Model-based Estimation of Driver Intentions Using Particle Filtering

Proactive vehicle alert systems that warn the driver about dangerous situations must be able to reason about, and predict, likely future states of the traffic environment. Our prediction method is based on a combination of a fuzzy logic model for intersection turning behavior and Gipps model for car following behavior. The stochastic models are used together with a particle filter to recursively approximate the state probability distribution as measurements are received over time. Estimates of the unobservable part of the state are used to predict path choice and thus driver intentions. The approach is evaluated on trajectory data gathered from video footage of an intersection, however it is also relevant for trajectories acquired through vehicle-to-vehicle communication.