PROJECT TITLE :

A Context-Based Approach to Vehicle Behavior Prediction

ABSTRACT :

Despite the most effective efforts of research and development administered in the automotive industry, accidents still occur resulting in many deaths and injuries every year. It has been shown that the vast majority of accidents occur hence (a minimum of in half) of human error. This paper introduces the model for the Intelligent Systems for Risk Assessment (ISRA) project that has the goal of eliminating accidents by detecting risk, alerting the operators when applicable, and ultimately removing some control of the vehicle from the operator when the risk is deemed unacceptable. The underlying premise is that vehicle dynamic info without contextual info is insufficient to perceive true well enough to enable the analysis of risk. This paper defines the contextual information required to research the case and shows how location context info can be derived using collected vehicle data. The method to infer high level vehicle state info using context data is additionally presented. The experimental results demonstrate the context based mostly inference method using knowledge collected from a fleet of mining vehicles during traditional operation. The systems developed for the mining business can later be extended to include additional complex traffic eventualities that exist within the domain of ITS.