Tightly coupling GPS with lane markings for autonomous vehicle navigation

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 also more and more key components for autonomous vehicle navigation and can enhance the localization system. In this paper, a video camera is used to get relative information with respect to lane markings and dead-reckoning sensors are also integrated to provide positioning information with high availability. A reduced order state space modeling of the observation problem is proposed in order to get an efficient real-time computing system. Thanks to an adequate error model of the GPS pseudoranges and a measured input state space formalization, a Kalman filter with correlated noises is developed. 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.