Acquisition and processing of vehicle dynamics data for predictive modeling

This paper presents a modular, low-latency data acquisition and analytics platform for vehicle dynamics modeling. The system integrates a physical front-right suspension prototype with STM32-based CAN-FD-enabled microcontrollers, custom analog front-end circuits, and a Raspberry Pi–based telemetry dashboard. Sensor data–including displacement, steering angle, damper velocity, and temperature–is streamed via a robust CAN-FD protocol and visualized using a Plotly-Dash interface with InfluxDB logging. Rule based and gaussian mixture models analyze damper behavior, classify damping regimes, and estimate tire thermal state using wheel speed, accelerometer, and temperature data. Experimental validation on a custom mechanical test rig demonstrates accurate damper curve generation and traction estimates. The project highlights a cross-domain approach that merges mechanical prototyping, embedded systems, and AI-driven analytics to deliver an extensible platform for real-time automotive subsystem evaluation.