Crash classification method and apparatus using multiple point crash sensing

Abstract

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.