# Anomalous Vehicle Recognition in Smart Urban Traffic Monitoring as an Edge Service +
## 2 Related Work  
### Anomaly Vehicle Behavior Detection  
A plethora of research efforts are reported in this area [25; 26; 27], and recently analysis and prediction of behaviors of autonomous driving have attracted more attention [28; 29; 30]. In this subsection, we provide a concise discussion on works that are closely related to our proposal. A novel deep-learning-based model is proposed for abnormal driving detection [31], in which a stacked sparse autoencoder model is adopted to learn generic driving behavior features from an unlabelled dataset in an unsupervised manner. Then,an error propagation algorithm is used with labeled data to fine-tune the model for a better performance. Corresponding to the behaviors under consideration, there are four patterns defined in this work: normal, drunk/fatigue, reckless, and phone use [31].  
A vehicle-edge-cloud framework is suggested to evaluate driving behaviors with deep learning [32], where driving behaviors are marked in three rankings with scores in a descending order: A, B, and C. In this framework, real-time vehicle driving data are transmitted to edge layers for reference, and the cloud exists to train or upgrade the predictive model. Similarly, in another work, researchers leverage edge layers to pre-process traffic video data to reduce network traffic [33]. The processed data will be transmitted to the cloud for vehicle detection and anomaly behavior detection. In contrast, [PERSON] et al. aim to detect drivers' inattention driving behaviors with large scale vehicle trajectory data [34]. In addition, based on the output derived from the inattention detection combining with point of interest and climate data, a Long Short Term Memory (LSTM) based model is deployed to predict driver's upcoming abnormal operations on the road [34].  
Although these reported efforts have achieved remarkable performance in abnormal driving behaviors detection, the application scenarios are different with ours. First, in some research works, the edge layer serves as more like a transponder which receives data from end devices and pre-processes it for preliminary learning or simple features extraction and transmits them to a cloud layer for a complicated reference. Secondly, to recognize abnormal behaviors, prior knowledge is required to pre-define what is an abnormal driving behavior. Therefore, on the one hand, these approaches necessitate large-scale, good quality data sets that cover a sufficient amount of scenarios; on the other hand, in real-world traffic scenarios, the reliability could be weak since, in some cases, normal and abnormal behaviors can exchange.  
In this paper, instead of trying to define a complete set of abnormal patterns, our approach trains models with normal patterns that are the most common cases in real-world. Additionally, mSSA is firstly utilized to identify vehicles that behave differently with others, and then use the first Cascaded Capsules Network to predict whether the different behavior is normal. Otherwise, the second Capsules Network is activated to decide whether this behavior will be agreed by other vehicles. In this way, SurMon is more robust to various abnormal behaviors in practice and easier to collect training data.