Wildlife vehicle collision, commonly called roadkill, is a nascent threat to both humans and wild animals. The collision results in property damage, injuries, death, and financial losses to society and mankind. An automobile system is integrated with alert notification, image processing, and machine learning models. This study explores a newer dimension for wild animal detection and signals the driver during active nocturnal hours. The intelligent system uses histogram of oriented gradients (HOG), which extracts the essential thermography image features; next, the extracted features are fed to the pre-trained, convolutional neural network (ID-CNN). This intelligent system has been tested on a set of real scenarios and gives approximately 91% and 92% accuracy in the alert notification and detection of the wild animals in the transportation road system in the city of San Antonio, TX, USA. This proposed system will contribute to the reduction of vehicle collisions caused by wild animals.
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