Potential applications of this research include enhanced design for next-generation ACC systems and countermeasures to improve safe driving with ACC.

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Optical Intra-oral Scanners (IOS) are widely used in digital dentistry, providing 3-Dimensional (3D) and high-resolution geometrical information of dental crowns and the gingiva. Accurate 3D tooth segmentation, which aims to precisely delineate the tooth and gingiva instances in IOS, plays a critical role in a variety of dental applications. However, segmentation performance of previous methods are error-prone in complicated tooth-tooth or tooth-gingiva boundaries, and usually exhibit unsatisfactory results across various patients, yet the clinically applicability is not verified with large-scale dataset. In this paper, we propose a novel method based on 3D transformer architectures that is evaluated with large-scale and high-resolution 3D IOS datasets. Our method, termed TFormer, captures both local and global dependencies among different teeth to distinguish various types of teeth with divergent anatomical structures and confusing boundaries. Moreover, we design a geometry guided loss based on a novel point curvature to exploit boundary geometric features, which helps refine the boundary predictions for more accurate and smooth segmentation. We further employ a multi-task learning scheme, where an additional teeth-gingiva segmentation head is introduced to improve the performance. Extensive experimental results in a large-scale dataset with 16,000 IOS, the largest IOS dataset to our best knowledge, demonstrate that our TFormer can surpass existing state-of-the-art baselines with a large margin, with its utility in real-world scenarios verified by a clinical applicability test.

The purpose of this study was to examine drivers’ cell phone use behavior as reflected in naturalistic driving data. Video data from 1 week's worth of driving for 108 participants were visually scored for all instances of cell phone use, including conversation and visual or manual (VM) tasks. The frequency of cell phone use for each participant was used to classify drivers’ behavior. Three frequency groups (low, moderate, and high) were scored across all drivers for conversation and VM tasks separately. The regression tree method was used to classify drivers’ cell phone use behavior and identify associated factors. Drivers’ individual factors, including age, annual driving mileage, and education levels, as well as situational factors, including use duration, time of day, road type, lighting (day and night), traffic conditions, and speed when initiating cell phone use, impacted drivers’ cell phone use behavior. The impacts of these factors were different for cell phone conversation and VM tasks. Traffic conditions were identified as affecting drivers’ cell phone VM task use frequency but not cell phone conversation frequency. The study also looked at driver self-regulation behavior based on the frequency of cell phone use.

The purpose of this study is to examine eye-glance patterns of drivers engaged in cell phone related tasks. To observe eye-glance patterns, researchers used naturalistic driving data from the Integrated Vehicle-Based Safety Systems field operational test to construct and tabulate two datasets. One dataset included gaze data that were coded from cell phone conversation clips by fifty different drivers under different driving conditions. The second dataset was created in a similar way using video clips from twenty-four drivers who engaged in visual-manual tasks (e.g., texting and dialing). Mixed-model analyses were conducted. Results showed that drivers' on-road gazes were longer when they were engaged in a cell phone conversation than when they were not engaged. Off-road gaze length was the same, regardless of task involvement. In contrast, drivers who engaged in visual-manual tasks had substantially shorter on-road gaze length compared to when those same drivers were not involved in visual-manual tasks.