EP4107051A1 - Procede de parametrage d'un calculateur, dispositif d'aide a la conduite comportant un tel calculateur et vehicule automobile equipe d'un tel dispositif - Google Patents
Procede de parametrage d'un calculateur, dispositif d'aide a la conduite comportant un tel calculateur et vehicule automobile equipe d'un tel dispositifInfo
- Publication number
- EP4107051A1 EP4107051A1 EP21700964.6A EP21700964A EP4107051A1 EP 4107051 A1 EP4107051 A1 EP 4107051A1 EP 21700964 A EP21700964 A EP 21700964A EP 4107051 A1 EP4107051 A1 EP 4107051A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- vehicle
- angle
- speed
- steering wheel
- template
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
- B62D—MOTOR VEHICLES; TRAILERS
- B62D15/00—Steering not otherwise provided for
- B62D15/02—Steering position indicators ; Steering position determination; Steering aids
- B62D15/029—Steering assistants using warnings or proposing actions to the driver without influencing the steering system
- B62D15/0295—Steering assistants using warnings or proposing actions to the driver without influencing the steering system by overlaying a vehicle path based on present steering angle over an image without processing that image
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3407—Route searching; Route guidance specially adapted for specific applications
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/0499—Feedforward networks
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/09—Supervised learning
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
Definitions
- the present invention relates generally to aids to the driver of a motor vehicle.
- Such a reversing camera is generally connected to a screen located on the dashboard of the vehicle so as to display, when the driver of the vehicle engages reverse gear, an image of the environment behind the vehicle.
- the dynamic template generally comprises two arcs of circles which extend from the rear of the vehicle, over a given length (for example 3 meters), and which represent the foreseeable trajectories of the two rear wheels of the vehicle taking into account the steering angle of the steering wheel.
- the dynamic template is thus intended to help the driver to park in reverse and in particular to appreciate the distance he has for parking.
- a conventional means of displaying this template comprises the use of a calculation unit capable of calculating point by point the dynamic template according to certain input parameters such as the angle at the steering wheel and the speed of the vehicle. This solution, described for example in the French patent application published under the number FR3031707, makes it possible to dispense with the recording of pre-established templates in the read-only memory of the computer.
- the dynamic template may include a first curve representative of the foreseeable trajectory of a first rear wheel and a second curve representative of the foreseeable trajectory of a second rear wheel.
- the training step may include:
- the training step can also include:
- the algorithm can be a neural network
- the approximation function of the shape of the template can be a polynomial whose parameters are the coefficients of the monomials;
- the polynomial can be a second degree polynomial
- the invention also provides a device for assisting the maneuver in reverse of a motor vehicle, comprising a means for acquiring a value relating to the angle at the steering wheel of the vehicle, a means for acquiring a value relating to the speed of the vehicle, a reversing camera, a display screen adapted to display images acquired by the reversing camera, a computer configured according to the parameterization method described above and programmed to determine a dynamic template representative of a foreseeable trajectory of the motor vehicle, and a display module on the display screen of said dynamic template superimposed on the images acquired by the reversing camera.
- the dynamic template may include a first curve representative of the foreseeable trajectory of a first rear wheel and a second curve representative of the foreseeable trajectory of a second rear wheel.
- the calculator can be configured to
- the invention further provides a motor vehicle equipped with a maneuvering aid device according to the invention.
- FIG. 1 is a schematic top view of a motor vehicle, behind which is an image of a dynamic template.
- FIG. 2 illustrates a method for setting up a computer in accordance with the invention.
- FIG. 3 is a portion of an array forming a training database for an algorithm of a computer according to the invention.
- FIG. 1 there is shown, seen from above, an embodiment of a motor vehicle 1 according to the invention.
- this motor vehicle 1 comprises a frame which defines a passenger compartment for the driver of the vehicle. It also has four wheels, a computer, a system for controlling the steering of the rear wheels, and a steering wheel for controlling the orientation of the steered wheels to the right or to the left.
- the motor vehicle 1 comprises two front wheels 2 steered and two rear wheels 3 non-steerable (that is to say which do not participate in the steering of the motor vehicle 1 towards right or left).
- This example is not limiting and the invention can for example be applied to a vehicle with four steered wheels.
- a reference (O, C, U, Z) attached to the motor vehicle 1.
- the reference considered is an orthonormal reference whose origin O is located at the center of the front axle of the motor vehicle 1, whose longitudinal axis X is oriented towards the rear of the motor vehicle, whose transverse axis Y is oriented towards the right of the motor vehicle and whose vertical axis Z is oriented upwards.
- top the terms “top”, “bottom”, “left” and “right” are to be understood from the point of view of a driver of the vehicle.
- transverse axis Y which is oriented towards the right of the vehicle when one is placed from the point of view of the driver appears oriented towards the left of FIG. 1.
- the motor vehicle 1 is equipped with a device for assisting in reversing maneuver comprising a reversing camera 50 and a display screen 51 adapted to display images acquired by the camera reversing camera 50.
- the reversing camera 50 is arranged at the rear of the chassis, for example above the vehicle registration plate, so that its lens opens out to the outside of the vehicle and is generally rearward and downward facing.
- the reversing camera 50 has an optical axis which is inclined:
- a yaw angle which may or may not be zero (which is here less than 10 degrees)
- a roll angle which may or may not be zero
- the display screen 51 is itself placed in the passenger compartment of the motor vehicle 1, for example on the dashboard of the latter. It therefore allows the driver to see the images acquired by the reversing camera 50.
- the device for assisting in reversing maneuvering to include a computer 52 comprising in particular a processor, random access memory, read only memory, analog-to-digital converters, and various input and output interfaces.
- the computer 52 is adapted to receive input signals from various sensors. Thanks to its analog-to-digital converters, the signals received by the computer 52 are sampled and digitized.
- the computer 52 In its random access memory, the computer 52 thus continuously stores: - the (instantaneous) image acquired by the reversing camera 50, and transmitted to the computer 52,
- the computer 52 is programmed to generate, for each operating condition of the vehicle, output signals.
- the computer 52 is suitable for transmitting these output signals to the audiovisual devices on board the motor vehicle 1, in particular to the display screen 51.
- the computer 52 is then suitable for implementing a method of assisting the maneuver in reverse of the motor vehicle 1.
- This method is implemented when the gear ratio engaged by the driver is reverse gear. This method consists in displaying a dynamic template superimposed on the images acquired by the reversing camera 50.
- FIG 1 there is shown an image of this dynamic template 20, seen in a plane parallel to the plane of the road on which the motor vehicle 1 travels (here in the plane (O, C, U)).
- the vehicle's reversing maneuver assistance device comprises a display module of this template in superimposition of the images acquired by the reversing camera so that it is visible to the driver.
- the dynamic template 20 is a geometric figure which allows the driver to understand the predictable path that the motor vehicle 1 will take when it backs up.
- the dynamic template here comprises a first curve 21 representing the foreseeable trajectory of the first rear wheel, and a second curve 22 representing the foreseeable trajectory of the second rear wheel.
- This geometric figure has a shape that varies depending at least on the steering angle of the steering wheel.
- the dynamic template 20 has a shape which is determined at each moment as a function of the input variables.
- the software stored in the read-only memory of the computer 52 comprises an algorithm previously trained to predict the shape of the template as a function of the input signals, and in particular here as a function of the speed of the vehicle and of the angle at the wheel .
- FIG. 2 illustrates the steps of a process for setting the computer 52. The example described below illustrates the setting of the computer 52 to calculate the first curve 21. It should be noted that the setting of the computer 52 for determining the second curve 21 is done in a similar fashion and will not be described here for the purposes of simplifying the description.
- This setting consists in practice in determining an algorithm which will make it possible, once installed in the vehicle 1, to easily calculate the shape of the dynamic template 20.
- This setting operation is therefore carried out during the design of the vehicle, on a unit computer calculation separate from the motor vehicle.
- this computer calculation unit calculates in a known manner, for example according to the manner described in the application FR3031707 cited above, for each pair of values of input parameters, here for each pair of steering wheel angle and vehicle speed values, the coordinates of the points of the first curve 20 in the plane (O, C, U). These coordinates are obtained and saved in the form of pairs of columns, a first column of which comprises the coordinates i of the points along X and a second column comprises the coordinates j of the points along Y.
- This first step E1 is known to those skilled in the art since it is already known to calculate the shape of the dynamic template analytically.
- the computer calculation unit determines, for each pair of columns, the parameters of an approximation function of the shape of the curve 20. For example, it calculates the coefficients d 'a polynomial, here a second degree polynomial, making it possible to predict the coordinates j of the points of the curve along the Y axis, from the coordinates i of the points of the curve along the X axis.
- the computer calculation unit generates a training database 4 from the information obtained in the previous steps.
- Figure 3 is a representation of part of the database 4, here the first thirteen lines.
- the database 4 is in the form of a table comprising, for each pair of steering wheel angle and speed values:
- a first coordinate ijni of a point of the first curve here the abscissa (along the X axis) of the initial point, that is to say of the point closest to the rear face of the vehicle therefore having the lowest abscissa value
- a second i_fin coordinate of a second point of the template here the abscissa of the end point, that is to say of the point of the curve furthest from the vehicle, for example previously chosen here to be located 3 meters from the initial point ijni along the curve
- the algorithm is configured to learn how to determine this curve from said database 4.
- the algorithm is formed from one or more neural networks. It comprises here five neural networks, which are preferably of the same type.
- each neural network has an input layer which considers at least the values of driving angle and speed of the motor vehicle.
- Each neural network has at least one hidden layer. In practice, it has two.
- each neural network comprises an output layer which returns a single value.
- the learning phase then consists in parameterizing the neurons of the different layers of the five networks, so that, when providing the algorithm (that is to say to the five neural networks) the values of steering wheel angle and speed of the motor vehicle, it provides an approximate value of the first coordinate ijni, of the second coordinate of point i_fin and of the coefficients a2, a1, aO of the second degree polynomial.
- Each neural network thus generates a prediction function of a value (the first coordinate ijni, or the second coordinate of point i_fin or the coefficients a2, a1, aO of the second degree polynomial) obtained using d 'a regression model.
- Each neural network is built here using the free software “TensorFlow”.
- a first regression model M1 is built to predict the first point coordinate ijni as a function of the steering wheel angle and the speed of the vehicle.
- the first regression model M1 is trained in a learning environment from the database 4.
- the regression model constructed is a multivariate linear regression model, comprising several inputs and a single output.
- a second regression model M2 is constructed to predict the second point i_fin coordinate as a function of the steering wheel angle, the vehicle speed and the first point ijni coordinate.
- the second M2 regression model is trained in a learning environment from database 4.
- a third regression model M3 is built to predict a first parameter of the approximation function, here the maximum order coefficient a2 of the polynomial, from the angle at the steering wheel , the speed of the vehicle, the first coordinate of point ijni and the second coordinate of point i_fin.
- the third M3 regression model is trained in a learning environment from database 4.
- a fourth regression model M4 is built to predict a second parameter of the approximation function, here the coefficient of order one a1 of the polynomial, from the angle at the steering wheel , of the speed of the vehicle, of the first coordinate of point Mni, of the second coordinate of point Jin, and of the coefficient a2.
- the fourth M4 regression model is trained in a learning environment from database 4.
- a fifth regression model M5 is built to predict a third parameter of the approximation function, here the zero order coefficient aO of the polynomial, from the angle at the steering wheel , the speed of the vehicle, the first coordinate of point ijni, the second coordinate of point Jin, the coefficient a2 and the coefficient a1.
- the fifth regression model M5 is trained in a learning environment from the database 4.
- regression models could be constructed differently. We could thus have started by predicting the values of the parameters of the polynomial before those of the x-coordinates ijni, i_fin.
- the computer 52 is thus configured to determine the first curve of the template using the regression models M1 to M5 as follows:
- i_fin M2 (angle_volant, speed, ijni)
- A2 M3 (angle_volant, speed, Mni, Jfin)
- A1 M4 (angle_volant, speed, Mni, Min, a2)
- AO M5 (angle_volant, speed, Mni, Min, a2, a1)
- This curve is also well positioned and limited in length on the display screen thanks to the abscissas of the first and last points of the curve.
- the invention therefore advantageously allows a determination of the dynamic template of the vehicle 1 in a simple manner, that is to say by implementing a limited number of operations per unit of time.
- the invention is in particular compatible with any other function suitable as an approximation function.
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- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Remote Sensing (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Artificial Intelligence (AREA)
- Computational Linguistics (AREA)
- Software Systems (AREA)
- Mathematical Physics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computing Systems (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Automation & Control Theory (AREA)
- Chemical & Material Sciences (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Combustion & Propulsion (AREA)
- Steering Control In Accordance With Driving Conditions (AREA)
Abstract
Description
Claims
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| FR2001541A FR3107380B1 (fr) | 2020-02-17 | 2020-02-17 | Procédé de paramétrage d’un calculateur, dispositif d’aide à la conduite comportant un tel calculateur et véhicule automobile équipe d’un tel dispositif |
| PCT/EP2021/051364 WO2021164975A1 (fr) | 2020-02-17 | 2021-01-21 | Procede de parametrage d'un calculateur, dispositif d'aide a la conduite comportant un tel calculateur et vehicule automobile equipe d'un tel dispositif |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| EP4107051A1 true EP4107051A1 (fr) | 2022-12-28 |
Family
ID=71452361
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| EP21700964.6A Pending EP4107051A1 (fr) | 2020-02-17 | 2021-01-21 | Procede de parametrage d'un calculateur, dispositif d'aide a la conduite comportant un tel calculateur et vehicule automobile equipe d'un tel dispositif |
Country Status (3)
| Country | Link |
|---|---|
| EP (1) | EP4107051A1 (fr) |
| FR (1) | FR3107380B1 (fr) |
| WO (1) | WO2021164975A1 (fr) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114822169B (zh) * | 2022-05-06 | 2023-06-09 | 辽宁科技大学 | 一种教练车驾驶辅助练习方法及装置 |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR3031707B1 (fr) | 2015-01-16 | 2018-06-29 | Renault S.A.S. | Procede et dispositif d'aide a la manœuvre en marche arriere d'un vehicule automobile |
| US11966838B2 (en) * | 2018-06-19 | 2024-04-23 | Nvidia Corporation | Behavior-guided path planning in autonomous machine applications |
-
2020
- 2020-02-17 FR FR2001541A patent/FR3107380B1/fr active Active
-
2021
- 2021-01-21 WO PCT/EP2021/051364 patent/WO2021164975A1/fr not_active Ceased
- 2021-01-21 EP EP21700964.6A patent/EP4107051A1/fr active Pending
Also Published As
| Publication number | Publication date |
|---|---|
| WO2021164975A1 (fr) | 2021-08-26 |
| FR3107380B1 (fr) | 2022-01-14 |
| FR3107380A1 (fr) | 2021-08-20 |
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