Calibration method for a road traffic control device

Abstract

Method (500) for calibrating a road control device (101) comprising a camera (201) and a radar (202), the method (500) comprising the following steps: - estimating (501) the position and orientation parameters of the camera (201) and the radar (202) in a reference frame (O, x, y, z) linked to a lane (102a) of a road (102); - acquiring (502), by the camera (201) and the radar (202), the data relating to the trajectory of at least one of the same vehicle (103) on a lane (102a) of the road (102); - determine (503), on the one hand, a camera trajectory of the vehicle (103) in the frame (O, x, y, z) linked to the lane (102,a) of the road (102) from the data acquired by the camera (201) and the estimated position and orientation parameters, and on the other hand, a radar trajectory of the vehicle (103) in the frame (O, x, y, z) linked to the lane (102,a) of the road (102) from the data acquired by the radar (202) and the estimated position and orientation parameters;- calculate (504), from the camera trajectory and the radar trajectory, corrected values ​​of at least some of the estimated position and orientation parameters of the camera (201), including an angle parameter, e, elevation parameter and a height parameter, h, of said camera (201).;

Description

Title of the invention: Method for calibrating a road traffic control device technical field

[0001] This disclosure relates to a method for calibrating a road traffic enforcement device comprising a camera and radar, and a road traffic enforcement device configured to implement this method. This disclosure applies in particular to the calibration of mobile road traffic enforcement devices, including those that can be placed in geographically variable locations over time, depending on the operational needs of these devices. Previous technique

[0002] Modern road traffic control devices include, in addition to radar, a camera whose resolution and field of view allow for tracking vehicle trajectories within the image stream. For a vehicle to be considered in violation, it is generally required that it be detected by both radar and camera, and that the vehicle trajectories acquired by the radar and camera be consistent.

[0003] To be usable, the data acquired by the camera, in particular the successive positions of the vehicle in the images obtained by the camera, must be converted into vehicle position data in a road-related frame of reference. However, the operating conditions of road traffic control devices are such that the camera is close to the ground with a grazing orientation, that is, located at a low height with a similarly low elevation angle. Under these conditions, it is necessary to determine the values ​​of the elevation and height parameters very precisely, because an uncertainty in these values ​​can significantly affect the determination of the vehicle's position in the road-related frame of reference. For example, an uncertainty of a few tenths of a degree in the elevation angle can represent an error in the vehicle's position of several meters.

[0004] This problem is even more relevant for mobile road traffic control devices. Indeed, for a device intended to be operated at a fixed location, the topography of the site can be precisely determined, and therefore the camera positioning parameters can be precisely determined.

[0005] FR 3096786 Al [IDEMIA IDENTITY & SECURITY FRANCE [FR]] 04.12.2020 describes a method for determining the position and orientation of a camera on a road traffic control device relative to a road, requiring the use of a total station. However, this is not compatible with operational constraints. mobile road control devices, which can be moved, for example every day or even several times a day, to different locations.

[0006] FR 3131777 Al [IDEMIA IDENTITY & SECURITY FRANCE [FR]] 14.07.2023 describes a method for aligning a camera near a road, particularly for mobile road control devices, which allows estimating the position and orientation parameters of the camera relative to the road. Summary

[0007] One purpose of the present disclosure is to propose a method for calibrating a road traffic control device that allows for a more accurate estimation of the height and elevation parameters of the camera of such a device, that is to say, an accurate estimation to the tenth of a meter and to the tenth of a degree, respectively.

[0008] In a first aspect of the invention, a method for calibrating a road traffic control device comprising a camera and a radar is provided, the method comprising the following steps: -estimate the position and orientation parameters of the camera and radar in a coordinate system (O, x, y, z) linked to a lane of a road; - to acquire, by camera and radar, data relating to the trajectory of at least one vehicle on the road; - determine, on the one hand, a camera trajectory of the vehicle in the frame (O, x, y, z) linked to the road lane from the data acquired by the camera and the estimated position and orientation parameters, and on the other hand, a radar trajectory of the vehicle in the frame (O, x, y, z) linked to the road lane from the data acquired by the radar and the estimated position and orientation parameters; - calculate, from the camera trajectory and the radar trajectory, the corrected values ​​of at least some of the estimated position and orientation parameters of the camera, including an angle parameter, e, elevation parameter and a height parameter, h, of said camera.

[0009] According to some embodiments, the radar trajectory and the camera trajectory each include vehicle position data in the (O, x, y, z) reference frame linked to the road lane, acquired at respective acquisition times, and the step of calculating the corrected values ​​of the parameters includes a substep of correcting the vehicle position data of the radar trajectory and the camera trajectory for identical acquisition times.

[0010] According to some embodiments, the radar and the camera have different data acquisition frequencies, and the correction substep includes an interpolation of one of the speed camera trajectory and / or the camera trajectory so that both trajectories include position data corresponding to identical acquisition times.

[0011] According to some embodiments, the calculation step includes the calculation of the corrected values ​​of angle, e, elevation and height, h, and said method includes a step of updating the value of at least one of a distance, d, of shore and of an angle, r, of camera roll from said corrected values.

[0012] According to some embodiments, the camera trajectory and the radar trajectory each comprise a time series of vehicle positions along an axis [Ox) of the frame (O, x, y, z) linked to the road lane, the axis [Ox) being tangent to an edge of the road lane, and the step of calculating the corrected values ​​comprises the calculation of corrected values ​​for the angle, e, of elevation and the height, h, of the camera, and a substep of minimizing the position differences between the time series of the camera trajectory and the radar trajectory.

[0013] According to some embodiments, the camera trajectory and the radar trajectory each include a time series of distances of the vehicle from the road control device, and the step of calculating the corrected values ​​for the position and orientation parameters of the camera includes a substep of minimizing this distance over the set of said time series.

[0014] According to certain embodiments, the step of calculating the corrected values ​​comprises the following sub-steps: - calculate a quantity Dl(t)*H2 / D2(t) as a function of Dl(t), where Dl(t) is the time sequence of distances of the vehicle from the road control device provided by the radar, D2(t) is the time sequence of distances of the vehicle (103) from the road control device provided by the camera, and H2 is the estimated height of the camera; - model the quantity Dl(t)*H2 / D2(t) as a function of Dl(t) by an affine function; - calculate a correction on the angle, e, of elevation using the value of the slope of the affine function, the corrected value of the height, h, of the camera from the ordinate at the origin of the affine function.

[0015] In a second aspect of the invention, a computer program is provided, comprising instructions which, when executed by a data processing unit, cause said device to implement the coding process for the implementation of a process comprising the following steps: - estimate the position and orientation parameters of the camera and radar in a coordinate system (O, x, y, z) linked to a lane of a road; - to control the camera and radar to acquire, by the camera and radar, data relating to the trajectory of at least one of the same vehicles on the road; - determine, on the one hand, a camera trajectory of the vehicle in the frame (O, x, y, z) linked to the road lane from the data acquired by the camera and the estimated position and orientation parameters, and on the other hand, a radar trajectory of the vehicle in the frame (O, x, y, z) linked to the road lane from the data acquired by the radar and the estimated position and orientation parameters; - calculate, from the camera trajectory and the radar trajectory, corrected values ​​of at least some of the estimated position and orientation parameters of the camera, including an angle parameter, e, elevation parameter and a height parameter, h, of said camera.

[0016] In a third aspect of the invention, a road control device is provided, comprising a camera, a radar, and a processing unit, the road control device being configured to implement the calibration process according to any one of the embodiments described above.

[0017] The method according to the invention makes it possible to precisely determine the height and elevation angle of the camera from the trajectories of one or more vehicles, acquired by the radar and the camera. The position data of a vehicle acquired by a radar such as, for example, a Doppler radar or LIDAR, are only slightly, or not at all, sensitive to the height and elevation angle of the radar, unlike a camera. Based on the radar data, it is then possible to correct the position and orientation parameters of the camera so that the vehicle position data acquired by the camera match the vehicle position data acquired by the radar. Accuracy to within a tenth of a degree for the camera's elevation angle can thus be achieved, and to within a hundredth of a meter for the height. Brief description of the drawings

[0018] [Fig.1] is a schematic representation of a road controlled by means of a road control device.

[0019] [Fig.2] is a schematic representation of a road traffic control device according to a method of implementation.

[0020] [Fig.3] is a schematic representation of the camera in an orthonormal coordinate system (O, x, y, z) linked to the road according to the (x, y) plane.

[0021] [Fig.4] is a schematic representation of a camera in an orthonormal coordinate system (O, x, y, z) linked to the road according to the (x, z) plane.

[0022] [Fig.5] is a flowchart of a calibration method for a control device road according to a first embodiment.

[0023] [Fig.6] is a flowchart of a calibration process for a control device road according to a second embodiment.

[0024] [Fig.7] is an example of a graphical representation of the trajectory, as a function of the time, of the same vehicle according to the camera and according to the speedometer of the road control device, each trajectory being formed of a time series of vehicle positions along an axis tangent to a side of the road.

[0025] [Fig.8] is a graphical representation of the trajectories of [Fig.6] after correction camera position and orientation parameters using a method according to the invention.

[0026] [Fig.9] is a schematic representation of the distance of a vehicle from the roadside control device.

[0027] [Fig. 10] is a graphical representation of the distances, as a function of time, of the same vehicle in relation to the road control device, as determined by the radar (empty circles) or the camera (crosses).

[0028] [Fig. 11] is a graphical representation of the ratio D1*H2 / D2 as a function of DI and its modeling by an affine function, where DI and D2 are respectively the time sequences of distances of the [Fig.9], and H2 is the estimated height of the camera of the road control device.

[0029] Detailed description of at least one embodiment

[0030] With reference to [Fig. 1], as an example of a road environment 100, a road control device 101 is positioned near a road 102 on which a vehicle 103 bearing a license plate 103a is traveling. The road 102 can be any type of traffic space authorizing the movement of vehicles, for example, a motorway, a street, a path, etc. In the example of [Fig. 1], the road 102 comprises two traffic lanes 102a, 102b, delimited by various markings 104 applied to the surface of the road 102 and / or separation elements 105 such as a central reservation. The markings 104 are generally in the form of visual signs such as a solid line, a broken line, or cones.

[0031] Generally, the control device 101 is oriented towards an offense line 106 which serves as a reference line for speed control. This offense line 106 is generally a virtual line whose position is determined during the installation of the control unit 101. In certain use cases, it may correspond to a traffic light line or a stop line.

[0032] With reference to [Fig. 2], the road traffic control device 101 comprises a camera 201 and a radar 202. The camera 201 and the radar 202 are adapted to be positioned at the edge of a road so as to be able to acquire data relating to the trajectory of vehicles traveling on a section of the road. In this regard, the camera 201 and the radar 202 are fixed to a support 203. The position and orientation The relative values ​​of the camera and radar are considered known and fixed at least during the data acquisition periods.

[0033] In some embodiments, the traffic control device 101 is mobile, i.e., the bracket 203, the camera 201, and the radar 202 can be moved from one location to another depending on the operating requirements of the traffic control device 201. The bracket 203 is generally configured to be placed on a tripod or attached to a chassis.

[0034] The term "radar" refers to any device that uses electromagnetic waves to detect the presence of an object and determine its position in space, and possibly its speed. Examples of "radar" systems suitable for road traffic control include Doppler radar and LiDAR.

[0035] The position measurements of a radar 202 are generally insensitive to its height and elevation angle. The data relating to the trajectory of a vehicle 103 tracked by a radar 202 include the vehicle's position along a radial axis defined between said vehicle and the radar. The speed of the vehicle 103 can be calculated from the azimuth angle between the radial axis and the velocity vector of said vehicle 103.

[0036] In some embodiments, the traffic control device 101 further comprises a controller 204. The controller 204 includes one or more processors for processing the data and signals necessary for the operation of the traffic control device 101. It further comprises a non-transient data storage memory, for example, of any type such as Flash, EEPROM, HDD, SSD, etc. It may also be connected to a user interface (not shown) to facilitate the configuration of the traffic control unit 200 by an operator. It may also be connected to a communication device (not shown) to transmit data collected during road monitoring 102 to a remote server. The controller 204 is generally configured to actuate the camera 201 and / or the radar 201, and optionally to process the data acquired by the camera 201 and the radar 202.

[0037] The controller 204 can also be configured to implement the method according to the invention. For this purpose, code instructions from a computer program can be stored in its non-transient storage memory; said instructions, when executed by one or more of its processors, implement the method according to the invention.

[0038] According to some embodiments, instead of the controller 204, the process is implemented by a processing unit located remotely, for example, on a remote server communicating with the controller 204 via a telecommunications network.

[0039] The calibration process for the road traffic control device advantageously allows for fine calibration of the camera positioning parameters 201 in a reference frame linked to lane 102a of Route 102, and in particular the height and elevation parameters of camera 201. The method is implemented once the traffic control device 101 is positioned at the edge of a Route 102 on which the vehicles 103 to be monitored are traveling. The traffic control device 101 is positioned at the edge of Route 102 so that the camera 201 and the radar 202 are oriented towards lane 102a of Route 202 to be monitored, and can thus acquire data relating to the trajectory of the vehicles 103. In the images acquired by camera 201, a portion of lane 102a of Route 102 at the level of the violation line 106 is visible.

[0040] With reference to [Fig.3] & 4, the position and orientation of the camera 201 are respectively represented by position and orientation parameters in a coordinate system (O, x, y, z) linked to lane 102a of road 102. For reasons of simplification, the radar 202 is not shown.

[0041] The coordinate system (O, x, y, z) linked to lane 102a of route 102 is a direct orthonormal coordinate system such that: - The axis [Ox) is tangent to one of the two edges of Route 102 and is included in the plane of lane 102a of Route 102, - The axis [Oy) is included in the plane of route 102, - The axis [Oz) is orthogonal to the plane of route 102.

[0042] The origin O of the frame of reference is such that the camera 201 has a position on the ground according to the coordinates (-L, d, h), where (-L, d and h) are the position parameters of the camera 201 such that: - L is a predefined constant, generally corresponding to the distance to the line of offence 106 - this distance is fixed arbitrarily, for example equal to 28m; - d represents the shore distance, that is, the distance between camera 201 and the edge of road 102, in particular at the edge of road closest to camera 201, along the axis [Oy) - this distance can be initially estimated; - h is the height of camera 201 relative to road 102 - this height can be initially estimated by an operator.

[0043] The orientation parameters of camera 201 in the coordinate system (O, x, y, z) linked to road 102 are: - The azimuth, a, corresponding to the angle between the (AV) line of sight of camera 201 and the [Ox) axis tangent to road 102; - The roll, r, corresponding to an angle of the camera 201 around its viewing axis (AV); and

[0044] The elevation, e, corresponding to the angle between the (AV) line of sight of the camera 201 and the plane of lane 102a of route 102.

[0045] With reference to [Fig. 5], the method 500 includes a first estimation step 501 of the positioning and orientation parameters of the camera 201 and the radar 202 in the (O, x, y, z) coordinate system linked to the road 102, so as to obtain an initial value for each of said parameters. In particular, with the value of the parameter L fixed, an initial estimation is made of the elevation angle e, the azimuth angle a, the roll angle r, the distance d, the shore d, and the height h.

[0046] Advantageously, step 501 of estimating the position and orientation parameters of the camera 201 can be implemented using a method such as that described in FR 3131777 A1 [IDEMIA IDENTITY & SECURITY FRANCE [FR]] 14.07.2023. In this method, the parameter estimation is based, on the one hand, on a straight road model, and, on the other hand, on two parametric curves, for example B-splines, representing the edges of said road and visible in the image acquired by the camera. The parameters are determined so that the straight curve model corresponds to the road represented in the image, the edges of which are modeled by the parametric curves.

[0047] In some embodiments, the distance, d, from the shore and the height, h, are estimated by the operator, and the method described in FR 3131777 A1 [IDEMIA IDENTITY & SECURITY FRANCE [FR]] 14.07.2023 is used only for estimating the values ​​of the orientation parameters (a, e, r). Alternatively, the distance, d, from the shore and the height, h, may be first estimated by the operator, and then the method described in FR 3131777 A1 [IDEMIA IDENTITY & SECURITY FRANCE [FR]] 14.07.2023 may be used to refine this first estimate and to estimate the orientation parameters (a, e, r).

[0048] Furthermore, the relative position and orientation of the radar 202 with respect to the camera 201 are considered known (determined during factory assembly). The positioning parameters of the radar 202 in the (O, x, y, z) coordinate system of lane 102a of route 102 can be determined from those of the camera 201.

[0049] The method then includes a step 502 of acquiring, by the camera 201 and the radar 202, data relating to the trajectory of at least one vehicle 103 on lane 102a of road 102. Generally, the greater the number of vehicles whose trajectory is tracked, the more precise and stable the calibration will be. Preferably, in practice, trajectory data are acquired for at least two or more vehicles, for example, between two and ten vehicles.

[0050] The data acquired by camera 201 during step 502 may include a succession of images representing vehicle 103 travelling on lane 102a of the road 102. This data can be acquired at the sampling frequency of camera 201.

[0051] During the acquisition step 502, the data acquired by the radar 202, for example in the case of a Doppler effect radar, may include a series of radial velocities of the vehicle 103 relative to the line of sight (AV) of the radar 202.

[0052] The data acquired by camera 201 and radar 202 are time-stamped.

[0053] The method includes a step 503 of determining the trajectories of the vehicle or vehicles 103 in the coordinate system (O, x, y, z) linked to the road 102, from data acquired on the one hand by the camera 201 and on the other hand by the radar 202. This step 503 comprises, for each vehicle, two sub-steps: - a sub-step 503a of determining a first trajectory, called "camera trajectory", of the vehicle 103 in the frame (O, x, y, z) linked to the road 102 from the data acquired by the camera 201 and the estimated position and orientation parameters of the camera 201 during the estimation step 501; - a substep 503b of determining a second trajectory, called "radar trajectory", of the same vehicle 103, in the frame (O, x, y, z) linked to the road 102, from the data acquired by the radar 202 and its known parameters of position and orientation, for example, in the case of a Doppler effect radar, of its azimuth angle and its positions along the axes [Ox) and [Oy) in the frame (O, x, y, z) linked to the road 102.

[0054] The radar trajectory and the camera trajectory each include position data of the vehicle 103 in the frame (O, x, y, z) linked to the road 102, and more specifically time sequences of position data in this frame.

[0055] These position data are determined, in a manner known to a person skilled in the art, from the data acquired by the camera 201 and the radar 202 using coordinate system transformation operations. These coordinate system transformation operations are performed based on the position and orientation parameters estimated in step 501 and the intrinsic parameters specific to the model of the camera 201 and the radar 202. By way of example, for the camera 201, the coordinate system transformation comprises a series of coordinate system transformations from the position of the vehicle 103 in the image acquired by the camera 102 to the coordinate system (O, x, y, z) linked to the road 102.

[0056] The method then includes a step 504 of calculating, from the camera trajectory and the radar trajectory, corrected values ​​of at least some of the estimated position and orientation parameters of the camera 201, including at least corrected values ​​for the angle, e, of elevation and the height, h, of said camera 201.

[0057] In some embodiments, calculation step 504 comprises only a calculation of corrected values ​​of the angle, e, of elevation and the height, h, of the camera 201. Alternatively, it includes a substep 504a for calculating corrected values ​​of the angle, e, of elevation and the height, h, and a substep 504b for correcting other position and orientation parameters of the camera 201, which may include the distance, d, from the shore and / or the angle, r, of roll, from the corrected values ​​of the angle, e, of elevation and the height, h. The distance L to the violation line 106, being arbitrarily fixed, is not recalculated. The azimuth angle, a, being the positioning parameter from which the radar trajectory used as a reference for the camera trajectory is derived, is also not recalculated.

[0058] As previously stated, the radar trajectory and the camera trajectory each comprise one or more time sequences of vehicle position data 103 in the (O, x, y, z) coordinate system linked to the road 102. In some embodiments, step 504, which calculates corrected values ​​for the position and orientation parameters, is implemented by comparing the vehicle position data 103 obtained by the radar 202 with those obtained by the camera 201. These position data correspond to identical acquisition times. "Comparison" means any suitable operation that allows for determining the difference between two data points.

[0059] In certain cases, particularly when the camera 201 and the radar 202 have different acquisition frequencies, the radar and camera trajectories may include vehicle position data 103 corresponding to different acquisition times. The calculation step 504 may then include a preliminary correction substep 50411 so that the two trajectories include vehicle position data corresponding to identical acquisition times, and thus become comparable. This correction substep 50411 may, in particular, include an interpolation of one or the other of the radar and camera trajectories. Generally, an interpolation is performed for the trajectory with the lower acquisition frequency. "Correction" means any appropriate operation to reduce or eliminate a discrepancy between two data points.

[0060] In some embodiments, step 504 may further include a preliminary substep 50442 for limiting the time sequence of data acquired by the radar 202 so that it corresponds to a given range of positions along the [Ox] axis. It may also include limiting the time sequence of data acquired by the camera 201 so that it corresponds to the same time sequence as that of the radar 202. Thus, the data processed in the subsequent stages of the process is reduced to only the data that is actually relevant.

[0061] In a first embodiment, the position data of vehicle 103 in the (O, x, y, z) frame of reference linked to road 102 comprises a time series of positions of said vehicle 102 along the [Ox] axis of the (O, x, y, z) frame of reference linked to road 102. The step Step 504 calculates the corrected values ​​for the angle, e, of elevation and the height, h, of the camera 201, and then includes a substep 504c1 for minimizing the discrepancies between the time series of the camera trajectory and the time series of the radar trajectory.

[0062] In some embodiments, a metric for the distance between the positions of the vehicle 103 along the [Ox] axis obtained on the one hand by the camera 201 and on the other hand by the radar 202 is determined. Step 504, for calculating the corrected values ​​of the angle, e, of elevation and the height, h, of the camera 201, then includes a substep 504c2 for minimizing this distance over the entire time series or, in the case of several vehicles, over all the time series.

[0063] The metric used can for example be the quadratic sum of the Euclidean distances between the coordinates along the axis [Ox) at each instant t of acquisition, for respectively the camera 201 and the radar 202. Alternatively, other distances can be considered, such as the Manhattan distance (or distance Ll), or the Mahalanobis distance.

[0064] Determining the values ​​of angle, e, elevation, and height, h, that minimize the determined metric can, for example, be achieved using a global minimum search function on a function of several variables (in this case, two variables corresponding to the angle of elevation and the height), or by nested iterative loops by successively varying the height and elevation by a determined step size. The step size can, for example, be between 0.005° and 0.05° for the angle of elevation, preferably between 0.005° and 0.02°, and between 0.005 m and 0.05 m for the height, preferably between 0.005 m and 0.02 m.

[0065] Advantageously, the method includes a step 505 of updating the angle, r, of roll and the distance, d, of shore, once the values ​​of the height, h, and the angle, e, of elevation are corrected using the corrected values ​​calculated in step 504. This update is carried out by reiterating the method described in FR 3131777 Al [IDEMIA IDENTITY & SECURITY FRANCE [FR]] 14.07.2023, where all parameters other than the angle, r, of roll and the distance, d, of shore are fixed.

[0066] On [Fig.7], the position (expressed in meters), as a function of time (expressed in seconds), of the same vehicle 103 is shown according to the camera 201 (empty circles) and according to the radar 202 (black squares) of the road control device 101, each trajectory being formed of a time series of positions of the vehicle 103 along an axis [Ox) tangent to an edge of the road 102. The two trajectories do not coincide.

[0067] Figure 8 shows the trajectories of Figure 7 after correction of the position and orientation parameters of camera 201 by corrected values ​​calculated using the method according to the invention. In this example, the following correction values ​​were calculated: - Elevation angle, e: +0.11° - Height, h: -0.1 m - Distance, d, from the shore: -0.3 m - Angle, r, roll: -0.2°

[0068] The corrections are on the order of a tenth, or even a hundredth of a degree for angles, and a tenth of a meter for distances. The offset along the [Ox) axis of the two radar and camera trajectories is substantially reduced.

[0069] In a second embodiment, the position data includes a time series of distances of the vehicle 103 from the traffic control device 101. By "distance of the vehicle from the traffic control device" is meant the distance of the vehicle from the camera 201 or the radar 202 of the traffic control device 101, the camera 201 and the radar 202 being assumed to be sufficiently close to each other for these two distances to be considered substantially equal.

[0070] Figure 9 schematically represents the distance of a vehicle 103 from the road traffic control device 101, as opposed to its position along the axis [Ox]. The following notations are used:

[0071] DI is the distance of vehicle 103 from the traffic control device 101 obtained by radar 202. This distance is considered accurate since its precision is independent of the height and elevation angle of the radar. A time sequence of values ​​Dl(t) for each vehicle is acquired during acquisition step 501. - D2 is the distance of vehicle 103 from the road control device 101 as obtained by camera 201. Before calibration, the distance D2 is generally different from the distance Dl. A time sequence D2(t) for each vehicle is acquired in acquisition step 501 (with, where necessary, interpolation of the values ​​of the sequence). - hl is the exact height of camera 201, its value is unknown. - h2=hl+Ah is the estimated height of camera 201; its value is known during step 501 of position and orientation parameter estimation. The parameter Ah is the height correction parameter between hl and h2, its value is unknown. - el is the exact elevation of the camera, its value is unknown. - e2 = el + Ae is the estimated elevation of camera 201; its value is known during step 501, which estimates the position and orientation parameters. The parameter Ae is the elevation correction parameter between el and e2; its value is unknown.

[0072] The spatial arrangement of the road control device 101 in relation to the road 102 results in the height hl and the distance Dl being large and the angle, e, of elevation, relatively small. Thus, the approximation sin(el) = el and sin(e2) = e2 is reasonable, and consequently, el = hl / Dl and e2 = h2 / D2.

[0073] This approximation allows for the following relationship:

[0074] TY)-- l'~ “ el+ Ae h2

[0075] Applied to the time sequences Dl(t) and D2(t), the previous relation takes the following form:

[0076] £>10*^ = h 1 + Ae*Dl(t)

[0077] Thus, according to the second embodiment, step 504 for calculating the corrected values ​​comprises a substep 504d-1 for calculating the ratio Dl(t)*h2 / D2(t) as a function of Dl(t), and a substep 504d-2 for modeling this ratio by an affine function dependent on Dl(t). The slope of the affine function corresponds to the correction parameter Ae for elevation, and the y-intercept corresponds to the parameter hl, that is, to the exact value of the height of the camera 102. In a substep 504d-3, the corrected values, hl and el, respectively for the height h and the angle, e, of elevation are calculated.

[0078] As with the first embodiment, the method 500 may include a step 505 of updating the roll angle, r, and the shore distance, d, once the values ​​of the height, h, and the elevation angle, e, have been corrected using the corrected values ​​calculated in step 504. This update is carried out by reiterating the method described in FR 3131777 Al [IDEMIA IDENTITY & SECURITY FRANCE [FR]] 14.07.2023, where all parameters other than the roll angle, r, and the shore distance, d, are fixed.

[0079] Figure 10 shows the distances D1(t) (crosses) and D2(t) (open circles) as a function of time, of the same vehicle 103 relative to the camera 201 and the radar 202 (in this case a Doppler radar) of the road traffic control device 101, each trajectory being formed by a time series of distances. This graphical representation shows that before calibration, the two trajectories do not coincide.

[0080] Figure 11 shows the variations of the ratio Dl(t)*h2 / D2(t) as a function of Dl(t) (open triangles), calculated for each acquisition instant, and its modeling by an affine function (solid line). The value obtained for the elevation correction parameter, corresponding to the slope of the affine function, is 0.005 radians or 0.29°, and the value of the height hl of camera 201, corresponding to the y-intercept of the affine function, is 1.01 m.

[0081] Thanks to the method of the invention, the position and orientation parameters of the camera 201 are determined with increased precision. The road traffic control device 101 can then perform speed checks, based on its camera 201, with precision, thus meeting the requirements of the legislative and / or administrative regulations currently in force.

Claims

Demands

1. Method (500) for calibrating a road traffic control device (101) comprising a camera (201) and a radar (202), the method (500) comprising the following steps: - estimating (501) the position and orientation parameters of the camera (201) and the radar (202) in a reference frame (O, x, y, z) linked to a lane (102a) of a road (102); - acquiring (502), by the camera (201) and the radar (202), the data relating to the trajectory of at least one of the same vehicle (103) on a lane (102a) of the road (102);- determine (503), on the one hand, a camera trajectory of the vehicle (103) in the frame (O, x, y, z) linked to the lane (102a) of the road (102) from the data acquired by the camera (201) and the estimated position and orientation parameters, and on the other hand, a radar trajectory of the vehicle (103) in the frame (O, x, y, z) linked to the lane (102,a) of the road (102) from the data acquired by the radar (202) and the estimated position and orientation parameters; - calculate (504), from the camera trajectory and the radar trajectory, the corrected values ​​of at least some of the estimated position and orientation parameters of the camera (201), including an angle parameter, e, elevation parameter and a height parameter, h, of said camera (201).

2. Method (500) according to claim 1, wherein the radar trajectory and the camera trajectory each comprise vehicle (103) position data in the (0, x, y, z) reference frame linked to the lane (102a) of the road (102), acquired at respective acquisition times, and the step (504) of calculating the corrected parameter values ​​comprises a substep (504-11) of correcting the vehicle (103) position data of the radar trajectory and the camera trajectory for identical acquisition times.

3. A method (500) according to claim 2, wherein the radar (202) and the camera (201) have different data acquisition frequencies, and the correction substep (504-11) comprises an interpolation of one of the speedometer trajectory and / or the camera trajectory such that the two trajectories comprise position data corresponding to identical acquisition times.

4. Method (500) according to any one of claims 1 to 3, wherein the calculation step (504) includes the calculation of the corrected values ​​of angle, e, elevation and height, h, and said method (500) includes a step (505) of updating the value of at least one of a distance, d, of shore and of a roll angle, r, of the camera (201) from said corrected values.

5. Method (500) according to any one of claims 1 to 4, wherein the camera trajectory and the radar trajectory each comprise a time series of vehicle positions along an axis [Ox) of the frame (O, x, y, z) attached to the lane (102a) of the road (102), the axis [Ox) being tangent to an edge of the lane (102a) of the road (102), and the step (504) of calculating the corrected values ​​comprises the calculation of corrected values ​​for the angle, e, of elevation and the height, h, of the camera (201), and a substep (504c 1) of minimizing the position differences between the time series of the camera trajectory and the radar trajectory.

6. Method (500) according to any one of claims 1 to 4, wherein the camera trajectory and the radar trajectory each comprise a time series of distances of the vehicle (103) from the road control device (101), and the step (504) of calculating the corrected values ​​for the position and orientation parameters of the camera (201) comprises a substep (504c2) of minimizing this distance over all of said time series.

7. A method (500) according to claim 6, wherein the step (504) of calculating the corrected values ​​comprises the following substeps: - calculating (504dl) a quantity Dl(t)*H2 / D2(t) as a function of Dl(t), where Dl(t) is the time sequence of distances of the vehicle (103) from the road control device (101) provided by the radar (202), D2(t) is the time sequence of distances of the vehicle (103) from the road control device (101) provided by the camera (201), and H2 is the estimated height of the camera (201); - modeling (504d2) the quantity Dl(t)*H2 / D2(t) as a function of Dl(t) by an affine function; - calculate (504d3) a correction on the angle, e, of elevation using the value of the slope of the affine function, the corrected value of the height, h, of the camera (202) from the ordinate at the origin of the affine function.

8. Computer program, comprising instructions which, when executed by a data processing unit, cause said device to implement the code method for the implementation of a method comprising the following steps: - estimating (501) the position and orientation parameters of the camera (201) and radar (202) in a frame (O, x, y, z) linked to a lane (102a) of a road (102); - to control the camera (201) and the radar (201) to acquire (502), by the camera (201) and the radar (202), the data relating to the trajectory of at least one of the same vehicle (103) on lane (102a) of the road (102); - determine (503), on the one hand, a camera trajectory of the vehicle (103) in the frame (O, x, y, z) linked to the lane (102a) of the road (102) from the data acquired by the camera (201) and the estimated position and orientation parameters, and on the other hand, a radar trajectory of the vehicle (103) in the frame (O, x, y, z) linked to the lane (102a) of the road (102) from the data acquired by the radar (202) and the estimated position and orientation parameters; - calculate (504), from the camera trajectory and the radar trajectory, the corrected values ​​of at least some of the estimated position and orientation parameters of the camera (201), including an angle parameter, e, elevation parameter and a height parameter, h, of said camera (201).

9. Road traffic control device (101), comprising a camera (201), a radar (202), and a processing unit, the road traffic control device (101) being configured to implement the calibration method according to any one of claims 1 to 7.

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