Map Matching for Vehicle Guidance (Draft)

The past years have revealed a dramatically increasing interest in the use of mobile Geographical Information Systems (GIS) in various automotive applications. Car navigation systems employ digital maps to guide the driver to the desired destination. Next generation driver assistance systems will use enhanced maps in order to present precise navigation hints (including speed limits, locations of gas stations, restaurants, etc.), and for assisted vehicle control. Furthermore, methods for autonomous enhancements of existing maps using video and lidar sensors are currently under development. An exhaustive overview of potential applications for upcoming mobile GI systems can be found in chapter 2. All applications share the need for a robust assignment of the measured vehicle position to a road segment in the digital map. This process is called map matching. Since the emergence of the field in the 1970s (French (1989)), considerable progress has been reported (cf. the surveying articles of Bernstein and Kornhauser (1998), White et al. (2000), Quddus et al. (2003) and Lakakis et al. (2004)). However, users of navigation systems still encounter some erroneous map matching results. On the contrary, manufacturers increasingly aim for a simple architecture of navigation systems. Hence, a growing number of navigation systems do not rely on multiple vehicle sensors such as a combination of DGPS, odometer and gyro but instead restrict themselves to GPS only. This is necessarily true for the newly evolving PDA (Personal Digital Assistant) navigation systems, which are identified in chapter 1 as key devices for next generation GIS, as well as for low cost in-car navigation solutions. In this chapter a robust map matching algorithm is presented which exclusively relies upon information from a standard GPS receiver (however, the integration of data from an integrated GPS dead-reckoning unit is straightforward). Mobile phone based location systems can equally serve as input. The achievable accuracy of mobile phone location is examined in detail in chapter 11. In contrast to standard map matching techniques the whole vehicle path is estimated for each time step within an iterative, statistically optimal Bayesian estimator (a Bayesian estimator using a different formulation has been developed by Scott and Drane (1994)) . The algorithm is suitable for all maps using the standard road segment representation as piecewise linear links. An introduction into digital maps and GPS is given in NCHRP (2002), for example. The map database used by the authors is off-the-shelf