  Animals have been a common sighting on roads in India which leads to several
accidents between them and vehicles every year. This makes it vital to develop
a support system for driverless vehicles that assists in preventing these forms
of accidents. In this paper, we propose a neoteric framework for avoiding
vehicle-to-animal collisions by developing an efficient approach for the
detection of animals on highways using deep learning and computer vision
techniques on dashcam video. Our approach leverages the Mask R-CNN model for
detecting and identifying various commonly found animals. Then, we perform lane
detection to deduce whether a detected animal is on the vehicle's lane or not
and track its location and direction of movement using a centroid based object
tracking algorithm. This approach ensures that the framework is effective at
determining whether an animal is obstructing the path or not of an autonomous
vehicle in addition to predicting its movement and giving feedback accordingly.
This system was tested under various lighting and weather conditions and was
observed to perform relatively well, which leads the way for prominent
driverless vehicle's support systems for avoiding vehicular collisions with
animals on Indian roads in real-time.
