IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. X, NO. X, XXXX 200X 1 In-car positioning and navigation technologies – a survey Isaac Skog and Peter H¨ andel Abstract—In-car positioning and navigation has been a killer application for global positioning services (GPS) receivers, and a variety of electronics for consumers and professionals have been launched on a large scale. Positioning technologies based on stand-alone GPS receivers are vulnerable, and thus have to be supported by additional information sources to obtain desired accuracy, integrity, availability, and continuity of service. A survey of the information sources and information fusion tech- nologies used in current in-car navigation systems is presented. The pros and cons of the four commonly used information sources receivers for radio based positioning using satellites, vehicle motion sensors, vehicle models, and digital map information are described. Common filters to combine the information from the various sources are discussed. The expansion of the number of satellites and the number of satellite systems, with their usage of available radio spectrum is an enabler for further development in combination with the rapid development of microelectromechanical inertial sensors and the refined digital maps. Index Terms—Vehicle navigation, dead reckoning, inertial nav- igation, satellite navigation, information fusion, vehicle models. I. I NTRODUCTION Today a large share of passenger cars is delivered from the factory with a GPS-based in-car navigation system. Owners of used cars can at, a reasonable cost, install one of the many third party in-car navigation systems on the market. Indeed, in Western Europe around 14.4 million portable satellite nav- igation systems were sold during 2007 [1]. These navigation aids are designed to support the driver by showing the vehicle’s current location on a map and by giving both visual and audio information on how to efficiently get from one location to another, i.e., route guidance. Moreover, many vehicles used in professional services, such as taxis, buses, ambulances, police cars and fire trucks, are today equipped with navigation systems that not only show the current location but also constantly communicate the vehicle location to a monitoring center. Operators at the center can use this information to direct the vehicle feet as effciently as possible. To further improve the usefulness of these in-car navigation systems, for example, with information such as when, where and how to make lane changes with respect to the planned course changes, the accuracy of both the navigation systems and digital maps has to be improved [2]–[5]. Increasing the accu- racy and robustness of navigation systems implies that traffic Manuscript received I. Skog and P. H¨ andel are with the ACCESS Linnaeus Center, Signal Processing Lab, Royal Institute of Technology (e-mail: isaac.skog@ee.kth.se; ph@ee.kth.se) Information sources GNSS/RF-based Positioning Vehicle motion Sensors Road maps Vehicle models Information Fusion Man-machine interface Vehicle state Guidance Traffic situation information Camera/Radar/Laser ADAS Fig. 1. Conceptional description of the available information sources and information flow for an in-car navigation system. The block with dashed lines is generally not part of current in-car navigation systems but will likely be a major part of next generation in-car navigation systems and advanced driver assistant systems (ADAS). coordinators could guide their vehicle fleets more effciently based on the traffic flow on different road lanes, etc. Refer to [6] and [7] for discussions on robustness enhancement of a bus fleet monitoring system and the use of GPS posi- tioning in bus priority control at traffic lights, respectively. Moreover, further development of intelligent transport system (ITS) applications such as advanced driver assistance systems (ADASs), traffic control, automatic positioning of accidents, electronic fee collection, goods tracking, etc. requires not only navigation systems with higher accuracy but also better reliability and integrity [8], i.e., redundant information sources are needed [9]. With reference to Fig. 1, looking at the in-car navigation problem from an information perspective there are basically four different sources of information available: various Global Navigation Satellite Systems (GNSSs) and other RF-based navigation systems, sensors observing vehicle dynamics, road maps and vehicle models. The GNSS receiver and vehicle motion sensors provide observations to estimate the vehicle’s state. The vehicle model and road map put constraints on the dynamics of the system and allow past information to be projected forward in time and to be combined with current ob- servation information [10]. Further, a fifth information source is indicated in Fig. 1 – visual, radar, or laser information. This kind of information is generally not used in current systems but plays a major role in the development of ADASs, etc. Presently one of the major bottlenecks for incorporating this type of information into safety systems is the price/performance ratio