Vision-based vehicle body slip angle estimation with multi-rate Kalman filter considering time delay

Body slip angle is one of the most important information for vehicle motion control; as specific sensors for body slip angle measurement are expensive, it is necessary to investigate estimation methods using existing popular sensors such as gyro sensor, encoder, camera, etc. For EV (electric vehicle), in particular, the motor response is several milliseconds which enables high performance control with short control period; fast signal feedback is consequently desired. Nevertheless, the sampling rate of a normal camera is much slower compared with other kinds of onboard sensors and the time delay caused by image processing cannot be neglected. In this paper, the two problems are solved using a multi-rate Kalman filter with measurement delay included; the estimated body slip angle can be updated every 1 ms. First of all, vehicle model and visual model are explained followed with experimental setup introduction; then, real-time image processing techniques are briefly introduced; and then, single-rate and multi-rate Kalman filters considering time delay are designed to estimate body slip angle; finally, conclusion and further works are presented.