A Road-Matching Method for Precise Vehicle Localization Using Belief Theory and Kalman Filtering

Abstract

This paper describes a novel road-matching method designed to support the real-time navigational function of cars for advanced systems applications in the area of driving assistance. This method provides an accurate estimation of position for a vehicle relative to a digital road map using Belief Theory and Kalman filtering. Firstly, an Extended Kalman Filter combines the DGPS and ABS sensor measurements to produce an approximation of the vehicle's pose, which is then used to select the most likely segment from the database. The selection strategy merges several criteria based on distance, direction and velocity measurements using Belief Theory. A new observation is then built using the selected segment, and the approximate pose adjusted in a second Kalman filter estimation stage. The particular attention given to the modeling of the system showed that incrementing the state by the bias (also called absolute error) of the map significantly increases the performance of the method. Real experimental results show that this approach, if correctly initialized, is able to work over a substantial period without GPS.

Road Network Construction and Hot Routes Inferring with Vehicular Trajectories

“…Many researchers have proposed a lot of effective, but highcomplexity algorithms [13][14][15][16][17] to improve the matching accuracy. However, since our road network structure is simple, does not include the roundabout, flyovers, and other complex structures we do not need to use these complex algorithms.…”

The Effects of Navigation Sensors and Spatial Road Network Data Quality on the Performance of Map Matching Algorithms

“…This is achieved by map matching algorithms which integrate the navigation data with spatial road network data. Procedures for map matching vary from those using simple search techniques [11], to those using more advanced techniques such as the use of an Extended Kalman Filter and Belief Theory [13]. Approaches for map matching algorithms found in the literature can be categorised into three groups: topological ( [2], [6], [19], [25]), probabilistic [16], [28], and other advanced techniques [13], [17], [21], [23], [26].…”

Road Selection Using Multicriteria Fusion for the Road-Matching Problem

“…For this reason, the GPS data is intermittent but one can give a high belief in this information when it is present. The equations are not described here but details can be found in [8]. Hereafter, we report the results of a test performed over a 4.5 km route (see Fig 3).…”

“…In order to take into account the estimation error on the position, a Gaussian ellipse is built using the covariance matrix P of the state vector X [8]. The probability that a given state Xs is included in a 40% ellipse centered on the estimate X is expressed by the formula:…”

“…The fusion of the selected segment with the estimated pose is not detailed in this paper. It can be performed recursively by using Kalman filtering as done in [8] when there is no ambiguity.…”