Animal-vehicle collisions (AVCs) pose a significant threat to road safety, wildlife conservation, and transportation systems worldwide. Advances in artificial intelligence (AI) and computer vision have enabled intelligent detection and mitigation systems aimed at reducing such collisions. This review synthesizes the current state of AI-powered AVC prevention systems, examining deep learning architectures, multimodal sensor technologies, real-time processing frameworks, and system-level integration strategies. We analyze the transition from traditional computer vision methods to modern deep neural networks, evaluate sensor fusion approaches, and assess existing wildlife detection datasets and benchmarking practices. Key technical challenges are identified, including environmental variability, long-range detection constraints, dataset scarcity, cross-species generalization limitations, and real-time safety requirements. Rather than framing AVC prevention solely as an object detection task, this review conceptualizes it as a safety-critical perception and risk assessment pipeline operating under strict latency and deployment constraints. Persistent gaps in wildlife-specific detection, standardized evaluation protocols, and scalable edge deployment are discussed. To organize these insights, we present WildSafe-Edge as a conceptual reference architecture derived from the literature, synthesizing system-level design considerations and highlighting open research directions. Future research directions include transfer learning, synthetic data augmentation, vehicle-to-everything (V2X) integration, and edge-centric architectures to enable robust, real-world collision mitigation systems.
Saraboji et al. (Tue,) studied this question.