Redesigning Today’s Driving Automation Toward Adaptive Backup Control With Context-Based and Invisible Interfaces

Objective: We investigated a driver monitoring system (DMS) designed to adaptively back up distracted drivers with automated driving. Background: Humans are likely inadequate for supervising today’s on-road driving automation. Conversely, backup concepts can use eye-tracker DMS to retain the human as the primary driver and use computerized...

Prediction of effort and eye movement measures from driving scene components

For transitions of control in automated vehicles, driver monitoring systems (DMS) may need to discern task difficulty and driver preparedness. Such DMS require models that relate driving scene components, driver effort, and eye measurements. Across two sessions, 15 participants enacted receiving control within 60 randomly ordered dashcam videos ...

How to keep drivers engaged while supervising driving automation? A literature survey and categorisation of six solution areas

This work aimed to organise recommendations for keeping people engaged during human supervision of driving automation, encouraging a safe and acceptable introduction of automated driving systems. First, heuristic knowledge of human factors, ergonomics, and psychological theory was used to propose solution areas to human supervisory control...

The 4D LINT model of function allocation: Spatial-temporal arrangement and levels of automation

Human factors researchers are well familiar with Sheridan and Verplank’s (1978) ‘levels of automation’. Although this automation dimension has proved useful, the last decade has seen a vast increase of automation in different forms, especially in transportation domains. To capture these and future developments, we propose an extended automation...

Validity and reliability of naturalistic driving scene categorization Judgments from crowdsourcing

A common challenge with processing naturalistic driving data is that humans may need to categorize great volumes of recorded visual information. By means of the online platform CrowdFlower, we investigated the potential of crowdsourcing to categorize driving scene features (i.e., presence of other road users, straight road segments, etc.) at...

What makes driving difficult?: Perceived effort and eye measures follow visible semantic complexity factors

A majority (95%) of crashes can be attributed to humans, with the highest cause category (41%) involving errors of recognition (i.e., inattention, distraction, inadequate surveillance) [1]. Driving safety research often claims that as much as 90% of the information that drivers use is visual. However, these claims have been hampered by a lack of...

Subjective and objective descriptions of driving scenes in support of driver-automation interactions

Background: Recent advances in the growing domain of automated driving suggest the need for thoughtful design of human-computer interaction strategies. For example, human drivers can process scene variability on implicit levels, but automated systems require explicit rule-based judgments of similarity and difference. What level of abstraction an...

Human factors of transitions in automated driving: A general framework and literature survey

The topic of transitions in automated driving is becoming important now that cars are automated to ever greater extents. This paper proposes a theoretical framework to support and align human factors research on transitions in automated driving. Driving states are defined based on the allocation of primary driving tasks (i.e., lateral control,...

From Mackworth’s clock to the open road: A literature review on driver vigilance task operationalization

Objective<br/>This review aimed to characterize tasks applied in driving research, in terms of instructions/conditions, signal types/rates, and component features in comparison to the classic vigilance literature.<br/><br/>Background<br/>Driver state monitoring is facing increased attention with evolving vehicle automation, and real-time...