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Road information, like lanes number, play an important role for intelligent vehicles (IV). Traditionally such road information are obtained through a vision-based measurement or by using a digital detailed map. In this paper, we present a new method for estimating the number of lanes using a low precision GPS receiver and OpenSteetMap (OSM). The method includes the integration of the GPS traces and OSM for a map matching. To this end we developed a probabilistic multicriteria algorithm for map matching that takes into account the accuracy of the GPS data and the attribute information of the road from OSM. Afterward we estimate the number of lanes from OSM. We tested our algorithm on a set of GPS data collected in an urban area near Paris for a total distance of 50km and the overall estimation accuracy reached 83.64%

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In this paper we propose a method for accurate ego-lane localization using camera images, on-board sensors and lanes number information from OpenStreetMap (OSM). The novelty relies in the probabilistic framework developed, as we introduce a modular Bayesian Network (BN) to infer the ego-lane position from multiple inaccurate information sources. The flexibility of the BN is proven, by first, using only information from surrounding lane-marking detections and second, by adding adjacent vehicles detection information. Afterward, we design a Hidden Markov Model (HMM) to temporary filter the outcome of the BN using the lane change information. The effectiveness of the algorithm is first verified on recorded images of national highway in the region of Clermont-Ferrand. Then, the performances are validated on more challenging scenarios and compared to an existing method, whose authors made their datasets public. Consequently, the results achieved highlight the modularity of the BN. In addition, our proposed algorithm outperforms the existing method, since it provides more accurate ego-lane localization: 85.35% compared to 77%.

Localizing the vehicle in its lane is a critical task for any autonomous vehicle. By and large, this task is carried out primarily through the identification of ego-lane markings. In recent years, ego-lane marking detection systems have been the subject of various research topics, using several inputs data such as camera or lidar sensors. Lately, the current trend is to use high accurate maps (HD maps) that provide accurate information about the road environment. However, these maps suffer from their availability and their price tag. An alternative is the use of affordable low-accurate maps. Yet, there is relatively little work on it. In this paper, we propose an information-driven approach that takes into account inaccurate prior geometry of the road from OpenStreetMap (OSM) to perform ego-lane marking detection using solely a lidar. The two major novelties presented in this paper are the use of the OSM datasets as prior for the road geometry, which reduces the research area in the lidar space, and the information-driven approach, which guarantees that the outcome of the detection is coherent to the road geometry. The robustness of the proposed method is proven on real datasets and statistical metrics are used to highlight our method's efficiency.