The development of artificial vision systems to support driving has been
of great interest in recent years, especially after new learning models based on deep
learning. In this work, a framework is proposed for detecting road speed anomalies,
taking as reference the driving vehicle. The objective is to warn the driver in realtime
that a vehicle is overtaking dangerously to prevent a possible accident. Thus,
taking the information captured by the rear camera integrated into the vehicle, the
system will automatically determine if the overtaking that other vehicles make is
considered abnormal or dangerous or is considered normal. Deep learning-based
object detection techniques will be used to detect the vehicles in the road image.
Each detected vehicle will be tracked over time, and its trajectory will be analyzed to
determine the approach speed. Finally, statistical regression techniques will estimate
the degree of anomaly or hazard of said overtaking as a preventive measure. This
proposal has been tested with a significant set of actual road sequences in different
lighting conditions with very satisfactory results.
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