DE102021118837A1 - Method for determining or estimating a collision probability of a vehicle with a surrounding object in the area surrounding the vehicle - Google Patents

Abstract

The invention relates to a method for determining or estimating a collision probability of a vehicle (2), in particular a motor vehicle or a motor vehicle combination, with at least one surrounding object (3) located in the surrounding area of the vehicle (2), the vehicle (2) having a surrounding sensor system (4 ) which can detect the at least one surrounding object (3) as sensor information. A virtual vehicle model is formed which, for example, is longer, wider and/or higher than the real vehicle and determines the probability of a collision between the surrounding object and the virtual vehicle model. Therefore, an additional external “virtual vehicle aura” is added to the real vehicle in the form of a vehicle model overhang area surrounding the real vehicle.

Description

The invention is based on a method for determining or estimating a collision probability of a vehicle, in particular a motor vehicle or a motor vehicle combination, with at least one surrounding object located in the surrounding area of the vehicle according to claim 1.

Known methods for determining or estimating a collision probability of a vehicle with a surrounding object deliver results in binary form, i.e. whether or not the vehicle is colliding with a surrounding object or whether a minimum distance in relation to a surrounding object is maintained or not. These binary results lead to discontinuities in the determination of manipulated variables that automatically affect the vehicle behavior, which leads to a jerky correction of the vehicle's movement. This jerky correction of the movement of the vehicle is perceived by the driver as unnatural and unpleasant, which is why there is a risk that the driver will intervene and the automatic correction will be partially withdrawn as a result.

A further disadvantage of current methods is that it is assumed that the data about the geometry of the surrounding object, which is obtained using sensor information from the surrounding sensor system, is correct. On the other hand, the sensor information obtained from the surrounding object, such as the size, type and dimensions of the surrounding object and possibly also the movement trajectory of the object, can deviate from reality as a result of measurement errors in the environment sensors, measurement fluctuations, calculation errors, model errors and model simplifications.

A method for determining or estimating a collision probability between a vehicle and a surrounding object that is more closely based on human behavior is desirable. This includes, for example, that people drive more slowly in narrow areas of the road or when the vehicle they are controlling is close to objects in the vicinity and faster in wide areas of the road or when the vehicle they are controlling is far away from objects in the environment, with the respective speed affecting the estimated collision probability taken into account.

The object of the invention is therefore to specify a method for determining or estimating a collision probability between a vehicle and a surrounding object, which is more closely oriented towards human behavior.

This object is achieved according to the invention by the features of claim 1.

These and other objects, advantages, purposes and features of the present invention will become apparent upon review of the following specification taken in conjunction with the drawings.

Disclosure of Invention

1. a) Bilden einer Fahrzeug-Bewegungs-Trajektorie, entlang welcher sich Fahrzeug bewegt oder bewegen soll,
2. b) Bestimmen wenigstens einer Fahrzeug-Abmessung des Fahrzeugs,
3. c) Bilden eines virtuellen Fahrzeugmodells, welches wenigstens eine Fahrzeugmodell-Abmessung aufweist, welche sich in Richtung der bestimmten Fahrzeug-Abmessung erstreckt, aber größer ist als diese Fahrzeug-Abmessung, wobei die Differenz zwischen der Fahrzeugmodell-Abmessung und der bestimmten Fahrzeug-Abmessung einen Fahrzeugmodell-Überstandsbereich definiert, mit welchem das virtuelle Fahrzeugmodell das Fahrzeug überragt, und/oder
4. d) Bilden eines virtuellen Umfeldobjektmodells, welches wenigstens eine Umfeldobjektmodell-Abmessung aufweist, welche sich in Richtung einer Umfeldobjekt-Abmessung des Umfeldobjekts erstreckt, aber größer ist als die Umfeldobjekt-Abmessung, wobei die Differenz zwischen der Umfeldobjektmodell-Abmessung und der Umfeldobjekt-Abmessung einen Umfeldobjektmodell-Überstandsbereich definiert, mit welchem das virtuelle Umfeldobjektmodell das Umfeldobjekt überragt und
5. e) Ermitteln eines Grades einer Überschneidung
   • e1) zwischen dem Fahrzeug und dem Umfeldobjekt, und
   • e2) zwischen dem Fahrzeugmodell-Überstandsbereich und dem Umfeldobjekt, oder
   • e3) zwischen dem Fahrzeug und dem Umfeldobjektmodell-Überstandsbereich, oder
   • e4) zwischen dem Fahrzeugmodell-Überstandsbereich und dem Umfeldobjektmodell-Überstandsbereich
   unter der Annahme, dass sich das Fahrzeug entlang der Fahrzeug-Bewegungs-Trajektorie bewegt, und
6. f) Bestimmen oder Schätzen der Kollisionswahrscheinlichkeit wenigstens unter Berücksichtigung der Fahrzeug-Bewegungs-Trajektorie, der Sensorinformation und des ermittelten Grads der Überschneidung.

The invention proposes a method for determining or estimating a collision probability of a vehicle, in particular a motor vehicle or a motor vehicle combination, with at least one surrounding object located in the surrounding area of the vehicle, the vehicle having a surrounding sensor system which detects or can detect the at least one surrounding object as sensor information , with the following steps:

1. a) Forming a vehicle movement trajectory along which the vehicle moves or should move,
2. b) determining at least one vehicle dimension of the vehicle,
3. c) Forming a virtual vehicle model, which has at least one vehicle model dimension, which extends in the direction of the determined vehicle dimension, but is larger than this vehicle dimension, the difference between the vehicle model dimension and the determined vehicle dimension being one Defines vehicle model overhang area with which the virtual vehicle model overhangs the vehicle, and/or
4. d) forming a virtual environment object model, which has at least one environment object model dimension, which extends in the direction of an environment object dimension of the environment object, but is larger than the environment object dimension, the difference between the environment object model dimension and the environment object dimension being one Environment object model overhang area defined, with which the virtual environment object model overhangs the environment object and
5. e) determining a degree of overlap
   • e1) between the vehicle and the surrounding object, and
   • e2) between the vehicle model overhang area and the surrounding object, or
   • e3) between the vehicle and the surrounding object model overhang area, or
   • e4) between the vehicle model overhang area and the surrounding object model overhang area
   assuming that the vehicle moves along the vehicle motion trajectory, and
6. f) Determining or estimating the collision probability at least taking into account the vehicle movement trajectory, the sensor information and the determined degree of overlap.

The method is preferably performed by routines implemented in an electronic controller that is preferably located on board the vehicle. The collision probability determined and estimated can then be used in a driver assistance system or in an autopilot of the vehicle, in particular in a method for collision protection.

“Vehicle” should be understood to mean the real vehicle and its real dimensions, in particular its real length, width and height. In a similar way, the “environmental object” should be understood to mean the real environmental object and its real dimensions, in particular its length, width and height, such as its at least one (geometric or spatial) environmental object dimension can be detected by the environmental sensors. The environmental object can be a static environmental object, such as a stationary vehicle, a bridge, a mast at the side of the road, or a moving environmental object, such as another vehicle that is moving.

The at least one environmental object dimension of the environmental object is determined, for example, on the basis of the sensor information from the environmental sensor system. Alternatively or additionally, the environmental object dimension of the environmental object can also be transmitted to the vehicle by the environmental object itself, for example via car-to-car communication (C2C), if the environmental object is formed by another vehicle. Furthermore, as an alternative, the environmental object dimensions of the environmental object can be transmitted to the vehicle from an external location X using X-to-car communication (X2C).

The dimensions of the real vehicle are known, for example, from the manufacturer and can be stored in a memory of the electronic controller and can be read from there.

In contrast, the virtual vehicle model differs geometrically from the real vehicle in at least one spatial dimension such as length, width and/or height. Likewise, the virtual environment object model differs geometrically from the real environment object in at least one spatial dimension such as length, width and/or height. The at least one spatial dimension relates in particular to an absolute or relative coordinate system and extends, for example, in the direction of a coordinate axis. In this respect, the length, the width and the height preferably each extend in the direction of a coordinate axis of a three-axis coordinate system whose coordinate axes are at right angles to one another.

Consequently, the virtual vehicle model is, for example, longer, wider and/or higher than the real vehicle, just as the virtual environment object model is longer, wider and/or higher than the real environment object. Therefore, an additional external “virtual vehicle aura” is added to the real vehicle in the form of the vehicle model overhang area surrounding the real vehicle. Alternatively or additionally, an additional external “virtual surrounding object aura” in the form of the surrounding object model overhang area is added to the real surrounding object, which surrounds the real surrounding object.

This external "virtual aura" of the vehicle and/or the object in the surrounding area is then additionally included in the determination or estimation of the probability of a collision between the real vehicle and the real object in the surrounding area. In the case of the external "virtual aura" of the vehicle and/or the surrounding object, at least one geometric dimension of the (real) vehicle and/or the (real) surrounding object is increased in order to establish a safe distance between the real vehicle and the real surrounding object if the vehicle moves along the vehicle trajectory and a surrounding object is detected in the vicinity of the vehicle. This additional external "virtual aura" forms a tolerance when determining or estimating the probability of a collision, which increases safety because the vehicle and/or the surrounding object is assumed to be larger than it is in reality due to the additional external "virtual aura". actually is.

Overall, the collision probability is determined or estimated closer to human behavior. Through the "virtual aura" the interventions, which are based on the determined or estimated collision probability in the driving behavior of the vehicle (brakes, steering, drive) become more constant.

Furthermore, the method according to the invention results in an adaptation to the real behavior of a human driver. For example, if the collision probability is determined using an external "virtual aura" around the vehicle, and this collision probability is then evaluated in an adaptive cruise control (ACC), then the ACC can reduce the speed of the vehicle at bottlenecks (e.g. a city gate). automatically reduce, although the vehicle in its real dimensions would not collide with the city gate because the vehicle movement trajectory excludes this, but the outer "virtual aura" of the vehicle collides with the bottleneck with a certain probability of collision, for example. Such a speed reduction by the ACC would then correspond to the natural behavior of a human driver who passes the bottleneck and then drives more slowly.

Furthermore, the method does not limit a determination of the collision probability between the real vehicle (without external "virtual aura") and the real object in the environment (without external "virtual aura"). Because the degree of overlap between the real vehicle (without external "virtual Aura") and the real environment object (without external "virtual aura") is determined in any case and is taken into account when estimating or determining the collision probability.

In addition, the degree of overlap is determined using at least one external "virtual aura" and is taken into account when estimating or determining the collision probability, namely the degree of overlap between the vehicle model overhang area and the surrounding object, or between the vehicle and the surrounding object model overhang area , or between the vehicle model overhang area and the surrounding object model overhang area.

Last but not least, tolerances or weaknesses in the environment sensors can be covered and flexibly adjusted by the additional external "virtual aura". With good measurement conditions, such as daylight and good visibility, the additional external "virtual aura" of the vehicle and/or the object can be smaller than in adverse conditions such as rain, poor visibility and/or frost, in which case the additional external "virtual Aura" of the vehicle and/or the surrounding object is larger. A greater external “virtual aura” of the vehicle and/or the surrounding object results in a greater probability of collision between the vehicle and the surrounding object, which is then taken into account or utilized in an ACC, for example, by reducing or limiting the vehicle speed. Again, this corresponds to the driving behavior of a human being.

Maneuvering and braking capabilities and meta-information or other data (e.g., geographic or elevation, season, climate, weather, urban versus rural location, traffic density, Car2Car/Car2X data, or the like) and/or other environmental characteristics may also be included or considerations of the vehicle are taken into account when determining or estimating the collision probability between the vehicle and the surrounding object.

For example, the collision probability is determined or estimated with 100 percent probability if any degree of interference between the vehicle and the surrounding object has been determined. This is the case, for example, when it is determined on the basis of the sensor information from the surroundings sensor system that the vehicle movement trajectory overlaps with the surroundings object.

With the method, the collision probability does not necessarily have to be specified as a percentage. Any variable by which the collision probability can be represented is conceivable, such as a categorization ("collision certain", "collision probable", collision less probable).

1. a) dem Fahrzeugmodell-Überstandsbereich und dem Umfeld-objekt, oder
2. b) dem Fahrzeug und dem Umfeldobjektmodell-Überstandsbereich, oder
3. c) dem Fahrzeugmodell-Überstandsbereich und dem Umfeldobjektmodell-Überstandsbereich ermittelt worden ist.

For example, the probability of collision is determined or estimated as 0 percent likely if there is no degree of overlap between

1. a) the vehicle model overhang area and the surrounding object, or
2. b) the vehicle and the surrounding object model overhang area, or
3. c) the vehicle model overhang area and the surrounding object model overhang area have been determined.

This represents the safest case because, despite the additional external “virtual aura” on the vehicle and/or on the object in the surrounding area, there is no overlap when the vehicle moves along the vehicle movement trajectory.

1. a) dem Fahrzeugmodell-Überstandsbereich und dem Umfeldobjekt, oder
2. b) dem Fahrzeug und dem Umfeldobjektmodell-Überstandsbereich, oder
3. c) dem Fahrzeugmodell-Überstandsbereich und dem Umfeldobjektmodell-Überstandsbereich ermittelt worden ist.

Furthermore, in the method, the probability of collision can be determined or estimated at a value between 0 and 100 percent (excluding 0 and 100) likely if (any) degree of overlap between

1. a) the vehicle model overhang area and the surrounding object, or
2. b) the vehicle and the surrounding object model overhang area, or
3. c) the vehicle model overhang area and the surrounding object model overhang area have been determined.

This concerns the case where there is (some) degree of overlap between the two outer "virtual auras" of the vehicle and the surrounding object, or between an outer "virtual aura" and the real vehicle or the real surrounding object. A mandatory collision can then be ruled out. However, the vehicle and the surrounding object then come so close that there is a certain probability of collision.

Depending on the determined or estimated collision probability, an active automatic intervention in a brake system and / or in a steering device and / or in a control of a drive motor of the vehicle can be carried out, in particular in a method for collision avoidance between the vehicle and the surrounding object, in which the determined or estimated collision probability is used.

1. a) Eine Adaptive-Cruise-Control (ACC),
2. b) ein Park-Assistenzsystem,
3. c) ein Highway-Pilot-System,
4. d) ein Autopilot-System,
5. e) ein Notbrems-Assistent.

For example, the determined or estimated collision probability can be evaluated or processed in at least one of the following driver assistance systems:

1. a) An Adaptive Cruise Control (ACC),
2. b) a parking assistance system,
3. c) a highway pilot system,
4. d) an autopilot system,
5. e) an emergency brake assistant.

According to a development of the method, the at least one surrounding object dimension can be a length, a width and/or a height of the surrounding object and the at least one vehicle dimension can be a length, a width and/or a height of the vehicle. The length, the width and/or the height or the dimension of the surrounding object and/or the vehicle dimension can in particular be related to a Cartesian coordinate system which is fixed to the vehicle or the surrounding object.

According to a preferred embodiment, the virtual vehicle model and/or the virtual environment object model can be formed in such a way that it corresponds geometrically to a cuboid. This virtual cuboid envelops the real vehicle or the real object, with the area between the outer surface of the cuboid and the vehicle forming the vehicle model overhang area and the area between the outer surface of the cuboid and the surrounding object then forming the surrounding object model overhanging area. Such a simple cuboid model advantageously reduces the computational effort in modeling.

The virtual vehicle model and/or the virtual environment object model can also be formed in such a way that it corresponds geometrically to at least two nested cuboids, an outer cuboid and an inner cuboid, and the collision probability is determined or estimated for each cuboid of the at least two nested cuboids. Then, for example, when the outer cuboid or a cuboid arranged further outside overlaps with the vehicle or with the surrounding object, the probability of collision is determined or estimated to be lower than is the case with an intersection of the inner cuboid or a cuboid arranged further inside with the vehicle or with the surrounding object is.

A surrounding object type of the at least one surrounding object can also be determined on the basis of the sensor information from the surrounding sensor system, and the virtual surrounding object model can then be formed as a function of the determined surrounding object type. If, for example, the environment sensor system identifies the environment object as a motorcycle, the virtual environment object model can be relatively large in terms of its geometric dimensions in order to compensate for the safety deficits of a motorcycle in the event of a collision.

According to one development, the sensor information from the surroundings sensors can be used to determine the relevance of the at least one surrounding object with regard to a collision with the vehicle, and the virtual surrounding object model and/or the virtual vehicle model can only be formed if the relevance of the at least one surrounding object exceeds a predefined limit relevance . This avoids unnecessary modeling and thus unnecessary computing effort in cases in which there is no relevant collision relevance, for example when the object in the surroundings is moving away from the vehicle.

1. a) erste Abschnitte des Fahrzeugmodell-Überstandsbereichs, welche näher an dem Fahrzeug liegen als zweite Abschnitte des Fahrzeugmodell-Überstandsbereichs, eine größere Gewichtung in Bezug auf die Bestimmung oder Schätzung der Kollisonswahrscheinlichkeit erhalten, und/oder bei welcher
2. b) erste Abschnitte des Umfeldobjektmodell-Überstandsbereichs, welche näher an dem Umfeldobjekt liegen als zweite Abschnitte des Umfeldobjektmodell-Überstandsbereichs, eine größere Gewichtung in Bezug auf die Bestimmung oder Schätzung der Kollisionswahrscheinlichkeit erhalten.

According to a development of the method, a weighting can also be carried out, in which

1. a) first sections of the vehicle model overhang area that are closer to the vehicle than second sections of the vehicle model overhang area, a greater weight with respect to the determination or estimation calculation of the collision probability obtained, and/or at which
2. b) first sections of the surrounding object model overhang area, which are closer to the surrounding object than second sections of the surrounding object model overhang area, receive a greater weighting with regard to the determination or estimation of the collision probability.

A spatial or geometric extension of the overhanging area of the vehicle model and/or the overhanging area of the surrounding object model is particularly preferably formed as a function of the speed of the vehicle and/or of at least one environmental condition. For example, the spatial or geometric extent of the overhanging area of the vehicle model and/or the overhanging area of the surrounding object model is greater the higher the speed of the vehicle and the smaller the lower the speed of the vehicle.

The environmental condition can be at least one of the following environmental conditions: the ambient temperature, the free visibility, the road surface, the ambient brightness, the ambient humidity.

A predicted driving path of the vehicle is preferably derived from the vehicle movement trajectory, the driving path of the vehicle being derived, for example, in such a way that it envelops the virtual vehicle model.

It can also be determined, for example on the basis of the sensor information, whether the surrounding object is moving, and if it has been determined that the surrounding object is moving, a surrounding object movement trajectory can be determined along which the surrounding object is moving and from the surrounding object movement -Trajectory a predicted driving path of the surrounding object can be derived. The driving path of the surrounding object can then be derived in such a way that it envelops the virtual surrounding object model.

character list

• 1 eine stark schematisierte Draufsicht eines sich entlang einer Fahrzeug-Bewegungs-Trajektorie bewegenden Fahrzeugs und virtuellen Fahrzeugmodells mit einem Fahrzeugmodell-Überstandsbereich sowie eines Umfeldobjekts und virtuellen Umfeldobjektmodells mit einem Umfeldobjektmodell-Überstandsbereich;
• 2 eine perspektivische Darstellung des Fahrzeugs und des virtuellen Fahrzeugmodells mit dem Fahrzeugmodell-Überstandsbereich;
• 3 ein Ablaufschema einer bevorzugten Ausführungsform eines Verfahrens zum Ermitteln oder Schätzen der Kollisionswahrscheinlichkeit zwischen dem Fahrzeug und dem Umfeldobjekt in 1.

Embodiments of the invention are shown in the drawing and explained in more detail in the following description. In the drawing shows

• 1 a highly schematized plan view of a vehicle moving along a vehicle movement trajectory and a virtual vehicle model with a vehicle model overhanging area and a surrounding object and virtual surrounding object model with a surrounding object model overhanging area;
• 2 a perspective view of the vehicle and the virtual vehicle model with the vehicle model overhang area;
• 3 a flow chart of a preferred embodiment of a method for determining or estimating the collision probability between the vehicle and the surrounding object 1 .

Description of the exemplary embodiments

1 1 is a highly schematic representation of a vehicle 2 moving along a vehicle movement trajectory 1 and a surrounding object 4 in the area surrounding the vehicle 2. Here, the surrounding object 4 is a static obstacle, for example.

The vehicle 2 is equipped with an environment sensor system 4, for example with at least one camera, at least one radar and/or at least one ultrasonic sensor, which supplies sensor information to an electronic evaluation and control device 5. The electronic evaluation and control device 5 can use the sensor information supplied by the surroundings sensor system 4 to detect or record the surroundings object 3, i.e. its existence and position in the surroundings of the vehicle 2 and here, for example, also a dimension of the surroundings object 2, in particular the width of the surroundings object 3 . Depending on the sensor information supplied, the evaluation and control device 5 then controls actuators 6 of the vehicle 2, such as a brake system, a drive motor and/or a steering device, in such a way that a collision of the vehicle 2 with the surrounding object 3 is avoided when the vehicle 2 moved along the vehicle movement trajectory 1. The collision avoidance is accomplished here, for example, by an ACC (adaptive cruise control) integrated into the evaluation and control device 5 . The vehicle 2 is controlled autonomously in level 4 or 5 here, for example, so that the vehicle is guided autonomously by an autopilot, which is preferably also integrated in the evaluation and control device 5 .

In addition, routines are implemented in the evaluation and control device 5 which can execute a method for determining or estimating a collision probability between the vehicle 2 and the object 3 in the surroundings. A flowchart of a preferred embodiment of this method is in 3 shown.

In a step 100, the vehicle movement trajectory 1 is formed, for example by a corresponding input into the autopilot, so that the autopilot, the vehicle 2 by a handles the actuators 6 such as a drive motor, a brake system and a steering device autonomously along the vehicle movement trajectory 1.

Furthermore, in a step 200 vehicle dimensions are determined, here for example the length, width and height of the vehicle 2. These data are stored, for example, in a memory of the evaluation and control device 5 and are read from it by the routines of the evaluation and control device 5 for this purpose .

In a step 300 a virtual vehicle model 7 is then formed from the vehicle dimensions in the evaluation and control device 5 . The virtual vehicle model, which, like the real vehicle, is 2 in 2 is shown has a greater length in relation to the length of the vehicle 2, a greater width in relation to the width of the real vehicle 2 and a greater height in relation to the height of the real vehicle 2, so that if you look at the real Vehicle 2 as in 2 simplified cuboid before, the virtual vehicle model 7 also simplified represents a cuboid, which then surrounds or envelops the cuboid of the real vehicle 2 at a distance.

Then defines the difference between the length of the virtual vehicle model 7 and the length of the real vehicle 2, the difference between the width of the virtual vehicle model 7 and the width of the real vehicle 2, and the difference between the height of the virtual vehicle model 7 and the height of the real one Vehicle has a vehicle model protruding area 8 surrounding the real vehicle 2 like an external “virtual aura”, with which the virtual vehicle model 7 protrudes over the real vehicle 2 . The vehicle model overhang area 8 is also in 1 shown in plan view.

In a similar way, in a step 400 in the evaluation and control device 5, a surrounding object model 9 is formed, which here, for example, has a greater length in relation to the length of the real surrounding object 3, a greater width in relation to the width of the real surrounding object 3, and in relation to the Height of the real surrounding object 3 has a greater height, so that if you look at the real surrounding object 3 analogously to 2 presented in a simplified cuboid form, the virtual environment object model 9 also represents a cuboid in simplified form, which then surrounds or envelops the cuboid of the real environment object 3 at a distance. The length, width and height of the real surrounding object 3 can be obtained, for example, from the sensor information from the surrounding sensor system 4 of the vehicle 2 .

Then defines the difference between the length of the virtual environment object model 9 and the length of the real environment object 3, the difference between the width of the virtual environment object model 9 and the width of the real environment object 3 and the difference between the height of the virtual environment object model 9 and the height of the real one Surrounding object 3 has a surrounding object model overhang area 10 which surrounds the real surrounding object 3 like an external "virtual aura" and with which the virtual surrounding object model 9 protrudes over the real surrounding object 3 . The environment object model overhang area 10 is also in 1 shown in plan view.

The evaluation and control device 5 then contains the data on the geometry of the real vehicle 2, the real surrounding object 3, the virtual vehicle model 7, the vehicle model protruding area 8 (external “vehicle aura”), the surrounding object model 9 and the surrounding object model Overhang area 10 (outer "environmental object aura").

In a step 500, a degree of a (possible) overlap is then determined in the evaluation and control device 5, namely in a sub-step 500.1 first a degree of a (possible) overlap between the real vehicle 2 and the real object 3, assuming that the vehicle 2 moves along the vehicle movement trajectory 1 . If it is determined that there is some degree of overlap ("YES"), a collision probability of 100% is assumed in step 600 because vehicle 2 and surrounding object 3 must then inevitably touch.

However, if it is determined in sub-step 500.1 that there is no overlap between the real vehicle 2 and the real object 3 ("NO"), then in a sub-step 500.2 a degree of a (possible) overlap between the vehicle model overhang area is determined 8 (exterior “vehicle aura”) and the surrounding object model overhang area 10 (exterior “surrounding object aura”) assuming that the vehicle 2 moves along the vehicle movement trajectory 1 .

If no overlap is determined ("NO"), the two outer "virtual auras" 8, 10 of the vehicle 2 and the surrounding object 3 do not even touch, so that in a step 700 a collision probability of 0% is determined or is appreciated.

If, on the other hand, it is determined in sub-step 500.2 that there is a certain degree of overlap between the vehicle model overhang area 8 (outer “vehicle aura”) and the environmental object model overhang area 10 (outer "environmental object aura"), which is also in 1 is illustrated, in a step 800 a collision probability is determined or estimated in a range between 0% and 100%, with 0% and 100% being excluded from the range. This therefore relates to the case in which the vehicle 2 and the object 3 come so close that, for example due to tolerances when determining the dimensions of the surrounding object 3, it cannot be ruled out that a collision will occur.

The collision probability estimated or determined using the method is then preferably used in the autopilot and/or in a collision protection system of the vehicle such as, for example, in an adaptive cruise control (ACC), a parking assistance system, a highway pilot system or an emergency brake Assistants utilized, in which case the autopilot or the collision protection system reacts depending on the determined or estimated probability of a collision.

Reference List

1

Vehicle Motion Trajectory

2

vehicle

3

environment object

4

environmental sensors

5

Evaluation and control device

6

actuators

7

vehicle model

8th

Vehicle model overhang area

9

environment object model

10

Environment object model overhang area

Claims (18)

Method for determining or estimating a collision probability of a vehicle (2), in particular a motor vehicle or a motor vehicle combination, with at least one surrounding object (3) located in the surrounding area of the vehicle (2), the vehicle (2) having a surrounding sensor system (4) which can detect the at least one surrounding object (3) as sensor information, with the following steps: a) Forming a vehicle movement trajectory (1) along which the vehicle (2) moves or should move, b) determining at least one vehicle dimension of the vehicle (2), c) forming a virtual vehicle model (7) which has at least one vehicle model dimension which extends in the direction of the determined vehicle dimension but is larger than this vehicle dimension, the difference between the vehicle model dimension and the determined vehicle - dimension defines a vehicle model projection area (8), with which the virtual vehicle model (7) projects beyond the vehicle (2), and/or d) Forming a virtual environment object model (9), which has at least one environment object model dimension, which extends in the direction of an environment object dimension, but is larger than the environment object dimension, the difference between the environment object model dimension and the environment object dimension defines a surrounding object model overhang area (10) with which the virtual surrounding object model (9) projects beyond the surrounding object (3) and e) determining a degree of overlap e1) between the vehicle (2) and the surrounding object (3), and e2) between the vehicle model overhang area (8) and the surrounding object (3), or e3) between the vehicle (2) and the projecting area (10) of the surrounding object model, or e4) between the vehicle model overhang area (8) and the surrounding object model overhang area (10) on the assumption that the vehicle (2) moves along the vehicle movement trajectory (1), and f) determining or estimating the collision probability at least taking into account the vehicle movement trajectory (1), the sensor information and the determined degree of overlap. procedure after claim 1 , characterized in that the collision probability is determined or estimated with 100 percent probability if any degree of overlap between the vehicle (2) and the surrounding object (3) has been determined. Method according to one of the preceding claims, characterized in that the collision probability is determined or estimated to be 0 percent probable if there is no degree of overlap between a) the vehicle model overhang area (8) and the surrounding object (3), or b) the vehicle ( 2) and the surrounding object model overhang area (10), or c) the vehicle model overhanging area (8) and the surrounding object model overhanging area (10). Method according to one of the preceding claims, characterized in that the collision probability is determined or estimated at a value between 0 and 100 percent likely if there is a degree of over-snowing between a) the vehicle model overlapping area (8) and the surrounding object (3), or b) the vehicle (2) and the surrounding object model overlapping area (10), or c) the vehicle model overlapping area (8) and the surrounding object model overlapping area (10) has been determined. Method according to one of the preceding claims, characterized in that depending on the determined or estimated collision probability, an active automatic intervention in a braking system and/or in a steering device and/or in a control of a drive motor of the vehicle (2) is carried out. Method according to one of the preceding claims, characterized in that the determined or estimated collision probability is processed in at least one of the following driver assistance systems: a) an adaptive cruise control (ACC), b) a parking assistance system, c) a highway pilot system, d) an autopilot system, e) an emergency braking assistant. Method according to one of the preceding claims, characterized in that the at least one surrounding object dimension is a length, a width and/or a height of the surrounding object (3) and the at least one vehicle dimension is a length, a width and/or a height of the Vehicle (2) is. Method according to one of the preceding claims, characterized in that the virtual vehicle model (7) and/or the virtual environment object model (9) is formed in such a way that it corresponds geometrically to a cuboid. procedure after claim 8 , characterized in that the virtual vehicle model (7) and/or the virtual environment object model (9) is formed in such a way that it corresponds geometrically to at least two nested cuboids and the collision probability is determined or estimated for each cuboid of the at least two nested cuboids. Method according to one of the preceding claims, characterized in that a surrounding object type of the at least one surrounding object is determined using the sensor information from the surrounding sensor system (4), and the virtual surrounding object model (9) is formed depending on the determined surrounding object type. Method according to one of the preceding claims, characterized in that using the sensor information from the environment sensors (4) determines a relevance of the at least one environment object (3) with regard to a collision with the vehicle (2) and the virtual environment object model (9) and / or the virtual vehicle model (7) is only formed when the relevance of the at least one surrounding object (3) exceeds a predetermined limit relevance. Method according to one of the preceding claims, characterized in that a weighting is carried out, in which a) first sections of the vehicle model overhang area (8), which are closer to the vehicle (2) than second sections of the vehicle model overhang area (8). receive greater weighting with regard to the determination or estimation of the collision probability, and/or in which b) first sections of the surrounding object model overhang area (10), which are closer to the surrounding object (3) than second sections of the surrounding object model overhanging area (10). given greater weight in determining or estimating the probability of collision. Method according to one of the preceding claims, characterized in that a spatial expansion of the vehicle overhanging area (8) and/or the surrounding object model overhanging area (10) is formed as a function of the speed of the vehicle (2) and/or of at least one environmental condition. procedure after Claim 13 , characterized in that the environmental condition is at least one of the following environmental conditions: the ambient temperature, the free visibility, the road surface, the ambient brightness, the ambient humidity. Method according to one of the preceding claims, characterized in that a predicted driving path of the vehicle is derived from the vehicle movement trajectory (1). procedure after claim 15 , characterized in that the driving path of the vehicle (2) is derived in such a way that it envelops the virtual vehicle model (7). Method according to one of the preceding claims, characterized in that it is determined whether the surrounding object (3) is moving, and if it has been determined that the surrounding object (3) is moving, a surrounding object movement Trajectory is determined, along which the surrounding object (3) moves and from the surrounding object movement trajectory a predicted driving path of the surrounding object (3) is derived. procedure after Claim 17 , characterized in that the driving path of the surrounding object (3) is derived in such a way that it envelops the virtual surrounding object model (9).

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