A Context-Based Approach to Vehicle Behavior Prediction

Abstract

Despite the best efforts of research and development carried out in the automotive industry, accidents continue to occur resulting in many deaths and injuries each year. It has been shown that the vast majority of accidents occur as a result (at least in part) of human error. This paper introduces the model for the Intelligent Systems for Risk Assessment (ISRA) project which has the goal of eliminating accidents by detecting risk, alerting the operators when appropriate, and ultimately removing some control of the vehicle from the operator when the risk is deemed unacceptable. The underlying premise is that vehicle dynamic information without contextual information is insufficient to understand the situation well enough to enable the analysis of risk. This paper defines the contextual information required to analyze the situation and shows how location context information can be derived using collected vehicle data. The process to infer high level vehicle state information using context information is also presented. The experimental results demonstrate the context based inference process using data collected from a fleet of mining vehicles during normal operation. The systems developed for the mining industry can later be extended to include more complex traffic scenarios that exist in the domain of ITS.