mounted on an autonomous car that provided the vehicle’s speed as opposed to commanding a go/no-go decision provided an additional 4 feet of safety for early adopters. One caveat to this approach is that the selection of a specific algorithm can significantly influence the results and more work is needed to determine the sensitivity of this approach with seemingly similar machine learning classification approaches.