Map-Matching Integrity Using Multihypothesis Road-Tracking

Abstract

Efficient and reliable map-matching algorithms are essential for Advanced Driver Assistance Systems. While most existing solutions fail to provide trustworthy outputs when the situation is ambiguous (such as at road intersections, at roundabouts, or when roads are parallel), we present a new map-matching method that overcomes this limitation. It is based on multihypothesis road-tracking that takes advantage of the geographical database road connectivity to provide a reliable road-matching solution with a confidence indicator that can be used for integrity-monitoring purposes. The presented multihypothesis road-tracking method combines proprioceptive sensors (odometers and gyrometers) with global positioning system and map information. While usually the algorithmic complexity of a multihypothesis method is exponential, because each hypothesis can generate new hypotheses at each sampling step, we propose using road connectivity information to overcome this drawback, so that new hypotheses are created only when they are really necessary. The proposed decision rule of the integrity monitoring strategy takes account of the estimated location with the map, as well as the respective probabilities of the different hypotheses to handle ambiguity zones. The performance of the method presented in this article is illustrated by tests that were carried out in real-world road conditions.