Automotive LIDAR objects detection and classification algorithm using the belief theory

Abstract

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 object-type 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.