This paper proposes a method for using previewed road geometry from a high-fidelity map to improve estimates of planar vehicle states in the presence of unmodeled sensor bias errors. Using well-established, linear models for representing human driver behavior and for planar vehicle states, a causal link between previewed road geometry and vehicle states can be derived. Cast as an augmented, closed-loop linear system, the total driver-vehicle-road system’s states are estimated using a Kalman filter. Estimation results from this filter using simulated noisy measurements of vehicle states and map-based measurements of previewed road geometry are compared to standard Kalman filters with identical measurements of vehicle states alone. The effects of errors in driver modeling, vehicle nonlinearity, and measurement disturbances on the estimator’s fidelity are also examined and discussed.

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