Automotive Systems Laboratory, Inc.

Shan Cong

Lang Hong

Shi Shen

Leonard W Behr

Michael Edward Farmer

James Paul Ebling

Craig S Jacobs

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.

Road curvature estimation and automotive target state estimation system

A plurality of events representative of a situation in which the host vehicle is operated are selected, including at least one set of related events. Input data is provided to an inference engine from either a first set of data representative of a target in a field of view of the host vehicle, a second set of data representative of the position or motion of the host vehicle, or a third set of data is representative of an environment of said host vehicle. The inference engine operates in accordance with an inference method to generate an output representative of a probability of occurrence of at least one event of the set of events, responsive to the input data, and possibly to one or more outputs at a past time. A countermeasure may be invoked responsive to one or more outputs from one or more inference engines.

Situation awareness processor

A radar system ( ) generates a plurality of data points (x, y) representative of the position of a tracked object ( ), and a representation of an associated path ( ) is formed therefrom. At least one quality measure, and at least one heading measure, of said representation, is calculated corresponding to at least one map coordinate ( ). The quality and heading measures are stored in memory as a track map. Data from other tracked objects ( ) is used to update the track map ( ), resulting a plurality of heading values at associated map coordinates ( ) that are representative of a path ( ) followed by the tracked objects ( ).

Track map generator

A neural network radar processor ( ) comprises a multilayer perceptron neural network ( ) comprising an input layer ( ), a second layer ( ), and at least a third layer ( ), wherein each layer has a plurality of nodes ( ), and respective subsets of nodes ( ) of the second ( ) and third ( ) layers are interconnected so as to form mutually exclusive subnetworks ( ). In-phase and quadrature phase time series from a sampled down-converted FMCW radar signal ( ) are applied to the input layer, and the neural network ( ) is trained so that the nodes of the output layer ( ) are responsive to targets in corresponding range cells, and different subnetworks ( ) are responsive to respectively different non-overlapping sets of target ranges. The neural network is trained with signals that are germane to an FMCW radar, including a wide range of target scenarios as well as leakage signals, DC bias signals, and background clutter signals.

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Ann Arbor, MI, United States of America

Shan Cong