Driver Visual Attention Before and After Take-Over Requests During Automated Driving on Public Roads

OBJECTIVE

This study aims to understand drivers' visual attention before and after take-over requests during automated driving (AD), when the vehicle is fully responsible for the driving task on public roads.

BACKGROUND

Existing research on transitions of control from AD to manual driving has mainly focused on take-over times. Despite its relevance for vehicle safety, drivers' visual attention has received little consideration.

METHOD

Thirty participants took part in a Wizard of Oz study on public roads. Drivers' visual attention was analyzed before and after four take-over requests. Visual attention during manual driving was also recorded to serve as a baseline for comparison.

RESULTS

During AD, the participants showed reduced visual attention to the forward road and increased duration of single off-road glances compared to manual driving. In response to take-over requests, the participants looked away from the forward road toward the instrument cluster. Levels of visual attention towards the forward road did not return to the levels observed during manual driving until after 15 s had passed.

CONCLUSION

During AD, drivers may look toward non-driving related task items (e.g., mobile phone) instead of forward. Further, when a transition of control is required, drivers may take over control before they are aware of the driving environment or potential threat(s). Thus, it cannot be assumed that drivers are ready to respond to events shortly after the take-over request.

APPLICATION

It is important to consider the effect of the design of take-over requests on drivers' visual attention alongside take-over times.

Calibration of Trust in Automated Driving: A Matter of Initial Level of Trust and Automated Driving Style?

OBJECTIVE

Automated driving is becoming a reality, and such technology raises new concerns about human-machine interaction on road. This paper aims to investigate factors influencing trust calibration and evolution over time.

BACKGROUND

Numerous studies showed trust was a determinant in automation use and misuse, particularly in the automated driving context.

METHOD

Sixty-one drivers participated in an experiment aiming to better understand the influence of initial level of trust (Trustful vs. Distrustful) on drivers' behaviors and trust calibration during two sessions of simulated automated driving. The automated driving style was manipulated as positive (smooth) or negative (abrupt) to investigate human-machine early interactions. Trust was assessed over time through questionnaires. Drivers' visual behaviors and take-over performances during an unplanned take-over request were also investigated.

RESULTS

Results showed an increase of trust over time, for both Trustful and Distrustful drivers regardless the automated driving style. Trust was also found to fluctuate over time depending on the specific events handled by the automated vehicle. Take-over performances were not influenced by the initial level of trust nor automated driving style.

CONCLUSION

Trust in automated driving increases rapidly when drivers' experience such a system. Initial level of trust seems to be crucial in further trust calibration and modulate the effect of automation performance. Long-term trust evolutions suggest that experience modify drivers' mental model about automated driving systems.

APPLICATION

In the automated driving context, trust calibration is a decisive question to guide such systems' proper utilization, and road safety.

Evaluation of Imminent Take-Over Requests With Real Automation on a Test Track

OBJECTIVE

Investigating take-over, driving, non-driving related task (NDRT) performance, and trust of conditionally automated vehicles (AVs) in critical transitions on a test track.

BACKGROUND

Most experimental results addressing driver take-over were obtained in simulators. The presented experiment aimed at validating relevant findings while uncovering potential effects of motion cues and real risk.

METHOD

Twenty-two participants responded to four critical transitions on a test track. Non-driving related task modality (reading on a handheld device vs. auditory) and take-over timing (cognitive load) were varied on two levels. We evaluated take-over and NDRT performance as well as gaze behavior. Further, trust and workload were assessed with scales and interviews.

RESULTS

Reaction times were significantly faster than in simulator studies. Further, reaction times were only barely affected by varying visual, physical, or cognitive load. Post-take-over control was significantly degraded with the handheld device. Experiencing the system reduced participants' distrust, and distrusting participants monitored the system longer and more frequently. NDRTs on a handheld device resulted in more safety-critical situations.

CONCLUSION

The results confirm that take-over performance is mainly influenced by visual-cognitive load, while physical load did not significantly affect responses. Future take-over request (TOR) studies may investigate situation awareness and post-take-over control rather than reaction times only. Trust and distrust can be considered as different dimensions in AV research.

APPLICATION

Conditionally AVs should offer dedicated interfaces for NDRTs to provide an alternative to using nomadic devices. These interfaces should be designed in a way to maintain drivers' situation awareness.

PRÉCIS

This paper presents a test track experiment addressing conditionally automated driving systems. Twenty-two participants responded to critical TORs, where we varied NDRT modality and take-over timing. In addition, we assessed trust and workload with standardized scales and interviews.

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 emergency takes place, the automation uses the human machine interface (HMI) to send a take-over request (TOR) to the driver.

Methods

We investigated the influence of a color themed HMI on the trust and take-over performance in automated vehicles. Using a driving simulator, we tested 45 participants divided in three groups with a baseline auditory HMI and two advanced color themed HMIs consisting of a display and ambient lighting with the colors red and blue. Trust in automation was assessed using questionnaires while take-over performance was assessed through response time and success rate.

Results

Compared to the baseline HMI, the color themed HMI is more trustworthy, and participants understood their driving tasks better. Results show that the color themed HMI is perceived as more pleasant compared to the baseline HMI and leads to shorter reaction times. Red ambient lighting is seen as more urging than blue, but HMI color did not significantly affect the general HMI perception and TOR performance.

Discussion

Further research can explore the use of color and other modalities to express varying urgency levels and validate findings in complex on road driving conditions.

Task Interruption and Control Recovery Strategies After Take-Over Requests Emphasize Need for Measures of Situation Awareness

OBJECTIVE

Our objective was to determine whether there is a need to go beyond measures of automation deactivation time to understand the transition to manual driving after take-over requests (TORs) using the example of office tasks as nondriving-related tasks (NDRTs).

BACKGROUND

Office tasks are likely NDRTs during automated commutes to/from work. Complex tasks can influence how manual control and visual attention is recovered after TORs.

METHOD

N = 51 participants in a driving simulator performed either one of two office tasks or no task (between subjects). We recorded reaction times in a high-urgency and low-urgency scenario (within subjects) and analyzed task interruption strategies.

RESULTS

90% of the participants who performed an NDRT deactivated the automation after 7 to 8 s. However, 90% of the same drivers looked at the side mirror for the first time only after 11 to 14 s. Drivers with office tasks either interrupted the tasks sequentially or in parallel. Strategies were not adapted to the take-over situation or the task but appeared to be due to individual preferences.

CONCLUSION

Drivers engaged in NDRTs may neglect lower priority subtasks after a TOR, such as mirror checking. Therefore, there is a need to go beyond measures of automation deactivation time to understand the transition to manual driving. Using analyses of attentional dynamics during take-over situations may enhance the safety of future car-driver handover assistance systems.

APPLICATION

If low driver availability is detected, TORs should only be used as a fallback option if sufficient time and adaptive driver support can be provided.