Method and device for controlling a cruise control system of an autonomous vehicle using a modulated lateral distance

Abstract

The invention relates to a method and a device for controlling an adaptive cruise control system (ACC system) of an autonomous vehicle, referred to as ego vehicle, the ACC system being based on a selection of a target vehicle, the method comprising the steps of:  receiving (200) first data;  receiving (210) second data; and, for each object,  determining (220) a trajectory of the ego vehicle;  determining (230) a lateral deviation;  determining (240) whether the position of the object is located inside or outside a curvature of the trajectory of the ego vehicle;  determining (250) a modulated lateral distance of the object in relation to the trajectory; the method further comprising a step of selecting (260) a target vehicle, said selection being based on the modulated lateral distance determined for each object.

Description

DESCRIPTION TITLE: Method and device for controlling an adaptive cruise control system of an autonomous vehicle by modulated lateral distance. The present invention claims priority from French application 2213895 filed on 20.12.2022, the content of which (text, drawings and claims) is incorporated herein by reference. Technical field of the invention

[0001] The invention relates to the field of autonomous vehicle driving assistance systems. In particular, the invention relates to a method and a device for controlling an adaptive cruise control system of an autonomous vehicle. State of the art

[0002] A "vehicle" means any type of vehicle such as a motor vehicle, a moped, a motorcycle, a storage robot in a warehouse, etc. "Autonomous driving" of an "autonomous vehicle" means any process capable of assisting the driving of the vehicle. The process may thus consist of partially or totally steering the vehicle or providing any type of assistance to a natural person driving the vehicle. The process thus covers all autonomous driving, from level 0 to level 5 in the OICA scale, for International Organization of Motor Vehicle Manufacturers.

[0003] Systems that assist vehicle driving are also called ADAS (Advanced Driver Assistance Systems), ADAS functions, ADAS systems, or driver assistance systems. These ADAS systems include adaptive cruise control.

[0004] Adaptive cruise control is also known as ACC or ACC system (from the English acronym "Auto Cruise Control"). This system automatically maintains a vehicle speed, called ego vehicle, at a value set by an occupant or driver of the ego vehicle, a value called the set speed, while respecting a safety distance from a vehicle, called target vehicle, preceding the ego vehicle. For example, a target vehicle is a land vehicle, any type of obstacle, any disturbance ... The safety distance is determined from an inter-vehicle time, Tiv, set by the driver and from a speed of the ego vehicle.

[0005] An ACC uses data from several sensors, usually a camera installed at the top of the ego vehicle's windshield and a radar or lidar installed in the ego vehicle's front bumper. These sensors are able to detect and identify objects in the vehicle's immediate environment. A vehicle equipped with an ACC is able to detect several objects in front of the ego vehicle, and is able to determine data associated with these objects such as speed, position, distance, inter-vehicle distance, shape, characteristics typifying or defining the object, etc. An object can be a land vehicle, any type of obstacle, any disturbance, etc. These sensors are also configured to identify characteristics of a road and lanes on which the ego vehicle is traveling.

[0006] A target vehicle selection controls the adaptive cruise control. This selection determines, on the one hand, a target vehicle speed, called target speed, and, on the other hand, a distance between the target vehicle and the ego vehicle, called target distance. The target speed and the target distance are important inputs to the ACC system in order to regulate the speed of the ego vehicle while maintaining a safe distance from the target vehicle. There are still situations where the selection of the target vehicle to control the adaptive cruise control is crucial.

[0007] Methods are known that identify whether a vehicle is ahead of the ego vehicle based on recognition of road markings. This is a mode of detecting a target vehicle called lane mode. However, very often and particularly when turning, these road markings are poorly recognized (because they are hidden, for example, by other cars, etc.). Methods are known that identify whether a vehicle, which is ahead of the ego vehicle, is on an estimated trajectory of the ego vehicle. However, when turning, particularly at the beginning of a turn and at the end of a turn, the estimated trajectories of the ego vehicle vary enormously (a radius of curvature of the road varies from infinity to approximately 30 meters for example). When turning, when the lane mode is not functional, the selection or deselection of a target vehicle, or simply keeping a target vehicle selected, is unstable. A selection of a target vehicle, vehicle preceding the ego vehicle in a turn, remains unstable with current methods. Summary of the invention

[0008] An object of the present invention is to remedy the aforementioned problem, in particular to improve a selection, when the lane mode is not functional, of a target vehicle in a turn.

[0009] To this end, a first aspect of the invention relates to a method for controlling an adaptive cruise control system, called an ACC system, of an autonomous vehicle, called an ego vehicle, said ACC system being based on a selection of a target vehicle, a reference frame, comprising an abscissa axis and a ordinate axis, being associated with said ego vehicle, said reference frame making it possible to express positions, speeds and accelerations, the method comprising the steps of: • Reception of first data characterizing at least one detected object, called object, said first data making it possible, for each object, to determine a position of said object, said position comprising an abscissa of said object, noted x0, and an ordinate of said object, noted y0; • Receiving second data representative of a movement of said ego vehicle, said second data comprising a speed of said ego vehicle and comprising a lateral acceleration of said ego vehicle; and for each object, • Determination, from said first data and said second data, of a trajectory of said ego vehicle, said trajectory determining a future position of said ego vehicle, called future position, said future position comprising a future abscissa, noted xt, and a future ordinate, noted yt, said future position being determined for a future abscissa equal to said abscissa of said object, xr=x0 • Determination, from said position of said object and said future position, of a lateral deviation, Ay, between said ordinate of said object and said future ordinate, Ay=y0-yf; • Determination, from said lateral deviation and said future position, whether said position of said object is located inside or outside a curvature of said trajectory of said ego vehicle; • Determining a modulated lateral distance of said object relative to said trajectory, said modulated lateral distance being smaller than an absolute value of said lateral deviation when said position of said object is inside said curvature of said trajectory, and, said modulated lateral distance being greater than said absolute value of said lateral deviation when said position of said object is outside said curvature of said trajectory; said method further comprising a step of selecting a target vehicle, said selection being based on said modulated lateral distance determined for each object.

[0010] Thus, virtually, detected objects that are located inside the trajectory curvature are brought closer to the trajectory, the modulated lateral distance is smaller than the absolute value of the lateral deviation. Conversely, detected objects that are located outside the trajectory curvature are moved away from the trajectory, the modulated distance is greater than the absolute value of the lateral deviation. The closer an object is laterally to the trajectory, the more relevant this object is to be selected as a target vehicle. The invention makes it possible to better select the objects that must be taken into account by the ACC system.

[0011] At the start of a turn for the ego vehicle, a second vehicle, preceding the ego vehicle and traveling on the same lane as the ego vehicle, is located inside the curvature of the trajectory of the ego vehicle. The invention will make it possible to virtually bring the second vehicle closer to the trajectory and thus make it possible to better take the second vehicle compared to another vehicle, a third vehicle, traveling on an overtaking lane of the lane on which the ego vehicle is traveling, the third vehicle traveling at the level of the second vehicle. This improves the selection of a target in a turn by approximately 0.5 seconds. It is also possible to better maintain the targets in the turns.

[0012] At the end of a turn, or when the second vehicle is traveling in a roundabout, the ego vehicle also traveling in the roundabout, and when the second vehicle leaves the roundabout by a first exit, while, for example, the ego vehicle wishes to leave the roundabout by a second exit, the second exit being after the first exit, the second vehicle will be located outside the curvature. The second vehicle will virtually be moved away from the trajectory. The second vehicle will become less relevant and will then be deselected as a target vehicle more quickly (in the order of 0.3 seconds).

[0013] Advantageously: • said position of said object is located inside said curvature of said trajectory when an equality sign(Ay)=sign(yf) is verified, sign() being a sign function; • if said equality is not verified, the position of said object is located outside said curvature of said trajectory.

[0014] Thus, in a simple and efficient way, we determine whether the object is inside or outside the curvature.

[0015] Advantageously, said trajectory is determined by the equation y = 2 a * la V t e E g a o° 2 * x 2 where x is an abscissa of said vehicle ego, y is an ordinate of said vehicle ego, alatEgo is said lateral acceleration of the vehicle ego, Vego is said speed of said vehicle ego, and said future ordinate, yt, is determined by the equation

[0016] Thus, in a simple and efficient way, without requiring complex calculations, we determine the future position of the ego vehicle, when the ego vehicle will be at the level of the object (same abscissa).

[0017] Advantageously: • when said position of said object is located inside said curvature of said trajectory, said modulated lateral distance, dm, is determined (250) by the equation dm=max(\Ay\-yaMaxR(Vego) ; 0), max() being a maximum function, \Ay\ being said absolute value of said lateral deviation, yaMaxR(Vego) being a positive function decreasing as a function of said speed of said vehicle ego; • when said position of said object is located outside said curvature of said trajectory, said modulated lateral distance is determined (250) by the equation dm=min(\Ay\+yaMaxE(Vego) ; 0), min() being a minimum function, \Ay\ being said absolute value of said lateral deviation, yaMaxE(Vego) being a positive function decreasing as a function of said speed of said vehicle ego.

[0018] The modulated lateral distance is a distance and, therefore, it is positive. The absolute value of the deviation is positive. When the object is located inside the curvature, we subtract from the absolute value of the deviation a positive value depending on the speed of the vehicle ego. The max function makes it possible to guarantee having a positive modulated lateral distance and less than the absolute value of the deviation. If \Ay\ <yaMaxR(Vego) alors la distance latérale modulée est égale à 0. La fonction yaMaxR(Vego) allows the distance to be reduced more significantly at low speed, for example less than 50 km / h, than at high speed, for example greater than 100 km / h. Indeed, in town for example, therefore at lower speed, the variations in the radius of curvature of a road are greater (we have a radius of curvature from infinity, in a perfectly straight line, at 30 meters) than on a motorway, a road adapted to traffic with a higher speed (we have a radius of curvature from infinity to 300 meters).

[0019] When the object is located outside the curvature, a positive value dependent on the speed of the vehicle ego is added to the absolute value of the gap. The min function ensures that the lateral distance is modulated positive and greater than the absolute value of the gap. The yaMaxE(Vego) function reduces the distance more significantly at low speeds, for example less than 50 km / h, than at high speeds, for example greater than 100 km / h.

[0020] The yaMaxR(Vego) and yaMaxE(Vego) functions, having larger values ​​at low speeds than at high speeds, allow for greater responsiveness at low speeds. The max(.) and min(.) functions create a threshold effect. It is as if the trajectory has a certain thickness, a larger thickness inside the curvature than outside the curvature.

[0021] Advantageously: • for each object, a relevance indicator, ind, is determined, said relevance indicator being data characteristic of the presence of an object representing a probability of the presence of the object, and • when said position of said object is located inside said curvature of said trajectory, said modulated lateral distance, dm, is determined (250) by the equation dm=max(\Ay\-kR(ind) ; \Ay\-yaMaxR(Vego) ; 0), kR(ind) being a positive increasing function as a function of said relevance indicator ind, • when said position of said object is located outside said curvature of said trajectory, said modulated lateral distance is determined by the equation dm=min(\Ay\+kE(ind) ; \Ay\+yaMaxE(Vego) ; 0), kE(ind) being a decreasing positive function as a function of said relevance indicator ind.

[0022] Thus, if an object has been detected as most likely present at a position, it is moved closer if this object is inside the curvature, and further away if this object is outside the curvature. The indicator can be determined over several time steps, over a period of time, thus allowing the indicator to be increased or decreased. This contributes to greater detection reliability and to better maintaining a selection of a target vehicle.

[0023] Advantageously, said relevance indicator is updated as a function of said modulated lateral distance.

[0024] Advantageously, the selection of the target vehicle is based on the relevance indicator.

[0025] This increases the quality and reliability of target vehicle selection. It is easier to keep a target vehicle, especially at low speeds or when the environment around the target vehicle is very changeable.

[0026] A second aspect of the invention relates to a device comprising a memory associated with at least one processor configured to implement the method according to the first aspect of the invention.

[0027] The invention also relates to a vehicle comprising the device.

[0028] The invention also relates to a computer program comprising instructions which, when the program is executed by the device according to the second aspect of the invention, cause the latter to implement the method according to the first aspect of the invention. Brief description of the figures

[0029] Other characteristics and advantages of the invention will emerge from the description of the non-limiting embodiments of the invention below, with reference to the appended figures, in which:

[0030] [Fig. 1] schematically illustrates a device, according to a particular exemplary embodiment of the present invention.

[0031] [Fig. 2] schematically illustrates a method for controlling an adaptive cruise control system of an autonomous vehicle, according to a particular exemplary embodiment of the present invention. Detailed description of the invention

[0032] The invention is described below in its non-limiting application to the case of an autonomous motor vehicle traveling on a road or on a traffic lane. Other applications such as a robot in a storage warehouse or a motorcycle on a country road are also conceivable.

[0033] Figure 1 represents an example of a device 101 included in the vehicle, in a network (“cloud”) or in a server. This device 101 can be used as a centralized device in charge of at least certain steps of the method described below with reference to Figure 2. In one embodiment, it corresponds to an autonomous driving computer.

[0034] In the present invention, the device 101 is included in the vehicle.

[0035] This device 101 can take the form of a box comprising printed circuits, any type of computer or even a mobile telephone (“smartphone”).

[0036] The device 101 comprises a random access memory 102 for storing instructions for the implementation by a processor 103 of at least one step of the method as described above. The device also comprises a mass memory 104 for storing data intended to be retained after the implementation of the method.

[0037] The device 101 may further comprise a digital signal processor (DSP) 105. This DSP 105 receives data to format, demodulate and amplify, in a manner known per se, this data.

[0038] The device 101 also comprises an input interface 106 for receiving the data implemented by the method according to the invention and an output interface 107 for transmitting the data implemented by the method according to the invention.

[0039] For example, the input interface 106 can receive the following data: position or geographical location of the vehicle, speed and / or acceleration of the vehicle, set or predetermined positions / speeds / accelerations, engine speed, position and / or travel of the clutch, brake and / or acceleration pedal, detection of other vehicles or objects, position or geographical location of the other vehicles or objects detected, speed and / or acceleration of the other vehicles or objects detected, operating states of sensors, confidence index of data originating from or processed by sensors and / or devices similar to the device 101. For example, the sensors capable of providing data are: GPS associated or not with mapping, tachometers, accelerometers, RADAR, LIDAR, lasers, ultrasound, camera, etc. The input interface 106 can also receive other data such as a position of detected objects, said position comprising an abscissa and an ordinate relative to a reference point linked to the ego vehicle, a longitudinal speed of the ego vehicle, called vehicle speed, a lateral acceleration of the ego vehicle, a relevance indicator associated with each object.

[0040] For example, the output interface 107 may transmit data similar to the data received by the input interface 106 or data such as a trajectory of the ego vehicle, a future position of the ego vehicle, a lateral deviation of an object relative to a future position of the ego vehicle, a lateral distance, a modulated lateral distance, a target vehicle relevance indicator for each object, the selection of a relevant object for the ACC system, i.e., a target vehicle, a target speed and an inter-vehicle distance between the target vehicle and the ego vehicle, ...

[0041] Figure 2 schematically illustrates a method for controlling an adaptive cruise control system of an autonomous vehicle, according to a particular embodiment of the present invention. Said adaptive cruise control system is called an ACC system. Said autonomous vehicle is called an ego vehicle. Said ACC system is based on a selection of a target vehicle. A selection of a target object, a target vehicle, thus controls said ACC system since depending on the selected vehicle, the regulator takes into account, for the speed regulation of the ego vehicle, a target speed and an inter-vehicle distance (between the target vehicle and the ego vehicle).

[0042] A reference frame, comprising an abscissa axis and a ordinate axis, is associated with said ego vehicle, said reference frame making it possible to express positions, speeds and accelerations. In particular, said reference frame makes it possible to express a position of an object, a future position of the ego vehicle, a speed and a lateral acceleration of the ego vehicle. The origin of said reference frame can be taken at the middle of the front axle of the ego vehicle, at a calculated or predetermined center of gravity of the ego vehicle, or any other point linked to the ego vehicle by a translation or other geometric conversion. Generally, the abscissa axis, also called the x-axis, is an axis passing through the origin and is coincident with the normal forward axis of the ego vehicle. The abscissa axis has a positive direction towards the front of the ego vehicle. Generally, the ordinate axis, also called the y-axis, is an axis passing through the origin and is perpendicular to the abscissa axis. The plane determined by the abscissa axis and the ordinate axis is horizontal, substantially parallel to the plane made by the road at the level where the vehicle is traveling. Classically the ordinate axis is a 90° rotation of the abscissa axis.The y-axis has a positive direction towards a left side of the ego vehicle when the ego vehicle is moving normally. Thus defined, the reference frame determines a longitudinal axis coincident with the x-axis, and a lateral axis coincident with the y-axis. A position is thus determined longitudinally and laterally relative to the origin of the ego vehicle, also called relative to the ego vehicle.

[0043] Step 200, Rx1, is a step of receiving first data characterizing at least one detected object, called object, said first data making it possible, for each object, to determine a position of said object, said position comprising an abscissa of said object, noted x0, and an ordinate of said object, noted y0. The input interface 106 is configured to receive said first information. The position of the object may correspond to the middle of a segment representing a rear width of the object. In one operating mode, the position of the object corresponds to a point of the nearest object, longitudinally and / or laterally in a normal direction of advance of the ego vehicle, relative to the ego vehicle. Devices similar to the device 101 are capable of determining the position of objects. Generally, coordinates of the position of the object, xo and yo, are positive when the object is located in front of and to the left of the ego vehicle.

[0044] Step 210, Rx2, is a step of receiving second data representative of a movement of said ego vehicle, said second data comprising a speed of said ego vehicle, and comprising a lateral acceleration of said ego vehicle. The input interface 106 is configured to receive said second information. Devices similar to the device 101 are capable of determining the speed of said ego vehicle and the lateral acceleration of said ego vehicle.

[0045] Step 200 allowed the first data to be received for each object. Steps 220 to 255 allow the first data to be processed for each object.

[0046] Step 220, Traj, is a step of determining, from said first data and said second data, a trajectory of said ego vehicle, said trajectory determining a future position of said ego vehicle, called future position, said future position comprising a future abscissa, noted xt, and a future ordinate, noted yt, said future position being determined for a future abscissa equal to said abscissa of said object, xt=x0. It is known to determine said future position by several means.

[0047] In one operating mode, the said trajectory is determined, very simply and without the need for numerous calculation resources, by the equation y = alatEa ° * x 2 where x is an abscissa of said ego vehicle, y is an ordinate of said ego vehicle, alatEgo is said lateral acceleration of the ego vehicle, Vego is said velocity of said ego vehicle. This equation determines a parabola having only one center of curvature when the lateral acceleration of the ego vehicle is non-zero. Said future position is determined for a future abscissa equal to said abscissa of said object, xf=x0, thus said future ordinate, yt, is determined by I equation

[0048] Step 230, Ay, is a step of determining, from said position of said object and said future position, a lateral deviation, Ay, between said ordinate of said object and said future ordinate, Ay=y0-yf. This deviation can be positive or negative depending on the position of the object relative to the future lateral position of the ego vehicle.

[0049] Step 240, Int / Ext, is a step of determining, from said lateral deviation and said future position, whether said position of said object is located inside or outside a curvature of said trajectory of said ego vehicle. There are several means of determining whether the position of said object is inside or outside said curvature. These means may depend on how the reference frame and the trajectory are determined.

[0050] In a simple and efficient operating mode, said position of said object is located inside said curvature of said trajectory when an equality sign(Ay)=sign(yf) is verified, sign() being a sign function, and Ay=y0-yf being the lateral deviation. Compared to the reference frame defined above, the lateral deviation is positive if the lateral position of the object is further to the left, left and right being defined relative to the abscissa axis, than the future position of the vehicle ego (yo>yr). If said equality is not verified, the position of said object is located outside said curvature of said trajectory.

[0051] Step 250, dm, is a step of determining a modulated lateral distance of said object relative to said trajectory, said modulated lateral distance being smaller than an absolute value of said lateral deviation when said position of said object is inside said curvature of said trajectory, and, said modulated lateral distance being greater than said absolute value of said lateral deviation when said position of said object is outside said curvature of said trajectory.

[0052] Thus, virtually, detected objects that are located inside the trajectory curvature are brought closer to the trajectory, the modulated lateral distance is smaller than the absolute value of the lateral deviation. Conversely, detected objects that are located outside the trajectory curvature are moved away from the trajectory, the modulated distance is greater than the absolute value of the lateral deviation. The closer an object is laterally to the trajectory, the more relevant this object is to be selected as a target vehicle. The invention makes it possible to better select the objects that must be taken into account by the ACC system.

[0053] Different methods are possible to calculate this approximation or distancing. In one operating mode, • when said position of said object is located inside said curvature of said trajectory, said modulated lateral distance, dm, is determined by the equation dm=max(\Ay\-yaMaxR(Vego) ; 0), max() being a maximum function, \Ay\ being said absolute value of said lateral deviation, yaMaxR(Vego) being a positive function decreasing as a function of said speed of said vehicle ego; • when said position of said object is located outside said curvature of said trajectory, said modulated lateral distance is determined by the equation dm=min(\Ay\+yaMaxE(Vego) ; 0), min() being a minimum function, \Ay\ being said absolute value of said lateral deviation, yaMaxE(Vego) being a positive function decreasing as a function of said speed of said vehicle ego.

[0054] The modulated lateral distance is a distance and, therefore, it is positive. The absolute value of the deviation is positive (|zly| >0). When the object is located inside the curvature, we subtract from the absolute value of the deviation a positive value depending on the speed of the vehicle ego. The max function makes it possible to guarantee having a positive modulated lateral distance and less than the absolute value of the deviation. If \Ay\ <yaMaxR(Vego) alors la distance latérale modulée est égale à 0. La fonction yaMaxR(Vego) permet de diminuer la distance latérale modulée de manière plus importante à faible vitesse, par exemple inférieure à 50 km / h, qu’à forte vitesse, par exemple supérieure à 100 km / h.Indeed, in town for example, therefore at lower speed, the variations in the radius of curvature of a road are greater (we have a radius of curvature of infinity, in a perfectly straight line, at 30 meters) than on a motorway, a road adapted to traffic at a higher speed (we have a radius of curvature of infinity at 300 meters).

[0055] When the object is located outside the curvature, a positive value dependent on the speed of the vehicle ego is added to the absolute value of the deviation. The min function ensures that the modulated lateral distance is positive and greater than the absolute value of the deviation. The yaMaxE(Vego) function reduces the modulated lateral distance more significantly at low speeds, for example less than 50 km / h, than at high speeds, for example greater than 100 km / h.

[0056] The yaMaxR(Vego) and yaMaxE(Vego) functions, being larger values ​​at low speed than at high speed, allow for reactive selection or deselection at low speed. The max(.) and min(.) functions create an effect threshold. It is as if the trajectory has a certain thickness, a thickness that is wider inside the curvature. Objects that are substantially close to the inside of the trajectory are considered, in the rest of the process, to be on the trajectory (dm=0). Objects on the outside of the trajectory are moved away from the trajectory.

[0057] In another mode of operation, • for each object, a relevance indicator, ind, is determined, said relevance indicator being data characteristic of the presence of an object representing a probability of the presence of the object, and • when said position of said object is located inside said curvature of said trajectory, said modulated lateral distance, dm, is determined by the equation dm=max(\Ay\-kR(ind) ; \Ay\-yaMaxR(Vego) ; 0), kR(ind) being a positive increasing function as a function of said relevance indicator ind, • when said position of said object is located outside said curvature of said trajectory, said modulated lateral distance is determined by the equation dm=min(\Ay\+kE(ind) ; \Ay\+yaMaxE(Vego) ; 0), kE(ind) being a decreasing positive function as a function of said relevance indicator ind.

[0058] In this operating mode, the objects located inside the curvature are brought closer to the trajectory if these objects are more relevant, and the objects are moved further away from the trajectory if these objects are less relevant. Methods for consolidating an indicator of the presence of a target object for autonomous driving are known, this consolidation giving a relevance indicator. Other methods are possible. For example, a relevance indicator is an indicator associated with an object whose value is between 0 and 1. The lower the value, therefore closer to zero, the more the object is considered irrelevant (less relevant) to be a target vehicle. Conversely, the higher the value, therefore closer to 1, the more the object is considered relevant as a target vehicle.

[0059] kR(ind) being a positive increasing function as a function of said relevance indicator ind, thus \Ay\-kR(ind) is all the smaller as the relevance indicator is large and therefore the object is considered relevant. An object is brought closer to the trajectory or an object is considered to be on the trajectory (dm=0), when the detected object is located inside the curvature, is close to the trajectory and is considered relevant. Conversely, kE(ind) being a positive decreasing function as a function of said relevance indicator ind, thus \Ay\+kE(ind) is all the larger as the relevance indicator is large and therefore the object is considered relevant. An object is moved away from the trajectory when the detected object is located outside the curvature and is considered relevant.

[0060]

[0061] Step 255, Ind, is a step in which said relevance indicator is updated as a function of said modulated lateral distance. In the previous step, objects were brought closer or further away from the trajectory of the ego vehicle. Objects very close to or confused with the trajectory of the ego vehicle are very relevant for an ACC system. In the case of a recent detection of a relevant object, for example a vehicle changing lanes to go into the lane of the ego vehicle, the relevance indicator must be increased very quickly.

[0062] Step 260, Sel, is a step of selecting a target vehicle, said selection being based on said modulated lateral distance determined for each object. The method makes it possible to bring detected objects closer to or further away from a trajectory of the ego vehicle. When, for an object, the modulated lateral distance is small (for example 1 meter, 2 meters, the equivalent of half a lane width, etc. other values ​​are possible), this object is on the trajectory of the ego vehicle. This object can be taken as the target vehicle. When several objects have a small modulated lateral distance, among these objects, it is interesting to take the object closest, longitudinally, to the ego vehicle as the target vehicle.

[0063] In one operating mode, the selection of the target vehicle is based on the relevance indicator. The more relevant an object is, the more this object should be taken as a target vehicle. For example, when several objects have a low modulated lateral distance, among these objects, it is interesting to make the relevance indicator of the object closest longitudinally to the ego vehicle higher.

[0064] The present invention is not limited to the embodiments described above as examples: it extends to other variants.

[0065] Equations and calculations have further been detailed. The invention is not limited to the form of these equations and calculations, and extends to any type of other mathematically equivalent form.

[0066] Thus, an exemplary embodiment has been described above in which an orthonormal reference frame has been defined whose orientation of the longitudinal axis is positive towards the front. The invention and the equations adapt in the event of a change of reference frame.

[0067] Furthermore, the steps referred to in Figure 2 have been described in a specific order. A different order is also possible. For example, steps 200 and 210 may be reversed or performed simultaneously. Other steps may be reversed or performed simultaneously.

Claims

CLAIMS 1. Method for controlling an adaptive cruise control system, called ACC system, of an autonomous vehicle, called ego vehicle, said ACC system being based on a selection of a target vehicle, a reference frame, comprising an abscissa axis and a ordinate axis, being associated with said ego vehicle, said reference frame making it possible to express positions, speeds and accelerations, the method comprising the steps of: • Reception (200) of first data characterizing at least one detected object, called object, said first data making it possible, for each object, to determine a position of said object, said position comprising an abscissa of said object, noted x0, and an ordinate of said object, noted y0 • Reception (210) of second data representative of a movement of said ego vehicle, said second data comprising a speed of said ego vehicle and comprising a lateral acceleration of said ego vehicle; and for each object, • Determination (220), from said first data and said second data, of a trajectory of said ego vehicle, said trajectory determining a future position of said ego vehicle, called future position, said future position comprising a future abscissa, noted xt, and a future ordinate, noted yt, said future position being determined for a future abscissa equal to said abscissa of said object, xr=x0 • Determination (230), from said position of said object and said future position, of a lateral deviation, Ay, between said ordinate of said object and said future ordinate, Ay=y0-yf; • Determination (240), from said lateral deviation and said future position, whether said position of said object is located inside or outside a curvature of said trajectory of said ego vehicle; • Determining (250) a modulated lateral distance of said object relative to said trajectory, said modulated lateral distance being smaller than an absolute value of said lateral deviation when said position of said object is inside said curvature of said trajectory, and, said modulated lateral distance being greater than said absolute value of said lateral deviation when said position of said object is outside said curvature of said trajectory; said method further comprising a step of selecting (260) a target vehicle, said selection being based on said modulated lateral distance determined for each object.

2. Method according to claim 1, in which: • said position of said object is located inside said curvature of said trajectory when an equality sign(Ay)=sign(yf) is verified, sign() being a sign function; • if said equality is not verified, the position of said object is located outside said curvature of said trajectory.

3. Method according to one of the preceding claims, in which said trajectory is determined by the equation x 2 , where x is an abscissa of said ego vehicle, y is an ordinate of said ego vehicle, alatEgo is said lateral acceleration of the ego vehicle, Vego is said velocity of said ego vehicle, and wherein said future ordinate, yr, is determined by the equation y f = x0 2 .

4. Method according to one of the preceding claims, in which: • when said position of said object is located inside said curvature of said trajectory, said modulated lateral distance, dm, is determined (250) by the equation dm=max(\Ay\-yaMaxR(Vego) ; 0), max() being a maximum function, \Ay\ being said absolute value of said lateral deviation, yaMaxR(Vego) being a positive function decreasing as a function of said speed of said vehicle ego; • when said position of said object is located outside said curvature of said trajectory, said modulated lateral distance is determined (250) by the equation dm=min(\Ay\+yaMaxE(Vego) ; 0), min() being a minimum function, \Ay\ being said absolute value of said lateral deviation, yaMaxE(Vego) being a positive function decreasing as a function of said speed of said vehicle ego.

5. Method according to one of the preceding claims, in which: • for each object, a relevance indicator, ind, is determined, said relevance indicator being data characteristic of the presence of an object representing a probability of the presence of the object, and • when said position of said object is located inside said curvature of said trajectory, said modulated lateral distance, dm, is determined (250) by the equation dm=max(\Ay\-kR(ind) ; \Ay\-yaMaxR(Vego) ; 0), kR(ind) being a positive increasing function as a function of said relevance indicator ind, • when said position of said object is located outside said curvature of said trajectory, said modulated lateral distance is determined (250) by the equation dm=min(\Ay\+kE(ind) ; \Ay\+yaMaxE(Vego) ; 0), kE(ind) being a decreasing positive function as a function of said relevance indicator ind.

6. The method of claim 5, wherein said relevance indicator is updated based on said modulated lateral distance.

7. Method according to claims 5 or 6, wherein the selection of the target vehicle is based on the relevance indicator.

8. Device (101) comprising a memory (102) associated with at least one processor (103) configured to implement the method according to one of the preceding claims.

9. Vehicle comprising the device according to the preceding claim.

10. Computer program comprising instructions which, when the program is executed by the device (101), cause the latter to implement the method according to one of claims 1 to 7.

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