Application of Evidence Accumulation Based on Estimation Theory and Human Psychology for Automotive Airbag Suppression

The traditional D-S conditioning is based on a collection of ‘experts’ inputting their evidence and accumulating the beliefs. Researchers have often adopted this same mechanism for integrating evidence from single sources of evidence over time, such as seen in sensor networks. The traditional D-S conditioning ensures the order of inputs does not matter. While this is sensible for a collection of experts we propose that it is not suitable for a single input providing streams of evidence. Research in psychology show order of integration of evidence does matter, and depending on the application humans have a preference for recency or primacy. Estimation theory provides frameworks for analyzing data over time, and recently some researchers have proposed integrating evidence in an estimation-inspired manner. We then propose a Kalman-filter based approach for integrating temporal streams of evidence from a single sensor. We then propose the system uncertainty be modeled by the conflict defined by Dempster. We then define a real-time evidence accumulation system for airbag suppression and demonstrate that the Kalman filter-based approach indeed out-performs Dempster-Shafer based evidence accumulation.