Position Estimation in Urban U-Turn Section for Autonomous Vehicles Using Multiple Vehicle Model and Interacting Multiple Model Filter

Abstract

A positioning system estimates the position and orientation of a vehicle. Autonomous driving systems plan paths and control the vehicle based on the information from the positioning system. Recently, methods of estimating the position in urban areas have been actively studied. In U-turn sections, which are common in urban areas, vehicles perform a rotation to reverse the direction of travel. Through these sections, drivers can reduce the travel distance and save time but with a high risk of an accident. Despite there being a need for the development of autonomous driving schemes for U-turn sections, the existing research is limited. This study proposes an interacting multiple model (IMM) filter-based position estimation algorithm for urban U-turn sections. To reflect the dynamic characteristics of a vehicle during U-turn maneuvers, a multiple vehicle model was used. This model includes kinematic and dynamic vehicle models. The state estimates of the vehicle model and gyroscope are combined using an IMM filter. The position estimation algorithm developed in this study is verified via experiments. The experimental results indicate that, during urban U-turn maneuvers, the position estimation accuracy of the IMM filter-based algorithm is improved than that of the single vehicle model.