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1777results about "Belt control systems" patented technology

RFID buckle closure and presence sensor system for safety childseat

In the preferred embodiment of the present invention a passive, wireless, RFID-based wireless buckle-closure sensor determines whether the buckle of a child safety seat is secured. Sensors also are provided to determine if the child is in the seat, the temperature and if the vehicle is in operation, and alarms are sounded if an unsafe condition is detected by the system. Child safety seats utilize a 3-point or 5-point locking-mechanism for seat belt and harness restraints. The locking-mechanism requires that metal belt / harness components latch together and are released by depression of a lock-mounted release button. In the preferred embodiment of the present invention a passive RF transponder is affixed to the buckle. Essentially the RF transponder comprises an RFID device without a data component. The RF transponder is interrogated by a frequency-scanning reader, which determines the resonant frequency of the transponder. The resonant frequency of the transponder is affected by the presence of the metal fittings local to the RF transponder. Thus, since the major components of the buckle and latch are metal, the detection of the change in the resonant frequency of the transponder, also referred to as “detuning,” permits the determination of the state of the belt / harness buckle—latched or unlatched. This sensing is wireless, unobtrusive, and requires only a passive component be attached to the buckle. Further disclosed is a method of determining the status of a child in a child safety seat, including: whether or not a child is in the seat, whether or not the belt / harness buckle is latched, whether the vehicle's engine is in operation, and whether or not the surrounding temperature exceeds the temperature range. If an unsafe condition is detected, an alarm is activated.
Owner:GRACO CHILDRENS PROD INC

Intelligent training system for vehicle driver based on actual vehicle

The invention relates to an intelligent training system for a vehicle driver based on an actual vehicle. The intelligent training system comprises a vehicle-mounted intelligent training subsystem, a data processing subsystem, an operation management and control subsystem and a system-level data communication subsystem, wherein the vehicle-mounted intelligent training subsystem is mounted on the actual vehicle and is used for training interactive driving of the driver and acquiring and reporting data of a driver training process; the data processing subsystem manages basic information and is used for receiving, storing and processing the data of the training process acquired and reported by the vehicle-mounted intelligent training subsystem; the operation management and control subsystem monitors an operated state and a running state of the vehicle in real time and remotely controls the monitored vehicle according to actual conditions; the vehicle-mounted intelligent training subsystem is connected with the data processing subsystem and the operation management and control subsystem through the system-level data communication subsystem and realizes bidirectional information interaction.
Owner:易显智能科技有限责任公司

Crash classification method and apparatus using multiple point crash sensing

An improved method of using multiple point crash sensing and multiple sensor occupant position sensing for classifying a crash event and determining which restraints should be deployed. A central controller collects crash data from multiple crash sensors and combines severity characterization data from each of the multiple sensors to construct a characterization table or matrix for the entire system. Each possible crash event classification is represented by a characterization value mask, and the various masks are sequentially applied to the system characterization table until a match is found, with a match identifying the appropriate crash event classification. The classification decision, in turn, is used to determine which, if any, of the restraint devices should be deployed based upon the crash severity. Similarly, the controller collects data from various occupant position sensors to construct a characterization table or matrix for the occupant position detection system. Each possible occupant position sensor classification is represented by a characterization value mask, and various masks are sequentially applied to the table until a match is found, with a match identifying the appropriate occupant position status. The occupant position status, in turn, is used to determine which, if any, of the restraints may be deployed. The system also includes a centrally located crash sensor, and the controller constructs an intrusion table based on differences between the remote and central sensors. The intrusion classification is determined and combined with the crash classification and occupant position status to determine which restraints should ultimately be deployed.
Owner:APTIV TECH LTD

Weight classification system

A method and apparatus is provided that classifies a seat occupant into one of several different weight classes based on an estimated value of the seat occupant weight. An occupant's measured weight varies when the occupant's seating position changes or when the vehicle travels over adverse road conditions. A plurality of weight sensors are used to measure the weight exerted by a seat occupant against a seat bottom and are used to determine center of gravity for the seat occupant. A seat belt force sensor is also used to assist in classifying the seat occupant. Compensation factors using the seat belt force and center of gravity information are used to generate an estimated weight value. The estimated value of the occupant weight is compared to a series of upper and lower weight thresholds assigned to each of the weight classes to generate an occupant weight sample class. Over a period of time, several estimated weight values are compared to the weight class thresholds. Once a predetermined number of consistent and consecutive occupant weight sample classes is achieved, the occupant is locked into a specific occupant weight class. When the weight class is locked, the separation value between the upper and lower thresholds is increased to account for minor weight variations due to adverse road conditions and changes in occupant position.
Owner:SIEMENS VDO AUTOMOTIVE CORP
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