International Journal of Automotive Technology, Vol. 10, No. 5, pp. 607−610 (2009)
DOI 10.1007/s12239−009−0071−8
Copyright © 2009 KSAE 1229−9138/2009/048−11
PRELIMINARY CLASSIFICATION OF DRIVING STYLE WITH OBJECTIVE RANK METHOD A. AUGUSTYNOWICZ
*
Chair of Road and Agricultural Vehicles, Opole University of Technology, ul. Miko′lajczyka 5, 45-271 Opole, Poland (Received 11 April 2008; Revised 8 November 2008)
ABSTRACT−This paper puts forward a method of preliminary driver classification, which applies two-criteria based analysis of the phenomenon of driving style. The resulting rank enabled the author to order the drivers according to their driving style, from the most active to the extremely mild. The most active driver in the sense of the two-criteria analysis is the one who covered a test stretch of the road the fastest while changing the position of the accelerator pedal most intensively. The classification of the drivers, along with the measurement data registered during road tests, will subsequently provide the foundation for later modeling of a dynamic classification of the driving style, in the form of a recurrent learning neural network of the Elman’s type. KEY WORDS : Driver style, Objective rank method, Modeling
1. INTRODUCTION
driving characteristics of a vehicle, and the active safety functions of a vehicle. One such function is stability control of a car in motion. The modeling of a driver’s characteristics is a more detailed issue that has attracted much attention (Augustynowicz, 2004; Hayakawa et al., 1998).
The steady increase in the number of requirements regarding the management of an ever growing number of control systems in a contemporary car results from constantly applied objectives including: the reduction of the toxicity of emissions, the reduction of fuel consumption, an increase in safety standards, the comfort of driver and their passengers, and finally an improvement in maneuveringand driving-related characteristics. The modernization of systems for the automatic control of vehicles includes a tendency towards the replacement of mechanical systems with “X-by-Wire” systems. Such systems are created to receive a driver’s commands with the aid of sensors, which consists of electronically processing signals and transferring them to the executionoriented part. The system commonly applied nowadays is the “Drive by Wire” system - the electronic acceleration pedal. In the area of braking systems and steer control systems, numerous companies are devoting effort to electronic systems such as “Steer by Wire” and “Brake by Wire”. Such systems constitute the foundation for the development of new systems, aiming to increase safety and driving comfort. The X-by-Wire systems enable the control system to be co-ordinated with the brake system. In addition, the X-by-Wire systems provide for the further possibility of integration with other functions of the chassis and the entire vehicle. Furthermore, such systems, which are fully programmable, allow for the optimization of the
2. ISSUE OF DRIVER TYPE MODELLING The driver controls the direction and speed of a car by enforcing the appropriate parameters of the car in motion. Subsequently, the driver experiences a feeling due to motion resulting from a sensation of enforced pressure associated with the impact of a load from the inertia force, associated with a change in velocity and direction. This sensation, in addition to visual observation, constitutes the basic source of driver information regarding motion characteristics of the vehicle, since the vehicle is not equipped with other devices that serve to indicate changes in motion characteristics, except for the speedometer. Likewise, the resultant roadwheel angles are an indirect reflection of the driver’s observation of the driving course. The characteristics of a vehicle in motion are the quick change of the velocity and direction as affected by the environment; in particular by the characteristics of a stretch of road and the presence of other road users. The greatest range of these changes occurs in the case of driving within a town. In contrast, the smallest range of these changes is observed for driving on the motorway and freeway. However, the majority of all roads can be described as stretches of straight road joined by road sections with various curvatures and bends. It is, however, the actual
*Corresponding author. e-mail:
[email protected] 607
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A. AUGUSTYNOWICZ
road conditions that affect the circumstances for a change in vehicle speed. The history of speed for a drive over a stretch of road is called the velocity profile (Augustynowica and Korniak, 2002). The profile of vehicle speed covering a sufficiently long road stretch is composed of the basic motion phases; acceleration, driving at a constant speed and delayed motion. The acceleration and delay constitute the intermediary phases between the various speed levels. The speed profile resulting from the road conditions and safety requirements is associated with necessary changes in the devices used in controlling engine power and the resulting gear changes. Examples of speed profile histories, along with the resulting throttle position, acceleration and rotation speed, are presented in Figure 1. The models of driving style applied so far in the simulation of vehicle in motion constitute the portions of the control system, which may fulfill a defined driving strategy. Such models, however, do not account for the full variety of the possible driver’s reactions in various traffic conditions and changes in the operation of the car itself, which is the object of the driving. As a consequence, these
characteristics are not sufficient for a full analysis of the human-vehicle-environment system. The complexity of the driver’s behavior and the large number of factors affecting it constitute the primary conclusion that the development of a comprehensive and more widely applicable driver model is still undeveloped. For a number of reasons the modeling of a driver’s characteristics is limited to one of the selected aspects of this model; the driving style. The appropriate analysis of signals through which the driver affects the vehicle offers a possibility for the establishment of driver’s type and, subsequently, interpretation of the behaviors in various road conditions. On the basis of this information the control system is capable of selecting the most applicable control algorithm, along with the parameters of engine and transmission operation which are best suited to the expectations of the driver (Augustynowicz, 2004; Augustynowicz and Bartecki, 2006; Augustynowica and Korniak, 2002; Hayakawa ., 1998). The current paper presents a method for an initial driver classification in terms of the driver’s driving characteristics. The final effect of the classification takes the form of a rank which orders drivers from the most “active” to the “mildest” ones. The data adapted to the subsequent driving characteristics registered in road conditions enable us to model dynamic classification of driving style in the form of a learning recurrent Elma type neural network, described by the current author in detail in a separate publication (Augustynowicz and Bartecki, 2006). et al
3. STRUCTURE OF DRIVERS RANK A comparison between various objects located in space, in terms of complex phenomena, is associated with the necessity to prepare their evaluations and, subsequently, the development of a rank. Complex phenomena by definition are characterized with a number of multi-criteria characteristics, which are identified with various units and indicate various values. The multi-criteria phenomenon assessment with various objects becomes an accessible option when a transformation of the original characteristics is undertaken with the aim of their unification. The transformed variables are stripped of their individual characteristics and assume similar values. The methods of the transformation of the original diagnostic values are called the methods of standardization. The standardized values of diagnostic variables may undergo the process of aggregation, which leads to the origin of an aggregated variable. Each object is characterized in terms of the evaluated complex phenomena. The awareness of the evaluation of the objects provides for the construction of their rank, from the worst to the best one, with regard to the value of the synthetic variable (Jedrzejczak ., 2004). This paper involves a search for the most “aggressive” drivers, based on two-criteria; those who change the position of the accelerator pedal most intensively during test stretches, while their speed varied to the greatest degree. et al
Figure 1. History change of vehicle speed, accelerator pedal position, longitudinal acceleration and engine speed ype the caption here.
PRELIMINARY CLASSIFICATION OF DRIVING STYLE WITH OBJECTIVE RANK METHOD
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The analyzed criteria (termed synthetic variables) include the standard deviation of accelerator pedal and standard deviation of vehicle speed. The two analyzed criteria are given in various units, since it is necessary to determine a synthetic measure for them in the first place, which accounts for their realization. Since both criteria are maximized, a formula for the standard diagnostic variable has been applied in both cases zij
z
x – min x = ----------------------------------lim maxx – lim min x ij
ij
ij
ij
i
ij
i
⎛i ⎝j
= 1, 2, …, r ⎞ = 1, 2, …, s ⎠
(1)
where, : diagnostic variables, : the number of objects, : the number of synthetic variables, max : the biggest realization of a phenomenon in an analyzed set, min : the smallest realization of a phenomenon in an analyzed set. Subsequently, the objects have been ordered due to their mean realization. The value of the synthetic variable is determined from relation (2). The lower the synthetic variable, the better the object properties are. xij
i
j
xij
xij
= 1--s- ∑ z (i = 1, 2, …, r) s
Q
i
(2)
ij
j
=1
The material subjected to analysis is taken from previously undertaken road tests. It included test drives of a passenger car in urban driving conditions. The various drivers covered a stretch of a road two times, attempting to drive in two different manners. In order to ensure similar conditions for each test drive, tests were conducted at the time of the day when the traffic was smallest. For the analysis, the number of drives was selected at = 76, by applying two synthetic variables (hence = 2). The distribution of the investigated drivers is indicated in Figure 2. Drivers were ranked based upon the two criteria establishi
j
Figure 3. Driver rank. 38 - most aggressive driver, 15 - mildest driver.Area Table 1. Selected parameters of trials performed by different types of drivers. Type of driver Selected parameters most most “mild” “aggressive” Mean car speed, m/s 12.3 15,9 Maximum car speed, m/s 22.6 29.7 Maximum longitudinal car 2.2 3.3 acceleration, m/s Mean position of the active 12.9 35.7 accelerator pedal, % Maximum value of the active accelerator pedal velocity (while 219 602 pressing), %/s 2
ed prior to testing. The drivers described in a rank are presented in the diagram in Figure 3. Table 1 contains a combination of selected parameters for two test drives corresponding to the extreme types of drivers identified in the rank: the most active and mildest one. The data included in Table 1 confirm the fact that the person who drove the most aggressively (no. 38) showed higher instantaneous values of car speed and acceleration, in comparison to the mildest driver (no.15).
4. CLASSIFICATION OF DRIVING STYLE
Figure 2. Area of two-criteria analysis
.
The classification of the driving style of each driver was based on the application of an Elman type neural network with a single output and single output neuron (Elman, 1990; Rutkowski, 2005). The network input is supplied with signals, representing the instantaneous position of the accelerator pedal. This value, prior to being supplied to input, undergoes standardization based on the rescaling of its value, to a value in the range (−1, 1). The input signals were identified as −1 for the corresponding “mild” driving
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A. AUGUSTYNOWICZ the development of driver models, have produced satisfactory results. We have devised a neural classificator of driving style requiring supply with an appropriate signal input. Because this paper applies a selected method or rank development, it would be useful to undertake more comprehensive research into prior studies already published and cited throughout this article. REFERENCES
Figure 4. Diagram of comprehensive classification of driving style based on recurrent Elman’s Neural Network. style, 0 was “neutral”, and 1 was attributed to the “active” driving style. While the network was in operation, the output signal underwent further filtration in an attempt to avoid an abrupt change in the output signal in the model. The learning and test data in this analysis only applied data from these specific test drives, which were indicated by their rank to be representative of a given driving style. It enabled the author to provide teaching data for the neural network. A block diagram of a classificatory of the driving style is presented in Figure 4. 5. CONCLUSION
The procedures presented in this paper, which contribute to
Augustynowicz, A. (2004). Estimation of driver’s intention based on acceleration pedal signal. Int. Automotive Cong. Konmot-Autoprogres, Zakopane, Poland, 59−66. Augustynowicz, A. and Bartecki, K. (2006). Estimation of driving characteristics by the application of Elman’s recurrent neural network. The Archives of Transport, Warsaw , , 5−13. Augustynowicz, A. and Korniak, J. (2002). Steering of car speed at use of fuzzy logic. Int. Automotive Cong. Konmot-Autoprogres, Pasym, Poland, 27−36. Elman, J. L. (1990). Finding structure in time, Cognitive Science, , 179−211. Hayakawa, K., Osawa, M., Yoshida, H., Oshima, M., Ito, Y. and Nakamura, Y. (1998). Real time estimation of driver’s intention and environment based on operational signals. FISITA World Automotive Cong., Paris, F98S204. Jedrzejczak, Z., Kukula, K., Skrzypek, J. and Walkosz, A. (2004). Operations Research in Tasks and Examples. Polish Scientific Publishers PWN. Warsaw. Rutkowski, L. (2005). Methods and Techniques of Artificial Intelligence. Polish Scientific Publishers PWN. Warsaw. 18 4
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