Data processing: vehicles – navigation – and relative location – Vehicle control – guidance – operation – or indication – Vehicle diagnosis or maintenance indication
Reexamination Certificate
1999-08-31
2001-11-13
Cuchlinski, Jr., William A. (Department: 3661)
Data processing: vehicles, navigation, and relative location
Vehicle control, guidance, operation, or indication
Vehicle diagnosis or maintenance indication
C701S301000, C701S091000, C701S110000, C701S111000, C701S101000, C701S102000, C180S065100, C180S065310, C180S076000, C180S076000, C180S197000, C180S272000, C123S205000, C123S297000, C123S325000, C123S333000, C123S336000, C123S357000, C123S463000, C123S463000, C123S406440, C123S463000, C123S501000, C123S502000, C303S090000, C303S090000, C303S115600, C303S115600, C340S576000, C340S439000, C340S425500
Reexamination Certificate
active
06317666
ABSTRACT:
BACKGROUND OF THE INVENTION
This invention relates to a procedure for analyzing and influencing the driveability of motor vehicles.
A major criterion in the subjective assessment of a motor vehicle by the driver or passengers is vehicle driveability. This term refers in general to a driver's personal impression of the vehicle's general operation, particularly in transient operational states.
DESCRIPTION OF THE PRIOR ART
In EP 0 846 945 A of the applicant a method for analyzing the driveability of motor vehicles is described, which includes the following steps:
Running tests with the use of a real vehicle to obtain measurement variables on its driveability;
Continuous monitoring to check whether one of a number of predefined trigger conditions is fulfilled, i.e., whether a certain set of variables takes on certain values;
If trigger conditions are fulfilled, computation of at least one rating representing vehicle driveability from one or more measured values, using a predefined functional relationship.
Output of the rating.
The rating concerned is a variable which represents the quality of the vehicle with regard to driveability. By means of a complex computing procedure and calibration conducted with the assistance of test drivers a close correlation is established between the rating computed from the measured values and the subjective judgment passed by the drivers.
It is natural that assessments on the driveability of one and the same vehicle made by different test drivers will differ within a certain range. It has been found, however, that such differences have a certain systematic component in addition to a stochastic component.
Similarly, the expectations and demands of different drivers with regard to a vehicle's driveability will always differ to some extent. This implies that it will not be possible generally to optimize driveability in such way that it is actually experienced as the optimum by all drivers. A comfort-oriented driver, for example, may find a jolting car motion most irritating, whilst a sporty driver may even enjoy jolting in certain manoeuvers.
In EP 0 304 089 A and DE 37 15 423 A methods and devices are described where different sensor signals provide information on the particular type of driver involved, and the engine or vehicle in general is adjusted accordingly. From the point of view of control theory this corresponds to open-loop control where a certain measured value will initiate an adjusting process, which will have no feedback on the measured value, however. Naturally, such a procedure is only moderately effective.
SUMMARY OF THE INVENTION
It is an object of this invention to refine the procedure for analyzing the driveability of a motor vehicle such that a closer correspondence will be obtained between computed results and subjective assessments. Furthermore, a procedure for influencing driveability is proposed which will be less dependent on the driver involved.
It has been found that the systematic component in the differences between driveability assessments of different drivers may be characterized by at least one variable. This variable is principally derived from the same data base as vehicle driveability; if required, additional variables are measured. The variable can be correlated with verbally defined types of drivers. Basically, there are several different ways of deriving one or more variables from the measured values. It is possible, for instance, to define a variable which represents a comfort-sportiness index. The driver would receive ratings on a continuous scale ranging from very comfort-oriented to very sporty. As an alternative it would be possible to define an independent variable each for comfort and sportiness. Another possibility would be to permit only certain discrete values for the variable, each of these values corresponding to a certain predefined type of driver. In words, such types could be referred to as cautious, comfort-oriented, unpracticed, sporty, hectic, etc. It would further be possible to define a number of behavioral variables and, in a second step, to build a classification into different types. Such behavioral variables could be comfort/sportiness, economy-mindedness, driving skill, etc. In such instance a multidimensional rating is produced, i.e., a specific driver'profile is prepared from the different variables assessing different characteristics.
The above described verbal interpretation of the meaning of the variables will only serve to promote comprehension and improve the presentation of the procedure according to the invention. In the actual implementation of the procedure mathematical and statistical methods are used in order to ensure the required accuracy and reproducibility.
The main difference between the present invention and known procedures using driver classifications is that the type of driver determined will enter the driveability computation, such that a multi-loop control system is obtained which will result in a more or less adaptive-self-learning optimization of vehicle driveability.
It is an essential feature of the invention that vehicle driveability is perceived as a quality which is not independent of the specific driver of the vehicle. The driver enters the computation in such way that the measured values will supply information on his behaviour. The frequency with which the throttle valve opens or closes, for example, will indicate whether a driver is relaxed and comfort-oriented, or sporty and aggressive. For proper assessment of the type of driver involved, measured values may be employed which do not directly enter the determination of driveability, such as the steering angle or rate of change of the steering angle.
When the system is calibrated with the assistance of test drivers, the drivers are classified and assigned to a corresponding type, and the variable classifying the driver type is given a certain value. For this purpose one and the same vehicle in one and the same parameter setting should be given more or less the same ratings by drivers of the same type, whilst it may be assessed differently by drivers of other types.
There are several different possibilities of influencing the driveability of a vehicle in dependence of the type of driver using it. First of all, the type of driver is found by means of the actions performed by the driver. Depending on the type of driver determined in this way a change in parameter settings may be effected in a first variant of the invention. This implies that engine characteristics are selected from a set of several possibilities such that the expected driveability will be optimally matched with the specific type of driver involved. To this open-loop control a closed loop system is added, which will be further discussed below.
In a preferred variant of the invention a deviation of the momentary rating or ratings from one or several target values is continuously monitored, the target values being derived according to predefined functional relationships from the simultaneously obtained variables representing the driver type. By means of known multi-dimensional control methods, for example systems based on neural networks, it is attempted to minimize the deviation error. Neural networks are simplified, computer-adapted simulations of human brain cells and comprise an optional number of inputs, outputs, nodes and node links on different levels. By training a neural network node weights can be determined, which will permit the effects of any changes in input quantities on the output quantities, i.e., the measured values, to be predicted. In the instance of a deviation error an estimate is provided in which way the input quantities, i.e., choice of engine characteristics, etc., are to be modified in order to reduce the error. Since the internal working of the system is not fully known, such a procedure will be an iterative process which may require a large number of steps to find an optimum.
In an especially preferred variant, the above control scheme is characterized by different time-scales. Some control ac
List Helmut
Schoggl Peter
AVL List GmbH
Cuchlinski Jr. William A.
Dykema Gossett PLLC
Mancho Ronnie
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