Modeling Drivers’ Takeover Behavior Depending on the Criticality of Driving Situations and the Complexity of Secondary Tasks

Tanshi, Foghor; Söffker, Dirk

doi:10.1109/cogsima.2019.8724304

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…The effects of NDRTs on driver and takeover time quality are examined using a simulator [15][16][17][18][19][20]. These studies examine the influence of the different traffic scenarios and NDRTs on the takeover process during automated driving, because NDRTs are the physical, mental, or visual activities that may affect the human factor and endanger driving safety.…”

Section: Case Description/taskmentioning

confidence: 99%

Evaluation of Takeover Time Performance of Drivers in Partially Autonomous Vehicles Using a Wearable Sensor

2022

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Advanced driver assistance systems and conditional automation (SAE level L3) bring full of challenges, central to the human role in such automated driving and are the transitions from automated to manual driving mode: driver monitoring systems should be able to adapt to the detected human state adjusting time given for takeover request. In this study, the human driver’s control authority during the transition from the automated system is evaluated by using a large set of sensors, including the galvanic skin response sensor, the speed, and the current sensor of the electric vehicle. The Local Outlier Factor (LOF) method detects the driver’s readiness to take control during the control authority transition transient stage. Eighteen drivers (8 females and 10 males) between 24 and 65 years of age participate in the experimental evaluation and data collection campaign. Takeover request time analysis evaluates each driver and three driver categories, such as experienced, semiexperienced, and inexperienced, and the questionnaire validates the takeover request time analysis. Galvanic skin response measurements and the LOF approach illustrate successful detection of the driver’s status about his/her readiness to take over and regulate the driving task when the transition of driving authority from the automated system is requested in a real road driving scenario. The takeover request time is personalized for each driver, which may improve the current conditional automated driving technologies’ penetration and acceptance.

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Section: Case Description/taskmentioning

confidence: 99%

Evaluation of Takeover Time Performance of Drivers in Partially Autonomous Vehicles Using a Wearable Sensor

2022

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“…Takeover readiness defines the driver's ability to regain control of the vehicle from the automated mode. Non-driving related tasks during automated driving may interfere with a driver's ability to regain control of the vehicle [56]. Thus, it is necessary to help the driver stay prepared for a takeover.…”

Section: Take-over Readiness Evaluationmentioning

confidence: 99%

“…Factors may include traffic situation complexity, ego-motion of the vehicle, and type of secondary tasks, etc. [56] studies how the complexity of the driving task and secondary task impact takeover reaction time. A mathematical formula estimates takeover reaction time based on experimental data.…”

Section: Take-over Readiness Evaluationmentioning

confidence: 99%

Artificial Intelligence Methods in In-Cabin Use Cases: A Survey

Han

Hellert

et al. 2021

Preprint

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As interest in autonomous driving increases, efforts are being made to meet requirements for the high-level automation of vehicles. In this context, the functionality inside the vehicle cabin plays a key role in ensuring a safe and pleasant journey for driver and passenger alike. At the same time, recent advances in the field of artificial intelligence (AI) have enabled a whole range of new applications and assistance systems to solve automated problems in the vehicle cabin. This paper presents a thorough survey on existing work that utilizes AI methods for use-cases inside the driving cabin, focusing, in particular, on application scenarios related to (1) driving safety and (2) driving comfort. Results from the surveyed works show that AI technology has a promising future in tackling in-cabin tasks within the autonomous driving aspect.

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“…While these studies show how context factors have an influence in highway scenarios, this has not been studied yet in an urban environment. As Tanshi and Söffker (2019) mentions, the complexity of a situation increases not only with the number of surrounding vehicles but also pedestrians, as more interactions are then possible.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Mobility in the City: The Influence of System and Context Factors on Drivers’ Takeover Willingness and Trust in Automated Vehicles

Lanzer¹,

Stoll²,

Colley

et al. 2021

Front. Hum. Dyn.

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Automated driving in urban environments not only has the potential to improve traffic flow and heighten driver comfort but also to increase traffic safety, particularly for vulnerable road users such as pedestrians. For these benefits to take effect, drivers need to trust and use automated vehicles. This decision is influenced by both system and context factors. However, it is not yet clear how these factors interact with each other, especially for automated driving in city scenarios with crossing pedestrians. Therefore, we conducted an online experiment in which participants (N = 68) experienced short automated rides from the driver’s perspective through an urban environment. In each of the presented videos, a pedestrian crossed the street in front of the automated vehicle while system and context factors were varied: 1) the crossing pedestrian’s intention was either visualized correctly (as crossing) or incorrectly (visualization missing) by the automated vehicle (system factor), 2) the pedestrian was either distracted by using a smartphone while crossing or not (context factor), and 3) the scenario was either more or less complex depending on the number of other vehicles and pedestrians being present (context factor). In situations with a system malfunction where the crossing pedestrian’s intention was not visualized, participants perceived the situation as more critical, had less trust in the automated system, and a higher willingness to take over control regardless of any context factors. However, when the system worked correctly, the crossing pedestrian’s smartphone usage came into play, especially in the less complex scenario. Participants perceived situations with a distracted pedestrian as more critical, trusted the system less, indicated a higher willingness to take over control, and were more uncertain about their decision. As this study demonstrates the influence of distracted pedestrians, more research is needed on context factors and their inclusion in the design of interfaces to keep drivers informed during automated driving in urban environments.

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Modeling Drivers’ Takeover Behavior Depending on the Criticality of Driving Situations and the Complexity of Secondary Tasks

Cited by 7 publications

References 9 publications

Evaluation of Takeover Time Performance of Drivers in Partially Autonomous Vehicles Using a Wearable Sensor

Evaluation of Takeover Time Performance of Drivers in Partially Autonomous Vehicles Using a Wearable Sensor

Artificial Intelligence Methods in In-Cabin Use Cases: A Survey

Intelligent Mobility in the City: The Influence of System and Context Factors on Drivers’ Takeover Willingness and Trust in Automated Vehicles

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