How to Keep Drivers Attentive during Level 2 Automation? Development and Evaluation of an HMI Concept Using Affective Elements and Message Framing

With Level 3 and 4 automated driving activated, users will be allowed to engage in a wide range of non-driving related activities (NDRAs). Although Level 2 automation can appear very similar to L3 and L4, drivers are required to always monitor the system. However, past research has found drivers neglect this obligation at least partly and instead engage in NDRAs. Since this behavior can have negative impacts on traffic safety, the goal of this work was to develop a human–machine interface (HMI) concept to motivate users to continue their supervision task. This work’s concept used message framing in connection with affective elements. Every three minutes, messages were displayed on the head-up display. To evaluate the affective message concept’s (AMC) effectiveness, we conducted a between-subject driving simulator study (baseline vs. advanced HMI) with 32 participants and 45 min of driving time with both L2 and L4 phases and a silent system malfunction. Results show the road attention ratio decreases and the NDRA engagement ratio increases over time only for baseline participants. Participants supported by the AMC did not show a change over time in monitoring behavior and NDRA engagement. However, no effect on the drivers’ reaction to the system failure became apparent. No effects on subjective workload and user experience were found. Additional research is needed to further investigate the safety implications and long-term effectiveness of the concept, as well as a driver-state-dependent design.

Evaluating Driver Features for Cognitive Distraction Detection and Validation in Manual and Level 2 Automated Driving

OBJECTIVE

This study aimed to investigate the impacts of feature selection on driver cognitive distraction (CD) detection and validation in real-world nonautomated and Level 2 automated driving scenarios.

BACKGROUND

Real-time driver state monitoring is critical to promote road user safety.

METHOD

Twenty-four participants were recruited to drive a Tesla Model S in manual and Autopilot modes on the highway while engaging in the N-back task. In each driving mode, CD was classified by the random forest algorithm built on three "hand-crafted" glance features (i.e., percent road center [PRC], the standard deviation of gaze pitch, and yaw angles), or through a large number of features that were transformed from the output of a driver monitoring system (DMS) and other sensing systems.

RESULTS

In manual driving, the small set of glance features was as effective as the large set of machine-generated features in terms of classification accuracy. Whereas in Level 2 automated driving, both glance and vehicle features were less sensitive to CD. The glance features also revealed that the misclassified driver state was the result of the dynamic fluctuations and individual differences of cognitive loads under CD.

CONCLUSION

Glance metrics are critical for the detection and validation of CD in on-road driving.

APPLICATIONS

The paper suggests the practical value of human factors domain knowledge in feature selection and ground truth validation for the development of driver monitoring technologies.

Effects of Distraction in On-Road Level 2 Automated Driving: Impacts on Glance Behavior and Takeover Performance

OBJECTIVE

The paper aimed to investigate glance behaviors under different levels of distraction in automated driving (AD) and understand the impact of distraction levels on driver takeover performance.

BACKGROUND

Driver distraction detrimentally affects takeover performance. Glance-based distraction measurement could be a promising method to remind drivers to maintain enough attentiveness before the takeover request in partially AD.

METHOD

Thirty-six participants were recruited to drive a Tesla Model S in manual and Autopilot modes on a test track while engaging in secondary tasks, including temperature-control, email-sorting, and music-selection, to impose low and high distractions. During the test drive, participants needed to quickly change the lane as if avoiding an immediate road hazard if they heard an unexpected takeover request (an auditory warning). Driver state and behavior over the test drive were recorded in real time by a driver monitoring system and several other sensors installed in the Tesla vehicle.

RESULTS

The distribution of off-road glance duration was heavily skewed (with a long tail) by high distractions, with extreme glance duration more than 30 s. Moreover, being eyes-off-road before takeover could cause more delay in the urgent takeover reaction compared to being hands-off-wheel.

CONCLUSION

The study measured off-road glance duration under different levels of distraction and demonstrated the impacts of being eyes-off-road and hands-off-wheel on the following takeover performance.

APPLICATION

The findings provide new insights about engagement in Level 2 AD and are useful for the design of driver monitoring technologies for distraction management.