CN117953674A

CN117953674A - Method for creating a vehicle model for a motor vehicle, in particular for a truck

Info

Publication number: CN117953674A
Application number: CN202311396980.7A
Authority: CN
Inventors: P·恩格尔
Original assignee: Robert Bosch GmbH
Current assignee: Robert Bosch GmbH
Priority date: 2022-10-28
Filing date: 2023-10-26
Publication date: 2024-04-30
Also published as: DE102022211436A1

Abstract

According to a first aspect of the invention, a method for creating a vehicle model for a motor vehicle, in particular for a truck, is proposed, wherein data of the motor vehicle are detected by means of at least one infrastructure sensor, the data are transmitted to an infrastructure system and the data are evaluated by a computing unit of the infrastructure system, wherein vehicle parameters are determined and a vehicle model is selected based on the determined vehicle parameters and parameterized.

Description

Method for creating a vehicle model for a motor vehicle, in particular for a truck

Technical Field

The invention relates to a method for creating a vehicle model for a motor vehicle, in particular for a truck. Furthermore, the invention relates to an infrastructure system. The invention further relates to a method for guiding a motor vehicle at least partially automatically. Furthermore, the invention relates to a computer program.

Background

At present, automated vehicles are typically controlled from the vehicle origin, i.e., vehicle models and vehicle parameters (e.g., length, width) adapted thereto are stored in the vehicle for the purpose of driving planning. The process works properly as long as the vehicle parameters do not change significantly. In particular in the case of a truck, the models and parameters required for the path planning may always change again and again due to the use of different trailers and semi-trailers, so that no current and adapted vehicle models or parameters are present in the vehicle.

It is also known to transfer the planning of the travel path into the infrastructure. For planning and control in the infrastructure, detailed models for any vehicle are required in the infrastructure. In addition, it is known, for example, to detect parameters of the size and the number of axles of the vehicle (for example, toll collection) by a Toll bridge. Here, the dimensions are typically used to reject passenger cars. The number of axles is included in the determination of the road toll amount. No detection is performed with respect to the vehicle model (saddle tractor, articulated tractor, long truck or the like) and its specific parameters.

A method for generating an environment model of an autonomously controlled vehicle is known from WO 2020,259,892 a 1. In this method, measured values of sensors located in an infrastructure surrounding the vehicle are recorded, and measured data of a plurality of sensors are fused and fixedly stored. In order to identify objects in the infrastructure and to classify the objects, the measured values are analyzed. Environmental model data is generated from the results of the analysis of the measured values and transmitted to the vehicle. The vehicle generates an environmental model from the transmitted environmental model data by means of an environmental model generation device. In particular, sensor data from sensors of the vehicle are additionally used in the generation of the environmental model. Control of the autonomously controlled vehicle may be based on environmental model data.

In traction operation (traction load of the truck while it is traveling forward; main steering axle is at the head of the vehicle in the traveling direction) it is also possible to estimate the traveling path to some extent for the truck. In a push operation (load vehicle pushes the load when driving backwards; main steering axle is at the rear of the vehicle in the direction of travel), this is only possible in the case of vehicles without a trailer or a saddle-type semitrailer. In saddle-riding or trailer-riding, an unstable inverted double pendulum (inverses Doppel-Pendel) is produced during the backward travel. In this case, an exact parameterization of the vehicle model is required, so that different turning and steering points and the travel path in the rearward travel direction can be determined precisely.

Disclosure of Invention

It may therefore be seen as an object of the present invention to provide a method by means of which a currently configured vehicle model and adapted vehicle parameters for a motor vehicle, in particular a load carrier, can be created.

It may be seen as a further object of the present invention to provide an infrastructure system for driving support of networked motor vehicles that is guided at least partially automatically, which uses the vehicle model thus created to guide one or more motor vehicles at least partially automatically.

In this context, a vehicle model is to be understood as meaning, in particular, the following mathematical model: the mathematical model describes the movement of the motor vehicle and is associated with different model parameters, which in turn are determined by the geometry and construction of the motor vehicle in question. With the aid of the vehicle model, it is furthermore possible, for example, to determine how much space is required for the motor vehicle when executing a determined driving maneuver, for example, a curve. In particular in the case of a truck with a trailer, for example, the distance and the cornering ability of the different axles and the turning and steering points can be taken into account as parameters.

The invention makes it possible to automatically generate a vehicle model by means of sensors and calculation logic in the infrastructure (for example on the flow field house) by performing at least partially automatic guidance of the vehicle model required for this purpose, in particular of the truck with a converted trailer/semitrailer. The travel path planning implemented by means of the vehicle model can be carried out in the infrastructure (parameterized vehicle model remains in the infrastructure and can be assigned to the motor vehicle in question) or in the motor vehicle (parameterized vehicle model is transferred from the infrastructure to the motor vehicle).

In addition, parameterized vehicle models may also be used to predict travel paths, for example, to enable cloud-based collaboration systems to achieve traffic coordination (e.g., a haul truck (Lastzug) turns left when a passenger vehicle has left an intersection area, or other vehicles are parked a distance ahead of a stop line to provide space for the haul truck to maneuver).

Preferably, the vehicle parameters include at least one axle distance and/or front and/or rear overrun and/or maximum pivot angle (EINSCHLAGWINKEL) and/or vehicle width and/or position of rear wheel point and/or position of pivot point (Einschlagpunkt) and/or parameters derived therefrom.

If the motor vehicle is a truck with at least one trailer, the vehicle model can preferably be parameterized alternatively or additionally by at least one of the rotatability of the individual axles and/or the length of the drawbar (Deichsel) and/or the distance between the drawbar stop point and the trailer axles and/or the number of trailers and/or the position of the support points of the saddle-type semitrailer and/or the variables derived therefrom.

The vehicle parameters may be determined by means of at least one infrastructure sensor, for example by one or more of the following: the vehicle width can be determined, for example, by means of an imaged video sensor, a lidar sensor and/or a radar sensor, for example, from a (diagonally) upper viewing angle. Alternatively or additionally, the vehicle width can be determined during the travel through the measuring bridge by means of laterally arranged distance-measuring sensors (for example ultrasonic sensors), by measuring the distance between the two sides and calculating the vehicle width from the distance values thus determined and the known sensor distances.

The vehicle length can be determined, for example, by means of an imaged video sensor, a lidar sensor and/or a radar sensor, for example from a (oblique) upper or lower view.

The number of axles and the axle position can be determined by means of an image analysis of the complete side view of the motor vehicle. Alternatively or additionally, the number and position of the axles may be detected by means of sensors (e.g. pressure sensors) in the road surface of the traffic lane when driving through the measuring bridge and/or by means of a grating mounted a few centimeters above the traffic lane when driving through. The overall position of the vehicle is preferably continuously detected (for example directly by means of distance measurement or indirectly by determining the measured vehicle speed) and is incorporated into the position determination of the axle.

The overrun can be derived from the position of the first and last axle and the total length.

Some parameters cannot be determined in the case of straight-line driving, but can only be determined by observing the dynamic behavior of the motor vehicle, for example by means of (oblique) upper, lower and/or lateral viewing angles during cornering. The rigid elements of the truck can thus be determined by means of the length, spacing and rotation points of the individual elements. The steerable axles can be identified by a corresponding evaluation of the wheel position from the side during the curve travel. The maximum turning angle may be determined, for example, by observing the behavior when traveling at a minimum turning circle. The number of trailers can be determined, for example, by evaluating the camera images recorded from above, below and from the side by means of an interruption in the detection configuration.

The lever length can preferably be determined, for example, by detecting an interruption and evaluating the camera images recorded from below (as described in DE 102011020111791 A1, DE 1020110209327 A1 and DE 102011003553 A1) or by evaluating the behavior of the individual vehicle segments during the curve, which is detected, for example, by means of a video sensor, taking into account the parameters determined previously.

The bearing point of the saddle semitrailer can be derived, for example, from the previously determined dimensions and positions of the rigid vehicle element, and the determination of the pivot point can be derived from dynamic behavior.

In a preferred embodiment of the invention, a drag curve (Schleppkurve) for the motor vehicle can furthermore be determined using the vehicle parameters determined in this way. Thus, advantageously, the space requirement of the motor vehicle when performing certain driving maneuvers (e.g. turning around, driving around a curve, driving backwards) can be ascertained.

According to another aspect of the invention, an infrastructure system is presented, which is configured for implementing the method according to the first aspect of the invention. For this purpose, the infrastructure system comprises at least one infrastructure sensor, which is designed to detect data of the motor vehicle and to transmit the data to a computing unit of the infrastructure system, wherein the computing unit is designed to evaluate the data and to determine vehicle parameters therefrom and to select a vehicle model based on the determined vehicle parameters and to parameterize the vehicle model.

Preferably, the at least one infrastructure sensor for detecting data of the motor vehicle is configured as a lidar sensor and/or an ultrasound sensor and/or a radar sensor and/or a camera system and/or a pressure sensor and/or a grating.

According to a third aspect of the invention, a method for at least partially automatically guiding a motor vehicle, in particular a truck, is proposed, wherein the at least partially automatic guiding of the motor vehicle is performed on the basis of a vehicle model, wherein the vehicle model is created according to the method according to the first aspect of the invention.

The expression "at least partly automatically" includes one or more of the following cases: assisted guidance, partially automated guidance, highly automated guidance, and fully automated guidance of the motor vehicle.

Assisted guidance means that the driver of the motor vehicle is constantly guided either transversely or longitudinally. A corresponding further driving task, i.e. the control of the longitudinal or transverse guidance of the motor vehicle, is automatically performed. That is to say, in the assisted guidance of the motor vehicle, either the transverse guidance or the longitudinal guidance is automatically controlled.

By partially automated guidance is meant that the longitudinal and transverse guidance of the motor vehicle is controlled automatically in certain situations (e.g. driving on a highway, driving in a parking lot, exceeding an object, driving in a lane determined by lane markings) and/or for certain time periods. The driver of the motor vehicle does not have to manually control the longitudinal and transverse guidance of the motor vehicle himself. However, the driver must continuously monitor the automatic control of the longitudinal and transverse guidance in order to be able to intervene manually when required. The driver must be ready to take over the vehicle guidance completely.

Highly automated guidance means that the longitudinal and transverse guidance of the motor vehicle is automatically controlled in specific situations (e.g. driving on a highway, driving in a parking space, exceeding objects, driving in a lane defined by lane markings) over a certain period of time. The driver of the motor vehicle does not have to manually control the longitudinal and transverse guidance of the motor vehicle himself. The driver does not have to constantly monitor the automatic control of the longitudinal and transverse guidance in order to be able to intervene manually when required. If necessary, a take-over request is automatically output to the driver in order to take over the control of the longitudinal and transverse guidance, in particular in a manner with a sufficient time margin. That is, the driver must potentially be able to take over control of the longitudinal and lateral guidance. The limits of automatic control of the lateral guidance and the longitudinal guidance are automatically identified. In the case of highly automated guidance, it is not possible to automatically reach a state of least risk in any initial situation.

Fully automated guidance means that the longitudinal and transverse guidance of the motor vehicle is automatically controlled in certain situations (e.g. driving on highways, in parking lots, over objects, in lanes determined by lane markings). The driver of the motor vehicle does not have to manually control the longitudinal and transverse guidance of the motor vehicle himself. The driver does not have to monitor the automatic control of the longitudinal and transverse guidance in order to be able to intervene manually when required. Before the automatic control of the transverse and longitudinal guidance is completed, the request to the driver for taking over the driving task (control of the transverse and longitudinal guidance of the motor vehicle) is automatically effected, in particular with a sufficient time margin. If the driver does not take over the driving task, the state is automatically returned to the state in which the risk is minimum. The limits of automatic control of the lateral guidance and the longitudinal guidance are automatically identified. In any case, it is possible to automatically return to the system state with the least risk.

Control or guidance without driver means that the longitudinal guidance and the transverse guidance of the motor vehicle are automatically controlled independently of the specific application situation, for example, driving on a highway, driving in a parking space, exceeding an object, driving in a lane determined by lane markings. The driver of the motor vehicle does not have to manually control the longitudinal and transverse guidance of the motor vehicle himself. The driver does not have to monitor the automatic control of the longitudinal and transverse guidance in order to be able to intervene manually when required. Thus, for example, the longitudinal and transverse guidance of the vehicle is automatically controlled in all road types, speed ranges and environmental conditions. Thus, the entire driving task of the driver is automatically taken over. And thus no driver is required. The motor vehicle can thus also be driven from any starting position to any target position without the driver. The potential problem is automatically resolved without driver assistance.

According to a fourth aspect of the invention, a method for automated traffic control by means of an infrastructure system according to the invention is proposed, wherein one or more vehicle models are created by a computing unit of the infrastructure system according to the method according to the first aspect of the invention and traffic control is performed on the basis of the vehicle models.

Preferably, for at least one motor vehicle, an optimized driving path is determined by means of a vehicle model associated with the motor vehicle and is transmitted to the motor vehicle.

Preferably, for at least one motor vehicle, a travel path is predicted by means of a vehicle model associated with the motor vehicle and the predicted travel path is taken into account in the traffic control.

Preferably, the infrastructure system is configured for implementing the method according to the third aspect of the invention.

A motor vehicle for use in a method according to a fourth aspect of the invention is proposed, wherein the motor vehicle comprises a control unit for at least partially automating the movement of the motor vehicle using a driving model, and wherein the motor vehicle comprises a communication interface for wireless data reception, which is connected to the control unit and is designed for wireless reception of a vehicle model created according to the method of the first aspect of the invention and for forwarding the vehicle model to the control unit.

A computer program is proposed, comprising instructions which, when the computer program is implemented by a computer, cause the computer to implement the method according to the invention.

A machine-readable storage medium is proposed, on which a computer program according to the invention is stored.

The solution according to the invention advantageously enables an automated guidance of a motor vehicle, in particular a truck with a changing trailer/semitrailer, by means of the vehicle model required for this purpose in the following manner: the matched vehicle model is automatically generated by means of sensors and computational logic in the infrastructure (for example on the object flow field house). The travel path planning implemented by means of the vehicle model can be carried out in the infrastructure (parameterized vehicle model remains in the infrastructure and can be assigned to the vehicle in question) or in the vehicle (parameterized model is transferred from the infrastructure to the motor vehicle).

In addition, parameterized models may also be used to predict travel paths so that, for example, cloud-based collaboration systems can achieve traffic coordination (e.g., a haul truck turns left when a passenger vehicle has left an intersection area, or other vehicles stop a distance in front of a stop line to provide space for the haul truck to maneuver).

Drawings

Embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Fig. 1 shows various types of motor vehicles. Fig. 1a shows a different motor vehicle in a side view. Fig. 1b shows a different motor vehicle in a plan view.

Fig. 2 shows the motor vehicle in a plan view and a rod model representing the motor vehicle.

Fig. 3a and 3b show the towing curve of a saddle tractor for two different driving maneuvers.

Fig. 4 shows a flow chart of an embodiment of a method according to the first aspect of the invention.

Fig. 5 schematically illustrates an infrastructure system according to an embodiment of the invention.

Fig. 6 shows a machine-readable storage medium on which a computer program according to the invention is stored.

Fig. 7 shows a motor vehicle.

In the following description of the embodiments of the present invention, like elements are denoted by like reference numerals, and repetitive description of these elements is omitted if necessary. The figures only schematically illustrate the subject matter of the invention.

Detailed Description

In fig. 1a, a plurality of differently configured motor vehicles 10a-h are schematically shown in a side view. The motor vehicles 10a-h each have a different configuration of the towing vehicle 3 and of the trailers 7, 8. The motor vehicles 10a-h differ, for example, in the number and/or position of their axles 13, the number and position of steerable axles, the front and/or rear overrun and/or the maximum pivot angle and/or the corresponding vehicle width, the length of the drawbar and/or the distance between the drawbar stop and the trailer axles and/or the number of trailers and/or the position of the supporting points of the saddle-type semitrailer. In a structure of double drivingIn the example of a truck motor vehicle 10d, the motor vehicle 10d has a traction link 5 of adjustable length. All these parameters or specifications are necessary for the selection and parameterization of the vehicle model for the respective motor vehicle 10a-h and are detected according to the invention by means of a sensor system provided on the infrastructure side. For this purpose, measurement data, such as distance values, image data and/or weight data, are detected and evaluated by the sensor device. Accordingly, corresponding vehicle parameters can be determined, and on the basis of this, a matching vehicle model can be selected and parameterized.

Three further motor vehicles 10i-k are schematically shown in top view in fig. 1 b. Taking the vehicle 10k as an example, certain parameters are measured, for example the distance a' from the bearing point S of the saddle-type semitrailer to the axle.

Fig. 2 schematically shows a motor vehicle 10 and a rod model 11 created from selected vehicle parameters of the motor vehicle 10. The motor vehicle 10 has two axles with an axle distance a. The front axle is configured to be steerable at a maximum turning angle α. The motor vehicle 10 has a rear extensionAnd front excess/>Furthermore, the motor vehicle 10 has a width b.

In the rod-type model 11, the front is extended from the frontRear excess/>And the axle distance a to obtain a rear wheel point H and a turning point E. The stick model 11 can be used as a vehicle model for the motor vehicle 10 together with the corresponding parameters. Now, in combination with a known vehicle width, it is possible, for example, to calculate a drag curve of the motor vehicle 10 for different driving maneuvers.

Fig. 3 shows exemplary two drag curves for a saddle tractor 10'. FIG. 3a shows a turn-in maneuverFig. 3b shows a curve driving. The drag curves 70 respectively show the area swept by the saddle tractor 10' during driving maneuvers.

Fig. 4 shows a flow chart of a method implemented according to an embodiment of the invention. In a first step 101, data of the motor vehicle are detected by means of at least one infrastructure sensor. This involves, for example, measurement data. In a second step 103, the data are transmitted to the infrastructure system and analyzed by a computing unit of the infrastructure system, wherein the vehicle parameters are determined. In this case, for example, the axle distance and/or the front and/or rear overrun and/or the maximum pivot angle and/or the vehicle width and/or the position of the rear wheel point and/or the position of the pivot point can be determined. If the motor vehicle is a truck with at least one trailer, it is furthermore possible to determine as vehicle parameters, for example, at least one rotatability of the individual axles and/or the length of the drawbar and/or the distance between the drawbar stop point and the trailer axles and/or the number of trailers and/or the position of the bearing points of the saddle semitrailer. In step 105, a vehicle model is selected based on the determined vehicle parameters and parameterized. In step 107, the motor vehicle is guided at least partially automatically on the basis of the parameterized vehicle model. In this case, a travel path (path) for the motor vehicle is determined in particular on the basis of the parameterized vehicle model, and the motor vehicle is guided at least partially automatically along the travel path.

Fig. 5 shows a device 201 which is designed to carry out all the steps of the method according to the first aspect. The device 201 is an infrastructure system and may be configured as, for example, a Road-Side-Unit (RSU).

The device 201 comprises a communication interface 203 which is designed to receive data from one or more infrastructure sensors and to determine one or more parameters of the motor vehicle detected by the one or more infrastructure sensors on the basis thereof. By means of the one or more parameters, the device 201 can select a vehicle model by means of the correspondingly configured computing unit and parameterize the vehicle model. The parameterized vehicle model may be used further by the device 201 and/or transmitted to the motor vehicle via the communication interface 203.

The device 201 in this example comprises a data storage 205 in which a number of vehicle models are stored, wherein the calculation unit 207 is configured for selecting a determined vehicle model based on the data and parameterizing the vehicle model. The data are evaluated by means of the computing unit 207 and vehicle parameters, such as axle distance and/or front and/or rear overrun and/or maximum pivot angle and/or vehicle width and/or position of rear wheel point and/or position of pivot point, are determined. If the motor vehicle is a truck with at least one trailer, at least one cornering and/or a drawbar length and/or a distance between a drawbar stop and a trailer axle and/or the number of trailers and/or the position of the bearing points of the saddle-type semitrailer can furthermore be determined as vehicle parameters.

Fig. 6 shows a machine-readable storage medium 301 on which a computer program 303 is stored. The computer program 303 comprises instructions which, when the computer program 303 is executed by a computer, for example by the first road side unit 201 according to fig. 5, cause the computer to carry out the method according to the first aspect.

Fig. 7 shows an embodiment of a motor vehicle 10 for use in the method according to the invention. The motor vehicle has a control unit 12 for at least partially automatically moving the motor vehicle 10 using the driving model. In addition, the motor vehicle 10 comprises a communication interface 14 for wireless data reception, which is connected to the control unit 12 and is designed to receive the vehicle model created according to the invention wirelessly and to forward the vehicle model to the control unit 12.

Claims

1. Method for creating a vehicle model for a motor vehicle (10, 10a-k,10 '), in particular for a truck, wherein data of the motor vehicle (10, 10a-k, 10') are detected by means of at least one infrastructure sensor, transmitted to an infrastructure system (201) and evaluated by a computing unit (207) of the infrastructure system (201), wherein vehicle parameters are determined and a vehicle model is selected and parameterized on the basis of the determined vehicle parameters.

2. The method according to claim 1, wherein the vehicle parameters include at least one axle spacing (a) and/or a front overrunAnd/or the rear extension (U _H) and/or the maximum pivot angle (alpha) and/or the vehicle width (b) and/or the position of the rear wheel point (H) and/or the position of the pivot point (E) or a variable derived therefrom.

3. Method according to claim 1 or 2, wherein a vehicle model is created for a truck with at least one trailer, characterized in that the vehicle parameters comprise at least one rotatability of the individual axles and/or the length of the drawbar and/or the distance between the drawbar stop and the trailer axles and/or the number of trailers and/or the position of the bearing points of the saddle-type semitrailer or parameters derived therefrom.

4. A method according to any one of claims 1 to 3, wherein a drag curve (70) for the motor vehicle (10, 10a-k, 10') is also determined by means of the vehicle parameters.

5. Infrastructure system (201) configured for carrying out the method according to any one of claims 1 to 4, comprising at least one infrastructure sensor configured for detecting data of a motor vehicle (10, 10a-k, 10') and for transmitting the data to a computing unit (207) of the infrastructure system (201), which is configured for carrying out an analysis process on the data, wherein vehicle parameters are determined and a vehicle model is selected based on the determined vehicle parameters and parameterized.

6. Infrastructure system according to claim 5, wherein at least one infrastructure sensor for detecting data of the motor vehicle (10, 10a-k, 10') is configured as a lidar sensor and/or an ultrasound sensor and/or a radar sensor and/or a camera system and/or a pressure sensor and/or a grating.

7. Method for at least partially automatically guiding a motor vehicle (10, 10a-k,10 '), in particular a truck, wherein at least partially automatically guiding the motor vehicle (10, 10a-k, 10') is performed on the basis of a vehicle model, wherein the vehicle model is created according to any one of claims 2 to 4.

8. A method for automated traffic control by means of an infrastructure system (201) according to any of claims 5 or 6, wherein one or more vehicle models are created by a computing unit (207) of the infrastructure system (201) according to the method according to any of claims 1 to 4 and the traffic control is performed on the basis of the vehicle models.

9. The method according to claim 8, wherein for at least one motor vehicle (10, 10a-k,10 '), an optimized driving path is determined by means of a vehicle model associated with the motor vehicle (10, 10a-k,10 ') and said driving path is transmitted to the motor vehicle (10, 10a-k,10 ').

10. The method according to claim 8, wherein for at least one motor vehicle (10, 10a-k,10 '), a travel path is predicted by means of a vehicle model associated with the motor vehicle (10, 10a-k, 10') and the predicted travel path is taken into account in the traffic control.

11. The infrastructure system (201) of any of claims 5 or 6, wherein the infrastructure system is configured for implementing the method of at least one of claims 7 to 10.

12. Motor vehicle (10, 10a-k,10 ') for use in a method according to any one of claims 7 to 10, characterized in that the motor vehicle (10, 10a-k, 10') comprises a control unit (12) for at least partially automatically moving the motor vehicle (10, 10a-k,10 ') with use of a driving model, and in that the motor vehicle (10, 10a-k, 10') comprises a communication interface (14) for wireless data reception, which is connected to the control unit (12) and is set up for wirelessly receiving a vehicle model created according to any one of claims 1 to 4 and forwarding the vehicle model to the control unit (12).

13. A computer program (303) comprising instructions which, when the computer program (303) is implemented by a computer, cause the computer to implement the method according to any one of claims 1 to 4 or 7 to 10.

14. A machine readable storage medium (301) on which a computer program (303) according to claim 13 is stored.