Vision-Based Lateral State Estimation for Integrated Control of Automated Vehicles Considering Multirate and Unevenly Delayed Measurements

Integrated position and motion control are critical for future automated vehicles, but the sampling sequences of the two control loops are different. Normally, the position-related measurements from an onboard vision system are infrequent and with delay. Aiming at improved position states estimation, this paper develops a multisensor fusion estimation scheme based on a vision-vehicle model. Different from conventional methods such as state augmentation, an algorithm solving the sensor sampling mismatch issues in a single step is proposed. To design the estimator, a combined vision-vehicle model for lateral vehicle state estimation is first introduced, and the multirate and uneven sampling delay problems are explained from the perspectives of sensor fusion. Then, a measurement reconstruction algorithm with intersample compensation is introduced to solve the sensor measurement mismatch issue. Using this method, vehicle lateral states such as lateral position to the lane and heading angle can be updated faster and more reliably. Performance of the proposed lateral state estimation algorithm is verified by field test data.