Real-time Collision Risk Estimation based on Pearson's Correlation Coefficient

Arthur Miranda Neto (1) , Alessandro Corrêa Victorino (2) , Isabelle Fantoni (2) , Janito Vaqueiro Ferreira (1)

1 UNICAMP - Universidade Estadual de Campinas = University of Campinas
2 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]

The perception of the environment is a major issue in autonomous robots. In our previous works, we have proposed a visual perception system based on an automatic image discarding method as a simple solution to improve the performance of a real-time navigation system. In this paper, we take place in the obstacle avoidance context for vehicles in dynamic and unknown environments, and we propose a new method for Collision Risk Estimation based on Pearson's Correlation Coefficient (PCC). Applying the PCC to real-time CRE has not been done yet, making the concept unique. This paper provides a novel way of calculating collision risk and applying it for object avoidance using the PCC. This real-time perception system has been evaluated from real data obtained by our intelligent vehicle.

Domaines: Automatique / Robotique

Arthur Miranda Neto, Alessandro Corrêa Victorino, Isabelle Fantoni, Janito Vaqueiro Ferreira. Real-time Collision Risk Estimation based on Pearson's Correlation Coefficient. IEEE Workshop on Robot Vision (WORV 2013), Jan 2013, Clearwater Beach, FL, United States. pp.40-45, ⟨10.1109/WORV.2013.6521911⟩. ⟨hal-00861087⟩