Moving Into the Loop: An Investigation of Drivers' Steering Behavior in Highly Automated Vehicles

Trusting Automation: Designing for Responsivity and Resilience

OBJECTIVE

This paper reviews recent articles related to human trust in automation to guide research and design for increasingly capable automation in complex work environments.

BACKGROUND

Two recent trends-the development of increasingly capable automation and the flattening of organizational hierarchies-suggest a reframing of trust in automation is needed.

METHOD

Many publications related to human trust and human-automation interaction were integrated in this narrative literature review.

RESULTS

Much research has focused on calibrating human trust to promote appropriate reliance on automation. This approach neglects relational aspects of increasingly capable automation and system-level outcomes, such as cooperation and resilience. To address these limitations, we adopt a relational framing of trust based on the decision situation, semiotics, interaction sequence, and strategy. This relational framework stresses that the goal is not to maximize trust, or to even calibrate trust, but to support a process of trusting through automation responsivity.

CONCLUSION

This framing clarifies why future work on trust in automation should consider not just individual characteristics and how automation influences people, but also how people can influence automation and how interdependent interactions affect trusting automation. In these new technological and organizational contexts that shift human operators to co-operators of automation, automation responsivity and the ability to resolve conflicting goals may be more relevant than reliability and reliance for advancing system design.

APPLICATION

A conceptual model comprising four concepts-situation, semiotics, strategy, and sequence-can guide future trust research and design for automation responsivity and more resilient human-automation systems.

Analysis of Driving Control Characteristics in Typical Road Types

Vehicle control requirements for longitudinal and lateral driver control are varied in different road geometries; this makes it irrational and superfluous to represent driving control characteristics with repetitive indices. To address this problem, the present study used multiple cross-analysis methods of vehicle running state parameters from experienced drivers in order to deeply study driving control characteristics in different road geometries. Six common road geometries with different driving control emphases were selected as typical road types and twenty-five experienced drivers were asked to perform an actual driving test. Taking the indices in the long straight road as the control variable, the indices in other roads were compared with it and judged according to the three methods: the overall distribution by box plots, significant difference test by analysis of variance (ANOVA) and relative distance calculation by technique for order preference by similarity to an ideal solution (TOPSIS). Moreover, the weight of the driving control characteristic index was calculated through the entropy weight method to reflect its importance. In this paper, the relationships between road geometry and driving control characteristics explicate the influence mechanism and interaction of road geometry on driving behavior, and the indicators that can reflect the control characteristics in different road types are obtained.

Assessing Drivers' Trust of Automated Vehicle Driving Styles With a Two-Part Mixed Model of Intervention Tendency and Magnitude

OBJECTIVE

This study examines how driving styles of fully automated vehicles affect drivers' trust using a statistical technique-the two-part mixed model-that considers the frequency and magnitude of drivers' interventions.

BACKGROUND

Adoption of fully automated vehicles depends on how people accept and trust them, and the vehicle's driving style might have an important influence.

METHOD

A driving simulator experiment exposed participants to a fully automated vehicle with three driving styles (aggressive, moderate, and conservative) across four intersection types (with and without a stop sign and with and without crossing path traffic). Drivers indicated their dissatisfaction with the automation by depressing the brake or accelerator pedals. A two-part mixed model examined how automation style, intersection type, and the distance between the automation's driving style and the person's driving style affected the frequency and magnitude of their pedal depression.

RESULTS

The conservative automated driving style increased the frequency and magnitude of accelerator pedal inputs; conversely, the aggressive style increased the frequency and magnitude of brake pedal inputs. The two-part mixed model showed a similar pattern for the factors influencing driver response, but the distance between driving styles affected how often the brake pedal was pressed, but it had little effect on how much it was pressed.

CONCLUSION

Eliciting brake and accelerator pedal responses provides a temporally precise indicator of drivers' trust of automated driving styles, and the two-part model considers both the discrete and continuous characteristics of this indicator.

APPLICATION

We offer a measure and method for assessing driving styles.