Lutz Lorenz

“Take over!” How long does it take to get the driver back into the loop?

Raising the automation level in cars is an imaginable scenario for the future in order to improve traffic safety. However, as long as there are situations that cannot be handled by the automation, the driver has to be enabled to take over the driving task in a safe manner. The focus of the current study is to understand at which point in time a driver’s attention must be directed back to the driving task. To investigate this issue, an experiment was conducted in a dynamic driving simulator and two take-over times were examined and compared to manual driving. The conditions of the experiment were designed to examine the take-over process of inattentive drivers engaged in an interaction with a tablet computer. The results show distinct automation effects in both take-over conditions. With shorter take-over time, decision making and reactions are faster but generally worse in quality.

How traffic situations and non-driving related tasks affect the take-over quality in highly automated driving

Highly automated driving constitutes a temporary transfer of the primary driving task from the driver to the automated vehicle. In case of system limits, drivers take back control of the vehicle. This study investigates the effect of varying traffic situations and non-driving related tasks on the take-over process and quality. The experiment is conducted in a high-fidelity driving simulator. The standardized visual Surrogate Reference Task (SuRT) and the cognitive n-back Task are used to simulate the non-driving related tasks. Participants experience four different traffic situations. Results of this experiment show a strong influence of the traffic situations on the take-over quality in a highway setting, if the traffic density is high. The non-driving related tasks SuRT and the n-back Task show similar effects on the take-over process with a higher total number of collisions by the SuRT in the high density traffic situation.

Designing take over scenarios for automated driving: How does augmented reality support the driver to get back into the loop?

Highly automated driving allows the driver to temporarily turn away from the driving task, meaning he or she does not have to monitor the system. This leads to the challenge of getting the driver back into the loop, if the automation reaches a system boundary. This study investigates, whether augmented reality information can positively influence the take over process. Therefore we evaluated two augmented reality concepts. The concept “AR red” displays a corridor on the road to be avoided by the driver in a take over scenario. The concept “AR green” suggests a corridor the driver can safely steer through. Results indicate that the type of augmented reality information does not influence take over times, but considerably affects reaction type. Visual inspection revealed higher consistency in driving trajectories for participants following the proposed corridor of “AR green” concept as compared to trajectories of drivers confronted with the restricted zone of “AR red”.

Keep Your Scanners Peeled: Gaze Behavior as a Measure of Automation Trust During Highly Automated Driving

Objective: The feasibility of measuring drivers’ automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving simulator study. Background: Earlier research from other domains indicates that drivers’ automation trust might be inferred from gaze behavior, such as monitoring frequency. Method: The gaze behavior and self-reported automation trust of 35 participants attending to a visually demanding non-driving-related task (NDRT) during highly automated driving was evaluated. The relationship between dispositional, situational, and learned automation trust with gaze behavior was compared. Results: Overall, there was a consistent relationship between drivers’ automation trust and gaze behavior. Participants reporting higher automation trust tended to monitor the automation less frequently. Further analyses revealed that higher automation trust was associated with lower monitoring frequency of the automation during NDRTs, and an increase in trust over the experimental session was connected with a decrease in monitoring frequency. Conclusion: We suggest that (a) the current results indicate a negative relationship between drivers’ self-reported automation trust and monitoring frequency, (b) gaze behavior provides a more direct measure of automation trust than other behavioral measures, and (c) with further refinement, drivers’ automation trust during highly automated driving might be inferred from gaze behavior. Application: Potential applications of this research include the estimation of drivers’ automation trust and reliance during highly automated driving.

Prior Familiarization with Takeover Requests Affects Drivers’ Takeover Performance and Automation Trust

Objective: The objective for this study was to investigate the effects of prior familiarization with takeover requests (TORs) during conditional automated driving on drivers’ initial takeover performance and automation trust. Background: System-initiated TORs are one of the biggest concerns for conditional automated driving and have been studied extensively in the past. Most, but not all, of these studies have included training sessions to familiarize participants with TORs. This makes them hard to compare and might obscure first-failure-like effects on takeover performance and automation trust formation. Method: A driving simulator study compared drivers’ takeover performance in two takeover situations across four prior familiarization groups (no familiarization, description, experience, description and experience) and automation trust before and after experiencing the system. Results: As hypothesized, prior familiarization with TORs had a more positive effect on takeover performance in the first than in a subsequent takeover situation. In all groups, automation trust increased after participants experienced the system. Participants who were given no prior familiarization with TORs reported highest automation trust both before and after experiencing the system. Conclusion: The current results extend earlier findings suggesting that prior familiarization with TORs during conditional automated driving will be most relevant for takeover performance in the first takeover situation and that it lowers drivers’ automation trust. Application: Potential applications of this research include different approaches to familiarize users with automated driving systems, better integration of earlier findings, and sophistication of experimental designs.

A transforming steering wheel for highly automated cars

In the near future, highly automated driving will almost certainly be available in commercial vehicles. Concerning the human-machine interface in such cars, two main issues have to be addressed. First, the detrimental effects of automation have to be avoided. Second, cars should provide an interface that allows the driver to utilize the time while driving highly automated. We conducted a driving simulator study to investigate the concept of geometrical transformation of the steering wheel to address both issues. Therefore, we implemented a prototype steering wheel which changes its shape depending on the current driving mode to improve mode awareness and comfort when driving highly automated. The study was focused on possible negative effects of the mechanical transformation in front of the driver during the take-over process. Results indicate that on average participants reacted faster and took over control later. The number of lane change errors, for example changing lanes without looking into the mirror, even somewhat decreased when using the transforming steering wheel. Furthermore, participants mainly rated the proposed concept as usable without problems during the take-over process.

Highly automated driving with a decoupled steering wheel

Future cars will almost certainly provide an increasing level of automation. Under certain conditions, they will allow the driver to withdraw from the control loop and deal with non-driving related tasks. To provide a convenient and safe user interface for this case, it can be advantageous to have the steering wheel de-coupled from the steering link and stationary. In this study, we evaluated two alternative steering wheel concepts. The first concept represents a state of the art steering wheel that decouples from the steering link and remains stationary at an angle of 0° during highly automated driving. In the second concept, the steering wheel shows the same behavior but does not have visible spokes. Hence, it does not display its physical orientation to the driver. Using a dynamic driving simulator, we evaluated the concepts in a comparison drive and a take-over scenario in a curve. A permanently coupled state of the art steering wheel served as control condition. Results show that the decoupling was only noticed by a small number of participants. Further, no negative impacts on the take-over process could be determined. The steering wheel with no visible spokes led to an even better performance compared to the control condition.

Designing the human-machine interface for highly automated cars — Challenges, exemplary concepts and studies

During the last years, intensive research has been conducted to make high degrees of automation available in cars. However, driver assistance systems today still need the driver to monitor the system. This will most probably change in near future, as highly automated driving becomes available. With the driver out of the control loop, this driving mode has beneficial aspects for the driver and could improve traffic safety as a high portion of traffic accidents are due to human error. On the other hand, high degrees of automation can have detrimental effects which are well known from other domains like aviation. These effects have led to fatal accidents in the past. The authors investigate various aspects of this topic and the corresponding challenges for the human-machine interface in future cars. In this paper, we present three of our research areas: the take-over process, trust in automation and utilization of drivetime. For each area, we explain theoretical background, current challenges and studies we conducted.