Car accidents remain a significant public safety issue worldwide, with the
majority of them attributed to driver errors stemming from inadequate driving
knowledge, non-compliance with regulations, and poor driving habits. To improve
road safety, Driving Behavior Detection (DBD) systems have been proposed in
several studies to identify safe and unsafe driving behavior. Many of these
studies have utilized sensor data obtained from the Controller Area Network
(CAN) bus to construct their models. However, the use of publicly available
sensors is known to reduce the accuracy of detection models, while
incorporating vendor-specific sensors into the dataset increases accuracy. To
address the limitations of existing approaches, we present a reliable DBD
system based on Graph Convolutional Long Short-Term Memory Networks (GConvLSTM)
that enhances the precision and practicality of DBD models using public
sensors. Additionally, we incorporate non-public sensors to evaluate the
model's effectiveness. Our proposed model achieved a high accuracy of 97.5\%
for public sensors and an average accuracy of 98.1\% for non-public sensors,
indicating its consistency and accuracy in both settings. To enable local
driver behavior analysis, we deployed our DBD system on a Raspberry Pi at the
network edge, with drivers able to access daily driving condition reports,
sensor data, and prediction results through a monitoring dashboard.
Furthermore, the dashboard issues voice warnings to alert drivers of hazardous
driving conditions. Our findings demonstrate that the proposed system can
effectively detect hazardous and unsafe driving behavior, with potential
applications in improving road safety and reducing the number of accidents
caused by driver errors.

提出了一种基于图卷积长短期记忆网络模型的可靠的驾驶行为检测系统，它使用公共传感器增强了传感器数据模型的精度和实用性。同时，我们引入了非公共传感器用于评估模型的有效性，结果显示该模型在两种情况下的准确率均表现出一致性和高精度，这个系统可以有效地检测危险和不安全的驾驶行为，具有提高道路安全性和减少由驾驶员错误引起的事故的潜在应用。

利用图卷积循环网络准确检测危险驾驶行为以增强道路安全

Enhancing Road Safety through Accurate Detection of Hazardous Driving  Behaviors with Graph Convolutional Recurrent Networks

This paper examines the problem of dynamic traffic scene classification under
space-time variations in viewpoint that arise from video captured on-board a
moving vehicle. Solutions to this problem are important for realization of
effective driving assistance technologies required to interpret or predict road
user behavior. Currently, dynamic traffic scene classification has not been
adequately addressed due to a lack of benchmark datasets that consider
spatiotemporal evolution of traffic scenes resulting from a vehicle's
ego-motion. This paper has three main contributions. First, an annotated
dataset is released to enable dynamic scene classification that includes 80
hours of diverse high quality driving video data clips collected in the San
Francisco Bay area. The dataset includes temporal annotations for road places,
road types, weather, and road surface conditions. Second, we introduce novel
and baseline algorithms that utilize semantic context and temporal nature of
the dataset for dynamic classification of road scenes. Finally, we showcase
algorithms and experimental results that highlight how extracted features from
scene classification serve as strong priors and help with tactical driver
behavior understanding. The results show significant improvement from
previously reported driving behavior detection baselines in the literature.

本文介绍了一种面临道路运动相机拍摄时空视角变化而实现动态交通场景分类的解决方案，包括创建了 80 小时的数据集和使用语义上下文和时间性的新型算法开发了一种道路场景动态分类系统，可以帮助理解驾驶员的驾驶行为。