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.

Estimating Trunk Angle Kinematics During Lifting Using a Computationally Efficient Computer Vision Method.

Objective: A computer vision method was developed for estimating the trunk flexion angle, angular speed, and angular acceleration by extracting simple features from the moving image during lifting.

Background: Trunk kinematics is an important risk factor for lower back pain, but is often difficult to measure by practitioners for lifting risk assessments.

Methods: Mannequins representing a wide range of hand locations for different lifting postures were systematically generated using the University of Michigan 3DSSPP software.

Preface to the Special Issue on Human Factors and Advanced Vehicle Automation: Of Benefits, Barriers, and Bridges to Safe and Effective Implementation.

Objective: The aim of this special issue is to bring together the latest research related to driver interaction with various types of vehicle automation.

Background: Vehicle technology has undergone significant progress over the past decade, bringing new support features that can assist the driver and take on more and more of the driving responsibilities.

Method: This issue is comprised of eight articles from international research teams, focusing on different types of automation and different user populations, including driver support features through to highly automated driving systems.

Designing for the Extremes: Modeling Drivers' Response Time to Take Back Control From Automation Using Bayesian Quantile Regression.

Objective: Understanding the factors that affect drivers' response time in takeover from automation can help guide the design of vehicle systems to aid drivers. Higher quantiles of the response time distribution might indicate a higher risk of an unsuccessful takeover. Therefore, assessments of these systems should consider upper quantiles rather than focusing on the central tendency.

Exploring Trust in Self-Driving Vehicles Through Text Analysis.

Objective: This study examined attitudes toward self-driving vehicles and the factors motivating those attitudes.

Background: Self-driving vehicles represent potentially transformative technology, but achieving this potential depends on consumers' attitudes. Ratings from surveys estimate these attitudes, and open-ended comments provide an opportunity to understand their basis.

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

Objective: This paper investigates driver engagement with vehicle automation and the transition to manual control in the context of a phenomenon that we have termed vicarious steering-drivers steering when the vehicle is under automated control.

Background: Automated vehicles introduce many challenges, including disengagement from the driving task and out-of-the-loop performance decrement. We examine drivers' steering behavior when the automation is engaged, and steering input has no effect on the vehicle state.