Automotive LIDAR objects Detection and Classification Algorithm Using the Belief Theory

Valentin Magnier (1) , Dominique Gruyer (1) , Jérôme Godelle (2)

1 IFSTTAR/COSYS/LIVIC - Laboratoire sur les Interactions Véhicules-Infrastructure-Conducteurs
2 Centre technique Aubevoye

In Autonomous driving applications, the LIDAR is becoming one of the key sensors for the perception of the environment. Indeed its work principle which is based on distance ranging using a laser beam scanning the environment allows highly accurate measurements. Among sensors commonly used in autonomous driving applications, which are cameras, RADARs and LIDARs, the LIDAR is the most suited to estimate the shape of objects. However, for the moment, LIDARs dedicated to pure automotive application have only up to four measurement layers (4 laser beams scanning the environment at different height). Hence objects detection algorithm have to rely on very few layers to detected and classify the type of objects perceived on the road scene, that makes them specific. In this paper we will present an Detection and Tracking of Moving Objects (DATMO) algorithm featuring an objecttype classification based on the belief theory. This algorithm is specific to automotive application therefore, the classification of perceived vehicles is between bike, car and truck. At the end of this paper we will present an application of this algorithm in real-world context.

Mots clés: POINT CLUSTERING; SEGMENTATION; DATA-ASSOCIATION; TRACK UPDATE

Mots clés: TRAITEMENT DES DONNEES

Domaines: Automatique / Robotique

Valentin Magnier, Dominique Gruyer, Jérôme Godelle. Automotive LIDAR objects Detection and Classification Algorithm Using the Belief Theory. IV 2017 - IEEE Intelligent Vehicles Symposium, Jun 2017, Los Angeles, United States. 6p, ⟨10.1109/IVS.2017.7995806⟩. ⟨hal-01675243⟩