Lateral State Estimation for Lane Keeping Control of Electric Vehicles Considering Sensor Sampling Mismatch Issue 2016-01-1900

Vehicle lateral states such as lateral distance at a preview point and heading angle are indispensable for lane keeping control systems, and such states are normally estimated by fusing signals from an onboard vision system and inertial sensors. However, the sampling rates and measurement delays are different between the two kinds of sensing devices. Most of the conventional methods simply neglect measurement delay and reduce sampling rate of the estimator to adapt to the slow sensors/devices. However, the estimation accuracy is deteriorated, especially considering the delay of visual signals may not be constant. In case of electric vehicles, the actuators for steering and traction are motors that have high control frequency. Therefore, the frequency of vehicle state feedback may not match the control frequency if the estimator is infrequently updated. In this paper, a multi-rate estimation algorithm based on Kalman filter is proposed to provide lateral states with high frequency. First of all, a combined vehicle and vision model for lateral position estimation is introduced, and then, the necessity to compensate uneven sampling delay for lane keeping control is briefly explained. Next, a measurement reconstruction algorithm is introduced to solve the uneven delay and multi-rate issues of the sensing devices. That is, real-time and precise vehicle lateral position and heading angle signals are available for lane keeping control systems with the proposed method. Finally, the effectiveness of the proposed estimation algorithm is verified by simulations.