  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.
