  Traffic near-crash events serve as critical data sources for various smart
transportation applications, such as being surrogate safety measures for
traffic safety research and corner case data for automated vehicle testing.
However, there are several key challenges for near-crash detection. First,
extracting near-crashes from original data sources requires significant
computing, communication, and storage resources. Also, existing methods lack
efficiency and transferability, which bottlenecks prospective large-scale
applications. To this end, this paper leverages the power of edge computing to
address these challenges by processing the video streams from existing dashcams
onboard in a real-time manner. We design a multi-thread system architecture
that operates on edge devices and model the bounding boxes generated by object
detection and tracking in linear complexity. The method is insensitive to
camera parameters and backward compatible with different vehicles. The edge
computing system has been evaluated with recorded videos and real-world tests
on two cars and four buses for over ten thousand hours. It filters out
irrelevant videos in real-time thereby saving labor cost, processing time,
network bandwidth, and data storage. It collects not only event videos but also
other valuable data such as road user type, event location, time to collision,
vehicle trajectory, vehicle speed, brake switch, and throttle. The experiments
demonstrate the promising performance of the system regarding efficiency,
accuracy, reliability, and transferability. It is among the first efforts in
applying edge computing for real-time traffic video analytics and is expected
to benefit multiple sub-fields in smart transportation research and
applications.
