A Systematic Review and Meta-Analysis of Takeover Performance During Conditionally Automated Driving

OBJECTIVE

The aim of this paper was to synthesize the experimental research on factors that affect takeover performance during conditionally automated driving.

BACKGROUND

For conditionally automated driving, the automated driving system (ADS) can handle the entire dynamic driving task but only for limited domains. When the system reaches a limit, the driver is responsible for taking over vehicle control, which may be affected by how much time they are provided to take over, what they were doing prior to the takeover, or the type of information provided to them during the takeover.

METHOD

Out of 8446 articles identified by a systematic literature search, 48 articles containing 51 experiments were included in the meta-analysis. Coded independent variables were time budget, non-driving related task engagement and resource demands, and information support during the takeover. Coded dependent variables were takeover timing and quality measures.

RESULTS

Engaging in non-driving related tasks results in degraded takeover performance, particularly if it has overlapping resource demands with the driving task. Weak evidence suggests takeover performance is impaired with shorter time budgets. Current implementations of information support did not affect takeover performance.

CONCLUSION

Future research and implementation should focus on providing the driver more time to take over while automation is active and should further explore information support.

APPLICATION

The results of the current paper indicate the need for the development and deployment of vehicle-to-everything (V2X) services and driver monitoring.

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.