Abstract:
Sharp curves are hazardous because of narrow, poorly maintained, and limited visibility
roads. Drivers in heterogeneous traffic may engage in dangerous driving behaviors such
as overtaking on sharp curves or driving at high speeds. As per the Ministry of Road
Transport and Highway (MoRTH 2016), 21% of traffic accidents occur due to the
overtaking of vehicles. The main reason for accidents on curve roads is due to the high
speed of the driver or unawareness of the oncoming vehicle to the curve. Lack of
communication between the ends of the curve and visibility problems around the curve
make hazardous situations. Existing methodologies like convex mirrors, sensor-based
collision avoidance systems, headlights, and horns are used to reduce collisions. In
modern transportation systems, in-vehicle and in-road technology are important for
collision avoidance. User perception studied through a questionnaire survey is first
conducted to analyze how the users perceive the new concept of the driving assistance
system instead of the conventional system (eg: convex mirror) that are commonly
provided. For the detection process, vehicle detection algorithms (YOLOv5l) are used.
Vehicle detection involves analyzing an image or video to identify the presence of
vehicles by detecting patterns, shapes, and colors that correspond to vehicles, and then
distinguishing them from the background. The annotation and augmentation process
helps to improve the accuracy of the model. Curve collision warning systems use
cameras and signal boards, to detect when a vehicle is approaching a curve too quickly,
and then issue a visual warning to the driver to slow down or take other corrective
action to avoid a potential collision. The accuracy and precision obtained for the curve
collision warning are analyzed and both results are above the acceptable limit. So, this
collision avoidance system is essential in preventing accidents and saving lives by
detecting and responding to the potential collision in real-time.
