Analyzing Human-Automation Interdependence Based on the Situational Property and Task Timescale

Abstract

Automation’s imperfection requires driver engagement to handle challenging tasks, creating interdependent relationships where both parties influence each other. Therefore, it is vital to support driver-automation interdependence to align their behaviors with team goals. We expand the Interdependence Analysis (IA) method to evaluate interdependent relationships within driver-automation teams, considering their situational property (e.g., cost of task failure) and the timescale of team tasks. We used the proposed IA to assess the interdependence between a driver and the Tesla Model Y when its Automatic Lane Change feature is engaged. The proposed IA revealed that trajectory planning for lane-changing is performed without driver involvement despite the task’s high cost of failure. Timescale decomposition suggested that trajectory planning relies on short-term road user movement and lane predictions, identifying the specific task and timescale to be supported. This study demonstrates the benefits of considering situational property and task timescale in analyzing interdependence within driver-automation teams.