Automotive Systems Laboratory, Inc.

Shi Shen

Lang Hong

Shan Cong

A processor operatively coupled to a speed sensor adapted to generate a measure of a longitudinal speed of a vehicle on a roadway, and to a source of a measure of yaw rate of the vehicle, provides for selecting a most likely roadway model of a plurality of different roadway models and for outputting a corresponding associated at least one curvature parameter as an estimate of curvature of the roadway, wherein the processor incorporates a plurality of curvature estimators associated with the corresponding plurality of different roadway models.

Road curvature estimation system

A processor using a first Kalman filter estimates a host vehicle state from speed and yaw rate, the latter of which may be from a yaw rate sensor if speed is greater than a threshold, and, if less, from a steer angle sensor and speed. Road curvature parameters are estimated from a curve fit of a host vehicle trajectory or from a second Kalman filter for which a state variable may be responsive to a plurality of host state variables. Kalman filters may incorporate adaptive sliding windows. Curvature of a most likely road type is estimated with an interacting multiple model (IMM) algorithm using models of different road types. A road curvature fusion subsystem provides for fusing road curvature estimates from a plurality of curvature estimators using either host vehicle state, a map database responsive to vehicle location, or measurements of a target vehicle with a radar system.

A first Kalman filter estimates true measures of yaw rate and vehicle speed from associated noisy measures thereof generated by respective sensors in a host vehicle, and a second Kalman filter estimates therefrom parameters of a clothoid model of road curvature. Measures of range, range rate, and azimuth angle from a target state estimation subsystem, e.g. a radar system, are processed by an extended Kalman filter to provide an unconstrained estimate of the state of a target vehicle. Associated road constrained target state estimates are generated for one or more roadway lanes, and are compared&#x2014;either individually or in combination&#x2014;with the unconstrained estimate. If a constrained target state estimate corresponds to the unconstrained estimate, then the state of the target vehicle is generated by fusing the unconstrained and constrained estimates; and otherwise is given by the unconstrained estimate alone.

Growing community of inventors

Farmington Hills, MI, United States of America

Shi Shen