NEFCOP: A Neuro-Fuzzy Vehicle Collision Prediction System

Given that road accidents occur in a real-time environment, simple crisp functions would barely provide an estimate of the gravity of the life situation. Fuzzy-based systems are able to establish complex non-linear relationships between variables with ease, making them perfect in the domain of vehicle collision prediction. However a sole fuzzy-based system, would fail to provide the user-specificity and intuition needed here. Neural networks, through their intelligent learning capabilities are ideal for this requirement. We propose NEFCOP, a neuro-fuzzy vehicle collision prediction system. NEFCOP uses laser ranging to obtain information about the road environment, which is then passed to a two-stage prediction system. The first stage clusters these data in order to prioritize them based on their relevance. The second stage is a neuro-fuzzy sub-system, which processes these data analyzing the possibility of a collision, following which the driver is warned accordingly. Thus, NEFCOP ach...

• Type: Conference
• Year: 2005
• Where: CIMCA
• Authors: K. Venkatesh, Archana Ramesh, M. Alagusundaram, J. V. Sahana
• Keywords: CIMCA 2005, Fuzzy-based System, Prediction System, Vehicle Collision Prediction