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In highways, lane markings are undoubtedly the most widely used landmarks for vehicle 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 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).

Low-Cost Precise Vehicle Localization Using Lane Endpoints and Road Signs for Highway Situations