Road geometry estimation using a precise clothoid road model and observations of moving vehicles
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An important part of any advanced driver assistance system is road geometry estimation. In this paper, we develop a Bayesian estimation algorithm using lane marking measurements received from a camera and measurements of the leading vehicles received from a radar-camera fusion system, to estimate the road up to 200 meters ahead in highway scenarios. The filtering algorithm uses a segmented clothoid-based road model. In order to use the heading of leading vehicles we need to detect if each vehicle is keeping lane or changing lane. Hence, we propose to jointly detect the motion state of the leading vehicles and estimate the road geometry using a multiple model filter. Finally the proposed algorithm is compared to an existing method using real data collected from highways. The results indicate that it provides a more accurate road estimation in some scenarios.
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