Method and device for controlling an adaptive cruise control system of an autonomous vehicle using a corridor mode.

Abstract

The invention relates to a method and a device 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, said method comprising the steps of: Receiving (200) first data; Receiving (210) second data; Determining (220) a trajectory of said ego vehicle; and for each object, Determining (230), from said trajectory and a predetermined width, a corridor, said corridor representing, over said predetermined time horizon, possible locations of said ego vehicle; Determining (240), from said corridor and said position of the object, whether said object is present in said corridor; said method further comprising a step of selecting (250) a target vehicle, said selection being based on said position of said object and on the presence of an object in the corridor.Figure to be published for abstract: Figure 2.

Description

Title of the invention: Method and device for controlling an adaptive cruise control system of an autonomous vehicle using a corridor mode. Technical field of the invention

[0001] The invention is in 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] The term "vehicle" means any type of vehicle such as a motor vehicle, a moped, a motorcycle, a storage robot in a warehouse, etc. The term "autonomous driving" of an "autonomous vehicle" means any method capable of assisting the driving of the vehicle. The method may thus consist of partially or totally steering the vehicle or providing any type of assistance to a natural person driving the vehicle. The method thus covers all autonomous driving, from level 0 to level 5 in the OICA scale, for International Organization of Motor Vehicle Manufacturers.

[0003] Methods capable of assisting the driving of the vehicle are also called AD AS (from the English acronym "Advanced Driver Assistance Systems"), AD AS functions, AD AS systems or driving assistance systems. Among these AD AS systems, an adaptive cruise control is known.

[0004] Adaptive cruise control is also known as ACC or ACC system (from the English acronym "Auto Cruise Control"). This system automatically maintains a speed of the vehicle, called the 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 with a vehicle, called the target vehicle, preceding the ego vehicle. For example, a target vehicle is a land vehicle, any type of obstacle, any disturbance, etc. 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, generally including a camera installed at the top of a windshield of the ego vehicle and a radar or lidar installed in a front bumper of the ego vehicle. These sensors are capable of detecting and identifying objects in an environment close to the vehicle. A vehicle equipped with an ACC is capable of detecting several objects in front of the ego vehicle, and is capable of determining data associated with these objects such as a speed, a position, a distance, an inter-vehicle distance, a shape, characteristics typifying or defining the object, ... An object can be a land vehicle, any type of obstacle, any disturbance ... These sensors are also configured to identify characteristics of a road and lanes on which the ego vehicle is traveling.

[0006] A selection of a target vehicle controls the adaptive cruise control. This selection makes it possible to determine, on the one hand, a speed of the target vehicle, called the target speed, and, on the other hand, a distance between the target vehicle and the ego vehicle, called the target distance. The target speed and the target distance are important input elements 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 which identify whether a vehicle is preceding the ego vehicle from 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 which determine a trajectory of the ego vehicle and identify whether a vehicle, which is preceding the ego vehicle, is on an estimated trajectory of the ego vehicle. However, reliably determining the presence of a target vehicle on the trajectory is not entirely satisfactory, particularly in the case of rain, rain or humidity having an impact on the measurement quality of sensors of the ACC system, a perception of the environment of the ego vehicle being degraded. Summary of the invention

[0008] An object of the present invention is to remedy the aforementioned problem, in particular to improve, make more reliable and simplify a selection, when the track mode is not functional, of a target vehicle.

[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, said 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 relative to the ego vehicle; • Reception of second data representative of a movement of said ego vehicle; • Determination, from said second data, of a trajectory of said ego vehicle, said trajectory determining future positions of said ego vehicle over a predetermined time horizon; and for each object, • Determination, from said trajectory and a predetermined width, of a corridor, said corridor representing, over said predetermined time horizon, possible locations of said ego vehicle; • Determination, from said corridor and said position of the object, whether said object is present in said corridor; said method further comprising a step of selecting a target vehicle, said selection being based on said position of said object and on the presence of an object in the corridor.

[0010] Thus, very simply, we determine a corridor, the future locations of the ego vehicle over a time horizon. The corridor has a width determined by the predetermined width. The width can be a width of said ego vehicle or a slightly larger width to give itself a safety space. If only one vehicle is present in the corridor, this vehicle will be selected as the target vehicle. If several vehicles are present in the corridor, a target vehicle will be selected according to its position relative to the ego vehicle.

[0011] Advantageously, the predetermined width is a function of a width of said ego vehicle and / or a function of a width of the lane on which said ego vehicle is traveling, said widths being determined from third received data.

[0012] Thus, the width of the corridor is adapted according to a characteristic of the vehicle, the width of the vehicle ego, and / or is adapted according to a characteristic of the lane on which the vehicle is traveling. It is not necessary to resort to expensive empirical parameterizations on a test vehicle, thus simplifying the development of the method.

[0013] Advantageously, said predetermined width is a function of said future positions of said ego vehicle.

[0014] At a current time, T, a future trajectory of the ego vehicle is estimated. This estimation is generally very reliable over the next few moments (a few tens of milliseconds for example), therefore over a few meters for example if the vehicle is traveling on a highway. However, over more distant moments, several seconds, therefore over more distant future positions, for example a hundred meters for an ego vehicle traveling on a highway, this estimation is less reliable. To take this loss of reliability into account, said width is made to depend on the future positions of said ego vehicle. The further the future position of the ego vehicle is from the position at the current time, the more the width of the corridor is reduced. Thus, one avoids selecting an object that is too far away as a target vehicle, so as not to limit the speed regulated by a vehicle that will not be reliably detected in the corridor.

[0015] Advantageously, said method further comprises a step of receiving a precipitation rate, and said predetermined width is a function of a first parameter dependent on said precipitation rate, said first parameter being a decreasing function as a function of said precipitation rate, the greater said precipitation rate, the smaller the first parameter, the smaller the first parameter, the less wide the corridor.

[0016] Some sensors of the ACC system, such as a camera or a lidar, are disturbed by precipitation, in liquid form such as water drops or in solid form such as snow. Thus, the width of the corridor is dependent on weather conditions, in particular a precipitation rate. A drop of water or a snowflake in front of a camera lens has the effect of diffracting the received light. A vehicle located outside said corridor can then be perceived as being in the corridor. Reducing the width of the corridor according to weather conditions makes the detection of a target vehicle more reliable, by reducing false detections.

[0017] Advantageously, for each object, a relevance indicator is determined, said relevance indicator representing a probability of a presence of the object, and in which the selection of the target vehicle is also based on said relevance indicator.

[0018] Thus, the selection of a target vehicle is made more reliable. The indicator can be determined over several time steps, over a period of time, thus making it possible to increase or decrease the indicator. This contributes to greater detection reliability and to better maintaining a selection of a target vehicle. This avoids false detections, sporadic detections for example, which may be linked to weather conditions.

[0019] Advantageously, said relevance indicator is updated periodically, with a period AT, said update being based on a second parameter, said second parameter being an increasing function as a function of said precipitation rate, said probability of presence of the object increasing when said second parameter increases.

[0020] Thus, when a vehicle is detected in the corridor, when the width of the corridor has been reduced because of the first parameter, the detection of the vehicle is more certain than when the corridor is wider. Since the detection is more certain, the second parameter makes it possible to increase the presence indicator more quickly during the update. As the relevance indicator increases more quickly, the decision to select a new target vehicle that enters the corridor is faster.

[0021] Advantageously, said update of the relevance indicator is given by the equation: IPertinence (T) = IPertinence (T- AT) + kD (TxPrecipitation)* A Relevance (T), where IP Relevance is said relevance indicator, T is a current instant, T- AT representing a previous instant, TxPrecipitation being said precipitation rate, kD ( TxPrecipitation ) being the second parameter dependent on said precipitation rate, and A Relevance (T) being a predefined increment dependent on the current instant.

[0022] Very simply, the indicator is updated according to the second parameter.

[0023] 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.

[0024] The invention also relates to a vehicle comprising the device.

[0025] 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, lead the latter to implement the method according to the first aspect of the invention. Brief description of the figures

[0026] 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:

[0027] [Fig-1] schematically illustrates a device, according to a particular example of embodiment of the present invention.

[0028] [Fig.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.

[0029] [Fig.3] schematically illustrates a life situation, according to a particular example of embodiment of the present invention. Detailed description of the invention

[0030] The invention is described below in its non-limiting application to the case of an autonomous motor vehicle, called an ego 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.

[0031] [Fig. 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 [Fig. 2]. In one embodiment, it corresponds to an autonomous driving computer.

[0032] In the present description, the device 101 is included in the vehicle.

[0033] This device 101 can take the form of a box comprising printed circuits, any type of computer or even a mobile telephone (“smartphone”).

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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 data characterizing at least one detected object, for example a position of each object relative to the ego vehicle, data representative of a movement of said ego vehicle, for example a speed, a lateral acceleration, a yaw rate, an angle or steering wheel or rack torque of the ego vehicle, a predetermined corridor width, a width of the ego vehicle, a width of the lane on which the ego vehicle is traveling, a precipitation rate, a humidity rate, a windshield wiper activation, a windshield wiper sweeping speed, precipitation or meteorological data, an update period, a current time, a predefined increment, etc.

[0038] For example, the output interface 107 may transmit data similar to the data received by the input interface 106 or data such as a selected target vehicle, a target speed, a target distance, a target inter-vehicle distance, a corridor width, a precipitation rate, values ​​of a first parameter dependent on a precipitation rate, values ​​of a second parameter dependent on a precipitation rate, a relevance indicator, ...

[0039] [Fig.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. The adaptive cruise control system is called an ACC system. The autonomous vehicle is called an ego vehicle. Said method comprises several steps.

[0040] Step 200, Rxl, 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 relative to the ego vehicle. The input interface 106 is configured to receive said first data. 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 closest 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 the objects. The position of the objects is generally expressed in a reference frame linked to the ego vehicle.

[0041] Step 210, Rx2, is a step of receiving second data representative of a movement of said ego vehicle. The input interface 106 is configured to receive said second data. Data representative of a movement of said ego vehicle are, for example, a speed of said ego vehicle, an acceleration, longitudinal or lateral, of said ego vehicle, a yaw rate of said ego vehicle, a steering wheel angle of said ego vehicle, a steering wheel or rack torque of the ego vehicle, ... Devices similar to the device 101 are capable of determining these data representative of a movement of said ego vehicle.

[0042] Step 220, Traj, is a step of determining, from said second data, a trajectory of said ego vehicle, said trajectory determining future positions of said ego vehicle over a predetermined time horizon. In [Fig.3] an example of an ego vehicle 300 and a trajectory 310 are shown.

[0043] For example, the predetermined time horizon is 1 second, 10 seconds, or any other values. These other values ​​may depend on other data such as the vehicle speed. The trajectory predicts the possible positions and locations of the ego vehicle over a time horizon, relative to the ego vehicle and relative to the lanes

[0044] It is known to determine the future position by several means. In one operating mode, said trajectory is determined, very simply and without the need for numerous computational resources, by the equation _ alatEgo 2, where x is 2*Vego2 an abscissa of said ego vehicle, y is an ordinate of said ego vehicle, alatEgo is a lateral acceleration of the ego vehicle, Vego is said speed of said ego vehicle, the abscissa and the ordinate being, for example, expressed in an orthonormal reference frame related to the ego vehicle. For example, the vehicle's future abscissas are determined from a measured speed.

[0045] In another operating mode, it is possible to use a so-called "bicycle" model, a model conventionally used in the field of vehicle dynamics. In another operating mode, it is possible to extrapolate the position of the vehicle as a function of the previous positions. It is also possible to combine several of these operating modes.

[0046] Step 200 allows the first data to be received for each object. Steps 225 to 250 allow the first data to be processed for each object.

[0047] Step 230, Corr, is a step of determining, from said trajectory and a predetermined width, a corridor, said corridor representing, over said predetermined time horizon, possible locations of said ego vehicle.

[0048] Step 225, Width, preliminary to step 230, is a step of determining said predetermined width. This step may require receiving third data. The input interface 106 is configured to receive said third data. Said third data may be a width of the vehicle, data characterizing the lane on which the vehicle is traveling, such as a lane width, parameters ...

[0049] Advantageously, the predetermined width is a function of a width of said ego vehicle. Very simply, the corridor is determined. For example, in [Fig.3], the space between curves 330 and 331 represents the corridor. In this example, half the width of the corridor is equal to half the width of the ego vehicle. Curve 330 represents the left side of the corridor. The lateral distance between trajectory 310 and the left side of the corridor corresponds to half the width of the ego vehicle. Curve 331 represents the right side of the corridor. The lateral distance between trajectory 310 and the right side of the corridor corresponds to half the width of the ego vehicle.

[0050] Advantageously, the predetermined width is a function of a width of the lane on which said ego vehicle travels, said widths being determined from third received data. In the example of [Fig.3], the lane is represented by the surface between curves 320 and 321. Curve 320 represents the left side of the lane. Curve 321 represents the right side of the lane. The lateral distance between curve 320 and curve 321 is the width of the lane.

[0051] Depending on the country and location, lane widths may vary. Depending on driving habits, a vehicle may travel closer to the left side than to the right side, or vice versa. It is therefore interesting to make the width of the corridor dependent on the width of the vehicle and / or the lane. If the width of the corridor is wider than the width of the vehicle, this allows a vehicle to be detected earlier in the corridor. It is then possible to select this vehicle detected earlier as the target vehicle. However, the risk of making a false detection increases. If the corridor is wider, a target vehicle is de-selected less quickly. If the corridor width is narrower than the vehicle width, this allows a vehicle to be detected later in the corridor. The detected vehicle will be selected later as the target vehicle. However, detection is more reliable, the target vehicle is close to the trajectory and not on the edge of the trajectory. Vehicles whose trajectory oscillates a lot or during a very brief false maneuver are no longer detected. If the corridor is narrower, a target vehicle that no longer remains in front of the ego vehicle on the trajectory is de-selected more quickly.

[0052] Advantageously, said predetermined width is a function of said future positions of said ego vehicle. Generally, the determined trajectory is quite accurate over the first tens of milliseconds. However, this trajectory determination is less reliable, because, for example, the ACC system can change the speed of the ego vehicle, because a driver of the ego vehicle can change direction or accelerate or brake, ... from several hundred milliseconds. These times can also depend on the speed of the ego vehicle or other parameters. Thus, the further an estimated future position of the ego vehicle is from an initial position, the more uncertain the position, and therefore the trajectory, is. It is useful to reduce, for example, the width of the corridor as a function of the estimated future position of the ego vehicle, thus making it possible to make fewer false detections.

[0053] Advantageously, said method further comprises a step of receiving a precipitation rate. The input interface 106 is configured to receive said precipitation rate. The precipitation rate is an indicator of the quantity of precipitation around the ego vehicle at a given time. This indicator may also depend on a humidity rate, a temperature, a temperature difference between the interior of the ego vehicle and the exterior of the ego vehicle, etc. The rate may be represented by a value equal to or between 0 and 1, 0 indicating that there is no precipitation, 1 indicating that there is precipitation. Several nuances or levels of precipitation are possible: weak, normal, strong, extreme, etc. These nuances or levels are represented by intermediate values ​​of the precipitation rate. The smaller the precipitation rate, the less chance there is of precipitation around the vehicle.For example, the higher the precipitation rate, the more reliably heavy precipitation was detected.

[0054] The precipitation rate can be determined by a rain sensor on a windshield of the vehicle, a wiper speed sensor, by receiving meteorological data, by processing captured image data, etc.

[0055] Advantageously, in step 225, the predetermined width is a function of a first parameter dependent on said precipitation rate, said first parameter being a decreasing function as a function of said precipitation rate, the greater said precipitation rate, the smaller the first parameter, the smaller the first parameter, the less wide the corridor. In other words, the more precipitation detected, the less wide the corridor.

[0056] Some sensors of the ACC system, such as a camera or a lidar, are disturbed by precipitation, in liquid form such as water drops or in solid form such as snow. A drop of water in front of a camera lens has the effect of diffracting the received light. A vehicle located outside of said corridor can then be perceived as being in the corridor. For example in [Fig. 3], the vehicle 340 is a vehicle traveling on a lane adjacent to the lane on which the ego vehicle 300 is traveling. For example, because of a drop of water in front of the camera lens, the position of the vehicle 340 is perceived as being the position of the vehicle 341. The vehicle 341 being on the corridor, the width of which is not dependent on weather conditions but only the width of the ego vehicle, can be selected as the target vehicle.The invention reduces the width of the corridor depending on the precipitation rate, as certain precipitation levels cause perception problems. The invention makes it possible to manage perception problems due to a degraded camera image. In the presence of precipitation, an image acquired by a camera of the ACC system is more complicated to analyze. However, the image represents the best source for the lateral positioning of an object. In the presence of precipitation, there is less confidence in the accuracy of the lateral detection of an object. This reduces bad selections.

[0057] Reducing the width of the corridor according to weather conditions makes it possible to make the detection of a target vehicle more reliable, by reducing false detections which will cause untimely braking, which is particularly dangerous in the event of precipitation because the road becomes more slippery (tire / road grip becomes lower than in dry weather and without precipitation).

[0058] Step 240, Near, is a step of determining, from said corridor and said position of the object, whether said object is present in said corridor. If the object is in the corridor, this object is a good candidate for selection as a target vehicle. The invention makes it possible to modify the width of the corridor as a function of different parameters including characteristics of the ego vehicle and / or the lane on which the ego vehicle is traveling, including the future position of the ego vehicle, including perception problem conditions including a precipitation rate.

[0059] Step 245, Ind, is a step where, for each object, a relevance indicator is determined, said relevance indicator representing a probability of the presence of the object. Methods for consolidating a presence indicator of a target object for autonomous driving are known, this consolidation giving an indicator of relevance. 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, i.e., closer to zero, the more the object is considered irrelevant to being a target vehicle. Conversely, the higher the value, i.e., closer to 1, the more the object is considered relevant as a target vehicle.

[0060] Advantageously, said relevance indicator is updated periodically, with a period AT, said update being based on a second parameter, said second parameter being an increasing function as a function of said precipitation rate, said probability of presence of the object increasing when said second parameter increases.

[0061] The indicator can be determined over several time steps, over a period of time, thus making it possible to increase or decrease the indicator. This contributes to greater detection reliability and to better maintaining a selection of a target vehicle. This avoids false detections, sporadic detections for example, which may be linked to weather conditions or perception problems.

[0062] There are different means for determining the relevance indicator. Advantageously, said updating of the relevance indicator is given by the equation: IPertinence (T) = IPertinence (T- AT) + kD ( TxPrecipitation ) * A Relevance (T), where IP Relevance is said relevance indicator, T is a current instant, T- AT representing a preceding instant, TxPrecipitation being said precipitation rate, kD ( TxPrecipitation ) being the second parameter dependent on said precipitation rate, and A Relevance (T) being a predefined increment dependent on the current instant. For example, the predefined increment is positive if the object is detected in the corridor, the predefined increment is negative if the object is not detected in the corridor. In this case, the more the object is detected in the corridor (detected present over several periods) the more the indicator increases. Thanks to the second parameter, the indicator increases all the more as the precipitation rate is high.For example, if no precipitation is detected, the second indicator is equal to 1, and if heavy precipitation is detected, the second indicator is equal to 1.2. Other values ​​are possible. Thanks to the second parameter, the relevance indicator varies more or less quickly depending on the precipitation rate. There is no need to modify existing strategies for determining a relevance indicator much.

[0063] Step 250, Sel, is a step of selecting a target vehicle, said selection being based on said position of said object and on the presence of an object in the corridor. In one operating mode, the selection of the target vehicle is also based on the relevance indicator.

[0064] If there is only one object in the corridor, this object can be selected as the target vehicle. However, if the relevance indicator is too low, this object may not not be selected. If the relevance indicator becomes too low, this object may no longer be selected.

[0065] When several objects are determined to be present in the corridor, among these objects, it is interesting to take as target vehicle the object closest, longitudinally, to the ego vehicle.

[0066] The more relevant an object is, the more this object must be taken as a target vehicle. For example, when several objects are determined to be present in the corridor, said objects having a similar longitudinal distance from the ego vehicle, among these objects, it is interesting to take the most relevant object as a target vehicle. By similar longitudinal distance, we mean a few tens of centimeters to a few meters, the similar distance can depend on the speed of the ego vehicle, the distance of the detected objects, or other parameters. A degraded perception, by a drop of water for example, can cause a sporadic detection of the same object to within a few tens of centimeters or meters. The variation in the width of the corridor combined with the relevance indicator makes it possible to select an object as a target vehicle in a more reliable and robust manner.

[0067] The present invention is not limited to the embodiments described above as examples: it extends to other variants.

[0068] 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.

[0069] Furthermore, the steps with reference to [Fig. 2] have been described in a specific order. A different order is also possible. For example, steps 200 and 210, just like steps 220 to 245, can be reversed or even carried out simultaneously.

Claims

1. Claims Method for controlling an adaptive cruise control system, called ACC system, of an autonomous vehicle (300), called ego vehicle, said ACC system being based on a selection of a target vehicle, said method comprising the steps of: • Reception (200) of first data characterizing at least one detected object (340, 341), called object, said first data making it possible, for each object, to determine a position of said object relative to the ego vehicle; • Reception (210) of second data representative of a movement of said ego vehicle (300); • Determination (220), from said second data, of a trajectory (310) of said ego vehicle, said trajectory (310) determining future positions of said ego vehicle over a predetermined time horizon; and for each object, • Determination (230), from said trajectory and a predetermined width, of a corridor (330, 331), said corridor representing, on said predetermined time horizon, possible locations of said ego vehicle; • Determination (240), from said corridor and said position of the object, whether said object is present in said corridor; said method further comprising a step of selecting (250) a target vehicle, said selection being based on said position of said object and on the presence of an object in the corridor (330, 331), said method further comprising a step of receiving a precipitation rate, and said predetermined width is a function (225) of a first parameter dependent on said precipitation rate, said first parameter being a decreasing function as a function of said precipitation rate, the greater said precipitation rate, the smaller the first parameter, the smaller the first parameter, the less wide the corridor.

2. Method according to claim 1, in which the predetermined width is a function (225) of a width of said ego vehicle and / or a function of a width of the lane (320, 321) on which said ego vehicle travels, said widths being determined from third received data.

3. Method according to one of the preceding claims, wherein said predetermined width is a function (225) of said future positions of said ego vehicle.

4. Method according to one of the preceding claims, in which for each object, a relevance indicator is determined (245), said relevance indicator representing a probability of a presence of the object, and in which the selection of the target vehicle is also based on said relevance indicator.

5. Method according to the preceding claim, in which said relevance indicator is updated periodically, with a period AT, said update being based on a second parameter, said second parameter being an increasing function as a function of said precipitation rate, said probability of presence of the object increasing when said second parameter increases.

6. Method according to the preceding claim, wherein said updating of the relevance indicator is given by the equation: IPertinence (T) = IPertinence (T- AT) + kD ( TxPrecipitation ) * A Relevance (T), where IP Relevance is said relevance indicator, T is a current instant, T- AT representing a preceding instant, TxPrecipitation being said precipitation rate, kD ( TxPrecipitation ) being the second parameter dependent on said precipitation rate, and A Relevance (T) being a predefined increment dependent on the current instant.

7. 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.

8. Vehicle (300) comprising the device according to the preceding claim.

9. 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 6.

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