Effect of cognitive load on drivers’ State and task performance during automated driving: Introducing a novel method for determining stabilisation time following take-over of control

Melnicuk, Vadim; Thompson, Simon G.; Jennings, Paul; Birrell, Stewart A.

doi:10.1016/j.aap.2020.105967

Cited by 34 publications

(20 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meanwhile, our method could be potentially employed to predict stabilisation time and specific driving behaviors following TORs [34], which provide a more comprehensive description of takeover performance. Broadly speaking, our findings can facilitate the interactions between drivers and automated vehicles, enhance driving safety in intelligent transportation systems, and improve automated vehicle acceptance across the whole population.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Predicting Driver Takeover Time in Conditionally Automated Driving

Ayoub¹,

Du²,

Yang³

et al. 2021

Preprint

View full text Add to dashboard Cite

It is extremely important to ensure a safe takeover transition in conditionally automated driving. One of the critical factors that quantifies the safe takeover transition is takeover time. Previous studies identified the effects of many factors on takeover time, such as takeover lead time, non-driving tasks, modalities of the takeover requests (TORs), and scenario urgency. However, there is a lack of research to predict takeover time by considering these factors all at the same time. Toward this end, we used eXtreme Gradient Boosting (XGBoost) to predict the takeover time using a dataset from a meta-analysis study [1]. In addition, we used SHAP (SHapley Additive exPlanation) to analyze and explain the effects of the predictors on takeover time. We identified seven most critical predictors that resulted in the best prediction performance. Their main effects and interaction effects on takeover time were examined. The results showed that the proposed approach provided both good performance and explainability. Our findings have implications on the design of invehicle monitoring and alert systems to facilitate the interaction between the drivers and the automated vehicle.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Driving environments such as traffic density, scenario types, and weather are other important variables that influence takeover time [9], [13], [31], [33], [34]. When the traffic was in the same direction, studies found that heavy traffic increased takeover time [9], [31].…”

Section: A Factors Influencing Takeover Timementioning

confidence: 99%

Predicting Driver Takeover Time in Conditionally Automated Driving

Ayoub¹,

Du²,

Yang³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…A 2-back is a working memory task involving speech and is well established for generating mental workload. Previous neuroergonomics studies have used fNIRS to measure mental workload elicited by an N-back task, both in flight and driving simulators [41], [42], [43]; as well as human factors studies measuring mental workload with other physiological devices [44], [45], [46], [47]. This NDRT was carried out while the highly automated vehicle (HAV) was driving across a highway scenario, and we expect this condition to be a control condition for mental workload, as the 2-back task should not affect TiA (Fig.…”

Section: Automated Driving Scenariosmentioning

confidence: 99%

Using Fnirs to Verify Trust in Highly Automated Driving

Perelló-March¹,

Burns

Woodman

et al. 2022

SSRN Journal

View full text Add to dashboard Cite

“…For example, the Chengkai highway in Chongqing bridges and tunnels account for 80%, and original roadbed sections only account for 20% [4], because the driver in the tunnel-interchange closely spacing section needs to not only experience the "light adaptation" stage but also to confirm the traffic signs along the line. When the driving environment is complicated, the driver needs to increase the additional driving load to deal with this information [5][6][7][8]. erefore, the driving behavior in highway tunnels is more complicated than that in original roadbed sections, leading to different traffic flow characteristics and accident risk levels.…”

Section: Introductionmentioning

confidence: 99%

Influences of Exit Advance Guide Signs on the Trajectory and Speed of Passenger Cars in Highway Tunnels

Shang

Lian

et al. 2021

Journal of Advanced Transportation

View full text Add to dashboard Cite

The driving behavior in highway tunnels is more complicated than that in regular roadbed sections because the former is usually affected by black and white hole effects, tunnel clearance, and bad illumination. Unfortunately, the current Chinese criteria and the Uniform Traffic Control Equipment Manual (MUTCD) 2009 guidelines provide no clear method for setting exit advance guide signs in highway tunnels. Hence, a driving simulator-based experiment was conducted in the current study to analyze the effects of exit advance guide signs on the trajectory, speed, and acceleration of passenger cars in a highway tunnel under three different service levels. It was found that when the service level is first service level, second service level, and third service level, the setting of the exit advance guide signs made the initial transverse location of the vehicle from the tunnel exit advance by 13.39%, 21.20%, and 5.73%, the lane change distance is shortened by 6.34%, 20.18%, and 15.34%, the average speed is decreased by 1.44%, 2.40%, and 0.08%, and the acceleration is decreased to −0.10 m·s−2, −0.11 m·s−2, and −0.06 m·s−2. Thus, the exit guide signs in the tunnel played a certain optimization role in improving the traffic flow state of the section and reducing the traffic accident rate.

show abstract

Effect of cognitive load on drivers’ State and task performance during automated driving: Introducing a novel method for determining stabilisation time following take-over of control

Cited by 34 publications

References 22 publications

Predicting Driver Takeover Time in Conditionally Automated Driving

Predicting Driver Takeover Time in Conditionally Automated Driving

Using Fnirs to Verify Trust in Highly Automated Driving

Influences of Exit Advance Guide Signs on the Trajectory and Speed of Passenger Cars in Highway Tunnels

Contact Info

Product

Resources

About