Detecting Socially Abnormal Highway Driving Behaviors via Recurrent Graph Attention Networks
Detecting Socially Abnormal Highway Driving Behaviors via Recurrent Graph Attention Networks
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DOI：
10.1145/3543507.3583452
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Yue Hu;Yuhang Zhang;Yanbing Wang;D. Work
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文献类型：
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作者：
Yue Hu;Yuhang Zhang;Yanbing Wang;D. Work
随着物联网技术的快速发展，下一代交通监控基础设施通过网络连接，以辅助交通数据收集和智能交通管理。交通中最重要的任务之一是异常检测，因为异常驾驶员会降低交通效率并导致安全问题。本文主要研究从高速公路视频监控系统产生的轨迹中检测异常驾驶行为。目前的异常驾驶行为检测方法大多集中在处理单个车辆的有限类别的异常行为，而不考虑车辆的相互作用。在这项工作中，我们考虑检测各种社会异常驾驶行为的问题，即，与附近其他驾驶员的行为不一致的行为。这项任务是复杂的各种车辆的相互作用和时空变化的性质，公路交通。为了解决这个问题，我们提出了一个自动编码器与循环图注意力网络，可以捕捉高速公路驾驶行为的上下文环境中周围的汽车，并检测偏离学习模式的异常。我们的模型可扩展到具有数千辆汽车的大型高速公路。通过对交通仿真软件生成的数据进行实验，证明了该模型是目前最先进的异常检测模型中唯一一个能够准确检测出进行社会异常行为的车辆的模型。我们进一步展示了真实的世界HighD交通数据集上的性能，其中我们的模型检测违反当地驾驶规范的车辆。
With the rapid development of Internet of Things technologies, the next generation traffic monitoring infrastructures are connected via the web, to aid traffic data collection and intelligent traffic management. One of the most important tasks in traffic is anomaly detection, since abnormal drivers can reduce traffic efficiency and cause safety issues. This work focuses on detecting abnormal driving behaviors from trajectories produced by highway video surveillance systems. Most of the current abnormal driving behavior detection methods focus on a limited category of abnormal behaviors that deal with a single vehicle without considering vehicular interactions. In this work, we consider the problem of detecting a variety of socially abnormal driving behaviors, i.e., behaviors that do not conform to the behavior of other nearby drivers. This task is complicated by the variety of vehicular interactions and the spatial-temporal varying nature of highway traffic. To solve this problem, we propose an autoencoder with a Recurrent Graph Attention Network that can capture the highway driving behaviors contextualized on the surrounding cars, and detect anomalies that deviate from learned patterns. Our model is scalable to large freeways with thousands of cars. Experiments on data generated from traffic simulation software show that our model is the only one that can spot the exact vehicle conducting socially abnormal behaviors, among the state-of-the-art anomaly detection models. We further show the performance on real world HighD traffic dataset, where our model detects vehicles that violate the local driving norms.