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Animal–vehicle collision is a common danger on highways, especially during nighttime driving. Its likelihood is affected not only by the low visibility during nighttime hours, but also by the unpredictability of animals’ actions when a vehicle is nearby. Extensive research has shown that the lack of visibility during nighttime hours can be addressed using thermal imaging. However, to our knowledge, little research has been undertaken on predicting animal action through an animal’s specific poses while a vehicle is moving. This paper proposes a new system that couples the use of a two-dimensional convolutional neural network (2D-CNN) and thermal image input, to determine the risk imposed by an animal in a specific pose to a passing automobile during nighttime hours. The proposed system was tested using a set of thermal images presenting real-life scenarios of animals in specific poses on the roadside and was found to classify animal poses accurately in 82% of cases. Overall, it provides a valuable basis for implementing an automotive tool to minimize animal–vehicle collisions during nighttime hours. 

Animal Vehicle Collision (AVC) is relatively an evolving source of fatality resulting in the deficit of wildlife conservancy along with carnage. It’s a globally distressing and disturbing experience that causes monetary damage, injury, and human-animal mortality. Roadkill has always been atop the research domain and serendipitously provided heterogeneous solutions for collision mitigation and prevention. Despite the abundant solution availability, this research throws a new spotlight on wildlife-vehicle collision mitigation using highly efficient artificial intelligence during nighttime hours. This study focuses mainly on arousal mechanisms of the “Histogram of Oriented Gradients (HOG)” intelligent system with extracted thermography image features, which are then processed by a trained, convolutional neural network (1D-CNN). The above computer vision – deep learning-based alert system has an accuracy between 94%, and 96% on the arousal mechanisms with the empowered real-time data set utilization.