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In Level-3 autonomous driving, drivers are required to take over in an emergency upon receiving a request from an autonomous vehicle (AV). However, before the deadline for the takeover request expires, drivers are not considered fully responsible for the accident, which may make them hesitant to assume control and take on full liability before the time runs out. Therefore, to prevent problems caused by late takeover, it is important to know which factors influence a driver's willingness to take over in an emergency. To address this issue, we recruited 250 participants each for both video-based and text-based surveys to investigate the takeover decision in a dilemmatic situation that can endanger the driver, with the AV either sacrificing a group of pedestrians or the driver if the participants do not intervene. The results showed that 88.2% of respondents chose to take over when the AV intended to sacrifice the driver, while only 59.4% wanted to take over when the pedestrians would be sacrificed. Additionally, when the AV's chosen path matched the participant's intention, 77.4% chose to take over when the car intended to sacrifice the driver compared with only 34.3% when the pedestrians would be sacrificed. Furthermore, other factors such as sex, driving experience, and driving preferences partially influenced takeover decisions; however, they had a smaller effect than the situational context. Overall, our findings show that regardless of the driving intention of an AV, informing drivers that their safety is at risk can enhance their willingness to take over control of an AV in critical situations.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11064069 | PMC |
http://dx.doi.org/10.1016/j.heliyon.2024.e29616 | DOI Listing |
Behav Sci (Basel)
July 2025
Key Laboratory of Human Factors and Ergonomics, State Administration for Market Regulation, China National Institute of Standardization, Beijing 100191, China.
With the increasing prevalence of autonomous vehicles (AVs), drivers' spatial cognition and takeover performance have become critical to traffic safety. This study investigates the effects of landmark salience-specifically visual and structural salience-on drivers' spatial cognition and takeover behavior in autonomous driving scenarios. Two simulator-based experiments were conducted.
View Article and Find Full Text PDFAccid Anal Prev
September 2025
School of Automobile and Traffic Engineering, Hefei University of Technology, Hefei 230009 Anhui, PR China.
With advances in autonomous driving technology, conditional automated driving (CAD) is gradually entering the consumer market. Before full automation is achieved, however, drivers are still required to take control of the vehicle in complex scenarios. To increase safety and comfort in CAD systems, drivers must be able to respond promptly to takeover requests (TORs).
View Article and Find Full Text PDFFront Zool
July 2025
Division of Behavioural Ecology, Institute of Ecology and Evolution, University of Bern, 3032, Hinterkappelen, Switzerland.
Background: Parental care is costly for the caregiver. Therefore, parents should be able to discriminate between their own and conspecific offspring to avoid costly misdirected care. Infanticide, the intentional killing of conspecific young by adult individuals, occurs in many animal taxa.
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June 2025
State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China.
Level 3 automated driving systems require drivers to occasionally retake control, making it crucial to understand factors influencing takeover performance. This study investigates how single versus multiple non-driving-related tasks (NDRTs) affect driver behavior, perceptions, and takeover capability. Using a high-fidelity driving simulator with 32 participants, conditions varying NDRT types and task-switching frequency were examined.
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July 2025
Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, Torino, Italy, 10129.
Conditionally Automated driving (CAD) represents a pivotal point in the evolution of automotive technology, bridging full automation and human intervention through effective control mechanisms that ensure safe driver-system transitions. This research consisted of a comparative analysis of take-over mechanisms, focusing on ordinary merging and diverging maneuvers and critical collision-avoidance scenarios. Three take-over control (TOC) methods, including (i) accelerating/braking, (ii) pressing a dedicated button, and (iii) steering, were investigated.
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