Driving Maneuver Anomaly Detection Based on Deep Auto-Encoder and Geographical Partitioning
Driving Maneuver Anomaly Detection Based on Deep Auto-Encoder and Geographical Partitioning
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DOI：
10.1145/3563217
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发表时间：
2022-09
影响因子：
4.1
通讯作者：
Miaomiao Liu;K. Yang;Yanjie Fu;Dapeng Oliver Wu;Wan Du
中科院分区：
文献类型：
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作者：
Miaomiao Liu;K. Yang;Yanjie Fu;Dapeng Oliver Wu;Wan Du
本文介绍了GeoDMA，它可以处理多辆车的GPS数据，以检测快速加速、突然刹车和快速转向等异常驾驶动作。首先，设计了一种无监督的深度自动编码器，从所有驾驶员的正常历史GPS数据中学习一组独特的特征。我们考虑了个体驾驶员驾驶数据的时间相关性和不同驾驶员之间的空间相关性。其次，为了结合驾驶员在局部区域的节点依赖关系，我们提出了一种地理划分算法，将一个城市划分为若干子区域进行驾驶异常检测。具体地说，我们将车辆对车辆的依赖扩展到道路对道路的依赖，并将地理划分问题转化为优化问题。优化问题的目标是最大化每个子区域内道路的依赖程度，最小化任意两个不同子区域之间的道路依赖程度。最后，我们针对每个子区域训练一个特定的驾驶异常检测模型，并通过增量训练对这些模型进行现场更新。我们在Pytorch中实现了GeoDMA，并使用真实的大型GPS轨迹对其性能进行了评估。实验结果表明，GeoDMA的检测准确率比基线方法提高了8.5%。
This paper presents GeoDMA, which processes the GPS data from multiple vehicles to detect anomalous driving maneuvers, such as rapid acceleration, sudden braking, and rapid swerving. First, an unsupervised deep auto-encoder is designed to learn a set of unique features from the normal historical GPS data of all drivers. We consider the temporal dependency of the driving data for individual drivers and the spatial correlation among different drivers. Second, to incorporate the peer dependency of drivers in local regions, we develop a geographical partitioning algorithm to partition a city into several sub-regions to do the driving anomaly detection. Specifically, we extend the vehicle-vehicle dependency to road-road dependency and formulate the geographical partitioning problem into an optimization problem. The objective of the optimization problem is to maximize the dependency of roads within each sub-region and minimize the dependency of roads between any two different sub-regions. Finally, we train a specific driving anomaly detection model for each sub-region and perform in-situ updating of these models by incremental training. We implement GeoDMA in Pytorch and evaluate its performance using a large real-world GPS trajectories. The experiment results demonstrate that GeoDMA achieves up to 8.5% higher detection accuracy than the baseline methods.