CORRESPONDING

CONTROL UNIT, COMPUTER PROGRAM, COMPUTER READABLE MEDIUM AND VEHICLE

TECHNICAL FIELD

The disclosure relates to a method for estimating one or more geometric properties of a vehicle. The disclosure further relates to a control unit, a computer program, a computer 5 readable medium and to a vehicle.

The invention can be applied in heavy-duty vehicles, such as trucks, buses and construction equipment. Although the invention will be described with respect to a truck, the invention is not restricted to this particular vehicle, but may also be used in other 10 vehicles such as passenger cars, buses and construction equipment, such as wheel loaders, excavators etc.

BACKGROUND

It is known to equip a vehicle with driving assistance systems. For example, such 15 assistance systems may be configured to take over the complete control of the vehicle during reversing or a driver may be assisted by receiving guiding instructions from the system during reversing, such as instructions to make a left or right turn. Other non limiting examples of driving assistance systems are lane keeping assistance systems and fully automated systems for guiding a vehicle during driving, at least for a limited time 20 period. In the future it is also expected to be more common of using fully autonomous vehicles. Such vehicles may not only be used in restricted areas, but also in public road networks.

For most of the above-mentioned systems, and in particular for more advanced systems,

25 it is important to have information about certain geometric properties of the vehicle, such as the vehicle length, width, height etc. Therefore, these systems often utilize stored information about the vehicle’s geometric properties. However, for certain vehicles, such as for vehicle combinations comprising one or more connected trailers, the required information about the trailer’s geometric properties may not always be available. For 30 example, a towing truck, or tractor, may during use be connected to different kinds of trailers, and the geometric properties of at least some of these trailers may be unknown. For this issue it has been proposed to make use of a camera which is mounted on the towing vehicle, wherein the camera can obtain an image of the shadow casted by the vehicle when the sun shines. By use of the shadow, the vehicle’s length and height may be estimated.

Even though it is known to make use of a vehicle’s shadow to estimate a geometric property of the vehicle, there is however still a strive of developing further improved methods for increasing the reliability and accuracy of the estimation.

SUMMARY

In view of the above, an object of the invention is to provide an improved method for estimating one or more geometric properties of a vehicle by use of a vehicle shadow. A further object of the invention is to provide an improved control unit, computer program, computer readable medium and/or vehicle.

According to a first aspect of the disclosure, the object is achieved by a method according to claim 1. Thus, a method for estimating one or more geometric properties of a vehicle is provided. The method comprises:

- identifying at least one shadow casted by the vehicle on a neighbouring surface by use of at least one visual perception sensor, wherein the at least one shadow is a consequence of a light source emitting light which is directed towards the vehicle;

- estimating the one or more geometric properties by use of the identified at least one shadow, wherein the estimation is performed based on information about a relative position and/or orientation of the vehicle with respect to the light source; and

- wherein identifying the at least one shadow comprises identifying a sequence of shadows while driving the vehicle along a known trajectory, wherein estimating the one or more geometric properties is further performed based on an expected evolvement of the identified sequence of shadows while driving the vehicle along the known trajectory.

By the provision of a method as disclosed herein according to the first aspect, an improved estimation of the one or more geometric properties is achieved. In particular, it has been realized that by identifying a sequence of shadows while driving the vehicle along a known trajectory, and by taking the knowledge of the known trajectory into account for the estimation, the reliability and accuracy of the estimation can be improved. More specifically, by letting the vehicle follow a known trajectory, an expected evolvement of the identified shadow can be considered in the estimation. For example, data indicative of the expected evolvement of the identified shadow while driving the vehicle along the known trajectory may be compared with the sequence of shadows identified by the at least one visual perception sensor. As such, a confidence level of for example the shape of the identified shadows may be obtained, which in turn may be used for improving the estimation. Additionally, or alternatively, data indicative of the expected evolvement of the identified shadow while driving the vehicle along the known trajectory may be used to compensate for and/or correct for any issues relating to the data collected by the at least one visual perception sensor. A data fusion algorithm may be used for this purpose, where data from more than one source is used for improving the estimation. For example, identification of outer boundaries, or edges, of the shadow may be improved if combining the aforementioned data. Thereby, the estimation accuracy can be improved.

Optionally, the known trajectory as disclosed herein is a predetermined trajectory. For example, the predetermined trajectory may thereby be formed such that the one or more geometric properties can be more easily estimated, implying reduced time for the estimation and/or reduced processing power required for the estimation. Additionally, or alternatively, the known trajectory may be identified while driving the vehicle, and/or after driving the vehicle, whereby the expected evolvement of the identified sequence of shadows while driving the vehicle along the known trajectory is determined when or after the trajectory is known.

Optionally, the vehicle may be associated with a yaw axis about which the vehicle is adapted to rotate while driving, wherein the known trajectory is formed such that the vehicle will rotate at least one full rotation in a first direction about its yaw axis. Thereby a further improved estimation of the one or more geometric properties may be achieved. More specifically, be letting the vehicle rotate at least one full rotation about its yaw axis, more complete data of all sides of the vehicle may be obtained. Thereby, more geometric properties of the vehicle may be obtained in a fast and efficient manner. For example, the known trajectory may be formed as a circle, implying reduced time for performing the estimation. Still optionally, the known trajectory may further be formed such that the vehicle will further rotate at least one full rotation in a second opposite direction about its yaw axis, wherein the known trajectory preferably comprises a figure-eight path for the vehicle. Thereby, more complete data of all sides may be obtained at least two times from different perspectives. For example, the figure-eight path may be formed as two connected circles, implying reduced time for performing the estimation.

Optionally, the method may further comprise:

- obtaining information about the shape of the neighbouring surface, wherein estimating the one or more geometric properties is further performed based on the information about the shape of the neighbouring surface.

It has further been realized that by considering the shape of the neighbouring surface, the estimation may be improved. For example, by considering the shape of the neighbouring surface, and compensating for this, the accuracy of the estimation of the one or more geometric properties can be increased. Purely by way of example, the information about the shape of the neighbouring surface may be used for performing a projection transformation of the identified at least one shadow to a prediction of the identified at least one shadow on a flat extending surface, such as a flat horizontally extending surface, wherein estimating the one or more geometric properties is further performed based on the prediction of the identified at least one shadow on the flat extending surface. Thereby, irregularities, inclinations etc., of the neighbouring surface which may impair the estimation can be compensated for by use of the prediction of the identified at least one shadow on the flat extending surface.

Optionally, the shape of the neighbouring surface may be at least partly inclined with respect to the vehicle. As such, any inclinations may be compensated for, thereby improving the estimation.

Optionally, estimating the one or more geometric properties may further be performed based on an expected evolvement of the shape of the neighbouring surface while driving the vehicle along the known trajectory. Accordingly, by e.g. using the expected evolvements of the shadows and of the shapes of the neighbouring surface while driving along the known trajectory, the accuracy and reliability of the estimation can be further improved.

Optionally, the information about the shape of the neighbouring surface may be obtained from map data and/or by a perception sensor which identifies geometric properties of the ground surface. Even though the perception sensor which identifies geometric properties of the ground surface may be one of the at least one visual perception sensors for identifying the at least one shadow, it may also be another different sensor. For example, the perception sensor which identifies geometric properties of the ground surface may be directed in the driving direction of the vehicle, whereby the shape of the neighbouring surface is obtained before the shadow is casted thereon.

Optionally, the method may further comprise:

- obtaining information about at least one geometric property of the vehicle from another source than from the at least one shadow, and wherein estimating the one or more geometric properties by use of the identified at least one shadow is further performed based on using the information about the at least one geometric property obtained from the other source. This may for example reduce the number of variables for the estimation, implying less processing power for the estimation. Optionally, the at least one geometric property from the other source may be different from the one or more geometric properties estimated by use of the at least one shadow. Still optionally, the at least one geometric property from the other source may be the same geometric property as one of the one or more geometric properties estimated by use of the at least one shadow. Still optionally, the information about the at least one geometric property of the vehicle from the other source may be obtained by use of a perception sensor and/or obtained from a database. For example, a perception sensor may be directed towards a trailer, wherein a value indicative of the trailer’s height is obtained. This value may then be used for estimating the length of the trailer by use of the at least one shadow.

Optionally, the one or more geometric properties of the vehicle estimated by use of the at least one shadow may be at least one of the following:

- a vehicle length, preferably a trailer length;

- a vehicle height, preferably a trailer height;

- a vehicle width, preferably a trailer width;

- a coupling point position between a trailer and another vehicle;

- one or more wheel axle positions of the vehicle, preferably one or more trailer wheel axle positions.

Optionally, estimating the one or more geometric properties by use of the identified at least one shadow may be performed by determining at least one coordinate of an edge of the at least one shadow. According to a second aspect of the disclosure, the object is achieved by a control unit according to claim 15. Thus, a control unit for estimating one or more geometric properties of a vehicle is provided, wherein the control unit is configured to perform the steps of the method according to any one of the embodiments as disclosed herein. Advantages and effects of the control unit are largely analogous to the advantages and effects of the method. Further, all embodiments of the method are applicable to and combinable with all embodiments of the control unit, and vice versa.

The control unit is preferably an electronic control unit comprising processing circuitry for performing the method. The control unit may be a computer. The control unit may comprise hardware or hardware and software. As such, according to a third aspect of the disclosure, the object is achieved by a computer program according to claim 16. The computer program comprises program code means for causing the control unit to perform the steps of the method according to any one of the embodiments as disclosed herein. In addition, according to a fourth aspect of the disclosure, the object is achieved by a computer readable medium according to claim 17. The computer readable medium is carrying a computer program comprising program code means for causing the control unit to perform the steps of the method according to any one of the embodiments as disclosed herein.

According to a fifth aspect of the disclosure, the object is achieved by a vehicle according to claim 18. Thus, a vehicle comprising at least one visual perception sensor for identifying a shadow of the vehicle is provided. The vehicle further comprises a control unit according to any one of the embodiments of the control unit as disclosed herein. It shall be noted that advantages and effects of the fifth aspect of the disclosure are applicable to and combinable with all advantages and effects of the other aspects of the disclosure. Further, all embodiments of the fifth aspect are combinable with all embodiments of the other aspects, and vice versa.

Optionally, the vehicle may be a vehicle combination comprising at least one trailer, wherein the at least one visual perception sensor is adapted to identify a shadow of the at least one trailer. The vehicle combination may comprise at least one articulation joint. Optionally, an articulation angle of the articulation joint may be known and used for the geometric property estimation. For example, the articulation angle may be measured in real-time. By use of the articulation angle, a relative position and/or orientation of connected vehicle bodies of the vehicle combination may be determined. This information may in turn be used for determining the relative position and/or orientation of the vehicle combination with respect to the light source.

Optionally, the at least one visual perception sensor may be a camera. However, it may also be any other type of visual perception sensor, such as a LIDAR (light detection and ranging). Still optionally, at least two visual perception sensors may be used for the estimation. Thereby it may be easier to determine shadow edges and image depth. As such, one or more coordinates of the edge of the at least one shadow may be more easily determined. The at least two visual perception sensors may be denoted a stereo camera configuration, implying facilitated determination of image depth. Additionally, or alternatively, the at least one visual perception sensor may be complemented by another perception sensor which is configured to determine depth, or distance. The above- mentioned LIDAR may be used for this purpose. Other possible perception sensors for determining depth, or distance, may be RADAR (range detection and ranging) or ultrasonic sensors.

In the following, possible features and feature combinations of embodiments of the present disclosure are disclosed as items. Advantages and effects of the features and feature combinations may be found in the above and also in the below detailed description.

ITEMS:

1. A method for estimating one or more geometric properties (L, H, W) of a vehicle (1), comprising:

- identifying (S1) at least one shadow (S) casted by the vehicle (1) on a neighbouring surface (GS) by use of at least one visual perception sensor (11), wherein the at least one shadow (S) is a consequence of a light source (20) emitting light which is directed towards the vehicle (1); - estimating (S2) the one or more geometric properties (L, H, W) by use of the identified at least one shadow (S), wherein the estimation is performed based on information about a relative position and/or orientation of the vehicle (1) with respect to the light source (20).

2. The method according to item 1, wherein identifying the at least one shadow (S) comprises identifying a sequence of shadows while driving the vehicle (1) along a known trajectory (T), wherein estimating the one or more geometric properties (L, H, W) is further performed based on an expected evolvement of the identified sequence of shadows while driving the vehicle (1) along the known trajectory (T).

3. The method according to item 2, wherein the vehicle (1) is associated with a yaw axis (z1) about which the vehicle (1) is adapted to rotate while driving, wherein the known trajectory (T) is formed such that the vehicle (1) will rotate at least one full rotation in a first direction about its yaw axis (z1).

4. The method according to item 3, wherein the known trajectory (T) is further formed such that the vehicle (1) will further rotate at least one full rotation in a second opposite direction about its yaw axis (z1), wherein the known trajectory (T) preferably comprises a figure-eight path for the vehicle (1).

5. The method according to any one of the preceding items, further comprising:

- obtaining information about the shape of the neighbouring surface (GS), wherein estimating the one or more geometric properties (L, H, W) is further performed based on the information about the shape of the neighbouring surface (GS).

6. The method according to item 5, wherein the information about the shape of the neighbouring surface (GS) is used for performing a projection transformation of the identified at least one shadow (S) to a prediction of the identified at least one shadow (S) on a flat extending surface, such as a flat horizontally extending surface, wherein estimating the one or more geometric properties (L, H, W) is further performed based on the prediction of the identified at least one shadow (S) on the flat extending surface.

7. The method according to any one of items 5-6, wherein the shape of the neighbouring surface (GS) is at least partly inclined with respect to the vehicle (1). 8. The method according to any one of items 5-7, wherein estimating the one or more geometric properties (L, H, W) is further performed based on an expected evolvement of the shape of the neighbouring surface (GS) while driving the vehicle (1) along a known trajectory (T).

9. The method according to any one of items 5-8, wherein the information about the shape of the neighbouring surface (GS) is obtained from map data and/or by a perception sensor which identifies geometric properties of the ground surface (GS).

10. The method according to any one of the preceding items, further comprising:

- obtaining information about at least one geometric property of the vehicle (1) from another source than from the at least one shadow (S), and wherein estimating the one or more geometric properties (L, H, W) by use of the identified at least one shadow (S) is further performed based on using the information about the at least one geometric property obtained from the other source.

11. The method according to item 10, wherein the at least one geometric property from the other source is different from the one or more geometric properties (L, H, W) estimated by use of the at least one shadow (S).

12. The method according to item 11 , wherein the at least one geometric property from the other source is the same geometric property as one of the one or more geometric properties (L, H, W) estimated by use of the at least one shadow (S).

13. The method according to any one of items 10-12, wherein the information about the at least one geometric property of the vehicle (1) from the other source is obtained by use of a perception sensor and/or obtained from a database.

14. The method according to any one of the preceding items, wherein the one or more geometric properties of the vehicle (1) estimated by use of the at least one shadow (S) is at least one of the following:

- a vehicle length (L), preferably a trailer length;

- a vehicle height (H), preferably a trailer height;

- a vehicle width (W), preferably a trailer width;

- a coupling point position between a trailer and another vehicle; - one or more wheel axle positions of the vehicle, preferably one or more trailer wheel axle positions.

15. The method according to any one of the preceding claims, wherein estimating the one or more geometric properties (L, H, W) by use of the identified at least one shadow (S) is performed by determining at least one coordinate (FT, F2’) of an edge of the at least one shadow (S).

16. A control unit (10) for estimating one or more geometric properties (L, H, W) of a vehicle (1), wherein the control unit (10) is configured to perform the steps of the method according to any one of the preceding items.

17. A computer program comprising program code means for causing the control unit (10) of item 16 to perform the steps of the method according to any one of items 1-15.

18. A computer readable medium carrying a computer program comprising program code means for causing the control unit (10) of item 16 to perform the steps of the method according to any one of items 1-15.

19. A vehicle (1) comprising at least one visual perception sensor (11) for identifying a shadow (S) of the vehicle (1), and further comprising a control unit (10) according to item 16.

20. The vehicle (1) according to item 19, wherein the vehicle (1) is a vehicle combination comprising at least one trailer (100), wherein the at least one visual perception sensor

(11) is adapted to identify a shadow (S) of the at least one trailer (100).

As such, according to a general aspect of the disclosure, a method for estimating one or more geometric properties of a vehicle according to item 1 is provided. Example embodiments can be found in the following items of the items list as disclosed in the above. The method comprises:

- identifying at least one shadow casted by the vehicle on a neighbouring surface by use of at least one visual perception sensor, wherein the at least one shadow is a consequence of a light source emitting light which is directed towards the vehicle; - estimating the one or more geometric properties by use of the identified at least one shadow, wherein the estimation is performed based on information about a relative position and/or orientation of the vehicle with respect to the light source.

Further advantages and advantageous features of the invention are disclosed in the following description and in the dependent claims.

BRIEF DESCRIPTION OF THE DRAWINGS

With reference to the appended drawings, below follows a more detailed description of embodiments of the invention cited as examples.

In the drawings:

Fig. 1 is a schematic rear view of a vehicle according to an example embodiment of the disclosure;

Fig. 2 is a schematic view from above of a vehicle driving along a known travelling path, i.e. known trajectory, according to an example embodiment of the disclosure;

Fig. 3 is a schematic rear view of a vehicle according to an example embodiment of the disclosure;

Fig. 4 is schematic side view of a vehicle according to an example embodiment of the disclosure;

Fig. 5 is schematic view from above of a vehicle according to an example embodiment of the disclosure; and

Figs. 6a-b show flowcharts of methods according to example embodiments of the disclosure.

The drawings show diagrammatic exemplifying embodiments of the present disclosure and are thus not necessarily drawn to scale. It shall be understood that the embodiments shown and described are exemplifying and that the invention is not limited to these embodiments. It shall also be noted that some details in the drawings may be exaggerated in order to better describe and illustrate the particular embodiment. Like reference characters refer to like elements throughout the description, unless expressed otherwise.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS OF THE INVENTION

Fig. 1 shows a schematic view from behind of a vehicle 1 according to an example embodiment of the disclosure. The vehicle 1 is here a truck, even though any other vehicle could be used, such as a bus, construction equipment, passenger car etc. In fig. 1, the sun 20 is shining, resulting in a shadow S on a neighbouring surface GS on the right- hand side of the vehicle 1.

Fig. 2 shows a schematic view of a vehicle 1 from above while it is driving along a known travelling path T. The vehicle 1 is here a vehicle combination comprising a towing vehicle 110 and a trailer 100. The trailer 100 can be articulated with respect to the towing vehicle 110 via an articulation joint (not shown). It shall be noted that a vehicle combination as disclosed herein may comprise any number of articulation joints and any number of trailers. For example, the vehicle combination may be a tractor and semi-trailer combination or a so-called Nordic combination comprising two articulation joints and a dolly. The known travelling path T is here a figure-eight path.

Fig. 3 shows another schematic view of a vehicle 1 according to an example embodiment of the disclosure. The figure differs from fig. 1 in that the neighbouring surface GS is inclined with an angle a with respect to a flat horizontally extending surface GS1 which the vehicle 1 is provided on.

Fig. 4 shows a schematic view of a vehicle 1 from the side. The vehicle 1 is here a vehicle combination comprising a truck 110 and a trailer 100. More specifically, the vehicle combination is here a so-called semi-trailer combination with one articulation joint 120.

The truck 110 further comprises a visual perception sensor 11 in the form of a camera which is directed rearwards such that it can view at least a portion of the trailer 100. The camera 11 is also configured to view a neighbouring surface GS (not shown) at a side of the vehicle 1 , in particular at a side of the trailer 100. Fig. 5 shows a schematic view from above of the vehicle 1 as shown in fig. 4. The sun 20 is shining towards the vehicle 1 such that a shadow S is casted by the vehicle 1 on the neighboring surface GS.

For convenience, the vehicles 1 as shown in fig. 1 and figs. 3-5 are here defined with respect to a Cartesian coordinate system, wherein a longitudinal extension of each vehicle 1 extends along an x-axis, a transverse extension of each vehicle 1 extends along a y-axis and a height extension of each vehicle 1 extends along a z-axis of the Cartesian coordinate system. It shall however be noted that directions, locations, orientations etc. may of course be expressed in any other type of coordinate system.

Fig. 6a depicts a flowchart of a method according to the general aspect of the disclosure, and fig. 6b depicts a more detailed flowchart with preferred embodiments of the disclosure.

With respect to fig. 6a and e.g. figs. 1 , 4 and 5, a method for estimating one or more geometric properties L, H, W of a vehicle 1 will be described. The method comprises:

S1: identifying at least one shadow S casted by the vehicle 1 on a neighbouring surface GS by use of at least one visual perception sensor 11 , wherein the at least one shadow S is a consequence of a light source 20 emitting light which is directed towards the vehicle 1; and

S2: estimating the one or more geometric properties L, H, W by use of the identified at least one shadow S, wherein the estimation is performed based on information about a relative position and/or orientation of the vehicle 1 with respect to the light source 20.

The geometric properties are here trailer length L, trailer height H and trailer width W. It shall however be noted that other lengths, widths and heights may be estimated by use of the shadow S, such as the total length of the vehicle combination, including also the truck 110.

The geometric properties may for example be estimated by identifying one or more coordinates of outer boundaries (or an edge), of the shadow S, such as the coordinates/positions FT and F2’ as shown in fig. 5, and the coordinates F1 and F2. FT and F2’ here represent the outer corners of the shadow S. F1 and F2 represent inner corners of the shadow S and/or outer corners of the trailer 100. Thereby, a distance between the coordinates F1’ and F2’ may be determined by use of the identified shadow S. This may be done by an analytical and/or iterative approach, including e.g. an image analysis algorithm. The determined distance between FT and F2’ may then be used for obtaining an estimated value of the trailer’s 100 length L. In addition, the estimation requires that the relative position and/or orientation of the vehicle 1 with respect to the light source 20 is identified. This may for example be done by knowledge about the time of day relating to a position of the sun 20, and also by knowledge of the position and/or orientation of the vehicle 1 with respect to the sun 20. This information may for example be obtained by use of a GPS (global positioning system) system of the vehicle 1 , or by any other type of GNSS (global navigation satellite system) system. As such, the length of the shadow S along the x-axis may be used to estimate the vehicle length L. In addition, the width of the shadow S along the y-axis may be used for identifying the vehicle’s height H.

Even though the sun 20 is here used as an example, it shall be noted that any other type of light source may be used for the estimation, as long it is possible to determine a relative position and/or orientation of the vehicle 1 with respect to the light source. According to an embodiment, when the light source is not the sun 20, the method may further comprise controlling the light emitted by the light source. For example, the direction of the emitted light may be controlled for obtaining a suitable shadow S for the estimation. The light source may additionally or alternatively be controlled to be turned on and off at a suitable point in time when the vehicle 1 is driving nearby the light source for the estimation. Such control of the light source may further improve the estimation, and also reduce the time for conducting the estimation.

The method as disclosed herein may be implemented in a control unit 10, which may be part of the vehicle 1 , as shown in fig. 5. Even though the control unit 10, represented by a box with dashed lines, is here one control unit, it shall be understood that the method may be implemented in several control units. Accordingly, the control unit 10 as disclosed herein may comprise a plurality of sub-control units which are communicatively connected, such as by a wired or wireless connection. The control unit 10 is also in communicative contact with the at least one visual perception sensor 11. The control unit 10 is preferably an electronic control unit (ECU) of the vehicle 1 which also may comprise other control functions for the vehicle 1. The method may preferably further comprise: identifying the at least one shadow S comprises identifying a sequence of shadows while driving the vehicle 1 along a known trajectory T, wherein estimating the one or more geometric properties L, H, W is further performed based on an expected evolvement of the identified sequence of shadows S while driving the vehicle 1 along the known trajectory T. This is represented by the box S5 in fig. 6b, where the vehicle 1 is driving along a known trajectory T, such as the trajectory T shown in fig. 2. Accordingly, the known travelling path T is herein also denoted a known trajectory T. As such, the estimation step S2 may obtain further information, represented by S5, which information is indicative of the known travelling path T. Thereby, the reliability and accuracy of the estimation may be improved by also considering the expected evolvement of the identified sequence of shadows S while driving along the known travelling path T, i.e. known trajectory T. Preferably, the known trajectory T is a predetermined trajectory T, or predetermined travelling path T.

Accordingly, a known trajectory T as used herein may mean that the trajectory T is determined at least just before the vehicle 1 drives along the trajectory T. As such, the known trajectory T may be a predetermined trajectory T and may be stored in e.g. a memory of the vehicle 1 or in a cloud memory. The known trajectory T may for example be a trajectory of an autonomous driving mission. Preferably, the known trajectory T is exclusively, or at least mainly, adapted for the geometric property estimation.

It shall be noted that a box with dashed lines in fig. 6b means that may be an optional embodiment of the method. In addition, even though the flowchart may imply a certain sequence of steps, the skilled person will realize that the steps may also be performed in other orders than the one shown in fig. 6b.

In addition, the vehicle 1 may be associated with a yaw axis z1, see fig. 4. In the shown embodiment the yaw axis z1 extends in the z direction, and the vehicle 1 is adapted to rotate about the yaw axis z1 while driving, wherein the known trajectory T is formed such that the vehicle 1 will rotate at least one full rotation in a first direction about its yaw axis z1. Thereby all sides of the vehicle 1 with respect to the yaw axis z1 may be exposed to the sun 20, or light source. This in turn may provide more data relating to the vehicle’s 1 geometric properties, thereby further improving the estimation. For example, by driving in a circle it may be easier to determine the vehicle’s width W, since all sides of the vehicle 1 will cast a shadow S on the ground surface GS. In an example embodiment, the width W may be estimated by use of a visual perception sensor (not shown) which is located so that it can obtain an image behind of the vehicle 1.

Still further, the known trajectory T may further be formed such that the vehicle 1 will further rotate at least one full rotation in a second opposite direction about its yaw axis z1, wherein the known trajectory T preferably comprises a figure-eight path for the vehicle 1. As mentioned in the above, the vehicle 1 may for example drive in the figure-eight path T as shown in fig. 2. Driving the vehicle 1 like this allows for an even further improved estimation, since more data will thereby be obtained. The data may thereby also be associated with more different positions and orientations with respect to e.g. the sun 20. Further, if driving along the known trajectory T more than one time, a further improved estimation may be achieved.

Additionally, or alternatively, the method may further comprise:

S31: obtaining information about the shape of the neighbouring surface GS, wherein estimating the one or more geometric properties L, H, W is further performed based on the information about the shape of the neighbouring surface GS.

The information obtained in step S31 may be obtained from map data, such as from a GPS system or the like, and/or by a perception sensor which identifies geometric properties of the ground surface GS. For example, the sensor 11 may be used for this.

The information about the shape of the neighbouring surface GS may be used for:

S32: performing a projection transformation of the identified at least one shadow S to a prediction P (see fig. 3) of the identified at least one shadow S on a flat extending surface GS1, such as a flat horizontally extending surface, wherein estimating the one or more geometric properties L, H, W is further performed based on the prediction P of the identified at least one shadow S on the flat extending surface. As shown in fig. 3, the shadow S casted on the at least partly inclined surface GS has by a projection transformation resulted in a prediction P of the shadow on a flat horizontally extending surface GS1, i.e. by here extending the ground surface GS1 which the vehicle 1 is provided on. By doing this, the estimation may be further improved, allowing any ground surface irregularities/inclinations to be compensated for, thereby further improving the accuracy of the estimation. Furthermore, estimating the one or more geometric properties L, H, W may further be performed based on an expected evolvement of the shape of the neighbouring surface GS while driving the vehicle 1 along a known trajectory T. As such, the shape of the surface associated with the known trajectory T may be known in advance, from e.g. map data and/or sensor data from a sensor on the vehicle. Thereby, by knowing an expected evolvement of the shape of the surface GS, this may be used to predict how the shadow S should evolve with respect to the shape of the ground surface GS while driving along the known trajectory T. This information may thereby be used to further improve the estimation.

Additionally, or alternatively, the method may further comprise:

S4: obtaining information about at least one geometric property of the vehicle 1 from another source than from the at least one shadow S, and wherein estimating the one or more geometric properties L, H, W by use of the identified at least one shadow S is further performed based on using the information about the at least one geometric property obtained from the other source. For example, the at least one geometric property from the other source may be different from the one or more geometric properties L, H, W estimated by use of the at least one shadow S. As an example, the geometric property obtained from the other source may be the vehicle’s height H. This may for example be obtained by the sensor 11. The vehicle’s height H may be easier to identify directly by e.g. the sensor 11. By having this information, proportions of the shadow S identified by use of the visual perception sensor 11 may be used to more easily e.g. estimate the vehicle’s length L. Additionally, or alternatively, the at least one geometric property from the other source may be the same geometric property as one of the one or more geometric properties L, H, W estimated by use of the at least one shadow S. For example, the height H may be estimated by obtaining an image of the vehicle’s side by use of the sensor 11 and/or by use of the shadow S.

As mentioned, the information about the at least one geometric property of the vehicle 1 from the other source may be obtained by use of a perception sensor. However, it may additionally or alternatively be obtained from a database. This may be a database located in the vehicle 1 and/or it may be remote database, whereby the information is communicated to the vehicle 1 by e.g. a wireless connection. In addition to the above-mentioned geometric properties L, H, W, the one or more geometric properties of the vehicle 1 estimated by use of the at least one shadow S may be at least one of the following:

- a coupling point position between a trailer 100 and another vehicle 110, e.g. at the articulation joint 120 shown in fig. 4;

- one or more wheel axle positions of the vehicle 1, preferably one or more trailer wheel axle positions.

For example, the wheels of the vehicle 1 may be identified in the shadow S if the light source, e.g. the sun 20, is low enough with respect to the z-axis, thereby the shadow of the wheels may become visible.

Other geometric properties which may be estimated by the method disclosed herein are track width, wheel base, number of wheels, number of wheel axles, number of vehicle body units, e.g. number of connected trailers.

In particular, if driving along a known travelling path T - such as along the above mentioned paths, including a figure-eight path - a coupling point position 120 and/or wheel axle positions may be more easily estimated, compared to if e.g. estimating geometric properties while driving along a substantially straight highway section.

If e.g. driving along a straight highway section, the shadow S will only be casted on one side of the vehicle 1 , and not on both sides as it would do if following a known travelling path T as mentioned in the above. Further, if using a curved known travelling path T as mentioned in the above, the shadow S casted on the ground surface GS will also be formed such that a relative position and/or orientation between the truck 110 and the trailer 100 will be represented in the shadow S. By determining the relative position and/or orientation from the shadow S, the coupling point position 120 may be estimated.

The above-mentioned control unit 10 may use a computer program comprising program code means for causing the control unit 10 to perform the steps of the method according to any one of the embodiments disclosed herein. Furthermore, the control unit may further use a computer readable medium carrying a computer program comprising program code means for causing the control unit 10 to perform the steps of the method according to any one of the embodiments disclosed herein. The control unit 10 as disclosed herein may include a microprocessor, microcontroller, programmable digital signal processor or another programmable device. The primary control unit may also, or instead, include an application specific integrated circuit, a programmable gate array or programmable array logic, a programmable logic device, or a digital signal processor. Where the control unit 10 includes a programmable device such as the microprocessor, microcontroller or programmable digital signal processor mentioned above, the processor may further include computer executable code that controls operation of the programmable device. The control unit 10 may comprise embedded hardware, sometimes with integrated software, where the hardware show close physical relationship. Examples of physical relationships are: shared casing and components mounted on one or several circuit boards. It shall also be noted that the control unit 10 may be a combination of several communicatively connected control units.

Even though the control unit 10 as shown in fig. 5 is preferably located in the vehicle 1, it may alternatively be located remotely from the vehicle 1, such as being part of a cloud- based system (not shown). As such, all parts, or at least some parts, of the method may alternatively be performed remotely from the vehicle 1.

The vehicle 1 may be a manually operated vehicle, i.e. operated by a driver. However, the vehicle 1 may alternatively be an autonomous vehicle, such as an at least partly or fully autonomous vehicle. It has been realized that the present invention is particularly advantageous for vehicles without a driver, since the geometric properties can be automatically obtained without any direct human involvement. For example, the geometric properties L, H W of a newly connected trailer 100 may be estimated before an autonomous driving mission is initiated. This information can then be used to inform the autonomous driving mission about the geometric properties L, H, W. For example, control signals for controlling the vehicle 1 according to the autonomous driving mission may be updated based on the new information about the vehicle’s 1 geometric properties L, H, W.

It is to be understood that the present invention is not limited to the embodiments described above and illustrated in the drawings; rather, the skilled person will recognize that many changes and modifications may be made within the scope of the appended claims.