The influence of a color themed HMI on trust and take-over performance in automated vehicles

Introduction: SAE Level 3 is known as conditional driving automation. As long as certain conditions are met, there is no need to supervise the technology and the driver can engage in non-driving related tasks (NDRTs). However, a human driver must be present and alert to take over when the automation is facing its system limits. When such an...

Effects of User Interfaces on Take-Over Performance: A Review of the Empirical Evidence

In automated driving, the user interface plays an essential role in guiding transitions between automated and manual driving. This literature review identified 25 studies that explicitly studied the effectiveness of user interfaces in automated driving. Our main selection criterion was how the user interface (UI) affected take-over performance...

Design of Control Transfer Rituals for Automated Vehicles

In 1886, humanity started a great effort to move away from horseback and build a carriage over which they have full control. A century-and-a-half later, we have succeeded to the degree that we are looking for a way to relax in our automobiles. The MEDIATOR project tackles the transitional period between conventional automobiles and fully...

Does personality affect responses to auditory take-over requests? Validating a simulator experiment setup through a N=1-study

Automated vehicles with conditional driving automation (SAE level 3 (SAE, 2018)) will request the human driver to intervene when reaching its system boundaries by issuing a take-over request (TOR). This study is investigating whether a speech-based auditory take-over request is influencing the time it takes from automated to manual driving,...

A method to assess safety implications during authority transitions in automated driving

The question of how well in terms of safety can a driver take over control of an automated vehicle in response to an emergency situation is of crucial importance. Most of the studies performed so far focus on the drivers’ reaction times and the mechanisms behind the transition. In this study, an effort is made to incorporate the braking times...

Quantifying Take-Over Quality: A Novel Approach Utilizing a Scenario-Specific Optimized Reference Trajectory

Due to recent advancements in automated driving systems, drivers are able to withdraw themselves from the control loop of the vehicle in certain driving situations. However, system limits occur relatively frequently, which results in the driver having to take back control. It is argued most currently used take-over quality quantification methods...

Evaluating take-over requests with directional audio: Assessing the effect of ipsilateral and contralateral verbal stimuli on response times and visual behavior

Taking over control from an automated vehicle may take a substantial amount of time if the driver is not engaged in the driving task. Take-over requests containing directional information of hazardous surrounding cars could aid the driver in taking over the vehicle faster. However, whether the directional information should be presented...

Comparing spatially static and dynamic vibrotactile take-over requests in the driver seat

Vibrotactile stimuli can be effective as warning signals, but their effectiveness as directional take-over requests in automated driving is yet unknown. This study aimed to investigate the correct response rate, reaction times, and eye and head orientation for static versus dynamic directional take-over requests presented via vibrating motors...

Take-over again: Investigating multimodal and directional TORs to get the driver back into the loop

When a highly automated car reaches its operational limits, it needs to provide a take-over request (TOR) in order for the driver to resume control. The aim of this simulator-based study was to investigate the effects of TOR modality and left/right directionality on drivers' steering behaviour when facing a head-on collision without having...