Sep 2026· Proceedings of the 18th International Conference on Automotive User Interfaces and Interactive Vehicular Applications· pp. 36-47· 0 citations· 34 references
Abstract
Automated vehicles require drivers to take over manual control upon receiving a takeover request (TOR). However, drivers often continue performing non-driving related tasks (NDRTs) after a TOR, alternating between the NDRT and driving — a behavior known as interleaving. This study investigated the influence of natural breakpoints (NBs) in NDRTs and of monitoring requests (MRs) on interleaving in a fixed-base driving simulator study (N = 36). Results showed that drivers interrupt NDRTs at NBs in over 90% of cases after a TOR, supporting strategic interruption behavior. However, the number of NBs did not significantly influence interleaving duration or takeover time. Notably, MRs significantly reduced the likelihood of interleaving by 88.83% and improved drivers’ cognitive orientation toward maneuver-relevant areas. These findings suggest that MRs are a promising design measure to reduce interleaving and enhance situational awareness during TORs, contributing to safer human-automation transitions in conditionally automated driving.
Non-Driving-Related Task (NDRT) interruption has been proposed as a strategy to mitigate control-transition challenges in Level 3 cars. However, it remains unclear whether the system should actively suspend ongoing NDRTs. This paper presents the first study to test empirically whether suspending the NDRT at the moment...
Rawan Srour Zreik, M. Harvey, S. Brewster· Proceedings of the 18th Inte...· 0 citations
Drivers’ trust in conditionally automated vehicles (AVs) is a critical concept affecting voluntary takeover behavior. However, few studies have examined how long drivers manually control an AV before re-activating the automation as a function of their level of trust. This study investigated the distribution of time in...
Myeongkyu Lee, Brandon J. Pitts· Proceedings of the Human Fac...· 0 citations
Guidance through remote assistants (RAs) is a key component for introducing driverless highly automated vehicle fleets (SAE level 4) into future mobility systems. RAs likely encounter situations with variable task demands depending on the frequency of incoming tasks and the complexity of the problems they encounter, po...
Fabian Walocha, Andrea Valerio, Hoai Phuong Nguyen et al.· Proceedings of the 18th Inte...· 0 citations
Human driver errors remain a central road-safety concern, but existing error and violation taxonomies were developed mainly for conventional traffic. In mixed traffic, human drivers must also interpret and respond to autonomous vehicles (AVs), whose behavior, intent, recognizability, and social role may differ from tho...
Olga Mironenko, Hadi Banaee, A. Loutfi· European Transport Research...· 0 citations
Distracted driving is a recognized pedestrian safety risk, yet behavioral heterogeneity within distraction periods remains poorly characterized. We analyze 39,271 one-second observations from 720 naturalistic crosswalk traversals (162 drivers, SHRP2 NDS, 2008-2013) using a Hidden Markov Model (HMM) and Gaussian Mixture...
Grace A. Douglas, Linda Ng Boyle· Accident Analysis and Preven...· 0 citations
This paper proposes an analytical framework that integrates the SOTIF perspective with System‐Theoretic Process Analysis (STPA) to systematically analyze driver misuse in Level 3 automated driving environments. In Lv.3 systems, drivers must intervene when Takeover Requests (TOR) occur, making misuse a critical safety...
Yangkoo Lee, Eun-Hye Jang, Mi Chang et al.· ETRI Journal· 0 citations
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