A SMART AIRBAG SYSTEM David S. Breed
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A SMART AIRBAG SYSTEM use with anticipatory sensing systems to identify threateningobjects, such as an approachingvehicle about David S. Breed to impact the side of the vehicle. Neural networks have Automotive Technologies International, Inc. also been applied to sense automobile crashes for the United States purposeof determiningwhether or not to deploy an airbag Paper Number: 98-%-O- 13 or other passiverestraint, or to tighten the seatbelts,cutoff the fuel system, or unlock the doors after the crash. ABSTRACT Heretofore, neural networks have not been applied to forecastthe severity of automobilecrashes for the purpose Pattern recognition techniques, such as neural of controlling the flow of gas into or out of an airbag in networks, have beenappiied to identify objects within the order to tailor the airbag inflation characteristicsto the passengercompartment of the vehicle, such as a rear crash severity. Neural networks have also not been used facing child seat or an out-of-position occupant, and to to tailor the airbag inflation characteristicsto the size, suppressthe airbagwhen an occupantis more likely to be position or relative velocity of the occupant or other injured by the air-bag than by the accident. Neural factors such as seatbelt usage, seat and seat back networks have also been applied to sense automobile positions,headrest position, vehicle velocity, etc. crashes. The use of neural networks is extendedhere to “Pattern recognition” as usedherein meansany system tailoring the airbag inflation to the severity of the crash, which processesa signal that is generatedby an object. or the size, position and relative velocity of the occupantand is modified by interacting with an object, in order to other factors such as seatbeltusage, seat and seat back determinewhich one of a set of classesthe object belongs positions, vehicle velocity, and any other relevant to. In this case, the object can be a vehicle with an information. accelerometerwhich generates a signal based on the It is well known that a neural network based crash deceleration of the vehicle. Such a system might sensor can forecast, basedon the first part of the crash determineonly that the object is or is not a memberof one pulse, that the crash wilI be of a severity which requires specified class (e.g., airbag required crashes),or it might that an airbag be deployed. This is extended here to attempt to assign the object to one of a larger set of enhancethe capabilities of this sensor to forecast the specified classes,or find that it is not a memberof any of velocity changeof the crash over the entire crash period. the classes in the set. One such class might consist of Then a patternrecognition occupantposition and velocity vehicles undergoinga crash of a certain severity into a determination sensor is added. Finaiy, an occupant pole. The signals processed are generaIly electrical weight sensor is inctuded to permit a measure of the signals coming from transducerswhich are sensitive to occupant’s momentum or kinetic energy. The either acceleration, or acoustic or electromagnetic combination of these systems in various forms will be radiation and, if electromagnetic, they can be either usedto optimize inflation and/or deflation of the airbagto visible light, intiared, ultraviolet or radar. The particular createa “‘smartairbag ” system. pattern recognition techniques used here are neural Crash sensorscan predict that a crash is of a severity networks. which requires the deployment of an airbag for the To “identifjr” as usedherein meansto determinethat majority of real world crashes. A more difficult problem the object belongsto a particular set or class. The class is to predict the crash velocity versus time function and may be one containing all frontal impact air-bag-desired then to adjust the airbag inflation/deflation over time so crashesinto a pole at 20 mph, one containing all events that just the proper amount of gas is in the airbag at all where the airbag is not required, or one containing all times even without considering the influence of the eventsrequiring a triggering of both stagesof a dual stage occupant. To also simultaneouslyconsider the occupant gas generatorwith a 15 millisecond deiay between the size, weight, position and velocity renders this problem triggering of the first and secondstages. unsolvableby conventionalmethods. SINGLE POINT CRASH SENSORS BACKGROUND All electronic crash sensorscurrently used in sensing Patternrecognition techniques, such as artificial neural frontal impacts include accelerometersthat detect and networks, are finding increasedapplication in solving a measurethe vehicle accelerationsduring the crash. The variety of problems such as optical characterrecognition, accelerometerproduces an analog signal proportional to voice recognition, and military target identification. In the acceleration experiencedby the accelerometer,and the automotive industry, neural networks have now been hencethe vehicle on which it is mounted. An analog to applied to engine control and to identify various objects digital converter(ADC) transformsthis analog signal into within the passengercompartment of the vehicle, such as a digital time series. Crash sensor designersstudy this a rear facing child seat. They have also beenproposed for digital accelerationdata and derive therefrom computer 1080 algorithms that determine whether the accelerationdata position, size and weight of the occupant. More Tom a particular crash event warrants deploymentof the particularly, no such system exists which uses pattern airbag. This is usually a trial and error processwherein recognition techniquesto match the airbagdeployment or the crash sensor designer observes data from crashes gas dischargefrom the airbag to the severity of the crash where the airbag is desiredand when it is not needed,and or the size, weight, position, velocity and seatbeltuse of other events where the airbag is not needed. Finally, the an occupant. crash sensordesigner settles on the “rules” for controlling Since there is insufficient information in the deployment of the airbag which are programmedinto an acceleration data, as measured in the passenger algorithm which appearsto satisfy the requirementsof the compartment,to senseall crashesand since some of the crashlibrary. The resulting algorithm is not universaland failure modesof published single point sensoralgorithms most such crash sensor designers will answer in the can be easily demonstratedusing the techniquesof crash negative when asked whether their algorithm will work and velocity scaling describedin the referencedtechnical for all vehicles. Such an algorithm also merely papers, and, moreover, since the process by which determines that the airbag should or should not be engineersdevelop crash sensor algorithms is based on triggered. Heretofore, no attempt has been made to trial and error, pattern recognition techniques such as ascertainor forecastthe eventual severity of the crash or, neural networks, should be able to create an algorithm more specifically, the velocity changeversus time of the basedon training the system on a large number of crash passenger compartment during the crash from the and non-crashevents which, although not perfect, will be accelerationdata obtained from the accelerometer. superior to all others. Such a crash sensor has been Severalpapers have beenpublished pointing out some demonstratedwhich is based on the ability of neural of the problemsand limitations of electronic crashsensors networks to forecast, basedon the first part of the crash that are mounted out of the crush zone, usually in a pulse, that the crashwill be of a severity requiring airbag protected location in the passengercompartment of the deployment. vehicle (I-6). The crush zone is defined, for the purposes herein, as that portion of the vehicle that has crushedat SMART AIRBAG CATEGORIES the time that the crash sensormust trigger deploymentof the restraint system. These sensorsare frequently called An improvementto this neural network based crash single point crashsensors. sensor carries this process fiuther by using the neural These papers demonstrate,among other things, that network to forecastthe velocity changeof the crash over there is no known theory which allows an engineer to time so that the inflation and/ordeflation of the airbag can develop an algorithm for sensingcrashes and selectively be optimized. Then by the addition of a neural network deploying the airbag except when the sensoris located in occupant position and velocity determination system as the crush zone of the vehicle. Thesepapers show that, in disclosed in (lo,1 I) the occupancy category (forward general, there is insufficient information within the facing human, rear facing child seat, box etc.), position acceleration signal measured in the passenger and velocity can be obtained. Finally, the addition of the compartment to sense all crashes. Another conclusion weight of the occupant provides a measure of the suggestedby thesetechnical papersis that if an algorithm occupantskinetic energyas a further input to the system. can be found which works for one vehicle, it will also The combination of these sub-systemsin various forms work for all vehicles since it is possible to create any can be called “smart airbags” or “smart restraints”. In a crash pulse measuredin one vehicle, in any other vehicle. preferred implementation, the crash severity is not Note in particular SAE paper920 124(3). explicitly forecasted. Rather, the value of a control In