Journal of Intelligent Computing Dominique Gruyer, Valentin Magnier, Mohamed-Cherif Rahal, Guillaume Bresson 8 2 2017 http://www.dline.info/jic/fulltext/v8n2/jicv8n2_1.pdf This paper deals with real-time obstacle detection and track- ing using multi-layer LIDAR data. We present two algorithms to cluster raw data coming from LIDAR sensors. The rst algorithm is based on a dynamic clustering approach while the second one relies on the con- nectivity between the laser impacts. Both algorithms take into account the inaccuracy and the uncertainty of the data sources. We propose a tracking approach based on the belief theory to estimate the dynamic state of the detected objects in order to predict their future maneuvers. The objects are then ltered using an intelligent ROI that depends on a dynamic evolution area computed from proprioceptive information of the ego-vehicle. We evaluate and validate the whole chained process on real data-sets.