EP2610836A1 - Dispositif et procédé pour la prédiction en ligne du cycle d'entraînement dans un véhicule automobile - Google Patents

Dispositif et procédé pour la prédiction en ligne du cycle d'entraînement dans un véhicule automobile Download PDF

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EP2610836A1
EP2610836A1 EP12382494.8A EP12382494A EP2610836A1 EP 2610836 A1 EP2610836 A1 EP 2610836A1 EP 12382494 A EP12382494 A EP 12382494A EP 2610836 A1 EP2610836 A1 EP 2610836A1
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vehicle
driving cycle
pat
speed
prediction
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EP2610836B1 (fr
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Juan José Valera García
Jordi Caus Roqueta
Gerhard Lux
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SEAT SA
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SEAT SA
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0129—Traffic data processing for creating historical data or processing based on historical data

Definitions

  • the present invention is encompassed within the automotive field, and more specifically within the devices and methods for the on-line prediction (while the vehicle is circulating) of the driving cycle of a hybrid vehicle with respect to a preselected prediction horizon.
  • the objective of the invention is to provide the prediction made to the energy management subsystem of the hybrid vehicle so that said vehicle adapts its energy strategy as a function of said prediction, and can thus reduce vehicle consumption as well as optimize the different energy flows found in a hybrid vehicle to increase its energy efficiency, autonomy and reduce CO 2 emissions.
  • the present invention focuses on the development of a system or device which contributes to solving the first drawback or barrier related to the prior knowledge of the driving cycle that the vehicle will perform.
  • the model of a driver refers to the representation by means of mathematical formulations or intelligent algorithms of the behavior of the driver of a vehicle, i.e., of the drivers tasks, for analyzing or inferring which actions the driver takes with said vehicle.
  • a broader approach relates the model of the driver with the purely dynamic model of the car, as well as with the environment of the driver and his/her vehicle, i.e., the city and other drivers.
  • Different types of driver models can therefore be defined in accordance with the reality which they want to best represent.
  • the first trend focuses on the human behavior characteristics of the driver, i.e., the analysis of said behavior, the interpretation of gestures and emotions on one hand and the inference of that behavior in vehicle control, maneuvers and driving strategy. It is obvious to include the Michon hierarchical control model (1985) within this umbrella.
  • the first distinction it makes is to differentiate between external state input-output type models and internal state type models.
  • the other distinction refers to functional models or taxonomy models. Michon asserts that the models are generally bottom-up (internal) and that top-down models are generally non-specifiic and too simplified.
  • His cognitive process type model the Hierarchical Control Model, divides the task of driving into three coupled and hierarchical levels:
  • a fourth level would be the purely behavior level, [2] (Panou, Bekiaris, & Papakostopoulos, 2007). Another suitable classification of driver behavior is that which distinguishes between following a desired trajectory and stability in the event of disturbances.
  • the second group of applications focuses purely on vehicle control task. This group has a direct correlation with the Michon control level.
  • the control of a vehicle is split in two, longitudinal control (accelerator and brake) and lateral control (steering wheel).
  • Tustin (1947) is considered to be the first author to publish driver model in mathematical form.
  • the third type of models focuses on traffic simulation both microscopically (individual behavior with respect to the traffic) and macroscopically (large environments with several drivers) ([6] Fernandez, 2010).
  • the fourth type of models refers to those used in searching for an optimum energy management system for hybrid vehicles.
  • the importance of the driver model in these applications stems from the fact that its behavior considerably affects the energy distribution.
  • a new driver model is specifically developed in literature reference [7] (Froberg & Nielsen, 2008) for dynamic inverse simulations for the purpose of optimizing the simulation times used in searching for energy and drive strategies. Its objective is driving cycle simulation efficiency, not real time implementation.
  • a relevant paper is that citied in reference [9] (Langari & Won, 2005).
  • This paper presents a system for identifying the driving cycle by means of an LQV neural network and a fuzzy logic system for the purpose of improving energy management (IEMA).
  • IEMA energy management
  • Another approach to the problem is by means of driving pattern recognition by means of fuzzy logic and the subsequent prediction by means of Hidden Markov Models ([13] Montazeri- Gh, Ahmadi, & Asadi, 2008). The optimum is not assured because fuzzy logic is used.
  • the success of the control strategy largely depends on the quality of that prediction as well as the length of said prediction ([14] Koot, Kessels, Jager, Heemels, den, & Steinbuch, 2005).
  • the algorithm used must be optimum, although given the complexity of the problem a quasi optimum is achieved.
  • data about the road state and traffic congestion and thoroughfare type and speed limit must be obtained. With the aid of modern GPS type navigation systems it would be possible to obtain this data. This technology can result in a greater efficiency and precision in predicting driver behavior and thereby the intelligent drive system behavior, which is fundamental in the global efficiency of electric hybrid vehicles.
  • the present invention consists of a device and a method for the on-line prediction (while the vehicle is circulating) of the driving cycle in a vehicle with respect to a preselected prediction horizon.
  • the proposed device is based on a prediction strategy made up of a step of pre-processing the inputs received, an artificial neural network (ANN), and a step of post-processing for obtaining the prediction.
  • ANN artificial neural network
  • the strategy is supported on three main information sources:
  • the device predicts the future driving cycle (speed vs. time, and path gradient vs. time) while the vehicle is circulating. For that purpose, the device uses certain information (measurements) of the driving cycle performed in the recent past, as well as certain information (from the new navigation systems which are connected with traffic management systems in real time, second generation systems)) of an advanced nature related to future traffic events that will occur in the path that it being taken.
  • the main objective of this device and method is to provide the prediction made to the energy management subsystem of the hybrid vehicle so that said vehicle adapts its energy strategy as a function of said prediction, and can thus reduce vehicle consumption as well as optimize the different energy flows found in a hybrid vehicle to increase its autonomy and reduce CO 2 emissions.
  • the proposed device has a communication channel for connecting to the vehicle second generation navigation system, and another communication channel for connecting to the subsystem (Electronic Control Unit, ECU) which performs energy management functions in a hybrid vehicle.
  • ECU Electronic Control Unit
  • the proposed device/method receives the current vehicle speed measurement, as well as navigation subsystem information related to information about the path that is being taken (speed limits, road slopes,%), as well as real time traffic events information about events that are occurring in future points where the vehicle will be circulating (jams, holdups,).
  • This information (speed measurement and navigation system information) is preprocessed and used as input in a neural network with a specific topology which obtains mainly two outputs.
  • the first output corresponds with the prediction of the driving cycle (speed vs. time*) made with respect to a selected prediction horizon (usually in Km), whereas the second output corresponds with the prediction of the slope cycle (slope vs. time*) with respect to the selected horizon.
  • the proposed device and method further obtains a third output related to the user driving style.
  • a fuzzy inference system is used using specific expert rules which are based on specific parameters which are being obtained using the driving cycle measurements from the recent past.
  • the proposed prediction algorithm and technique (based on neural network, fuzzy inference system and steps of pre-processing and post-processing of the information) works on-line while the vehicle is circulating, calculating and obtaining new predictions (prediction update) in each sampling instant selected for it.
  • the neural network used tries to obtain the non-linear function which best approximates the current vehicle-driver circulation. For that purpose, this network gradually learns the vehicle behavior and the different driving modes of the usual driver by means of the driving cycles that are ultimately being performed. Therefore, it relates to a device/method with self-learning capability.
  • this device/method does not try to characterize or classify the driving cycle which is being performed (like most of the reviewed systems/techniques do) according to pattern cycles, and take actions based on this classification as a function of a rule-based system, but rather the proposed device obtains the prediction using information taken from the recent driving past and from future traffic events information, thus being integrated with navigation systems using new technologies (mobile networks, traffic management, connection to management servers and traffic supervision, etc.) for the purpose of constructing on-line (while circulating) the most probable driving cycle that the vehicle-driver will follow in future kilometers of the path that is being taken.
  • new technologies mobile networks, traffic management, connection to management servers and traffic supervision, etc.
  • the device/method proposed is capable of making the prediction without knowing beforehand the final destination of the path, although it is more precise and accurate and offers enormous potential if the route and/or final destination is known beforehand: indicated in the navigation system by the driver or even recognizable by the system itself (paths usually taken). Therefore, if the final destination and route that is being taken are known beforehand, the selected prediction horizon can be very large, obtaining a high prediction precision. However, if the final destination and route are not known, the selected prediction horizon must be smaller if prediction precision is to be maintained, or otherwise (large prediction horizons) the prediction precision could be affected because the system or device has to opt for (or has to guess) one route from among all those possible routes that the driver will follow based on statistical data and probabilities.
  • the method for the on-line prediction of the driving cycle in an automotive vehicle comprises:
  • the step of data pre-processing can comprise receiving traffic events information corresponding to the expected path for the vehicle within at least the prediction horizon, and where said traffic events information received is used also for obtaining the reference driving cycle.
  • the neural network is preferably a previously trained recurrent dynamic neural network with NARX topology.
  • the vehicle speed is sampled in the step of data pre-processing according to a specific sampling time, and obtaining the reference pattern cycle and the calculation of the deviation of the vehicle speed with respect to the reference driving cycle are performed for each sampling time.
  • the information relating to the reference driving cycle can comprise a pattern speed moved forward a future sample number, which is equivalent to the vision distance of the driver and the anticipation of the driver with respect to future traffic situation changes.
  • the traffic information can additionally include at least one of the following pieces of information:
  • the traffic events information can include information relating to at least one of the following:
  • the traffic information and the traffic events information are preferably received within the interval [p, p+H], p being the current vehicle position and H the selected prediction horizon.
  • the method can comprise obtaining the driving style of the driver of the vehicle according to calculations depending on a parameter relating to the driving style calculation mode selected, where the calculation modes are based on at least one of the following:
  • Another object of the present invention is a device for the on-line prediction of the driving cycle in an automotive vehicle, comprising:
  • the communication means can additionally be configured for receiving traffic events information corresponding to the expected path for the vehicle within at least the prediction horizon from the navigation system, and where the data processing means are configured for obtaining the reference driving cycle also using said traffic events information received by the communication means.
  • the data processing means are preferably configured for sampling the vehicle speed according to a specific sampling time and for obtaining the reference pattern cycle and the calculation of the deviation of the vehicle speed with respect to the reference driving cycle for each sampling time.
  • the device can also comprise the actual navigation module.
  • the data processing means can be configured for performing the prediction calculation while the vehicle is circulating and every time the vehicle advances by a selected distance by means of a parameter.
  • the device for the prediction of the driving cycle 100 has the instantaneous vehicle speed measurement (in Km/h) and two specific inputs referring to traffic information obtained by means of a navigation system 104, Traffic Information (HTI, Horizon Traffic Information ) and Traffic Events Information (HTEI, Horizon Traffic Events Information ), as inputs.
  • the Traffic Information (HTI) input contains the speed limits, road slopes and traffic signals in the prediction horizon considered.
  • the Traffic Events Information (HTEI) input contains information such as state/flow of traffic, works, visibility and road surface conditions.
  • the prediction horizon (H) is the driving cycle prediction interval.
  • the input parameters of the device for the prediction of the driving cycle 100 are explained in detail below:
  • Size H / H_Resol. It should be indicated that instead of said Speed_Limits vector, it could be replaced with any other vector providing information from which the device can deduce speed limits; for example, the type of thoroughfare (highway, road with a wide shoulder, etc.) when there is no higher limitation.
  • the navigation systems to be used must have the characteristic of being able to obtain real time traffic information and events.
  • Some types of models which can obtain this type of real time information are starting to be sold today.
  • the devices are either connected to traffic management systems via communications (RDS, 802.11x, etc.) or obtain the information by creating communication networks the users of which are onboard vehicle navigation systems.
  • RDS traffic management systems
  • 802.11x 802.11x
  • These vehicles/users share information with a server which infers the traffic state based on the speed and position measurements it receives from the different vehicles forming the network.
  • the device for the prediction of the driving cycle 100 uses the following parameters:
  • the device for the prediction of the driving cycle 100 obtains the following outputs, shown in Figure 1 , which are provided to an external unit 108, which can be the Energy Management System (EMS) of the vehicle or any other third party application:
  • EMS Energy Management System
  • the created prediction strategy is based on using a previously trained Artificial Neural Network (ANN) with NARX (nonlinear autoregressive network with exogenous inputs) topology.
  • ANN Artificial Neural Network
  • NARX nononlinear autoregressive network with exogenous inputs
  • This strategy is completed with a series of pre- and post-processing functions of both the inputs and the outputs of this ANN-NARX.
  • the objective of the ANN-NARX is to learn the behavior and driving mode of the driver-vehicle combination by evaluating the deviations of vehicle speed with respect to a reference driving cycle or pattern corresponding to the path that is being taken.
  • the reference driving cycle or pattern for the future horizon is dynamically constructed in each sampling time as a function of the information received through the Traffic Information ( HTI ) and Traffic Events Information ( HTEI ) inputs.
  • the ANN-NARX also obtains the expected deviations of speed (with respect to the reference cycle) for the future horizon in each sampling instant using the reference driving cycle (pattern cycle) and the deviations of speed occurring in the recent past of the path for that purpose. Therefore, the real expected driving cycle with respect to the selected prediction horizon can finally be obtained by using the prediction of the expected deviations of speed and the expected reference cycle.
  • the block diagram corresponding to the strategy used by the device for the prediction of the driving cycle is shown in Figure 2 .
  • the three steps of the strategy, the pre-processing (200), the artificial neural network (202) and the post-processing (204), are explained below in detail.
  • the pre-processing (200), the artificial neural network (202) and the post-processing (204) are done by means of data processing, using for example a system or device based on a microcontroller or microprocessor supported by a set of memory elements and input/output and communication ports.
  • the first function that is performed consists of applying a real time filter 206 on the Vehicle Speed ( V sp ) input variable.
  • the type of filter applied is a real time Kalman Filter.
  • the second function in the pre-processing (200) consists of performing a domain transformation 207 on the V spkf variable obtained as output after applying the filter 206.
  • a transformation from the time domain to distance domain (kilometer marker) is performed by means of this second function 207 obtaining the internal Kilometer_Marker (PKm) variable. This variable is obtained by means of numerical integration of the speed V spkf .
  • this function gradually generates a two column vector [V spf (i) , PKm(i)] where each row " i " is calculated according to the algorithm presented in (1) in each sampling time " k ". It can be observed that the PKm(i) vector is always increasing and further has a constant sampling " i " which corresponds with a distance of H_Resol.
  • the V spf (i) variable represents the vehicle speed corresponding to each kilometer marker PKm(i).
  • the next function that is performed in this step is the calculation of the deviation vector 208, which consists of calculating' the deviation existing between the vehicle speed V spf (i) and the pattern speed or reference driving cycle V pat (i) (V pat (i) being the speed allowed for said kilometer marker i) for each kilometer marker PKm(i).
  • the calculation for constructing the deviation vector DV sp (i) is shown in the algorithm (2) and is performed as the PKm(i) and V spf (i) vectors are being constructed.
  • the pattern speed vector V pat (i) is constructed using the available traffic information from the navigation system ( Horizon Traffic. Information , HTI , input). If the path to take (final destination and route to follow) is known, the traffic signals corresponding to speed limits existing in each kilometer marker of said route or path to take can be known.
  • An example can be observed in Figure 3 , in which a real driving cycle performed on a specific path marked in blue the vehicle speed V spf (i) and the corresponding pattern speed vector V pat (i) marked in red, has been represented by way of example. It can be observed that both vectors are represented with respect to the Kilometer_Marker PKm(i) vector.
  • the cycle represented by way of example in Figure 3 has been performed with a vehicle that was equipped with an onboard data acquisition system.
  • the path to take is not known, said path must be estimated by probabilistic methods in order to construct the pattern speed vector for the future horizon. Logically in this case, the higher the selected prediction horizon, the error in obtaining the pattern speed vector and therefore in the final prediction made could be penalized.
  • the vectors NN_DV sp (i) , and NN_V pat (i) which are the inputs of the artificial neural network 202, are generated.
  • the NN_V pat (i) vector is generated in step 210 and on one hand contains the last DSN samples of the vector V pat (i) , where DSN represents the number of samples defining the size of the recent past, and on the other hand the future FSN samples of the V pat (i) vector, where FSN (parameter of the device for the prediction of the driving cycle 100) represents the number of samples defining the size of the near future. Therefore, the NN_V pat (i) vector contains the last DSN values of the V pat (i) vector, and the future FSN values of the V pat (i) vector, see algorithm (4).
  • the functions performed in this step of pre-processing 200 are aimed at constructing the vectors NN_DV sp (i) , and NN_V pat (i) which are the inputs to the artificial neural network 202 created and used for performing the prediction of the driving cycle.
  • the topology of the artificial neural network 202 selected for performing the prediction is within recurrent dynamic neural networks and is referred to as NARX (nonlinear autoregressive network with exogenous inputs).
  • the NARX model is based on the linear ARX model which is commonly used in time series analysis and prediction.
  • the equation defining the non-linear NARX model is shown in (5), where y represents the output variable and x 1 , x 2 , ..., x n , represent the possible inputs of the model or system to be modeled, f represents a possible non-linear function, and NumDelaysY, NumDelaysX 1..n ., represent the number of previous samples that are taken into account for calculating the output of the non-linear function.
  • y ⁇ k + 1 f ⁇ ( y k , y ⁇ k - 1 , y ⁇ k - 2 , ... , y ⁇ k - NumDelaysY , x 1 k , x 1 ⁇ k - 1 , x 1 ⁇ k - 2 , ... , x 1 ⁇ k - NumDelays ⁇ X 1 , x 2 k , x 2 ⁇ k - 1 , x 2 ⁇ k - 2 , ... , x 2 ⁇ k - NumDelays ⁇ X 2 , ... , x n k , x n ⁇ k - 1 , x n ⁇ k - 2 , ... , x n ⁇ k - NumDelays ⁇ X n
  • a non-linear NARX model can be implemented through a feed-forward neural network approaching the non-linear function f .
  • Figure 4 shows a diagram of the structure of the proposed neural network 202 in which the inputs 400, the output 408 and the input layer 402, hidden layer 404 and output layer 406 which are formed by neurons can be observed. As can be observed, the inputs and the output correspond with those present in (5).
  • Each input in a neural network corresponds to a series of parameters (weights and bias 410) joining each input neuron with the corresponding neurons forming the hidden neuron layer 404. Therefore, the value of each neuron belonging to the hidden layer 404 is calculated by applying a usually non-linear function to the sum of the product of the input neurons by their corresponding weights added to the biases. This operation is gradually transmitted to the neurons forming the output layer 406, where the value of each output is obtained by means of applying a linear or non-linear function to the sum of the product in feed-forward direction.
  • the process of training the neural network defined consists of obtaining the parameters thereof (weights and bias 410 of all the connections) which lead to obtaining the outputs of the examples with a minimal error for the inputs of the examples. Therefore, the neural network "learns” from these examples, acquiring the property of "generalizing” when other sequences and different inputs occur in the network.
  • the success of training the network depends on the training algorithm used, on the number of layers and neurons selected, and especially on the examples used for training it, which must have enough information for the network to acquire the property of generalizing and not the property of over-learning the examples used.
  • the output to be estimated is the deviation of speed in the next kilometer marker, D*V sp (k+1).
  • the variable NN_DV sp together with its corresponding previous DSN samples, the variable V pat together with its corresponding previous DSN samples and subsequent FSN samples, are inputs, see Figure 5 .
  • the network calculates the output in the instant k+1, but since in this case the prediction is to be made with respect to a horizon H , this calculation can be repeated n times in a recursive manner and the prediction of the expected deviation of speed ( D * V sp ) with respect to the selected prediction horizon H can therefore be calculated.
  • the actual estimated variables of the output of the network D*V sp ) are needed, as represented by the dotted line in Figure 5 .
  • the basic algorithm for performing the prediction with respect to the horizon H is shown in (6), where the input vectors in the network together with the corresponding samples are constructed in the preparation_inputs_NARX() sub-function, see Figures 2 and 5 .
  • the NARX neural network 202 obtains the estimation of the deviation of speed expected for the selected horizon, from i to i + H / H _ Reso /, as output for each kilometer marker i (PKm(i)).
  • the driving style is obtained in different manners according to the selection made by means of the DSCM ( Driving Style Calculation Mode) parameter.
  • DSCM Driving Style Calculation Mode
  • This method seeks to observe the speed variations being caused by the driver with respect to the vehicle as well as the frequencies thereof. It seems logical to think that, for example, in a section where the speed limit is 80 Km/h (pattern speed), if the vehicle speed is higher and oscillating around this limit, the probable conclusion would be that the driver is in a hurry and is driving aggressively.
  • the Fourier transform applied to the speed signal of the vehicle offers the possibility of calculating the mean or continuous value of the signal (DC value) as well as the amplitudes of the main harmonic and of other orders. By relating these values with the pattern speed signal, conclusions as to the driving mode can be drawn.
  • the calculation process consists of applying the Fourier transform to the vehicle speed signal with respect to an interval corresponding to the recent past, obtaining the mean value and amplitude magnitudes of the first harmonic (fundamental signal) and relating them with their corresponding magnitudes in the pattern speed signal to see the existing variation.
  • This process is shown in (12), where D represents the desired number of previous samples (recent past) that are evaluated in the algorithm, DC represents the continuous value of the signal after performing the Fast Fourier transform, and A1 represents the amplitude of the first harmonic (fundamental signal) after the transform.
  • D represents the desired number of previous samples (recent past) that are evaluated in the algorithm
  • DC represents the continuous value of the signal after performing the Fast Fourier transform
  • A1 represents the amplitude of the first harmonic (fundamental signal) after the transform.
  • the use of the index i in the algorithm should be pointed out.
  • This method seeks to observe the mean vehicle speed variation in a predetermined time interval. This measurement could also indicate the degree of aggressiveness in driving.
  • the process for obtaining it is presented in (13). As can be observed, this time the index k (time domain) is used, obtaining the mean speed variations with respect to the mean speed value in the time interval corresponding to D x ST , ST being the sampling time selected by means of the corresponding parameter.
  • This method is based on obtaining the driver anticipation time in the event of a future speed limit change. For example, a driver who starts to accelerate 100 meters before the signal of traffic corresponding to a speed limit change (to a higher value) is most likely driving aggressively. Also, a driver who starts to brake at a kilometer marker that is higher than the corresponding kilometer marker where a traffic signal indicating a speed limit change (to a lower value) is located is assumed to be driving aggressively. The measurement of these anticipation and delay values in the recent past combined with the deviation of speed occurring in the steady systems (at a constant speed, as in the preceding methods) could provide a more accurate driving style.
  • Figure 6 shows in a non-limiting manner the components of a device for the prediction of the driving cycle 100 which performs the steps of the method described above.
  • the device comprises first communication means 600 for receiving the vehicle speed and traffic information, usually from the navigation system 104 (the vehicle speed can be received by other means, for example from measurements taken by the vehicle itself).
  • Said first communication means 600 can include a CANbus communications port.
  • the device 100 can comprise second communication means 602 for receiving the ignition (IG) signal from the ignition system 102 or from energy management system itself of the vehicle 108.
  • said second communication means 602 comprise a digital input port.
  • the device 100 comprises data processing means 604, for example a DSP unit or a microcontroller with high computational capacity for performing the different steps of the calculation.
  • Said data processing means 604 have, or have access to, data storage means 606, for example a RAM memory and an EPROM memory.
  • the device has communication means for communicating with the energy management system of the vehicle 108, for example through a CANbus communications port 608. It can also have a digital output port 610.

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EP20120382494 2011-12-30 2012-12-12 Dispositif et procédé pour la prédiction en ligne du cycle d'entraînement dans un véhicule automobile Active EP2610836B1 (fr)

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