Accurate prediction, detection, and reconstruction of vehicle collisions are essential for improving road safety and reducing fatalities. AI models enable real-time collision detection, severity assessment, driver coaching, and risk forecasting, but they require large, high-quality datasets for training. Today, such data is often obtained through third-party hardware like AI dashcams or OBDII dongles. These approaches introduce uncertainty due to installation errors, device damage, proprietary preprocessing, and limited data coverage. As a result, models built on these sources may not generalize well and can perform poorly in real-world deployment. Using standardized vehicle data models such as VSS (Vehicle Signal Specification) and interfaces like VISS (Vehicle Information Service Specification) provides consistent, richer, and more reliable access to vehicle data. This enables the development of more advanced collision detection and real-time analytics, supporting driver coaching, risk mitigation, and ultimately safer driving experiences.