High Definition Map-Based Localization Using ADAS Environment Sensors for Application to Automated Driving Vehicles

Abstract

Featured Application High definition (HD) map, advanced driver assistance systems (ADASs), localization, iterative closest point (ICP), automated driving vehicle. This paper presents high definition (HD) map-based localization using advanced driver assistance system (ADAS) environment sensors for application to automated driving vehicles. A variety of autonomous driving technologies are being developed using expensive and high-performance sensors, but limitations exist due to several practical issues. In respect of the application of autonomous driving cars in the near future, it is necessary to ensure autonomous driving performance by effectively utilizing sensors that are already installed for ADAS purposes. Additionally, the most common localization algorithm, which is usually used lane information only, has a highly unstable disadvantage in the absence of that information. Therefore, it is essential to ensure localization performance with other road features such as guardrails when there are no lane markings. In this study, we would like to propose a localization algorithm that could be implemented in the near future by using low-cost sensors and HD maps. The proposed localization algorithm consists of several sections: environment feature representation with low-cost sensors, digital map analysis and application, position correction based on map-matching, designated validation gates, and extended Kalman filter (EKF)-based localization filtering and fusion. Lane information is detected by monocular vision in front of the vehicle. A guardrail is perceived by radar by distinguishing low-speed object measurements and by accumulating several steps to extract wall features. These lane and guardrail information are able to correct the host vehicle position by using the iterative closest point (ICP) algorithm. The rigid transformation between the digital high definition map (HD map) and environment features is calculated through ICP matching. Each corrected vehicle position by map-matching is selected and merged based on EKF with double updating. The proposed algorithm was verified through simulation based on actual driving log data.