论文标题
综合分心驾驶（SynDD2）数据集用于分析分心的行为和驾驶员的各种凝视区域
Synthetic Distracted Driving (SynDD2) dataset for analyzing distracted behaviors and various gaze zones of a driver
论文作者
论文摘要
本文为机器学习模型提供了一个综合分散注意力的驾驶（Syndd2 -syndd1的连续体）数据集，以检测和分析驾驶员的各种分心行为和不同的凝视区域。我们使用位于位置的三个车载摄像机在固定车辆中收集了数据：在仪表板上，后视镜附近以及右侧右侧窗户角。数据集包含两种活动类型：分散注意力的活动和每个参与者的凝视区域，每个活动类型都有两种集合：没有外观块，外观块，例如戴上帽子或太阳镜。每个参与者的每个活动的顺序和持续时间都是随机的。此外，数据集包含每个活动的手动注释，并具有注释的开始和结束时间。研究人员可以使用此数据集评估机器学习算法的性能，以对各种分心的活动和驾驶员的凝视区域进行分类。
This article presents a synthetic distracted driving (SynDD2 - a continuum of SynDD1) dataset for machine learning models to detect and analyze drivers' various distracted behavior and different gaze zones. We collected the data in a stationary vehicle using three in-vehicle cameras positioned at locations: on the dashboard, near the rearview mirror, and on the top right-side window corner. The dataset contains two activity types: distracted activities and gaze zones for each participant, and each activity type has two sets: without appearance blocks and with appearance blocks such as wearing a hat or sunglasses. The order and duration of each activity for each participant are random. In addition, the dataset contains manual annotations for each activity, having its start and end time annotated. Researchers could use this dataset to evaluate the performance of machine learning algorithms to classify various distracting activities and gaze zones of drivers.
