Abstract
Tightly coupling GPS with lane markings sensed by a camera system
Tightly coupling GPS pseudorange and Doppler measurements with other sensors is a way to increase accuracy and integrity of the positioning information, particularly when it is computed autonomously. Highly accurate digital maps are key components for autonomous vehicle navigation and can enhance the positioning system. In order to benefit from a highly accurate map and to remove non-zero mean errors introduced by map matching on macro-scale polylines, a video camera is used to get relative information with respect to lane markings. For this, a refined camera observation model is proposed. Dead-Reckoning sensors are also integrated to increase the performance particularly in terms of availability. Shaping filtering of L1-GPS pseudoranges errors is also studied to take into account biases. The data fusion of the different sensors is done using a reduced order state space modeling in order to get efficient real-time computation. An Extended Kalman Filter with measured input and correlated noises is studied. The map-matching of the camera measurements exploits the capability of the camera system to recognize the type of lane marking. Several experiments have been carried out with an equipped vehicle and an accurate lane marking map. Experimental results show that this tightly coupled approach clearly improves the performance in terms of accuracy and integrity compared to a loosely coupled method that uses GPS fixes computed by an external receiver.