This case study explains how AI-powered video telematics systems are transforming fleet safety and efficiency by performing real-time video, radar, and sensor analytics directly at the edge. As adoption accelerates, providers face challenges running complex vision and AI models on power- and cost-constrained SoCs, where latency, data flow, and hardware utilization are critical. The paper highlights how optimized AI models and streamlined video pipelines enable real-time hazard detection, driver alerts, and predictive insights without relying on constant cloud connectivity. By translating high-level AI workloads into hardware-efficient implementations across CPUs, GPUs, and NPUs, and by building scalable, low-latency vision pipelines, optimized edge AI deployments unlock new safety features, improve reliability, and position video telematics platforms for long-term growth as AI models and SoC capabilities continue to evolve.

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