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.
The instant application claims the benefit of U.S. Provisional Application Serial No. 60/210,878 filed on Jun. 9, 2000 (5701-00266).
The instant application is related to U.S. application Ser. No. 09/877,493, entitled Track Map Generator, filed on Jun. 8, 2001 (5701-01265),
A method of fabricating a mask which can endure use for a long time and can be used for forming an isolated pattern with a high aspect ratio. The method includes the steps of: forming a soft material layer by disposing a soft material having positive photo sensitivity and adhesion or adhesiveness on a material as a target of machining; forming a hard material layer by disposing an opaque hard material in which a desired mask pattern has been formed in advance on the soft material layer; and forming the mask pattern in the soft material layer by performing exposure to light and development on the soft material layer by using the hard material layer as a photomask.
An on-board intelligent vehicle system includes a sensor assembly to collect data and a processor to process the data to determine the occurrence of at least one event. The data may be collected from existing standard equipment such as the vehicle communication bus or add-on sensors. The data may be indicative of conditions relating to the vehicle, roadway infrastructure, and roadway utilization, such as vehicle performance, roadway design, roadway conditions, and traffic levels. The detection of an event may signify abnormal, substandard, or unacceptable conditions prevailing in the roadway, vehicle, or traffic. The vehicle transmits an event indicator and correlated vehicle location data to a central facility for further management of the information. The central facility sends communications reflecting event occurrence to various relevant or interested users. The user population can include other vehicle subscribers (e.g., to provide rerouting data based on location-relevant roadway or traffic events), roadway maintenance crews, vehicle manufacturers, and governmental agencies (e.g., transportation authorities, law enforcement, and legislative bodies).
A process of determining an imminent-collision between a vehicle and an object, said vehicle having a sensing system for obtaining one or more images representing at least one observed object within a field of detection, said process comprising: (a) obtaining one or more images representing an observed object within said field of detection; and (b) determining that a collision between said vehicle and said observed object is imminent when the ratio of the probability that said observed object is actually within a collision zone to the probability that said observed object is actually within a safe zone is greater than a certain value.
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--either individually or in combination--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.
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.
