Proposed Framework for Identifying and Predicting Operator Errors When using Advanced Vehicle Technologies

Abstract

Advanced vehicle technologies include systems that are defined by the Society for Automotive Engineers as automated driving features or driver support features. The latter are increasingly available in late model vehicles in the form of advanced driver assistance systems (ADAS). ADAS features remove some responsibilities from drivers, but still depend on the drivers for safe operation. This can result in drivers committing errors while using ADAS, especially if their understanding of these systems, that is, their mental model, is incorrect. To understand how these systems could be used incorrectly it is necessary to have an insight into these mental models. One approach is to characterize users’ mental representations of systems based on the errors that they commit during system use. Such an approach necessitates a classification of potential errors that may be committed, and the underlying cognitive and behavioral reasons for such errors. To that end, a framework is proposed that can, among other goals, help predict user errors while using ADAS based on human factors and task analysis techniques. A methodology is detailed for mapping operator-system interactions using state diagrams, error identification techniques using task analysis are proposed, and a categorization scheme based on classic error taxonomies is described. This proposed framework can subsequently be expanded for error identification for a wider range and versions of ADAS, as well as for future automated driving systems (ADS). Moreover, the framework provides a systematic approach that can be used toward operationalizing mental models, forming the basis for structured user training, and for human-centered design of advanced vehicle technologies.