DE102013220170A1 - Method for determining a kinematic state variable of an object - Google Patents

Abstract

The invention relates to a method for determining a kinematic state variable of an object in an environment of a vehicle, wherein an occupancy grid subdividing the environment is formed, whereby assigned cells of the occupancy grid assigned to an object of the environment are grouped to form a cell network, wherein a kinematic state variable for the cell network is appreciated. The invention further relates to a corresponding device, a corresponding driver assistance system and a corresponding computer program.

Description

The invention relates to a method and a device for determining a kinematic state variable of an object in an environment of a vehicle. The invention further relates to a driver assistance system and a computer program.

State of the art

Driver assistance systems are usually based on a model of the vehicle environment. Such an environment model may include, for example, properties of other objects, such as their location, or open space information. To build up an environment model, measurements of various sensors present in the vehicle can be used (for example: video sensor, radar sensor, ultrasound sensor or lidar sensor).

Occupancy gratings, which subdivide the vehicle environment, are generally well suited for the representation of static obstructions, as these usually always occupy the same grid cells and thus different measurements can easily be fused. Dynamic, that is, moving, obstacles are difficult to model in an occupancy grid.

Disclosure of the invention

The object underlying the invention can therefore be seen to provide an improved method for determining a kinematic state quantity of an object in an environment of a vehicle, which can be applied to dynamic obstacles.

The object underlying the invention can also be seen in providing a corresponding device for determining a kinematic state variable of an object in an environment of a vehicle.

The object underlying the invention can also be seen to provide a corresponding driver assistance system.

Furthermore, the object underlying the invention can be seen to provide a corresponding computer program.

These objects are achieved by means of the subject matter of the independent claims. Advantageous embodiments are the subject of each dependent subclaims.

According to one aspect, a method for determining a kinematic state variable of an object in an environment of a vehicle is provided, wherein an occupancy grid subdividing the environment is formed, wherein occupied cells of the occupancy grid assigned to an object of the environment are grouped into a cell network, wherein a kinematic state variable for the cell network is estimated.

According to a further aspect, an apparatus for determining a kinematic state variable of an object in an environment of a vehicle is provided, comprising an education device for forming an occupancy grid subdividing the environment, a grouping device for grouping occupied cells of the occupancy grid assigned to an object of the environment to form a cell network and an estimator for estimating a kinematic state quantity for the cell composite.

In yet another aspect, a driver assistance system is provided that includes the device and a sensor device for detecting an environment of the vehicle.

According to yet another aspect, a computer program is provided which comprises program code for carrying out the method when the computer program is executed in a computer, in particular in a driver assistance system.

A kinematic state variable in the sense of the present invention particularly describes a movement of the object. A kinematic state variable can be, for example, a position, a velocity or an acceleration. For example, the kinematic state variable may be an angle, an angular velocity or an angular acceleration. For example, the kinematic state variable may be a yaw rate. For example, a plurality of kinematic state variables can be provided, that is, in particular estimated. In the case of several kinematic state variables, these can be formed in particular the same or preferably different.

An occupancy grid in the sense of the present invention comprises a plurality of cells. Each cell is assigned in particular an occupancy value, which can be referred to in particular as occupancy. An occupancy value may include, for example, a probability value. Such a probability value is a measure of the probability that this cell is occupied by an object. This means in particular that at the position corresponding to this cell in the environment of the vehicle, the object is located according to the probability.

A cell having a probability value greater than a predetermined probability value, for example 1%, preferably 5%, in particular 10%, may be referred to as an occupied cell. A cell with a probability value less than the predetermined probability value may be referred to as an unoccupied or unoccupied cell.

Each cell is associated in particular with a kinematic state variable, which in particular can also be referred to as movement information. In the following, this kinematic state variable of a cell can also be referred to as an individual kinematic state variable. In particular, this describes a movement of the object of the environment of the vehicle which is assigned to this cell.

In particular, each cell is assigned an occupancy value as well as an individual kinematic state variable.

For example, a cell may be 10 cm x 10 cm (length x width) in size. For example, a cell may be 20 cm x 20 cm (length x width) in size.

The invention thus encompasses, in particular, the idea of no longer independently looking at cells of an occupancy grid grouped together to form a group of cells, but rather as mutually dependent cells. Because cells of an occupancy grid grouped into a cell grouping generally correspond to a single object. In this respect, respective individual state variables of these cells grouped into a cell grouping must be the same. Because an object usually can not have different kinematic state variables. In particular, only a single speed can be assigned to the object.

In the prior art, an individual kinematic state quantity was estimated for each cell. This can lead to artifacts in that cells have different estimated kinematic state variables, even though the cells are assigned to a single object and therefore they should all actually have the same state variable.

According to the invention, a single kinematic state variable is estimated for the cell composite. Artifacts can not occur at all. There is an object around the vehicle. Thus, for example, there may only be one speed for the object. In this respect, the fact is exploited that cells grouped into a cell group also belong to an object. In this respect, the individual cells are no longer considered independently of each other, but interdependent, to the effect that the cells are grouped or connected to form a cell group and that a joint kinematic state quantity is estimated for this cell group. The estimated kinematic state quantity for the cell group thus applies to each cell of the cell group. Each cell of the cell group thus has the estimated kinematic state variable.

For example, cells grouped into a cell grouping can be immediately adjacent cells. The phrase "immediately adjacent" means, in particular, that no cell is provided between two immediately adjacent cells that is not assigned to an object and therefore has no occupancy. The immediately adjacent cells are in particular occupied cells.

According to one embodiment, those occupied cells are grouped into a cell group, to each of which an occupancy value and / or a kinematic state variable are assigned, wherein the occupancy values and / or the kinematic state variables lie within a predetermined occupancy value interval or state variable interval. Thus, it can be ensured in an advantageous manner that only those occupied cells are grouped into a cell group, each of which has as similar as possible (depending on the selected intervals) occupancy values and / or kinematic state variables. For two occupied cells, for example, have a kinematic state variable of once 10 km / h and once 100 km / h, usually can not belong to an object. So to group such occupied cells into a cell group, would not make sense and purposeful.

According to one embodiment it can be provided that in each case an individual kinematic state variable is estimated for at least some of the cells of the cell cluster, wherein the kinematic state variable for the cell cluster is estimated based on the individual kinematic state variables.

The fact that several individual state variables are used to estimate the kinematic state variable of the cell grouping means that any errors or artifacts average out in the individual state variables. This will give a more accurate estimate.

According to one embodiment, it may be provided that the kinematic state variable for the cell network is estimated based on an average value or a median of the individual kinematic state variables.

According to one embodiment, it can be provided that an object property is determined, wherein the assignment of occupied cells to the object is carried out based on the determined object property.

This facilitates in particular an assignment of occupied cells to an object in the environment of the vehicle. An object property can be, for example, the size, that is to say in particular a length and / or a height and / or a width. Accordingly, it is then known which dimensions, ie how many and which cells are occupied by the object in the allocation grid.

According to one embodiment it can be provided that a further partitioning grid forming the environment is formed at a time distance from the formation of the occupation grid, one structure assigned to the cell grouping being searched for in the other allocation grid being selected in one of the allocation grid, so that upon detection the structure in the other allocation lattice, the kinematic state quantity is estimated based on the respective location of the structure in the two assignment lattices and a time interval between the respective forming of the allocation lattice.

Since the structure is based on a single object, the structure is usually also present in the other allocation grid. There usually in a different position. The use of such a structure thus advantageously facilitates the search for the object in the other allocation grid, ie the search which occupied cells belong to the object. If then the respective location and the time interval are known, so for example, an object speed can be calculated. If additional object speeds are known, then an acceleration can be calculated.

According to one embodiment it can be provided that the structure is the cell composite itself.

This results in a simpler and more reliable detectability, since the probability is small or negligible that there is another object with exactly the same structure in the occupancy grid.

According to one embodiment, it may be provided that the structure comprises a subset of the cells of the cell assembly.

Thus, fewer cells must be considered, which results in less computational effort and faster processing.

The invention will be explained in more detail below with reference to preferred embodiments. Show here

1 an occupancy grid,

2 two grids at different times,

3 two additional grids at different times,

4 two more grids at different times,

5 a flowchart of a method for determining a kinematic state variable,

6 a flowchart of a further method for determining a kinematic state variable,

7 a flowchart of another method for determining a kinematic state variable,

8th a device for determining a kinematic state variable and

9 a driver assistance system.

Hereinafter, like reference numerals may be used for like features.

1 shows an occupancy grid 101 that divides an environment of a vehicle.

The allocation grid 101 includes several cells 103 , Occupied cells are denoted by the reference numerals 105 marked and drawn in black. The occupied cells 105 are assigned to an object in the environment of a vehicle. The occupied cells 105 are immediately adjacent to each other in the allocation grid 101 arranged. The association between the occupied cells 105 and the object of the environment can in particular be effected by determining an object property of the object and that the assignment of occupied cells to the object is carried out based on the determined object property. For example, such an object property can be performed by means of a sensor, for example a radar sensor or an ultrasound sensor.

An object property may be, for example, a size, ie a length and / or a height and / or a width. As a result, it is then immediately known which dimensions the object in the allocation grid 101 occupied, so how many and which cells 103 in the allocation grid 101 be occupied by the object.

Because the corresponding occupied cells 105 occupied by the same object, the cell assignments must 105 have the same movement. So that means in particular that every occupied cell 105 a same individual kinematic state variable must be assigned.

In order to estimate a kinematic state quantity for the object, the occupied cells become 105 to a cell network 107 grouped or summarized or connected. The cell network 107 includes all occupied cells 105 , It will also be for every single occupied cell 105 an individual kinematic state quantity estimated symbolically by arrows with the reference numeral 111 is shown. In this estimation, however, no dependence of the individual cells 105 looked at each other to the effect that they belong to the same object. Thus, different individual kinematic state variables can result from the individual estimates.

It is then envisaged that a median or an average of the individual kinematic state variables 111 is determined. This mean or median then corresponds to the common kinematic state variable for the cell network 107 , Thus, a movement of the object is calculated on the basis of estimates of individual cells, in particular by means of median or mean. The common kinematic state variable for the cell network 107 is symbolic by means of an arrow with the reference numeral 113 characterized.

2 shows two occupancy grid 101 and 101b at different times.

The left allocation grid 101 has been formed at time t - 1. The right allocation grid 101b was formed at time t.

Like the occupancy grid 101 and 101b show, the cell network has become 107 from the time t-1 to the time t, since the object has moved around the vehicle. A dashed arrow with the reference numeral 201 points to the position (shown in dashed lines) of the cell group 107 in the allocation grid 101b that the cell composite 107 at the time t - 1 had.

It is in the allocation grid 101 a structure selected that the cell composite 107 assigned. In the embodiment according to 2 this structure is the cell composite 107 itself. According to this structure, ie according to the cell network 107 , becomes in the occupancy grid 101b searched. Upon detection of the structure in the occupancy grid 101b can then determine the kinematic state quantity based on the respective location of the structure in the two occupancy grids 101 and 101b and the time interval between the respective forming of the occupancy grid 101 and 101b to be appreciated. In particular, an object speed can thereby be estimated.

3 shows two more allocation grid 101 and 101b ,

Analogous to 2 became the left allocation grid 101 taken at time t-1. The right allocation grid 101b was recorded at time t. As a structure, in the occupancy grid 101 not the cell network 107 itself selected, but a substructure of the cell composite 107 , As such, this substructure is itself a structure again and with the reference numeral 301 characterized. It is in the chosen embodiment 3 × 3 cells 103 large. Of these, four are cells 103 busy. Five cells are not occupied and with the reference numeral 303 characterized. Thus, the structure includes 301 a subset of the occupied cells 105 of the cell network 107 , After this structure 301 , so according to the corresponding pattern, is in the occupancy grid 101b searched. The reference number 201 denotes in this context a dashed arrow, analogous to 2 the position (shown in dashed lines) of the structure 301 in the allocation grid 101b shows that it had exhibited at time t - 1.

Analogous to 2 can then be based on the particular location of the structure 301 in the two allocation grids 101 and 101b and the time interval, the kinematic state variable, such as the speed, determined or estimated.

So it becomes especially a busy cell 105 of the cell network 107 connected to a certain number of adjacent cells, so formed a corresponding sub-structure. For the thus formed substructure, which also represents a cell composite, this can be done in connection with the 2 described search method used to move the cell group 107 or the structure 301 close. In particular, for each occupied cell 105 of the cell network 107 such a substructure are formed. In this respect, individual kinematic state variables for the individual occupied cells can be based on it 107 determined or estimated.

4 shows two more allocation grid 101 and 101b at different times.

Analogous to the 2 and 3 is the left grid 101 taken at time t - 1 or been formed. The right allocation grid 101b has been recorded at time t.

The corresponding substructure of occupied cells 105 and blank cells 403 is with the reference numeral 401 characterized. In the allocation grid 101b exists with this substructure 401 an uncertainty to the extent that these in the selected cell network 107 occurs twice. This is with the reference numerals 405 and 407 shown schematically. In this respect, there is an uncertainty regarding the estimation or the determination of the kinematic state variable 109 , But since the cells are usually 2 cm × 2 cm, for example 10 cm × 10 cm, are large, the corresponding error or the corresponding uncertainty is usually negligible.

5 shows a flowchart of a method for determining a kinematic state quantity of an object in an environment of a vehicle.

In one step 501 At time t-1, the condition of the occupancy grid is estimated. Here, each cell of the occupancy grid is allocated an occupancy value and a movement information, that is to say a kinematic state variable. This means, in particular, that the condition of the occupancy grid describes occupancy values and movement information.

In one step 503 takes place based on the condition of the occupancy grid according to step 501 a prediction takes place to a predicated state 505 for the time t to form.

In one step 507 a measured value is provided, which was formed by means of a sensor for detecting the environment. Such a sensor can be referred to in particular as an environmental sensor. This measurement provides occupancy values and movement information for the occupancy grid at time t. The sensor thus measures the state at time t.

In one step 509 a comparison is made between the estimated state at time t - 1 and the measured state according to step 507 , Thus, in particular, there is a comparison between the estimated occupancy at time t-1 and the measured occupancy at time t. Based on this, kinematic state variables for the individual objects are determined or estimated, as described in connection with the 1 to 4 was explained in more detail.

The estimated kinematic state variables according to step 509 form a new reading 511 , the correction value according to a correction step 513 for the predicated state 505 is used. The correction step 513 then returns an estimated state 515 at time t, ie estimated occupancy values and estimated movement information at time t. The state 515 especially as a condition 501 to be used for a restart of the procedure.

6 shows a flowchart of another method for determining a kinematic state quantity of an object in an environment of a vehicle.

Partially in the process according to 6 the same steps as in the method according to 5 , so that the same reference numerals are used accordingly. The in connection with the 5 corresponding statements apply analogously for 6 ,

As a difference, the sensor value according to the step 507 directly to correct the predicted state 505 according to a step 601 used. After the correction step 601 becomes an estimated state 603 for the time t, which includes estimated movement information and estimated occupancy information for the occupancy grid.

In one step 605 becomes the estimated state 603 at time t with the estimated state 501 compared at time t - 1. Thus, in particular, the occupancy values and the movement information of the occupancy grids formed at t and t-1 are compared with each other. This particular as related to the 1 to 4 explained in more detail. Based on this comparison, kinematic state variables 607 be estimated for the individual objects of the environment. This means in particular that based on the comparison movement information for the objects can be estimated or determined.

The state variables 607 be in one step 609 as correction values for correcting the estimated state 603 used, so a newly estimated state 611 is formed at the time t. The state 611 especially as a condition 501 to be used for a restart of the procedure.

7 FIG. 12 shows a flow chart of another method for determining a kinematic state quantity of an object in an environment of a vehicle.

In one step 701 an occupancy grid subdividing the environment is formed. In one step 703 are assigned to an object of the environment occupied cells of the occupancy grid grouped into a cell group. In one step 705 a kinematic state variable for the cell network is estimated.

8th shows a device 801 for determining a kinematic state quantity of an object in an environment of a vehicle (not shown).

The device 801 includes an educational institution 803 for forming an occupancy grid dividing the environment. Furthermore, the device comprises 801 a grouping device 805 for grouping occupied cells of the assignment grid assigned to an environment object to form a cell group. Furthermore, the device comprises 801 an estimator 807 for estimating a kinematic state variable for the cell network.

9 shows a driver assistance system 901 ,

The driver assistance system 901 includes the device 801 according to 8th and a sensor device 903 to capture the environment. The sensor device 903 For example, it may include a radar sensor, an ultrasonic sensor, a lidar sensor, a video sensor, or a combination thereof.

Claims (10)

Method for determining a kinematic state variable ( 109 ) of an object in an environment of a vehicle, wherein an occupancy grid dividing the environment ( 101 . 101 . 101b ) is formed ( 701 ), assigned to an object of the environment occupied cells ( 103 . 105 ) of the assignment grid ( 101 . 101 . 101b ) to a cell network ( 107 ) are grouped ( 703 ), where a kinematic state quantity ( 109 ) for the cell network ( 107 ) is estimated ( 705 ). The method of claim 1, wherein for at least some of the cells ( 103 . 105 ) of the cell network ( 107 ) each have an individual kinematic state variable ( 109 ), the kinematic state quantity ( 109 ) for the cell network ( 107 ) based on the individual kinematic state variables ( 109 ) is estimated. Method according to claim 2, wherein the kinematic state quantity ( 109 ) for the cell network ( 107 ) based on an average or a median of the individual kinematic state variables ( 109 ) is estimated. Method according to one of the preceding claims, wherein an object property is determined and wherein the assignment of occupied cells ( 103 . 105 ) to the object based on the determined object property. Method according to one of the preceding claims, wherein temporally spaced from the formation of the occupancy grid ( 101 . 101 . 101b ) another occupancy grid subdividing the environment ( 101 . 101 . 101b ) is formed, wherein in one of the assignment grid ( 101 . 101 . 101b ) one the cell group ( 107 ) assigned structure ( 301 . 401 ) is selected according to the in the other occupancy grid ( 101 . 101 . 101b ), so that upon detection of the structure ( 301 . 401 ) in the other allocation grid ( 101 . 101 . 101b ) the kinematic state quantity ( 109 ) based on the particular location of the structure ( 301 . 401 ) in the two distribution grids ( 101 . 101 . 101b ) and a time interval between the respective forming of the grids ( 101 . 101 . 101b ) is estimated. Method according to claim 5, wherein the structure ( 301 . 401 ) the cell network ( 107 ) is itself. Method according to claim 5, wherein the structure ( 301 . 401 ) a subset of cells ( 103 . 105 ) of the cell network ( 107 ). Contraption ( 801 ) for determining a kinematic state variable ( 109 ) of an object in an environment of a vehicle, comprising an educational device ( 803 ) for forming an occupancy grid dividing the environment ( 101 . 101 . 101b ), a grouping device ( 805 ) for grouping occupied cells assigned to an object of the environment ( 103 . 105 ) of the assignment grid ( 101 . 101 . 101b ) to a cell network ( 107 ) and a treasury ( 807 ) for estimating a kinematic state quantity ( 109 ) for the cell network ( 107 ). Driver assistance system ( 901 ), comprising the device according to claim 8 and a sensor device ( 903 ) to capture the environment. Computer program comprising program code for carrying out the method according to one of claims 1 to 7, when the computer program is executed in a computer.

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