An adaptive interface for a programmable system, for predicting a desired user function, based on user history, as well as machine internal status and context. The apparatus receives an input from the user and other data. A predicted input is presented for confirmation by the user, and the predictive mechanism is updated based on this feedback. Also provided is a pattern recognition system for a multimedia device, wherein a user input is matched to a video stream on a conceptual basis, allowing inexact programming of a multimedia device. The system analyzes a data stream for correspondence with a data pattern for processing and storage. The data stream is subjected to adaptive pattern recognition to extract features of interest to provide a highly compressed representation that may be efficiently processed to determine correspondence. Applications of the interface and system include a video cassette recorder (VCR), medical device, vehicle control system, audio device, environmental control system, securities trading terminal, and smart house. The system optionally includes an actuator for effecting the environment of operation, allowing closed-loop feedback operation and automated learning.

Adaptive pattern recognition based control system and method

GPS satellite (4) ranging signals (6) received (32) on comm1, and DGPS auxiliary range correction signals and pseudolite carrier phase ambiguity resolution signals (8) from a fixed known earth base station (10) received (34) on comm2, at one of a plurality of vehicles/aircraft/automobiles (2) are computer processed (36) to continuously determine the one's kinematic tracking position on a pathway (14) with centimeter accuracy. That GPS-based position is communicated with selected other status information to each other one of the plurality of vehicles (2), to the one station (10), and/or to one of a plurality of control centers (16), and the one vehicle receives therefrom each of the others' status information and kinematic tracking position. Objects (22) are detected from all directions (300) by multiple supplemental mechanisms, e.g., video (54), radar/lidar (56), laser and optical scanners. Data and information are computer processed and analyzed (50,52,200,452) in neural networks (132, FIGS. 6-8) in the one vehicle to identify, rank, and evaluate collision hazards/objects, an expert operating response to which is determined in a fuzzy logic associative memory (484) which generates control signals which actuate a plurality of control systems of the one vehicle in a coordinated manner to maneuver it laterally and longitudinally to avoid each collision hazard, or, for motor vehicles, when a collision is unavoidable, to minimize injury or damage therefrom. The operator is warned by a heads up display and other modes and may override. An automotive auto-pilot mode is provided.

GPS vehicle collision avoidance warning and control system and method

A mobile communications device comprising a location sensing system, producing a location output; a memory, storing a set of locations and associated events; a telecommunications device, communicating event and location information between a remote system and said memory; and a processor, processing said location output in conjunction with said stored locations and associated events in said memory, to determine a priority thereof.

Mobile communication device

Means are provided for recording, storing, calculating, communicating and reviewing one or more operational aspects of a machine. Insurance costs are based, in part, on activities of the machine operator. A discount may be provided in exchange for recording the operational aspects and providing the recorded information to the insurer. The party may review information and decide whether to provide it to the insurer. The means for reviewing may present comparative information. Information that causes insurance costs to vary may be highlighted. Provided data may be used to verify insurance application information, generate actuarial information or determine insurance rates. Operating data may be reviewed on a computer, a Web site or other display medium so a party can observe how his operating behavior compares to that of other operators of similar machines and may be manipulated so a party can understand how changes in operating behavior can affect his insurance rates.

Monitoring system for determining and communicating a cost of insurance

A system and method assists the driver of a motor vehicle in preventing accidents or minimizing the effects of same. In one form, a television camera is mounted on a vehicle and scans the roadway ahead of the vehicle as the vehicle travels. Continuously generated video picture signals output by the camera are electronically processed and analyzed by an image analyzing computer, which generates codes that serve to identify obstacles. A decision computer mounted in the controlled vehicle receives such code signals along with code signals generated by the speedometer or one or more sensors sensing steering mechanism operation and generates control signals. Such code signals may be displayed, and a synthetic speech or special sound generating and warning means used, to warn the driver of the vehicle of approaching and existing hazards. The system may also use the control signals, particularly through application of fuzzy logic, to control the operation of the brakes and steering mechanism of the vehicle to avoid or lessen the effects of a collision. In a particular form, the decision computer may select the evasive action taken from a number of choices, depending on whether and where the detection device senses other vehicles or obstacles.

Motor vehicle warning and control system and method

An AI-based adaptive vehicle control system which controls the speed of a vehicle in accordance with the driving characteristics of the driver. In effecting the follow-up driving which corresponds to the driving characteristics of the driver, the vehicle environment such as the distance between the vehicle and the vehicle in front, the relative speed, and the relative position, and the running state such as the speed and the steering angle of the vehicle itself are detected, and a danger index is calculated from the detected vehicle environment and running state in accordance with the driver's sense of driving. The danger index is calculated by the fuzzy induction from the membership functions which are determined in advance in accordance with a general driver's sense of driving. The running state of the vehicle such as the speed is controlled in accordance with the danger index. When the manipulated variable is changed by the driving operation of the driver, the danger index is adjusted in accordance with the amount of change. The change by the driving operation of the driver is reflected as the change in the membership functions. Thus, the calculated danger index gradually agrees with the driving characteristic of the driver.

Norikazu Endo

Papers

AI-based adaptive vehicle control system