## 1 Introduction  
Recently, vehicles with partially automated driving capabilities developed from advanced driver assistance systems (ADASs) have been competitively introduced by major carmakers [1, 2]. Most vehicle manufacturers aim to commercialize fully automated driving cars in the near future, and they have concentrated on autonomous driving research to satisfy both safety and marketability. In order to realize urban autonomous driving with full navigation, it is considered important to have accurate and robust localization without the use of expensive Global Positioning System(GPS).  
A comprehensive overview of previous localization studies is shown in [3]. [PERSON] et al. [4] proposed a data fusion approach that utilizes both stationary and dynamic radar detections for estimating relevant road parameters by using only radar. This paper not only utilizes radar for the detection of low-speed objects but also implements HD maps to estimate the current vehicle position with a variety of road information such as road curvature.  
Lately, a great deal of attention has been paid to vehicle localization based on maps [5; 6; 7]. A digital HD map is made up of plenty of place information to estimate the current location of the autonomous vehicle. Consequently, the digital map is used as a strong extra sensor to raise the quality of vehicle localization performance. As the speed of ego-vehicles increases, the position estimation performance is degraded, which is the limitation or source of error for precise localization [8].  
In this research, we take into account the three major issues in vehicle position estimation based on map information: environment features, correction method of vehicle position, and filtering with information fusion. The localization algorithms are possible to categorize depending on the road features used for matching. Lane markings and road boundaries are generally found on most roads and are easy to use as they are standardized. Therefore, many studies have taken lane marking [6; 7] and road boundary [6; 9] for map-building. According to previous research, the effective utilization of road features can contribute to improving location performance. In order to correct the position of the host vehicle using environment features and a digital map, a proper map-matching algorithm is necessary. The most well-known way of map-matching is the iterative closest point (ICP) algorithm initially proposed in [10]. The point-to-plane approach is known to be quite fast and accurate among the different ICP algorithms [11]. That is the reason why we apply this method to matching part of our algorithm in this research.  
The key contribution of this paper is a thorough experimental evaluation of a vehicle localization algorithm for automated driving on real highway roads. The localization algorithm is based on well-understood approaches, including lane and guardrail detection, ICP point-to-plane matching, and extended Kalman filter. We analyze the accuracy of the integrated system and show that the proposed algorithm can robustly localize the vehicle for proper control on real expressways at high speeds.  
The rest of this paper is composed as follows: Section 2 gives the architecture of the overall automated driving system, the vehicle localization structure, and a description of the test vehicle for automated driving. Section 3 contains road environment information and the attributed HD map for map-matching. Section 4 explains the map-matching method, fusion process, and Extended Kalman Filter(EKF)-based position estimation. Section 5 includes simulation results of the proposed position estimation of the host vehicle by using real driving data acquired on the Korean Expressway, including junctions (JC). Finally, Section 6 shows the conclusions.