Understanding Driving Automation Limitations: A Taxonomy Bridging System Constraints and Observable Cues

Abstract

This study presents a taxonomy for classifying the limitations of Advanced Driver Assistance Systems (ADAS) based on both system characteristics and cues observable to drivers. The taxonomy includes four dimensions: source of uncertainty, operational domain, constraint mechanism, and cue type. It was developed using existing safety frameworks and applied to two real-world datasets: Tesla crash summaries and selected National Highway Traffic Safety Administration (NHTSA) incident reports. The taxonomy identified eight types of limitations and classified each case based on the type of system limitation involved, such as perception failures due to the environment or control issues during interactions with other road users. Results show differences in limitation patterns across datasets and highlight cases that resist clear classification, such as phantom braking. By linking system limitation types with cues observable to the driver, this taxonomy can support the design of driver interfaces and training materials that help users understand when the system may not perform as expected.