Vehicle Localization at an Intersection Using a Traffic Light Map

Traditional vehicle localization methods use information from a GPS, inertial navigation, and an odometer, but a GPS is often limited by availability in urban areas for its sensitivity to terrain and interference. Aiming at the above problems, this paper presents a method of intersection localization using a traffic lights' map. In particular, traffic lights with significant visual characteristics in the urban environment are used as landmarks. The localization accuracy of the autonomous vehicle at intersections is improved by combining the location information of traffic lights provided by a high-precision map. The whole scheme of the localization system is designed and the sensors used are determined. First, the coordinates of sensors are established, respectively, and the transformation and rotation are calibrated. Then, the state model and measurement model of the vehicle vision system are established. Combined with the location and height information of the traffic lights provided by the high-precision map, the extended Kalman filter is used to fuse the vision detection results of the traffic lights with the inertial measurement unit information. The experiments demonstrate that the method proposed in this paper improves the lateral localization accuracy and the accuracy of the vehicle's yaw angle.