Title :
The ArosDyn project: Robust analysis of dynamic scenes
Author :
Paromtchik, Igor E. ; Laugier, Christian ; Perrollaz, Mathias ; Yong, Mao ; Négre, Amaury ; Tay, Christopher
Author_Institution :
INRIA Grenoble Rhone-Alpes, St. Ismier, France
Abstract :
The ArosDyn project aims to develop embedded software for robust analysis of dynamic scenes in urban traffic environments, in order to estimate and predict collision risks during car driving. The on-board telemetric sensors (lidars) and visual sensors (stereo camera) are used to monitor the environment around the car. The algorithms make use of Bayesian fusion of heterogenous sensor data. The key objective is to process sensor data for robust detection and tracking of multiple moving objects for estimating and predicting collision risks in real time, in order to help avoid potentially dangerous situations.
Keywords :
Bayes methods; object detection; sensors; target tracking; traffic engineering computing; ArosDyn project; Bayesian fusion; car driving; collision risks prediction; dynamic scenes; embedded software; heterogenous sensor data; lidars; multiple moving object detection; multiple moving object tracking; onboard telemetric sensors; robust analysis; stereo camera; urban traffic environments; visual sensors; Cameras; Estimation; Hidden Markov models; Laser radar; Probabilistic logic; Probability distribution; Sensors; Bayesian filter; Mobile robot; collision risk; lidar; sensor fusion; stereo vision; traffic environment;
Conference_Titel :
Control Automation Robotics & Vision (ICARCV), 2010 11th International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-7814-9
DOI :
10.1109/ICARCV.2010.5707333
