Development of Vehicle State Estimation Method for Dedicated Sensor-Less Semi-active Suspension Using AI Technology
摘要
This paper presents a sensor-less vehicle state estimation method using a neural network for semi-active suspensions. This method surpasses conventional mathematical models in performance and reduces calibration effort. The developed system, logic, and learning method are designed to address AI-specific challenges such as increased processing load and learning techniques, and their performance is validated through simulations and real-world tests. The results show that this system performs on par with those using dedicated sensors.