Ahmedabad: Scientists have developed an efficient, low-cost obstacle detection and alert system that can help cars avoid colliding with animals on the road in real time.
Researchers from Gujarat Technological University have built a system that uses a dashboard camera and an algorithm that can determine whether an object near the vehicle is an on-road cow and whether or not its movements represent a risk to the vehicle.
A timely audio or visual indicator can then be triggered to nudge the driver to apply the brakes whether or not they have seen an animal.
Road infrastructure is not keeping pace with traffic demands especially in roads connecting villages and towns, researchers said.
"In our current work, we have proposed and designed a system based on histogram research including oriented gradients and boosted cascade classifiers for automatic cow detection," they said.
The method was tested on various video clips involving cow movements in various scenarios.
"The proposed system has achieved an overall efficiency of 80 percent in terms of cow detection," researchers said.
On busy, imperfect roads, a cow represents a significant obstacle that must also be taken into account.
Safety, security and comfort are generally considered important to vehicle design with performance, fuel economy and other factors also considered in terms of how marketable a given vehicle will be.
However, road traffic collisions are the leading cause of death of people between the ages of 15 and 29 years old, according to the World Health Organization, is road traffic collisions.
Technology to reduce this grave incidence should be a high priority of vehicle design. India has the second largest road network in the world and 1 in 20,000 people die there in a road traffic accident, 12 in 70,000 are seriously injured in such accidents.
"The proposed system is a low-cost, highly reliable system which can easily be implemented in automobiles for detection of cow or any other animal after proper training and testing on the highway," researchers said.
The study was published in the journal International Journal of Vehicle Autonomous Systems.