EP4348179A1 - Method for locating autonomous vehicles - Google Patents
Method for locating autonomous vehiclesInfo
- Publication number
- EP4348179A1 EP4348179A1 EP22728611.9A EP22728611A EP4348179A1 EP 4348179 A1 EP4348179 A1 EP 4348179A1 EP 22728611 A EP22728611 A EP 22728611A EP 4348179 A1 EP4348179 A1 EP 4348179A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- trajectory
- autonomous vehicle
- calculation
- extrapolated
- vehicle
- 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
- 238000000034 method Methods 0.000 title claims abstract description 41
- 230000033001 locomotion Effects 0.000 claims abstract description 19
- 238000004364 calculation method Methods 0.000 claims description 23
- 239000011159 matrix material Substances 0.000 claims description 10
- 238000004590 computer program Methods 0.000 claims description 7
- 230000004807 localization Effects 0.000 claims description 5
- 238000005259 measurement Methods 0.000 description 18
- 230000006870 function Effects 0.000 description 10
- 230000004927 fusion Effects 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 101000582320 Homo sapiens Neurogenic differentiation factor 6 Proteins 0.000 description 2
- 102100030589 Neurogenic differentiation factor 6 Human genes 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 2
- 238000013213 extrapolation Methods 0.000 description 2
- 238000013179 statistical model Methods 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
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- 238000013473 artificial intelligence Methods 0.000 description 1
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Classifications
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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
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
- G01S19/49—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system whereby the further system is an inertial position system, e.g. loosely-coupled
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
Definitions
- the invention relates to a localization method for an autonomous vehicle.
- the invention also relates to a location device for an autonomous vehicle.
- the invention also relates to a computer program implementing the mentioned method.
- the invention finally relates to a recording medium on which such a program is recorded.
- the precision of the location of the autonomous vehicle conditions its operational reliability and the safety of the passengers.
- the location of the vehicle is generally derived from a GNSS-type satellite positioning system and vehicle movement estimates from sensors placed on the chassis.
- Patent WO2018/175441 A1 describes a navigation system for an autonomous vehicle taking into account not only satellite positioning and sensors (speed sensors, cameras, suspension sensors) but also the planned trajectory for the vehicle.
- the planned trajectory is determined from the camera measurements, making it possible, for example, to estimate the shape of the road and to define—according to behaviors learned by the vehicle—a short-term trajectory adapted to the shape of the road.
- the system described in this patent predicts the location of the autonomous vehicle by applying a processing to the data from the sensors (the processing possibly being the application of a Kalman filter or neural network modeling) to estimate the location of the vehicle along a short-term planned trajectory.
- the object of the invention is to provide a location device and method for autonomous vehicles remedying the above drawbacks and improving the location devices and methods for autonomous vehicles known from the prior art.
- the invention makes it possible to produce a device and a method which are simple and reliable.
- the invention relates to a method for locating an autonomous vehicle comprising position sensors, a trajectory planning system, and motion actuators.
- the method includes an iteration of the following steps:
- a step of predicting the current position of the autonomous vehicle comprising a reception of a previous estimated position of the autonomous vehicle resulting from a previous iteration of the method, a reception of commands applied by the movement actuators, a calculation of a trajectory controlled according to the commands applied, and a calculation of an extrapolated position of the autonomous vehicle according to said previous estimated position, and said commanded trajectory,
- an updating step comprising a calculation of an updated estimated position of the autonomous vehicle taking into account a measured position from the position sensors and the extrapolated position.
- the method can comprise a step of determining a trajectory planned by the trajectory planning system, and the calculation of an extrapolated position of the autonomous vehicle can be carried out according to the planned trajectory.
- the method may comprise a determination of a confidence index associated with the extrapolated position as a function of the calculated difference between the planned trajectory and the commanded trajectory and the step of calculating the updated estimated position may take account of the index of confidence.
- the prediction step may include the implementation of a Kalman filter with which is associated a noise covariance matrix calculated as a function of the difference between the planned trajectory and the commanded trajectory.
- the calculation of the confidence index can depend on the noise covariance matrix of the Kalman filter.
- the updated estimated position can be a weighted average of the measured position and of the extrapolated position, the weighting coefficients being able to be a function of the confidence index of the extrapolated position.
- the invention further relates to a tracking device for an autonomous vehicle, the device comprising a computing unit, position sensors, a trajectory planning system and motion actuators.
- the device comprises hardware and/or software elements implementing the method as defined previously, in particular hardware and/or software elements designed to implement the method according to the invention, and/or the device comprising means of implement the method as defined above.
- the invention further relates to an autonomous vehicle comprising a location device according to the invention.
- the invention also relates to a computer program product comprising program code instructions recorded on a computer-readable medium for implementing the steps of the method as defined above when said program is running on a computer.
- the invention also relates to a computer program product downloadable from a communication network and/or recorded on a data carrier readable by a computer and/or executable by a computer, comprising instructions which, when the program is executed by the computer, lead it to implement the method as defined previously.
- the invention also relates to a data recording medium, readable by a computer, on which is recorded a computer program comprising program code instructions for implementing the method as defined previously.
- the invention also relates to a computer-readable recording medium comprising instructions which, when executed by a computer, lead the latter to implement the method as defined previously.
- the invention also relates to a signal from a data medium, carrying the computer program product as defined previously.
- the appended drawing shows, by way of example, an embodiment of a location device according to the invention and an embodiment of a location method according to the invention.
- Figure 1 shows an autonomous vehicle equipped with a tracking device.
- FIG. 2 shows an embodiment of the location device.
- FIG. 3 represents a flowchart of an embodiment of a location method.
- Figure 4 represents an iteration loop of steps of the localization process.
- Figure 5 represents an architecture of a localization process prediction module.
- An example of an autonomous vehicle 100 equipped with a location system is described below with reference to FIG.
- the autonomous vehicle 100 can be an autonomous vehicle of any type, in particular a passenger vehicle, or a utility vehicle or even a public transport vehicle.
- the autonomous vehicle 100 comprises a chassis 4 and a tracking system 10.
- the location system 10 mainly comprises the following elements:
- a calculation unit 7 comprising a microprocessor 1, a memory 5 and communication interfaces 6 allowing the microprocessor 1 to communicate with the set of position sensors 2 and the navigation system 3.
- the set of position sensors 2 can comprise different types of sensors, including a satellite or GNSS positioning system, a set of cameras and/or lidars and/or radars.
- the cameras can be placed on the vehicle and also in the environment of the vehicle.
- Other embodiments of position sensors 2 may, for example, include communication systems between vehicles (V2V system).
- the measurements from the position sensors 2 include measurements 61 of the position of the vehicle.
- the position measurements 61 can be absolute, for example when they come from a GNSS type system.
- the position measurements 61 can also be relative measurements, in particular when they relate to a movement from a known initial position, the movement of the vehicle being able for example to be calculated from measurements of rotation of the wheels of the vehicle.
- the measurements from the position sensors 2 comprise measurements 68 of the environment of the autonomous vehicle.
- the measurements 68 include measurements of the position of the various elements present in the environment of the vehicle.
- the processing of measurements 68 can also provide other parameters detailing the state of an object in the scene: its attitude (angular position), its speed vector, but also other attributes such as its class (pedestrian, car, truck , cycle, etc%) or its dimensions.
- the chassis 4 includes the various actuators which implement the movement of the autonomous vehicle, in particular an engine torque actuator 41, a brake actuator 42 and a steering wheel rotation actuator 43.
- the chassis receives control commands 66 from the navigation system 3 in order to move the vehicle along a planned trajectory 64 by the navigation system 3.
- the term “trajectory” is used to designate the temporal evolution of a state vector defining the characteristics of the movement of the motor vehicle 100.
- the state vector comprises a location, in particular x,y coordinates, longitudinal and lateral velocities and/or longitudinal and lateral accelerations and/or yaw rate.
- position is used to designate either the x, y coordinates of the state vector, or the state vector as a whole.
- the commands actually applied by the actuators 41, 42, 43, named in the remainder of the document "applied commands”, may however differ from the control commands 66 received from the navigation system 3. Indeed, certain parameters may disturb the implementation commands 66. For example, the state of the road, the wear of the tires, or the weather conditions can interfere with the implementation of the commands 66.
- the chassis 4 transmits in return the applied commands 70 to the navigation system 3.
- the navigation system 3 is aware of the commands actually applied by the actuators.
- the commands applied 70 by the actuators 41, 42 and 43 are very close to the command commands 66.
- the trajectory implemented by the autonomous vehicle 100 deviates substantially from the planned trajectory 64.
- the location system 10 aims to improve the location of the autonomous vehicle, in particular in situations where the real trajectory of the vehicle autonomous deviates significantly from the planned trajectory.
- the navigation system 3 receives as input, on the one hand, the measurements 68 of the environment of the autonomous vehicle, and, on the other hand, an updated estimated position 72 of the autonomous vehicle 100.
- the navigation system 3 includes an environmental data fusion module 31, which aggregates the data from the various sensors and relating to the environment of the autonomous vehicle 100.
- the merged data 69 is then processed in a situation analysis module 32, advantageously comprising the determination of an electronic horizon of the vehicle.
- the electronic horizon is a local map of the elements of the driving scene including the information of the area useful for driving.
- the electronic horizon includes the precise positions relative to the vehicle of all the elements of the driving scene that will allow the vehicle to make the relevant decisions for driving.
- the data 63 from the situation analysis module 32 are then used in a module for planning a short-term trajectory 33, to determine a short-term trajectory 64.
- the order of magnitude of the duration of the trajectory short term 64 is seconds.
- the short-term trajectory 64 thus determined is transmitted to the control laws 34, in order to be transformed into a command order 66 transmitted to the chassis 4.
- the latter transmits in return the applied commands 70 to the navigation system 3.
- the commands applied 70 are transmitted to the computer 1 by the navigation system 3.
- the computer 1 makes it possible to execute software comprising the following modules: - a module 10 for determining a planned trajectory, which collaborates with the navigation module 3,
- the method comprises a step E0, followed by an iteration over two steps E1 and E2.
- a planned trajectory 64 is determined.
- the planned trajectory 64 is defined by the module for planning a short-term trajectory 33 according to the data from the situation analysis module 32.
- the planned trajectory 64 provides for the movements of the vehicle over a time interval of the order of a few seconds, corresponding to the sighting distance of the vehicle, that is to say the detection limit of the sensors 2, or of the electronic horizon.
- the planned trajectory follows an ideal curve, i.e. a curve representing a theoretical movement of the autonomous vehicle.
- the theoretical displacement of the vehicle can be determined by statistical models.
- the planned trajectory 64 is stored in the memory 5 for its later use in the step E1.
- the prediction step E1 predicts an extrapolated position 71 of the autonomous vehicle 100 from a previous estimated position 62, a planned trajectory 64 and a commanded trajectory 67,
- the updating step E2 calculates an updated estimated position 72 at starting from a measured position 61 and from the extrapolated position 71 resulting from step E1 of prediction.
- a mode of execution of the prediction step E1 is described with reference to FIG. 5.
- the first step E1 comprises a sub-step E11 of receiving an estimated position 62 of the vehicle coming from a previous iteration of the method.
- the estimated position 62 includes the absolute coordinates of the autonomous vehicle 100 dated at a time TN-I.
- the first step E1 also includes a sub-step E12 of receiving commands 67 applied to the vehicle by the movement actuators 41, 42, 42.
- the commands 67 are dated at a time TCOM of command of the movement of the vehicle.
- the first step E1 also comprises a sub-step E13 of calculating a commanded trajectory 74 as a function of the applied commands 67.
- a conversion module 113 converts the commands 67 into a commanded trajectory 74.
- the commanded trajectory 74 follows a noisy curve, that is to say which differs significantly from the ideal curve defined by the planned trajectory 64.
- the first step E1 further comprises a sub-step E14 of calculating an extrapolated position 71.
- the calculation of an extrapolated position 71 takes into account both the planned trajectory 64 and the trajectory ordered 74.
- the calculation of an extrapolated position 71 can implement a filter 111, taking into input a previous estimated position 62, a planned trajectory 64 and a noise covariance matrix 73.
- a first mode of execution of the filter 111 can implement an extended Kalman filter, that is to say a Kalman filter based on a nonlinear model.
- a predicted state vector x' is then calculated according to the following Math 1 formula:
- - x is the state vector predicted during the previous iteration, i.e. position 62 estimated during a previous iteration
- - u is the state vector associated with the planned trajectory, i.e. the planned trajectory 64,
- control function i.e. a nonlinear function modeling the transition between two successive iterations of filter 111.
- a covariance matrix P′ associated with the predicted state vector x′ is calculated according to the following Math 2 formula.
- - P is the covariance associated with the state vector predicted during the previous iteration, i.e. the covariance associated with position 62,
- - P' is the covariance associated with the predicted state vector during the current iteration, i.e. the covariance associated with the extrapolated position 71 ,
- control model i.e. the function that converts the controls on the actuators or the trajectory in space of the state vector. This function is normally known in advance and constant over time,
- the noise covariance matrix 73 is used to model the uncertainty of the prediction linked to a measured deviation between the planned trajectory 64 and the commanded trajectory 74.
- a synchronized planned trajectory 65 is calculated. , so that it can be compared with the commanded trajectory 74.
- the first mode of execution of the filter 111 comprises a calculation of the noise covariance matrix W according to the mathematical expressions Math 3:
- - E() is the estimated function.
- an extrapolated position 71 and an associated covariance P′ are obtained.
- the covariance P' thus makes it possible to calculate a confidence index IC1 associated with the extrapolated position.
- the confidence index IC1 can, for example, be calculated from the covariance matrix, by selecting the values on the diagonal (or another similar function), which makes it possible to determine a confidence index per state variable of the positioning module.
- the extrapolated position 71 corresponds to a point M belonging to the planned trajectory 64, and the confidence index IC1 reflects the probability that the autonomous vehicle 100 is actually located at point M with a given level of precision.
- the controlled trajectory 74 only influences the confidence index IC1 associated with the extrapolated position 71:
- the filter 111 could be implemented, for example by using a linear Kalman filter, or a particle filter.
- the Kalman filter could be replaced by artificial intelligence using, for example, Machine Learning algorithms or neural networks.
- step E1 the calculation of an extrapolated position 71 could take into account the commanded trajectory 74 for the calculation of the coordinates of the autonomous vehicle.
- step E2 an updated estimated position 72 is calculated taking into account, on the one hand, a measured position 61 coming from the sensors 2 and, on the other hand, the extrapolated position 71 .
- Step E2 includes a sub-step of receiving data from the sensors 2, and determining a measured position 61 at a given instant TMES.
- the reception sub-step comprises a fusion of the data coming from different sensors.
- the data fusion makes it possible to associate a confidence index IC2 with the measured position 61.
- the confidence index notably takes into account the different sources of data taken into account in the data fusion.
- the confidence index can also take into account visibility conditions related to road infrastructure (for example, the presence of a tunnel or a sharp bend), or related to light or weather conditions.
- the confidence index can also take into account the environment of the vehicle, in particular areas of reflection that can disturb the measurements of the sensors.
- Step E2 further comprises a sub-step of calculating an updated estimated position 72 from the measured position 61 derived from the sensors and from the extrapolated position 71 calculated in step E1.
- the updated estimated position 72 is a weighted average of the measured position 61 and the extrapolated position 71.
- Both of the measured 61 and extrapolated 71 positions are advantageously associated with a confidence index, IC2, IC1.
- the confidence index IC1 represents the degree of confidence associated with the planned trajectory 64; in particular the confidence index IC1 provides a measurement of the consistency between the planned trajectory 64 and the commanded trajectory 74.
- the weighting coefficients of the weighted average are determined according to the confidence index IC1. For example, if the confidence index of IC1 is lower than a given threshold then the weighting coefficient associated with the extrapolated position 71 is strictly lower than the weighting coefficient associated with the measured position 61 . In other words, if the planned trajectory 64 is substantially far from the commanded trajectory 67, then the measured position 61 will have more weight than the extrapolated position 71 in the calculation of the updated estimated position 72.
- the calculation of the weighting coefficients could take into account the two confidence indices IC1, IC2, in particular the relative level of the confidence indices IC1, IC2.
- the weighting coefficient of each position 61, 71 could be proportional to the confidence index IC2, IC1 respectively associated with said position.
- this method of calculation could only apply if the ratio between the highest confidence index and the lowest confidence index is below a given threshold, for example 120%. Beyond the given threshold, one could for example assign a very low coefficient, or even zero, to the position of the lowest confidence index.
- the calculation of a weighted average between these two positions aims to improve the precision of the location, in particular by taking into account the inherent uncertainty on the one hand in the measurements of the sensors, and on the other hand in a position determined by extrapolation from a planned trajectory.
- the location method according to the invention has the advantage of detecting and processing as soon as possible the differences between the trajectory planned and the commanded trajectory, i.e. the trajectory actually implemented by the vehicle's motion actuators.
- the deviation between the commanded trajectory and the planned trajectory is evaluated.
- the commanded trajectory corresponds to a very short-term extrapolation of the future position of the vehicle from the commands applied by the motion actuators.
- the objective of the invention is to apply a planned trajectory while improving the prediction of the position of the autonomous vehicle.
- the invention instead of being in a reactive mode (in which one reacts to measurements), the invention makes it possible to be proactive by taking into account the maneuvers to come.
- the commanded trajectory could be used in the calculation of the vehicle's position itself.
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- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Aviation & Aerospace Engineering (AREA)
- Computer Networks & Wireless Communication (AREA)
- Navigation (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention relates to a method for locating an autonomous vehicle (100) comprising position sensors (2), a path planning system (33), and motion actuators (41), characterised in that it comprises an iteration of the following steps: - a step (E1) of predicting the current position of the autonomous vehicle (100) which comprises receiving (E11) a previous estimated position (62) of the autonomous vehicle (100) obtained from a previous iteration of the method, receiving (E12) applied commands (67) by means of the motion actuators (41, 42, 43), calculating (E13) a commanded path (74) on the basis of the applied commands (67), and calculating (E14) an extrapolated position (71) of the autonomous vehicle (100) on the basis of said previous estimated position (62) and said commanded path (74); - an updating step (E2) which comprises calculating an updated estimated position (72) of the autonomous vehicle (100) taking into account a measured position (61) obtained from the position sensors (2) and the extrapolated position (71).
Description
TITRE : Procédé de localisation pour véhicule autonome TITLE: Localization process for autonomous vehicle
L’invention concerne un procédé de localisation pour véhicule autonome. L’invention porte encore sur un dispositif de localisation pour véhicule autonome. L’invention porte également sur un programme d’ordinateur mettant en oeuvre le procédé mentionné. L’invention porte enfin sur un support d’enregistrement sur lequel est enregistré un tel programme. The invention relates to a localization method for an autonomous vehicle. The invention also relates to a location device for an autonomous vehicle. The invention also relates to a computer program implementing the mentioned method. The invention finally relates to a recording medium on which such a program is recorded.
Dans le contexte des systèmes de conduite autonome, la précision de la localisation du véhicule autonome conditionne sa fiabilité de fonctionnement et la sécurité des passagers. In the context of autonomous driving systems, the precision of the location of the autonomous vehicle conditions its operational reliability and the safety of the passengers.
La localisation du véhicule est généralement issue d’un système de positionnement par satellites de type GNSS et d’estimations de mouvements du véhicule à partir de capteurs placés sur le châssis. The location of the vehicle is generally derived from a GNSS-type satellite positioning system and vehicle movement estimates from sensors placed on the chassis.
Des technologies alternatives ont plus récemment été développées pour déterminer la position du véhicule autonome à partir de multiples capteurs, comme par exemple des caméras, radars ou lidars présents sur le véhicule. Alternative technologies have more recently been developed to determine the position of the autonomous vehicle from multiple sensors, such as cameras, radars or lidars present on the vehicle.
Le brevet WO2018/175441 A1 décrit un système de navigation pour véhicule autonome prenant en compte non seulement un positionnement par satellites et des capteurs (capteurs de vitesse, caméras, capteurs de suspensions) mais aussi la trajectoire planifiée pour le véhicule. La trajectoire planifiée est déterminée à partir des mesures de caméra, permettant par exemple d’estimer la forme de la route et de définir -en fonction de comportements appris par le véhicule- une trajectoire à court terme adaptée à la forme de la route. Ainsi le système décrit dans ce brevet prédit la localisation du véhicule autonome en appliquant un traitement aux données issues des capteurs (le traitement pouvant être l’application d’un
filtre de Kalman ou une modélisation par réseau de neurones) pour estimer l’emplacement du véhicule le long d’une trajectoire planifiée à court terme. Patent WO2018/175441 A1 describes a navigation system for an autonomous vehicle taking into account not only satellite positioning and sensors (speed sensors, cameras, suspension sensors) but also the planned trajectory for the vehicle. The planned trajectory is determined from the camera measurements, making it possible, for example, to estimate the shape of the road and to define—according to behaviors learned by the vehicle—a short-term trajectory adapted to the shape of the road. Thus the system described in this patent predicts the location of the autonomous vehicle by applying a processing to the data from the sensors (the processing possibly being the application of a Kalman filter or neural network modeling) to estimate the location of the vehicle along a short-term planned trajectory.
Toutefois cette solution présente des inconvénients, notamment sa précision et sa fiabilité peuvent être améliorées. However, this solution has drawbacks, in particular its precision and its reliability can be improved.
Le but de l’invention est de fournir un dispositif et un procédé de localisation pour véhicule autonome remédiant aux inconvénients ci- dessus et améliorant les dispositifs et procédés de localisation pour véhicule autonome connus de l’art antérieur. En particulier, l’invention permet de réaliser un dispositif et un procédé qui soient simples et fiables. The object of the invention is to provide a location device and method for autonomous vehicles remedying the above drawbacks and improving the location devices and methods for autonomous vehicles known from the prior art. In particular, the invention makes it possible to produce a device and a method which are simple and reliable.
A cet effet, l’invention porte sur un procédé de localisation d'un véhicule autonome comprenant des capteurs de position, un système de planification de trajectoire, et des actionneurs de mouvement. To this end, the invention relates to a method for locating an autonomous vehicle comprising position sensors, a trajectory planning system, and motion actuators.
Le procédé comprend une itération des étapes suivantes : The method includes an iteration of the following steps:
- une étape de prédiction de la position courante du véhicule autonome comprenant une réception d’une précédente position estimée du véhicule autonome issue d’une précédente itération du procédé, une réception de commandes appliquées par les actionneurs de mouvement, un calcul d'une trajectoire commandée en fonction des commandes appliquées, et un calcul d’une position extrapolée du véhicule autonome en fonction de ladite précédente position estimée, et de ladite trajectoire commandée,- a step of predicting the current position of the autonomous vehicle comprising a reception of a previous estimated position of the autonomous vehicle resulting from a previous iteration of the method, a reception of commands applied by the movement actuators, a calculation of a trajectory controlled according to the commands applied, and a calculation of an extrapolated position of the autonomous vehicle according to said previous estimated position, and said commanded trajectory,
- une étape d’actualisation comprenant un calcul d’une position estimée actualisée du véhicule autonome prenant en compte une position mesurée issue des capteurs de position et la position extrapolée. - an updating step comprising a calculation of an updated estimated position of the autonomous vehicle taking into account a measured position from the position sensors and the extrapolated position.
Le procédé peut comprendre une étape de détermination d'une trajectoire planifiée par le système de planification de trajectoire, et le calcul d’une position extrapolée du véhicule autonome peut être réalisé en fonction de la trajectoire planifiée.
Le procédé peut comprendre une détermination d'un indice de confiance associé à la position extrapolée en fonction de l’écart calculé entre la trajectoire planifiée et la trajectoire commandée et l’étape de calcul de la position estimée actualisée peut tenir compte de l’indice de confiance. The method can comprise a step of determining a trajectory planned by the trajectory planning system, and the calculation of an extrapolated position of the autonomous vehicle can be carried out according to the planned trajectory. The method may comprise a determination of a confidence index associated with the extrapolated position as a function of the calculated difference between the planned trajectory and the commanded trajectory and the step of calculating the updated estimated position may take account of the index of confidence.
L’étape de prédiction peut comprendre la mise en œuvre d’un filtre de Kalman auquel est associée une matrice de covariance de bruit calculée en fonction de l’écart entre la trajectoire planifiée et la trajectoire commandée. The prediction step may include the implementation of a Kalman filter with which is associated a noise covariance matrix calculated as a function of the difference between the planned trajectory and the commanded trajectory.
Le calcul de l’indice de confiance peut dépendre de la matrice de covariance de bruit du filtre de Kalman. The calculation of the confidence index can depend on the noise covariance matrix of the Kalman filter.
La position estimée actualisée peut être une moyenne pondérée de la position mesurée et de la position extrapolée, les coefficients de pondération pouvant être fonction de l’indice de confiance de la position extrapolée. The updated estimated position can be a weighted average of the measured position and of the extrapolated position, the weighting coefficients being able to be a function of the confidence index of the extrapolated position.
L’invention porte en outre sur un dispositif de localisation pour véhicule autonome, le dispositif comprenant une unité de calcul, des capteurs de position un système de planification de trajectoire et des actionneurs de mouvement. Le dispositif comprend des éléments matériels et/ou logiciels mettant en œuvre le procédé tel que défini précédemment, notamment des éléments matériels et/ou logiciels conçus pour mettre en œuvre le procédé selon l’invention, et/ou le dispositif comprenant des moyens de mettre en œuvre le procédé tel que défini précédemment. The invention further relates to a tracking device for an autonomous vehicle, the device comprising a computing unit, position sensors, a trajectory planning system and motion actuators. The device comprises hardware and/or software elements implementing the method as defined previously, in particular hardware and/or software elements designed to implement the method according to the invention, and/or the device comprising means of implement the method as defined above.
L’invention porte en outre sur un véhicule autonome comprenant un dispositif de localisation selon l’invention.
L’invention porte également sur un produit programme d’ordinateur comprenant des instructions de code de programme enregistrées sur un support lisible par ordinateur pour mettre en oeuvre les étapes du procédé tel que défini précédemment lorsque ledit programme fonctionne sur un ordinateur. L’invention porte également sur un produit programme d’ordinateur téléchargeable depuis un réseau de communication et/ou enregistré sur un support de données lisible par un ordinateur et/ou exécutable par un ordinateur, comprenant des instructions qui, lorsque le programme est exécuté par l’ordinateur, conduisent celui-ci à mettre en oeuvre le procédé tel que défini précédemment. The invention further relates to an autonomous vehicle comprising a location device according to the invention. The invention also relates to a computer program product comprising program code instructions recorded on a computer-readable medium for implementing the steps of the method as defined above when said program is running on a computer. The invention also relates to a computer program product downloadable from a communication network and/or recorded on a data carrier readable by a computer and/or executable by a computer, comprising instructions which, when the program is executed by the computer, lead it to implement the method as defined previously.
L’invention porte encore sur un support d’enregistrement de données, lisible par un ordinateur, sur lequel est enregistré un programme d’ordinateur comprenant des instructions de code de programme de mise en oeuvre du procédé tel que défini précédemment. L’invention porte encore sur un support d'enregistrement lisible par ordinateur comprenant des instructions qui, lorsqu'elles sont exécutées par un ordinateur, conduisent celui-ci à mettre en oeuvre le procédé tel que défini précédemment. The invention also relates to a data recording medium, readable by a computer, on which is recorded a computer program comprising program code instructions for implementing the method as defined previously. The invention also relates to a computer-readable recording medium comprising instructions which, when executed by a computer, lead the latter to implement the method as defined previously.
L’invention porte encore sur un signal d'un support de données, portant le produit programme d'ordinateur tel que défini précédemment. The invention also relates to a signal from a data medium, carrying the computer program product as defined previously.
Le dessin annexé représente, à titre d’exemple, un mode de réalisation d’un dispositif de localisation selon l’invention et un mode d’exécution d’un procédé de localisation selon l’invention. The appended drawing shows, by way of example, an embodiment of a location device according to the invention and an embodiment of a location method according to the invention.
[Fig. 1] La figure 1 représente un véhicule autonome équipé d’un dispositif de localisation. [Fig. 1] Figure 1 shows an autonomous vehicle equipped with a tracking device.
[Fig. 2] La figure 2 représente un mode de réalisation du dispositif de localisation.
[Fig. 3] La figure 3 représente un ordinogramme d’un mode d’exécution d’un procédé de localisation. [Fig. 2] Figure 2 shows an embodiment of the location device. [Fig. 3] FIG. 3 represents a flowchart of an embodiment of a location method.
[Fig. 4] La figure 4 représente une boucle d’itération d'étapes du procédé de localisation. [Fig. 4] Figure 4 represents an iteration loop of steps of the localization process.
[Fig. 5] La figure 5 représente une architecture d’un module de prédiction du procédé de localisation. [Fig. 5] Figure 5 represents an architecture of a localization process prediction module.
Un exemple d’un véhicule autonome 100 équipé d’un système de localisation est décrit ci-après en référence à la figure 1 . An example of an autonomous vehicle 100 equipped with a location system is described below with reference to FIG.
Le véhicule autonome 100 peut être un véhicule autonome de n’importe quel type, notamment un véhicule de tourisme, ou un véhicule utilitaire ou encore un véhicule de transport en commun. The autonomous vehicle 100 can be an autonomous vehicle of any type, in particular a passenger vehicle, or a utility vehicle or even a public transport vehicle.
Le véhicule autonome 100 comprend un châssis 4 et un système de localisation 10. The autonomous vehicle 100 comprises a chassis 4 and a tracking system 10.
Le système de localisation 10 comprend principalement les éléments suivants : The location system 10 mainly comprises the following elements:
- un ensemble de capteurs de position 2, - a set of 2 position sensors,
- un système de navigation 3, - a navigation system 3,
- et une unité de calcul 7 comprenant un microprocesseur 1 , une mémoire 5 et des interfaces de communication 6 permettant au microprocesseur 1 de communiquer avec l’ensemble de capteurs de position 2 et le système de navigation 3. - and a calculation unit 7 comprising a microprocessor 1, a memory 5 and communication interfaces 6 allowing the microprocessor 1 to communicate with the set of position sensors 2 and the navigation system 3.
L’ensemble de capteurs de position 2 peut comprendre différents types de capteurs, parmi lesquels un système de positionnement par satellites ou GNSS, un ensemble de caméras et/ou lidars et/ou radars. Les caméras peuvent être placées sur le véhicule et également dans l’environnement du véhicule. D’autres modes de réalisation des capteurs de position 2
peuvent, par exemple, comprendre des systèmes de communication entre véhicules (système V2V). The set of position sensors 2 can comprise different types of sensors, including a satellite or GNSS positioning system, a set of cameras and/or lidars and/or radars. The cameras can be placed on the vehicle and also in the environment of the vehicle. Other embodiments of position sensors 2 may, for example, include communication systems between vehicles (V2V system).
Les mesures issues des capteurs de position 2 comprennent des mesures 61 de la position du véhicule. Les mesures de position 61 peuvent être absolues, par exemple lorsqu’elles sont issues d’un système de type GNSS. Les mesures de position 61 peuvent également des mesures relatives, notamment lorsqu’elles concernent un déplacement à partir d’une position initiale connue, le déplacement du véhicule pouvant être par exemple calculé à partir de mesures de rotation des roues du véhicule. The measurements from the position sensors 2 include measurements 61 of the position of the vehicle. The position measurements 61 can be absolute, for example when they come from a GNSS type system. The position measurements 61 can also be relative measurements, in particular when they relate to a movement from a known initial position, the movement of the vehicle being able for example to be calculated from measurements of rotation of the wheels of the vehicle.
Les mesures issues des capteurs de position 2 comprennent des mesures 68 de l’environnement du véhicule autonome. Les mesures 68 comprennent des mesures de la position des différents éléments présents dans l’environnement du véhicule. Le traitement des mesures 68 peut fournir également d’autres paramètres détaillant l’état d’un objet de la scène : son attitude (position angulaire), son vecteur vitesse, mais aussi d’autres attributs comme sa classe (piéton, auto, camion, cycle, etc... ) ou ses dimensions. The measurements from the position sensors 2 comprise measurements 68 of the environment of the autonomous vehicle. The measurements 68 include measurements of the position of the various elements present in the environment of the vehicle. The processing of measurements 68 can also provide other parameters detailing the state of an object in the scene: its attitude (angular position), its speed vector, but also other attributes such as its class (pedestrian, car, truck , cycle, etc...) or its dimensions.
Le châssis 4 comprend les différents actionneurs qui mettent en œuvre le déplacement du véhicule autonome, notamment un actionneur de couple moteur 41 , un actionneur de freins 42 et un actionneur de rotation des roues directrices 43. The chassis 4 includes the various actuators which implement the movement of the autonomous vehicle, in particular an engine torque actuator 41, a brake actuator 42 and a steering wheel rotation actuator 43.
Le châssis reçoit des commandes de contrôle 66 issues du système de navigation 3 afin de déplacer le véhicule selon une trajectoire planifiée 64 par le système de navigation 3. The chassis receives control commands 66 from the navigation system 3 in order to move the vehicle along a planned trajectory 64 by the navigation system 3.
Dans la suite du document, le terme de « trajectoire » est utilisé pour désigner l’évolution temporelle d’un vecteur d’état définissant les caractéristiques du déplacement du véhicule automobile 100. Dans un
mode de réalisation préférentiel, le vecteur d’état comprend une localisation, notamment des coordonnées x, y, des vitesses longitudinales et latérales et/ou des accélérations longitudinales et latérales et/ou une vitesse de lacet. Dans la suite du document, le terme « position » est utilisé pour désigner soit les coordonnées x, y du vecteur d’état, soit le vecteur d’état dans son ensemble. In the rest of the document, the term “trajectory” is used to designate the temporal evolution of a state vector defining the characteristics of the movement of the motor vehicle 100. In a preferred embodiment, the state vector comprises a location, in particular x,y coordinates, longitudinal and lateral velocities and/or longitudinal and lateral accelerations and/or yaw rate. In the remainder of the document, the term “position” is used to designate either the x, y coordinates of the state vector, or the state vector as a whole.
Les commandes réellement appliquées par les actionneurs 41 , 42, 43, nommées dans la suite du document « commandes appliquées », peuvent toutefois différer des commandes de contrôle 66 reçues du système de navigation 3. En effet, certains paramètres peuvent perturber la mise en oeuvre des ordres de commande 66. Par exemple, l’état de la route, l’usure des pneus, ou encore les conditions météorologiques peuvent interférer avec la mise en oeuvre des ordres de commande 66. Le châssis 4 transmet en retour les commandes appliquées 70 au système de navigation 3. The commands actually applied by the actuators 41, 42, 43, named in the remainder of the document "applied commands", may however differ from the control commands 66 received from the navigation system 3. Indeed, certain parameters may disturb the implementation commands 66. For example, the state of the road, the wear of the tires, or the weather conditions can interfere with the implementation of the commands 66. The chassis 4 transmits in return the applied commands 70 to the navigation system 3.
Ainsi le système de navigation 3 a connaissance des commandes réellement appliquées par les actionneurs. De façon générale, les commandes appliquées 70 par les actionneurs 41 , 42 et 43 sont très proches des ordres de commande 66. Toutefois, il existe des situations où les commandes appliquées 70 sont sensiblement différentes des ordres de commande 66. Dans ce cas, la trajectoire mise en oeuvre par le véhicule autonome 100 s’éloigne sensiblement de la trajectoire planifiée 64. Le système de localisation 10 selon l’invention a pour objectif d’améliorer la localisation du véhicule autonome, notamment dans les situations où la trajectoire réelle du véhicule autonome s’éloigne sensiblement de la trajectoire planifiée. Thus the navigation system 3 is aware of the commands actually applied by the actuators. In general, the commands applied 70 by the actuators 41, 42 and 43 are very close to the command commands 66. However, there are situations where the commands applied 70 are significantly different from the command commands 66. In this case, the trajectory implemented by the autonomous vehicle 100 deviates substantially from the planned trajectory 64. The location system 10 according to the invention aims to improve the location of the autonomous vehicle, in particular in situations where the real trajectory of the vehicle autonomous deviates significantly from the planned trajectory.
Un mode de réalisation du système de navigation 3 est décrit en détails par la figure 2. Le système de navigation 3 reçoit en entrée, d’une part, les mesures 68 de l’environnement du véhicule autonome, et, d’autre part, une position estimée actualisée 72 du véhicule autonome 100.
Le système de navigation 3 comprend un module de fusion des données environnementales 31 , qui agrège les données issues des différents capteurs et relatives à l’environnement du véhicule autonome 100. An embodiment of the navigation system 3 is described in detail in FIG. 2. The navigation system 3 receives as input, on the one hand, the measurements 68 of the environment of the autonomous vehicle, and, on the other hand, an updated estimated position 72 of the autonomous vehicle 100. The navigation system 3 includes an environmental data fusion module 31, which aggregates the data from the various sensors and relating to the environment of the autonomous vehicle 100.
Les données fusionnées 69 sont ensuite traitées dans un module d’analyse de situation 32, comprenant avantageusement la détermination d’un horizon électronique du véhicule. L’horizon électronique est une carte locale des éléments de la scène de conduite comprenant les informations de la zone utile pour la conduite. Autrement dit, l’horizon électronique comprend les positions précises par rapport au véhicule de tous les éléments de la scène de conduite qui vont permettre au véhicule de prendre les décisions pertinentes pour la conduite. The merged data 69 is then processed in a situation analysis module 32, advantageously comprising the determination of an electronic horizon of the vehicle. The electronic horizon is a local map of the elements of the driving scene including the information of the area useful for driving. In other words, the electronic horizon includes the precise positions relative to the vehicle of all the elements of the driving scene that will allow the vehicle to make the relevant decisions for driving.
Les données 63 issues du module d’analyse de la situation 32 sont ensuite utilisées dans un module de planification d’une trajectoire à court terme 33, pour déterminer une trajectoire à court terme 64. L’ordre de grandeur de la durée de la trajectoire à court terme 64 est de quelques secondes. The data 63 from the situation analysis module 32 are then used in a module for planning a short-term trajectory 33, to determine a short-term trajectory 64. The order of magnitude of the duration of the trajectory short term 64 is seconds.
La trajectoire à court terme 64 ainsi déterminée est transmise aux lois de commande 34, afin d’être transformée en ordre de commandes 66 transmis au châssis 4. The short-term trajectory 64 thus determined is transmitted to the control laws 34, in order to be transformed into a command order 66 transmitted to the chassis 4.
Comme cela a été précédemment expliqué, après transmission des ordres de commandes 66 au châssis 4, ce dernier transmet en retour les commandes appliquées 70 au système de navigation 3. As previously explained, after transmission of the command orders 66 to the chassis 4, the latter transmits in return the applied commands 70 to the navigation system 3.
Les commandes appliquées 70 sont transmises au calculateur 1 par le système de navigation 3. The commands applied 70 are transmitted to the computer 1 by the navigation system 3.
Dans un mode de réalisation, le calculateur 1 permet d’exécuter un logiciel comprenant les modules suivants :
- un module 10 de de détermination d'une trajectoire planifiée, qui collabore avec le module de navigation 3, In one embodiment, the computer 1 makes it possible to execute software comprising the following modules: - a module 10 for determining a planned trajectory, which collaborates with the navigation module 3,
- un module 11 de prédiction d’une position courante du véhicule autonome 100, qui collabore avec le module de navigation 3, - a module 11 for predicting a current position of the autonomous vehicle 100, which collaborates with the navigation module 3,
- un module 12 d’actualisation, qui collabore avec l’ensemble de capteurs 2 et le module de navigation 3. - an update module 12, which collaborates with the set of sensors 2 and the navigation module 3.
Un mode d’exécution du procédé de localisation pour véhicule autonome est décrit ci-après en référence aux figures 3 à 5. Le procédé comprend une étape E0, suivie d’une itération sur deux étapes E1 et E2. An embodiment of the location method for an autonomous vehicle is described below with reference to FIGS. 3 to 5. The method comprises a step E0, followed by an iteration over two steps E1 and E2.
Dans l’étape E0, on détermine une trajectoire planifiée 64. La trajectoire planifiée 64 est définie par le module de planification d’une trajectoire à court terme 33 en fonction des données issues du module d’analyse de la situation 32. La trajectoire planifiée 64 prévoit les déplacements du véhicule sur un intervalle de temps de l’ordre de quelques secondes, correspondant à la distance de visée du véhicule, c’est-à-dire la limite de détection des capteurs 2, ou de l’horizon électronique. La trajectoire planifiée suit une courbe idéale, c’est à dire une courbe représentant un déplacement théorique du véhicule autonome. Le déplacement théorique du véhicule peut être déterminé par des modèles statistiques. In step E0, a planned trajectory 64 is determined. The planned trajectory 64 is defined by the module for planning a short-term trajectory 33 according to the data from the situation analysis module 32. The planned trajectory 64 provides for the movements of the vehicle over a time interval of the order of a few seconds, corresponding to the sighting distance of the vehicle, that is to say the detection limit of the sensors 2, or of the electronic horizon. The planned trajectory follows an ideal curve, i.e. a curve representing a theoretical movement of the autonomous vehicle. The theoretical displacement of the vehicle can be determined by statistical models.
La trajectoire planifiée 64 est mémorisée dans la mémoire 5 pour son utilisation ultérieure dans l’étape E1. The planned trajectory 64 is stored in the memory 5 for its later use in the step E1.
On enchaîne ensuite sur une itération sur les étapes E1 et E2. We then move on to an iteration of steps E1 and E2.
Comme cela est plus spécifiquement illustré par la figure 4, As more specifically illustrated by Figure 4,
- l’étape E1 de prédiction prédit une position extrapolée 71 du véhicule autonome 100 à partir d’une précédente position estimée 62, d’une trajectoire planifiée 64 et d’une trajectoire commandée 67, - the prediction step E1 predicts an extrapolated position 71 of the autonomous vehicle 100 from a previous estimated position 62, a planned trajectory 64 and a commanded trajectory 67,
- l’étape E2 d’actualisation calcule une position estimée actualisée 72 à
partir d’une position mesurée 61 et de la position extrapolée 71 issue l’étape E1 de prédiction. - the updating step E2 calculates an updated estimated position 72 at starting from a measured position 61 and from the extrapolated position 71 resulting from step E1 of prediction.
Un mode d’exécution de l’étape E1 de prédiction est décrit en référence à la figure 5. A mode of execution of the prediction step E1 is described with reference to FIG. 5.
La première étape E1 comprend une sous-étape E11 de réception d’une position estimée 62 du véhicule provenant d’une précédente itération du procédé. La position estimée 62 comprend les coordonnées absolues du véhicule autonome 100 datées à un instant TN-I . The first step E1 comprises a sub-step E11 of receiving an estimated position 62 of the vehicle coming from a previous iteration of the method. The estimated position 62 includes the absolute coordinates of the autonomous vehicle 100 dated at a time TN-I.
La première étape E1 comprend également une sous-étape E12 de réception des commandes 67 appliquées au véhicule par les actionneurs de mouvement 41 , 42, 42. Les commandes 67 sont datées à un instant TCOM de commande du mouvement du véhicule. The first step E1 also includes a sub-step E12 of receiving commands 67 applied to the vehicle by the movement actuators 41, 42, 42. The commands 67 are dated at a time TCOM of command of the movement of the vehicle.
La première étape E1 comprend par ailleurs une sous-étape E13 de calcul d'une trajectoire commandée 74 en fonction des commandes appliquées 67. Pour cela, un module de conversion 113 convertit les commandes 67 en une trajectoire commandée 74. The first step E1 also comprises a sub-step E13 of calculating a commanded trajectory 74 as a function of the applied commands 67. For this, a conversion module 113 converts the commands 67 into a commanded trajectory 74.
La trajectoire commandée 74 suit une courbe bruitée, c’est-à-dire qui diffère sensiblement de la courbe idéale définie par la trajectoire planifiée 64. The commanded trajectory 74 follows a noisy curve, that is to say which differs significantly from the ideal curve defined by the planned trajectory 64.
La première étape E1 comprend en outre une sous-étape E14 de calcul d’une position extrapolée 71. Dans le mode d’exécution décrit, le calcul d’une position extrapolée 71 prend en compte à la fois la trajectoire planifiée 64 et la trajectoire commandée 74. The first step E1 further comprises a sub-step E14 of calculating an extrapolated position 71. In the mode of execution described, the calculation of an extrapolated position 71 takes into account both the planned trajectory 64 and the trajectory ordered 74.
Comme cela est plus spécifiquement décrit par la figure 5, le calcul d’une position extrapolée 71 peut mettre en oeuvre un filtre 111 , prenant en
entrée une précédente position estimée 62, une trajectoire planifiée 64 et une matrice de covariance de bruit 73. As is more specifically described by FIG. 5, the calculation of an extrapolated position 71 can implement a filter 111, taking into input a previous estimated position 62, a planned trajectory 64 and a noise covariance matrix 73.
Un premier mode d’exécution du filtre 111 peut mettre en œuvre un filtre de Kalman étendu, c’est à dire un filtre de Kalman reposant sur un modèle non linéaire. On calcule alors un vecteur d’état prédit x’ selon la formule Math 1 suivante : A first mode of execution of the filter 111 can implement an extended Kalman filter, that is to say a Kalman filter based on a nonlinear model. A predicted state vector x' is then calculated according to the following Math 1 formula:
[Math 1] x' = f(x,u ) où : [Math 1] x' = f(x,u ) where:
- x est le vecteur d’état prédit lors de la précédente itération, c’est-à-dire la position 62 estimée lors d’une précédente itération, - x is the state vector predicted during the previous iteration, i.e. position 62 estimated during a previous iteration,
- x’ est le vecteur d’état prédit lors de l’itération courante, correspondant à la position extrapolée 71 , - x' is the predicted state vector during the current iteration, corresponding to the extrapolated position 71 ,
- u est le vecteur d’état associé à la trajectoire planifiée, c’est-à-dire la trajectoire planifiée 64, - u is the state vector associated with the planned trajectory, i.e. the planned trajectory 64,
- f est la fonction de contrôle, c’est-à-dire une fonction non linéaire modélisant la transition entre deux itérations successives du filtre 111. - f is the control function, i.e. a nonlinear function modeling the transition between two successive iterations of filter 111.
De plus, dans ce premier mode d’exécution du filtre 111 , on calcule une matrice de covariance P’ associée au vecteur d’état prédit x’ selon la formule Math 2 suivante. Moreover, in this first mode of execution of the filter 111 , a covariance matrix P′ associated with the predicted state vector x′ is calculated according to the following Math 2 formula.
[Math 2] [Math 2]
P' = FPFT + BWBT où : P' = FPF T + BWB T where:
- P est la covariance associée au vecteur d’état prédit lors de la précédente itération, c’est-à-dire la covariance associée à la position 62, - P is the covariance associated with the state vector predicted during the previous iteration, i.e. the covariance associated with position 62,
- P’ est la covariance associée au vecteur d’état prédit lors de l’itération courante, c’est-à-dire la covariance associée à la position extrapolée 71 ,- P' is the covariance associated with the predicted state vector during the current iteration, i.e. the covariance associated with the extrapolated position 71 ,
- B est le modèle de contrôle, c’est-à-dire la fonction qui convertit les
commandes sur les actionneurs ou la trajectoire dans l’espace du vecteur d’état. Cette fonction est normalement connue à l’avance et constante dans le temps, - B is the control model, i.e. the function that converts the controls on the actuators or the trajectory in space of the state vector. This function is normally known in advance and constant over time,
- W est la matrice de covariance de bruit 73. - W is the noise covariance matrix 73.
Dans le premier mode d’exécution du filtre 111 , la matrice de covariance de bruit 73 est utilisée pour modéliser l’incertitude de la prédiction liée à un écart mesuré entre la trajectoire planifiée 64 et la trajectoire commandée 74. In the first mode of execution of the filter 111 , the noise covariance matrix 73 is used to model the uncertainty of the prediction linked to a measured deviation between the planned trajectory 64 and the commanded trajectory 74.
Afin de pouvoir mesurer l’écart existant entre la trajectoire planifiée 64 et la trajectoire commandée 74, il est nécessaire de synchroniser temporellement ces deux trajectoires via un module de synchronisation 112. A partir de la trajectoire planifiée 64, on calcule une trajectoire planifiée synchronisée 65, de sorte à pouvoir la comparer avec la trajectoire commandée 74. In order to be able to measure the difference existing between the planned trajectory 64 and the commanded trajectory 74, it is necessary to temporally synchronize these two trajectories via a synchronization module 112. From the planned trajectory 64, a synchronized planned trajectory 65 is calculated. , so that it can be compared with the commanded trajectory 74.
Le premier mode d’exécution du filtre 111 comprend un calcul de la matrice de covariance de bruit W selon les expressions mathématiques Math 3 : The first mode of execution of the filter 111 comprises a calculation of the noise covariance matrix W according to the mathematical expressions Math 3:
[Math 3] u" = Zu [Math 3] u" = Zu
W = EÇ(u" — u')(u" — u')T) où : W = EÇ(u" — u')(u" — u') T ) where:
- u est un vecteur d’état correspondant à la trajectoire planifiée 64,- u is a state vector corresponding to the planned trajectory 64,
- u’ est un vecteur d’état correspondant à la trajectoire appliquée 74,- u' is a state vector corresponding to the applied trajectory 74,
- u” est un vecteur d’état correspondant à la trajectoire planifiée synchronisée 65, - u” is a state vector corresponding to the synchronized planned trajectory 65,
- Z est la transformation mise en œuvre dans le module de synchronisation- Z is the transformation implemented in the synchronization module
112, 112,
- E() est la fonction d’estimée.
Ainsi, selon le premier mode d’exécution du filtre 111 , à l’issue de l’étape E1 , on obtient une position extrapolée 71 et une covariance associée P’. La covariance P’ permet ainsi de calculer un indice de confiance IC1 associé à la position extrapolée. L’indice de confiance IC1 peut, par exemple, être calculé à partir de la matrice de covariance, en sélectionnant les valeurs sur la diagonale (ou une autre fonction similaire), ce qui permet de déterminer un indice de confiance par variable d’état du module de positionnement. - E() is the estimated function. Thus, according to the first mode of execution of the filter 111 , at the end of the step E1 , an extrapolated position 71 and an associated covariance P′ are obtained. The covariance P' thus makes it possible to calculate a confidence index IC1 associated with the extrapolated position. The confidence index IC1 can, for example, be calculated from the covariance matrix, by selecting the values on the diagonal (or another similar function), which makes it possible to determine a confidence index per state variable of the positioning module.
La position extrapolée 71 correspond à un point M appartenant à la trajectoire planifiée 64, et l’indice de confiance IC1 traduit la probabilité pour que le véhicule autonome 100 se situe effectivement au point M avec un niveau de précision donné. The extrapolated position 71 corresponds to a point M belonging to the planned trajectory 64, and the confidence index IC1 reflects the probability that the autonomous vehicle 100 is actually located at point M with a given level of precision.
Autrement dit, dans le premier mode d’exécution du filtre 111 , la trajectoire commandée 74 influence uniquement l’indice de confiance IC1 associé à la position extrapolée 71 : In other words, in the first mode of execution of the filter 111 , the controlled trajectory 74 only influences the confidence index IC1 associated with the extrapolated position 71:
- plus la trajectoire commandée 74 est proche de la trajectoire planifiée 64, plus l’indice de confiance IC1 est élevé, - the closer the commanded trajectory 74 is to the planned trajectory 64, the higher the confidence index IC1,
- plus la trajectoire commandée 74 est distante de la trajectoire planifiée 64, plus l’indice de confiance IC1 est faible. - the further the commanded trajectory 74 is from the planned trajectory 64, the lower the confidence index IC1.
D’autres modes d’exécution du filtre 111 pourraient être mis en oeuvre, par exemple en utilisant un filtre de Kalman linéaire, ou un filtre à particules. Alternativement, le filtre de Kalman pourrait être remplacé par une intelligence artificielle utilisant par exemple des algorithmes de Machine Learning ou des réseaux de neurones. Other embodiments of the filter 111 could be implemented, for example by using a linear Kalman filter, or a particle filter. Alternatively, the Kalman filter could be replaced by artificial intelligence using, for example, Machine Learning algorithms or neural networks.
En complément ou alternativement, dans d’autres modes d’exécution de l’étape E1 , le calcul d’une position extrapolée 71 pourrait prendre en compte la trajectoire commandée 74 pour le calcul des coordonnées du véhicule autonome.
Dans une deuxième étape E2 d’actualisation, on calcule une position estimée actualisée 72 prenant en compte, d’une part, une position mesurée 61 issue des capteurs 2 et, d’autre part, la position extrapolée 71 . In addition or alternatively, in other modes of execution of step E1, the calculation of an extrapolated position 71 could take into account the commanded trajectory 74 for the calculation of the coordinates of the autonomous vehicle. In a second updating step E2, an updated estimated position 72 is calculated taking into account, on the one hand, a measured position 61 coming from the sensors 2 and, on the other hand, the extrapolated position 71 .
L’étape E2 comprend une sous-étape de réception de données issues des capteurs 2, et de détermination d’une position mesurée 61 à un instant donné TMES. La sous-étape de réception comprend une fusion des données issues de différents capteurs. Dans un mode d’exécution préférentiel, la fusion de données permet d’associer un indice de confiance IC2 à la position mesurée 61. L’indice de confiance tient compte notamment des différentes sources de données prises en compte dans la fusion de donnée. L’indice de confiance peut également tenir compte de conditions de visibilité en lien avec l’infrastructure routière (par exemple, la présence d’un tunnel ou d’un virage serré), ou en lien avec la luminosité ou les conditions météorologiques. L’indice de confiance peut en outre tenir compte de l’environnement du véhicule, notamment de zones de réflexion pouvant perturber les mesures des capteurs. Step E2 includes a sub-step of receiving data from the sensors 2, and determining a measured position 61 at a given instant TMES. The reception sub-step comprises a fusion of the data coming from different sensors. In a preferred mode of execution, the data fusion makes it possible to associate a confidence index IC2 with the measured position 61. The confidence index notably takes into account the different sources of data taken into account in the data fusion. The confidence index can also take into account visibility conditions related to road infrastructure (for example, the presence of a tunnel or a sharp bend), or related to light or weather conditions. The confidence index can also take into account the environment of the vehicle, in particular areas of reflection that can disturb the measurements of the sensors.
L’étape E2 comprend en outre une sous-étape de calcul d’une position estimée actualisée 72 à partir la position mesurée 61 issue des capteurs et de la position extrapolée 71 calculé dans l’étape E1 . Step E2 further comprises a sub-step of calculating an updated estimated position 72 from the measured position 61 derived from the sensors and from the extrapolated position 71 calculated in step E1.
Dans un mode d’exécution, la position estimée actualisée 72 est une moyenne pondérée de la position mesurée 61 et de la position extrapolée 71. In one embodiment, the updated estimated position 72 is a weighted average of the measured position 61 and the extrapolated position 71.
L’une et l’autre des positions mesurée 61 et extrapolée 71 sont avantageusement associées à un indice de confiance, IC2, IC1. Both of the measured 61 and extrapolated 71 positions are advantageously associated with a confidence index, IC2, IC1.
L’indice de confiance IC1 représente le degré de confiance associé à la trajectoire planifiée 64 ; notamment l’indice de confiance IC1 fournit une
mesure de la cohérence entre la trajectoire planifiée 64 et la trajectoire commandée 74. The confidence index IC1 represents the degree of confidence associated with the planned trajectory 64; in particular the confidence index IC1 provides a measurement of the consistency between the planned trajectory 64 and the commanded trajectory 74.
Avantageusement, les coefficients de pondération de la moyenne pondérée sont déterminés en fonction de l’indice de confiance IC1. Par exemple, si l’indice de confiance de IC1 est inférieur à un seuil donné alors le coefficient de pondération associé à la position extrapolée 71 est strictement inférieur au coefficient de pondération associé à la position mesurée 61 . Autrement dit, si la trajectoire planifiée 64 est sensiblement éloignée de la trajectoire commandée 67, alors la position mesurée 61 aura plus de poids que la position extrapolée 71 dans le calcul de la position estimée actualisée 72. Advantageously, the weighting coefficients of the weighted average are determined according to the confidence index IC1. For example, if the confidence index of IC1 is lower than a given threshold then the weighting coefficient associated with the extrapolated position 71 is strictly lower than the weighting coefficient associated with the measured position 61 . In other words, if the planned trajectory 64 is substantially far from the commanded trajectory 67, then the measured position 61 will have more weight than the extrapolated position 71 in the calculation of the updated estimated position 72.
En complément, le calcul des coefficients de pondération pourrait tenir compte des deux indices de confiance IC1 , IC2, notamment du niveau relatif des indices de confiance IC1 , IC2. Par exemple le coefficient de pondération de chaque position 61 , 71 pourrait être proportionnel à l’indice de confiance IC2, IC1 respectivement associé à ladite position. En variante, ce mode de calcul pourrait s’appliquer uniquement si le ratio entre l’indice de confiance le plus élevé et l’indice de confiance le moins élevé est inférieur à un seuil donné, par exemple 120%. Au-delà du seuil donné, on pourrait par exemple affecter un coefficient très faible, voire nul, à la position de plus faible indice de confiance. In addition, the calculation of the weighting coefficients could take into account the two confidence indices IC1, IC2, in particular the relative level of the confidence indices IC1, IC2. For example, the weighting coefficient of each position 61, 71 could be proportional to the confidence index IC2, IC1 respectively associated with said position. Alternatively, this method of calculation could only apply if the ratio between the highest confidence index and the lowest confidence index is below a given threshold, for example 120%. Beyond the given threshold, one could for example assign a very low coefficient, or even zero, to the position of the lowest confidence index.
Ainsi, le calcul d’une moyenne pondérée entre ces deux positions a pour objectif d’améliorer la précision de la localisation, notamment en prenant en compte l’incertitude inhérente d’une part aux mesures des capteurs, et d’autre part à une position déterminée par extrapolation à partir d’une trajectoire planifiée. Thus, the calculation of a weighted average between these two positions aims to improve the precision of the location, in particular by taking into account the inherent uncertainty on the one hand in the measurements of the sensors, and on the other hand in a position determined by extrapolation from a planned trajectory.
Finalement, le procédé de localisation selon l’invention présente l’avantage de détecter et traiter au plus tôt des écarts entre la trajectoire
planifiée et la trajectoire commandée, c’est-à-dire la trajectoire réellement mise en œuvre par les actionneurs de mouvement du véhicule. Autrement dit, au lieu de considérer que le véhicule se déplace sur une trajectoire idéale, définie selon des modèles statistiques par la planification de trajectoire, on évalue l’écart entre la trajectoire commandée et la trajectoire planifiée. Finally, the location method according to the invention has the advantage of detecting and processing as soon as possible the differences between the trajectory planned and the commanded trajectory, i.e. the trajectory actually implemented by the vehicle's motion actuators. In other words, instead of considering that the vehicle is moving on an ideal trajectory, defined according to statistical models by trajectory planning, the deviation between the commanded trajectory and the planned trajectory is evaluated.
Ce traitement est d’autant plus pertinent que, contrairement à la trajectoire planifiée qui est définie sur une fenêtre temporelle de l’ordre de quelques secondes, la trajectoire commandée est actualisée toutes les 10 millisecondes, ce qui est comparable à la fréquence d’actualisation des mesures issues des capteurs. This processing is all the more relevant since, unlike the planned trajectory which is defined over a time window of the order of a few seconds, the commanded trajectory is updated every 10 milliseconds, which is comparable to the update frequency measurements from the sensors.
Par ailleurs, alors que les données issues des capteurs décrivent une situation passée, la trajectoire commandée correspond à une extrapolation à très court terme de la position future du véhicule à partir des commandes appliquées par les actionneurs de mouvement. Furthermore, while the data from the sensors describe a past situation, the commanded trajectory corresponds to a very short-term extrapolation of the future position of the vehicle from the commands applied by the motion actuators.
Dans un contexte de véhicule autonome, l’objectif de l’invention est d’appliquer une trajectoire planifiée tout en améliorant la prédiction de la position du véhicule autonome. Ainsi, au lieu d’être dans un mode réactif (dans lequel on réagit à des mesures), l’invention permet d’être proactif en prenant en compte les manœuvres à venir. In an autonomous vehicle context, the objective of the invention is to apply a planned trajectory while improving the prediction of the position of the autonomous vehicle. Thus, instead of being in a reactive mode (in which one reacts to measurements), the invention makes it possible to be proactive by taking into account the maneuvers to come.
Dans des modes de réalisation alternatifs, la trajectoire commandée pourrait être utilisée dans le calcul même de la position du véhicule.
In alternative embodiments, the commanded trajectory could be used in the calculation of the vehicle's position itself.
Claims
1. Procédé de localisation d'un véhicule autonome (100) comprenant des capteurs de position (2), un système de planification de trajectoire (33), et des actionneurs de mouvement (41), caractérisé en ce qu’il comprend une itération des étapes suivantes : 1. Method for locating an autonomous vehicle (100) comprising position sensors (2), a trajectory planning system (33), and movement actuators (41), characterized in that it comprises an iteration of the following steps:
- une étape (E1) de prédiction de la position courante du véhicule autonome (100) comprenant une réception (E11) d’une précédente position estimée (62) du véhicule autonome (100) issue d’une précédente itération du procédé, une réception (E12) de commandes appliquées (67) par les actionneurs de mouvement (41, 42, 43), un calcul (E13) d'une trajectoire commandée (74) en fonction des commandes appliquées (67), et un calcul (E14) d’une position extrapolée (71) du véhicule autonome (100) en fonction de ladite précédente position estimée (62), et de ladite trajectoire commandée (74), - a step (E1) of predicting the current position of the autonomous vehicle (100) comprising a reception (E11) of a previous estimated position (62) of the autonomous vehicle (100) resulting from a previous iteration of the method, a reception (E12) of commands applied (67) by the movement actuators (41, 42, 43), a calculation (E13) of a commanded trajectory (74) according to the commands applied (67), and a calculation (E14) an extrapolated position (71) of the autonomous vehicle (100) as a function of said previous estimated position (62), and of said commanded trajectory (74),
- une étape (E2) d’actualisation comprenant un calcul d’une position estimée actualisée (72) du véhicule autonome (100) prenant en compte une position mesurée (61) issue des capteurs de position (2) et la position extrapolée (71). - an updating step (E2) comprising a calculation of an updated estimated position (72) of the autonomous vehicle (100) taking into account a measured position (61) from the position sensors (2) and the extrapolated position (71 ).
2. Procédé selon la revendication précédente, caractérisé en ce qu'il comprend une étape (E0) de détermination d'une trajectoire planifiée (64) par le système de planification de trajectoire (33), et en ce que ledit calcul (E14) d’une position extrapolée (71) du véhicule autonome (100) est réalisé en fonction de ladite trajectoire planifiée (64). 2. Method according to the preceding claim, characterized in that it comprises a step (E0) of determining a planned trajectory (64) by the trajectory planning system (33), and in that said calculation (E14) of an extrapolated position (71) of the autonomous vehicle (100) is carried out according to said planned trajectory (64).
3. Procédé selon la revendication précédente, caractérisé en ce qu'il comprend une détermination d'un indice de confiance (IC1) associé à la position extrapolée (71) en fonction de l’écart calculé entre la trajectoire planifiée (64) et la trajectoire commandée (74) et en ce que
l’étape de calcul de la position estimée actualisée (72) tient compte de l’indice de confiance (IC1). 3. Method according to the preceding claim, characterized in that it comprises a determination of a confidence index (IC1) associated with the extrapolated position (71) according to the difference calculated between the planned trajectory (64) and the commanded trajectory (74) and in that the step of calculating the updated estimated position (72) takes account of the confidence index (IC1).
4. Procédé selon la revendication 2 ou 3 caractérisé en ce que l’étape de prédiction (E1) comprend la mise en œuvre d’un filtre de Kalman (111) auquel est associée une matrice de covariance de bruit (73) calculée en fonction de l’écart entre la trajectoire planifiée (64) et la trajectoire commandée (74). 4. Method according to claim 2 or 3 characterized in that the prediction step (E1) comprises the implementation of a Kalman filter (111) with which is associated a noise covariance matrix (73) calculated as a function the difference between the planned trajectory (64) and the commanded trajectory (74).
5. Procédé selon la revendication précédente, caractérisé en ce que le calcul de l’indice de confiance (IC1 ) dépend de la matrice de covariance de bruit du filtre de Kalman (111). 5. Method according to the preceding claim, characterized in that the calculation of the confidence index (IC1) depends on the noise covariance matrix of the Kalman filter (111).
6. Procédé selon l’une des revendications 3 à 5, caractérisé en ce que la position estimée actualisée (72) est une moyenne pondérée de la position mesurée (61) et de la position extrapolée (71), les coefficients de pondération étant fonction de l’indice de confiance (IC1) de la position extrapolée (71). 6. Method according to one of claims 3 to 5, characterized in that the updated estimated position (72) is a weighted average of the measured position (61) and of the extrapolated position (71), the weighting coefficients being a function the confidence index (CI1) of the extrapolated position (71).
7. Dispositif (10) de localisation pour véhicule autonome, le dispositif comprenant une unité de calcul (7), des capteurs de position (2), un système de planification de trajectoire (33) et des actionneurs de mouvement (41, 42, 43) mettant en œuvre le procédé selon l’une des revendications précédentes, notamment une unité de calcul (7) apte à mettre en œuvre le procédé selon l’une des revendications précédentes. 7. Localization device (10) for an autonomous vehicle, the device comprising a calculation unit (7), position sensors (2), a trajectory planning system (33) and movement actuators (41, 42, 43) implementing the method according to one of the preceding claims, in particular a calculation unit (7) capable of implementing the method according to one of the preceding claims.
8. Véhicule autonome (100) comprenant un dispositif de localisation selon la revendication précédente. 8. Autonomous vehicle (100) comprising a location device according to the preceding claim.
9. Produit programme d’ordinateur comprenant des instructions de code de programme enregistrées sur un support lisible par ordinateur pour mettre en œuvre les étapes du procédé selon l’une quelconque des
revendications 1 à 6 lorsque ledit programme fonctionne sur un ordinateur. 9. Computer program product comprising program code instructions recorded on a computer-readable medium to implement the steps of the method according to any of the claims 1 to 6 when said program runs on a computer.
10. Support (5) d’enregistrement de données, lisible par un ordinateur, sur lequel est enregistré un programme d’ordinateur comprenant des instructions de code de programme de mise en œuvre du procédé selon l’une des revendications 1 à 6.
10. Support (5) for recording data, readable by a computer, on which is recorded a computer program comprising program code instructions for implementing the method according to one of claims 1 to 6.
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FR2105131A FR3122920B1 (en) | 2021-05-17 | 2021-05-17 | Localization method for autonomous vehicle. |
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