Integrated Positioning Algorithms for Transport Telematics Applications

A range of transport telematics applications and services require continuous and accurate positioning information of the vehicles traveling on the road network. Two types of information are essential for such telematics applications and services. These are the determination of the vehicle position and the determination of the physical location of the vehicle on the road network. The most common devices used for vehicle positioning are based on GPS, Dead-Reckoning (DR) sensors, Map Matching (MM) and microwave beacons. The use of these devices either in isolation or combination depends on the Required Navigation Performance (RNP) parameter specifications (accuracy, integrity, continuity and availability). Furthermore, the capability to identify the physical location of a vehicle is a key requirement in transport telematics applications. In order to achieve the RNP, system and sensor complementarity, such as in the case of the integration of GPS, DR and digital map data could be used to enhance geometric positioning capability. MM not only enables the physical location of the vehicle to be identified but also improves the positioning capability if a good digital map is available. A key factor in the integration of different devices is the knowledge of the various failure modes (including error sources). This paper develops two integrated positioning algorithms for transport telematics applications and services. The first is an Extended Kalman Filter (EKF) algorithm for the integration of GPS and low cost DR sensors to provide continuous positioning in built-up areas. The second takes this further by integrating the GPS/DR output with map data in a novel a map-matching process to both identify the physical location of a vehicle on the road network and improve positioning capability. The proposed MM algorithm is validated using a higher accuracy reference (truth) of the vehicle trajectory as determined by high precision positioning achieved by the carrier phase observable from GPS. The results demonstrate a 90% coverage in a typical built-up environment over a 4-hour duration for a stand-alone GPS employing a single frequency high sensitivity receiver/antenna assembly. The integrated GPS/DR approach employing the EKF gives 100% coverage at an accuracy level better than 30m (2o). The MM validation results show that 100% link identification is achieved with the proposed MM algorithm, with a maximum horizontal positioning error of 6m. The results also demonstrate the importance of the quality of the digital map data to the map matching process.