Stochastic Path Prediction using the Unscented Transform with Numerical Integration

This paper illustrates that the combination of the unscented transform and numerical integration can provide significantly more accurate stochastic predictions of the future state of a nonlinear system for potentially less computation time than similar Kalman-like routines. Within the context of this paper, this improvement is shown in the vehicular path prediction environment, where computation power and memory are kept at an affordable level.