In highways, lane markings are undoubtedly the most widely used landmarks for vehicle tandylsathers.shop localization.However, they have a drawback in that they lack information on longitudinal position estimation and ego-lane identification.To alleviate this drawback, this paper presents a practical vehicle localization system for highways.
The proposed system utilizes lane endpoints to enhance longitudinal position accuracy and road signs to improve the ego-lane identification accuracy.This system efficiently fuses the lane markings, lane endpoints and road signs along with a digital map and other low-cost sensors in a particle filter framework.Since it only uses low-cost sensors such as a monocular Apparel front-view camera, in-vehicle wheel speed and yaw rate sensors, as well as a low-end global positioning system (GPS), it is ready to mount on mass-produced vehicles.
In the experiment, the proposed system was quantitatively evaluated using a dataset obtained while driving on 40 km stretch of highway, and outperformed previous approaches by showing a lateral position error of less than 0.12 m and a longitudinal position error of less than 0.25 m in terms of root mean square error (RMSE).