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.

Advanced Driver Assistance Systems (ADAS) provide safety and comfort while driving. However, to effectively use ADAS, it is necessary for users to have proper knowledge of the systems and to trust the system to operate safely. Providing knowledge about operational capabilities and limitations of a system may help improve drivers’ mental models and calibrate their trust resulting in proper use of ADAS. Traditionally system information is provided via the owner’s manual, which is known to be tedious and time-consuming and underscores the need for alternate training approaches. This study evaluates two training methods, Text-Based and System Visualization, to examine users’ perceptions of training and change in trust after training. Results show that although training did not affect users’ trust, a qualitative examination showed that users preferred the Text-Based method rather than the Visualization method.

Objectives: Driving simulation is an important platform for studying vehicle automation. There are different approaches to using this platformwith most using scripting or programmatic tools to simulate vehicle automation. A less frequently used approach, the Wizard-of-Oz method, has potential for increased flexibility and efficiency in designing and conducting experiments. This study designed and evaluated an experimental setup to examine the feasibility of this approach as an alternative for conducting automation studies. Methods: Twenty-four participants experienced simulated vehicle automation in two platforms, one where the automation was controlled by algorithms, and the other where the automation was simulated by an external operator. Surveys were administered after each drive and the drivers' takeover performance after the automation disengaged was measured. Results: Results indicate that while the kinematic parameters of the driving differed significantly for the two platforms, there were no significant differences in the perceptions of participants and in their takeover performance between the two platforms. Conclusion: These results provide evidence for the use of alternative approaches for the conduct of human factors studies on vehicle automation, potentially lowering barriers to undertaking such experiments while increasing flexibility in designing more complex studies.

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