Data processing: vehicles – navigation – and relative location – Vehicle control – guidance – operation – or indication – Vehicle subsystem or accessory control
Reexamination Certificate
2002-08-15
2004-01-13
Camby, Richard M. (Department: 3661)
Data processing: vehicles, navigation, and relative location
Vehicle control, guidance, operation, or indication
Vehicle subsystem or accessory control
C280S735000
Reexamination Certificate
active
06678599
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates to a device for impact detection in a vehicle.
BACKGROUND INFORMATION
PCT Publication No. WO 98/15435 describes a device having both a precrash sensor and an impact sensor. With the precrash sensor, it is possible to determine the point in time of the impact and the impact velocity.
German Published Patent Application No. 199 57 187 describes a device having precrash sensors and impact sensors, the time of impact being determined with the help of precrash sensors. German Patent No. 198 17 334 describes a device having precrash sensors and impact sensors, the deployment threshold being lowered as a function of the presence of a precrash sensor signal and an impact sensor signal. It is known from German Published Patent Application No. 100 12 434 that an impact sensor may be designed as an acceleration sensor, a deformation sensor, a pressure sensor, or a structure-borne noise sensor. It is known from German Published Patent Application No. 197 39 655 that the precrash sensor may be designed as a radar sensor, a video sensor, or a noise sensor. It is known from German Published Patent Application No. 197 36 840 that the processor determines the deployment of restraint devices as a function of impact velocity and time of impact from the second signals. It is known from German Published Patent Application No. 199 17 710 that a threshold function is formed from a crash test.
SUMMARY OF THE INVENTION
The device according to the present invention for impact detection in a vehicle has the advantage over the related art that the noise threshold for the impact sensor is lowered as a function of the signals of the precrash sensor. The algorithm for calculating the deployment time for the restraint devices may thus begin at an earlier point in time. This is possible because when a time of impact is determined, it is certain that an object will crash with the vehicle. In addition, by combining the signals of the precrash sensor and the impact sensor, it is possible to determine the severity of the crash. The impact velocity and the type of crash indicate the severity of the crash. The type of crash may be extracted from the acceleration signals, this extraction being performed over velocity-dependent features. This increases the certainty, because the restraint devices may thus be used with greater precision and greater ability to adapt to the impact. The crash may thus be better identified as such. On the whole, the device according to the present invention thus results in a more accurate determination of the deployment time.
It is especially advantageous that the impact sensor is designed either as an acceleration sensor, a deformation sensor, a pressure sensor, a temperature sensor, or a structure-borne noise sensor. Combinations of these sensors may also be used, in particular in systems for plausibility checking. A precrash sensor used for side impact sensing may be combined with a structure-borne noise sensor, for example, or an acceleration sensor as a plausibility sensor. The precrash sensor may be designed as a radar sensor, a video sensor or a sound sensor, preferably an ultrasonic sensor. Here again, it is possible to use a combination of these sensors, i.e., for example, a radar sensor combined with a video sensor, in order to utilize the different distances covered by these sensors.
In addition, it is advantageous that the processor derives features from the signals of the impact sensor, i.e., the second signals, and these features are then investigated and compared with a threshold value function as a function of the signals of the precrash sensor in particular in order to determine the deployment time from these features. This provides in a particularly robust manner how the deployment time may be determined accurately in order to thus provide greater safety for the passengers of the vehicle in the event of an impact. Possible features for use here include in particular the deceleration, the velocity or the predisplacement. Thus, if an acceleration sensor is used, features may be derived from these acceleration signals through single and double integration. The threshold value function is used here as a function of velocity to compare it with the features and thus determine whether or not the threshold has been exceeded. If the threshold is exceeded, a deployment is signaled. The threshold value function may be either continuous or discrete.
The threshold value function is determined on the basis of crash tests by discovering the relationship between the impact velocity and the required airbag deployment time. This relationship may be generalized through the knowledge of an expert to types of crashes for which there have not been sufficient tests, so that this relationship may be extracted. A set of curves is based on a ranking with respect to crash severity. Thus a certain crash severity may be allocated to each type of crash. On this basis, features may be extracted either for crashes at the same velocity or the same crash severity or with the same type of crash. These features may be generalized to the other velocities or crash severities.
In this case, the knowledge extracted from the data of a subset of crash signals is transferred to other subsets. Thus, a functional relationship found in the data on one type of crash may be transferred to another type, either automatically or through the knowledge of an expert. The same thing is also possible in a transition from one velocity to another. This is particularly advantageous if only at least one crash test or even no crash tests have been conducted for some types of crashes. Therefore, the airbag may be deployed precisely at the required point in time even in these real world cases.
A crash class is identified for each type of crash for extraction of the features. The method described here makes is possible to combine crash classes whose deployment times are similar into one deployment class. In this way, crashes having different signal curves may be mapped onto the same deployment time. This permits the most accurate possible identification of crash classes while also the data and/or computation complexity for the deployment times remains low.
REFERENCES:
patent: 6529810 (2003-03-01), Foo et al.
patent: 6533316 (2003-03-01), Breed et al.
patent: 6609053 (2003-08-01), Breed
patent: 197 39 655 (1998-11-01), None
patent: 197 36 840 (1999-02-01), None
patent: 198 17 334 (1999-08-01), None
patent: 199 17 710 (2000-10-01), None
patent: 100 12 434 (2001-05-01), None
patent: 199 57 187 (2001-05-01), None
patent: WO 98/15435 (1998-04-01), None
Eisele Sybille
Roelleke Michael
Theisen Marc
Camby Richard M.
Robert & Bosch GmbH
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