Abstract
Landmark-based vehicle localization is a key component of both autonomous driving and advanced driver assistance systems (ADAS). Previously used landmarks in highways such as lane markings lack information on longitudinal positions. To address this problem, lane endpoints can be used as landmarks. This paper proposes two essential components when using lane endpoints as landmarks: lane endpoint detection and its accuracy evaluation. First, it proposes a method to efficiently detect lane endpoints using a monocular forward-looking camera, which is the most widely installed perception sensor. Lane endpoints are detected with a small amount of computation based on the following steps: lane detection, lane endpoint candidate generation, and lane endpoint candidate verification. Second, it proposes a method to reliably measure the position accuracy of the lane endpoints detected from images taken while the camera is moving at high speed. A camera is installed with a mobile mapping system (MMS) in a vehicle, and the position accuracy of the lane endpoints detected by the camera is measured by comparing their positions with ground truths obtained by the MMS. In the experiment, the proposed methods were evaluated and compared with previous methods based on a dataset acquired while driving on 80 km of highway in both daytime and nighttime.
Highlights
Vehicle localization is one of the key components of both autonomous driving and advanced driver assistance systems (ADAS)
This method is robust against the status of the global navigation satellite systems (GNSS) signal and provides accurate results in a short period of time, but its error can accumulate over time
To overcome these drawbacks, localization methods that utilize a perception sensor and digital map have been widely researched [1]. These methods localize the ego-vehicle by matching the landmarks detected by the perception sensor and the landmarks stored in the digital map
Summary
Vehicle localization is one of the key components of both autonomous driving and advanced driver assistance systems (ADAS). This method provides global positions and its error is not accumulated, but produces inaccurate results when the GNSS signal is reflected or blocked To alleviate this problem, GNSS has often been fused with dead reckoning (DR). GNSS has often been fused with dead reckoning (DR) This method is robust against the status of the GNSS signal and provides accurate results in a short period of time, but its error can accumulate over time. To overcome these drawbacks, localization methods that utilize a perception sensor and digital map have been widely researched [1]. These methods localize the ego-vehicle by matching the landmarks detected by the perception sensor and the landmarks stored in the digital map
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