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Communication Dans Un Congrès Année : 2020

High Integrity Lane-level Occupancy Estimation of Road Obstacles Through LiDAR and HD Map Data Fusion

Résumé

In the paper a fast and consistent method to manage uncertainties on detected traffic agents providing reliable results is presented. The information provided by a LiDAR-based object detector is combined with a high-definition map to identify the drivable space of the carriageway. Because the use of a HD map requires the use of a localization system, the uncertainty of the estimated pose shall be handled carefully. A novel approach taking into account the localization uncertainty in the perception task by direct propagation of it onto the LiDAR points is proposed. It is compared with a classical propagation that relies on linearized approximation. The good performances of this approach in terms of integrity are demonstrated by the use of real data acquired at the entrance of a roundabout being a particularly complex situation.
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Dates et versions

hal-02947187 , version 1 (23-09-2020)

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Edoardo Bernardi, Stefano Masi, Philippe Xu, Philippe Bonnifait. High Integrity Lane-level Occupancy Estimation of Road Obstacles Through LiDAR and HD Map Data Fusion. 31st IEEE Intelligent Vehicles Symposium (IV 2020), Oct 2020, Las Vegas, United States. pp.1873-1878, ⟨10.1109/IV47402.2020.9304783⟩. ⟨hal-02947187⟩
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