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.

Using driver monitoring to estimate readiness in automation: A conceptual model based on simulator experimental data

“…There is evidence individual differences between drivers may be associated with the likelihood to safely resume control of the driving task from automation. For example, the adequacy of a drivers' mental model to the actual behaviour of the automated system may be paramount to avoid potential mode errors (Pradhan et al, 2021;Reason, 2004). Victor et al (2018) reported in a Wizard of Oz experiment that drivers were unable to avoid a crash due to a system malfunction even though they had their eyes on the road.…”

How does training influence use and understanding of advanced vehicle technologies: a simulator evaluation of driver behavior and mental models

“…The Visualization Training was based on prior conceptual work on advanced vehicle technologies (Pradhan et al, 2020). Accordingly, the training content included a visual representation of an ACC system as a state diagram (see Figure 2 for an example of the illustration).…”

Does training improve users' mental models about adaptive cruise control?

“…Moreover, the quality of the material presented in the owner's manual can also affect the driver's knowledge and shape their perceptions of these systems . Also, there are variances and inconsistencies in the reporting of system limitations across different manufacturers (Pradhan et al, 2021), and this could result in misconceptions and overestimation of the systems' capabilities.…”

“…There is evidence that a lack of knowledge or any misconceptions about such systems may affect drivers' mental models, which could potentially manifest as action or response-related driver errors (Dickie & Boyle, 2009;McDonald et al, 2018;Pradhan et al, 2021). Mental models have been defined as 'a representation of the typical causal interconnections involving actions and environmental factors that influence a system's functioning' (Durso & Gronlund, 1999).…”