Attentional warnings caused by driver monitoring systems: How often do they appear and how well are they understood?

The present study investigated the effects of a driver monitoring system that triggers attention warnings in case distraction is detected. Based on the EuroNCAP protocol, distraction could either be long glances away from the forward roadway (≥3s) or visual attention time sharing (>10 cumulative seconds within a 30 s time interval). In a series of manual driving simulator drives, 30 participants completed both driving related tasks (e.

Test procedure for the evaluation of partially automated driving HMI including driver monitoring systems in driving simulation.

The proposed test procedure presents an approach for the evaluation of the usability of partial automated driving HMI including driver monitoring systems in driving simulation. This procedure is based on a definition of requirements that a Level 2 HMI and its included driver monitoring system must fulfill in order to guarantee that the drivers understand their responsibilities of continuously monitoring the driving environment and the status of the partial automated driving system. These requirements are used to define the evaluation criteria that have to be validated in the test as well as the use cases in which these criteria can be assessed.

The impact of speech-based assistants on the driver's cognitive distraction.

Speech-based interfaces can be a promising alternative and/or addition to visual-manual interfaces since they reduce visual-manual distraction while driving. However, there are also findings indicating that speech-based assistants may be a source of cognitive distraction. The aim of this experiment was to quantify drivers' cognitive distraction while interacting with speech-based assistants.

The box task - a method for assessing in-vehicle system demand.

The use of advanced in-vehicle information systems (IVIS) and other complex devices such as smartphones while driving can lead to driver distraction, which, in turn, increases safety-critical event risk. Therefore, using methods for measuring driver distraction caused by IVIS is crucial when developing new in-vehicle systems. In this paper, we present the setup and implementation of the Box Task combined with a Detection Response Task (BT+DRT) as a tool to assess visual-manual and cognitive distraction effects.

Measuring driver distraction - Evaluation of the box task method as a tool for assessing in-vehicle system demand.

Several tools have been developed over the past twenty years to assess the degree of driver distraction caused by secondary task engagement. A relatively new and promising method in this area is the box task combined with a detection response task (BT + DRT). However, no evaluation regarding the BT's sensitivity currently exists.

Using European naturalistic driving data to assess secondary task engagement when stopped at a red light.

Problem: Some evidence exists that drivers choose to engage in secondary tasks when the driving demand is low (e.g., when the car is stopped).

Test procedure for evaluating the human-machine interface of vehicles with automated driving systems.

The human-machine interface (HMI) is a crucial part of every automated driving system (ADS). In the near future, it is likely that-depending on the operational design domain (ODD)-different levels of automation will be available within the same vehicle. The capabilities of a given automation level as well as the operator's responsibilities must be communicated in an appropriate way.

Noncritical State Transitions During Conditionally Automated Driving on German Freeways: Effects of Non-Driving Related Tasks on Takeover Time and Takeover Quality.

Objective: This study aimed at investigating the driver's takeover performance when switching from working on different non-driving related tasks (NDRTs) while driving with a conditionally automated driving function (SAE L3), which was simulated by a Wizard of Oz vehicle, to manual vehicle control under naturalistic driving conditions.

Background: Conditionally automated driving systems, which are currently close to market introduction, require the user to stay fallback ready. As users will be allowed to engage in more complex NDRTs during the automated drive than when driving manually, the time needed to regain full manual control could likely be increased.

From partial and high automation to manual driving: Relationship between non-driving related tasks, drowsiness and take-over performance.

Background: Until the level of full vehicle automation is reached, users of vehicle automation systems will be required to take over manual control of the vehicle occasionally and stay fallback-ready to some extent during the drive. Both, drowsiness caused by inactivity and the engagement in distracting non-driving related tasks (NDRTs) such as entertainment or office work have been suggested to impair the driver's ability to safely handle these transitions of control. Thus, it is an open question whether engagement in NDRTs will impair or improve take-over performance.

Expert-based controllability assessment of control transitions from automated to manual driving.

Up to a level of full vehicle automation, drivers will have to be available as a fallback level and take back manual control of the vehicle in case of system limits or failures. Before introducing automated vehicles to the consumer market, the controllability of these control transitions has to be demonstrated. This paper presents a novel procedure for an expert-based controllability assessment of control transitions from automated to manual driving.

Effect of different alcohol levels on take-over performance in conditionally automated driving.

Automated driving systems are getting pushed into the consumer market, with varying degrees of automation. Most often the driver's task will consist of being available as a fall-back level when the automation reaches its limits. These so-called take-over situations have attracted a great body of research, focusing on various human factors aspects (e.

Driver compliance to take-over requests with different auditory outputs in conditional automation.

Conditionally automated driving (CAD) systems are expected to improve traffic safety. Whenever the CAD system exceeds its limit of operation, designers of the system need to ensure a safe and timely enough transition from automated to manual mode. An existing visual Human-Machine Interface (HMI) was supplemented by different auditory outputs.

Driving performance at lateral system limits during partially automated driving.

This study investigated driver performance during system limits of partially automated driving. Using a motion-based driving simulator, drivers encountered different situations in which a partially automated vehicle could no longer safely keep the lateral guidance. Drivers were distracted by a non-driving related task on a touch display or driving without an additional secondary task.

Cooperative warning systems: The impact of false and unnecessary alarms on drivers' compliance.

Cooperative warning systems have a great potential to prevent traffic accidents. However, because of their predictive nature, they might also go along with an increased frequency of incorrect alarms that could limit their effectiveness. To better understand the consequences associated with incorrect alarms, a driving simulator study with N=80 drivers was conducted to investigate how situational context and warning urgency jointly influence drivers' compliance with an unreliable advisory warning system (AWS).

Evaluating distraction of in-vehicle information systems while driving by predicting total eyes-off-road times with keystroke level modeling.

Increasingly complex in-vehicle information systems (IVIS) have become available in the automotive vehicle interior. To ensure usability and safety of use while driving, the distraction potential of system-associated tasks is most often analyzed during the development process, either by employing empirical or analytical methods, with both families of methods offering certain advantages and disadvantages. The present paper introduces a method that combines the predictive precision of empirical methods with the economic advantages of analytical methods.