Development of Autonomous Driving Systems Using State Estimator with Multi-rate Sampled-data

This paper presents a state estimator design method using multi-rate sampled-data for autonomous vehicle driving system. The proposed state estimator is designed by using an affine matched T-S fuzzy model with the sampling information of each sensors. For the tracking control of autonomous driving system with fusion sensors, overall control structure is designed by using the error dynamic model based state estimator. The proposed method is verified by the experimental results on the Husky unmanned ground vehicle (UGV) equipped with light detection and ranging (LiDAR), camera and encoder.