Despite the continual advances in Advanced Driver Assistance Systems (ADAS)
and the development of high-level autonomous vehicles (AV), there is a general
consensus that for the short to medium term, there is a requirement for a human
supervisor to handle the edge cases that inevitably arise. Given this
requirement, it is essential that the state of the vehicle operator is
monitored to ensure they are contributing to the vehicle's safe operation. This
paper introduces a dual-source approach integrating data from an infrared
camera facing the vehicle operator and vehicle perception systems to produce a
metric for driver alertness in order to promote and ensure safe operator
behaviour. The infrared camera detects the driver's head, enabling the
calculation of head orientation, which is relevant as the head typically moves
according to the individual's focus of attention. By incorporating
environmental data from the perception system, it becomes possible to determine
whether the vehicle operator observes objects in the surroundings. Experiments
were conducted using data collected in Sydney, Australia, simulating AV
operations in an urban environment. Our results demonstrate that the proposed
system effectively determines a metric for the attention levels of the vehicle
operator, enabling interventions such as warnings or reducing autonomous
functionality as appropriate. This comprehensive solution shows promise in
contributing to ADAS and AVs' overall safety and efficiency in a real-world
setting.

这篇论文介绍了一种利用红外摄像机和车辆感知系统的双源方法，以便根据驾驶员的警觉程度评估其操作安全性，并在需要时进行干预，从而为 ADAS 和 AV 的整体安全和效率做出贡献。

自动驾驶车辆运行期间安全驾驶员注意力分类

Classification of Safety Driver Attention During Autonomous Vehicle  Operation

Hand gesture detection is a well-explored area in computer vision with
applications in various forms of Human-Computer Interactions. In this work, we
propose a technique for simultaneous hand gesture classification, handedness
detection, and hand keypoints localization using thermal data captured by an
infrared camera. Our method uses a novel deep multi-task learning architecture
that includes shared encoderdecoder layers followed by three branches dedicated
for each mentioned task. We performed extensive experimental validation of our
model on an in-house dataset consisting of 24 users data. The results confirm
higher than 98 percent accuracy for gesture classification, handedness
detection, and fingertips localization, and more than 91 percent accuracy for
wrist points localization.

本文提出了一种使用红外热成像技术进行的手势分类、左右手检测、手部关键点定位的深度多任务学习模型，通过在包含 24 个用户数据的自有数据集上进行实验验证，结果表明手势分类、左右手检测、指尖定位的准确率均高于 98％，手腕定位的准确率超过了 91％。