CN115009290A - Method, device and storage medium for infrastructure-assisted control of a motor vehicle - Google Patents
Method, device and storage medium for infrastructure-assisted control of a motor vehicle Download PDFInfo
- Publication number
- CN115009290A CN115009290A CN202210214618.2A CN202210214618A CN115009290A CN 115009290 A CN115009290 A CN 115009290A CN 202210214618 A CN202210214618 A CN 202210214618A CN 115009290 A CN115009290 A CN 115009290A
- Authority
- CN
- China
- Prior art keywords
- motor vehicle
- vehicle
- state
- control instruction
- function
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000004590 computer program Methods 0.000 claims description 10
- 230000008859 change Effects 0.000 claims description 9
- 230000001133 acceleration Effects 0.000 claims description 7
- 230000003213 activating effect Effects 0.000 claims description 3
- 230000007613 environmental effect Effects 0.000 description 18
- 238000004088 simulation Methods 0.000 description 10
- 230000008901 benefit Effects 0.000 description 8
- 238000005259 measurement Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000004913 activation Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000007257 malfunction Effects 0.000 description 1
- 230000001172 regenerating effect Effects 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 238000012552 review Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
- G05D1/028—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using a RF signal
- G05D1/0282—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using a RF signal generated in a local control room
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
- B60W2050/0019—Control system elements or transfer functions
- B60W2050/0028—Mathematical models, e.g. for simulation
- B60W2050/0031—Mathematical model of the vehicle
Landscapes
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Human Computer Interaction (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Steering Control In Accordance With Driving Conditions (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention relates to a method for controlling a motor vehicle with assistance from an infrastructure, comprising the following steps: receiving an environment signal representing an environment of the motor vehicle, receiving a motor vehicle data signal representing motor vehicle data of the motor vehicle which is sought within the motor vehicle, seeking a real state of the motor vehicle based on the environment signal and the motor vehicle data signal, simulating the motor vehicle based on the environment signal and a motor vehicle model of the motor vehicle to seek a simulated state of the motor vehicle, comparing the real state with the simulated state, seeking at least one control instruction for controlling the motor vehicle assisted by the infrastructure based on a result of the comparison of the real state with the simulated state; outputting a control instruction signal representing the at least one control instruction. The invention also relates to a device for implementing the method and to a storage medium.
Description
Technical Field
The invention relates to a method for infrastructure-assisted control of a motor vehicle, to a device, to a computer program and to a machine-readable storage medium.
Background
DE 102013206746B 4 discloses a method and a device for changing the configuration of a driver assistance system of a motor vehicle.
Publication EP 3438901B 1 discloses a test driving scenario database system for a near-real virtual test driving scenario.
US 9, 507, 346B 1 discloses a remote control system for a motor vehicle.
Disclosure of Invention
The task on which the invention is based is to be seen as providing a solution for effectively infrastructure-assisted control of a motor vehicle.
This object is achieved by means of the individual subject matter of the invention. Advantageous configurations of the invention are the subject of the individual preferred embodiments.
According to a first aspect, a method for infrastructure-assisted control of a motor vehicle is provided, comprising the following steps:
-receiving an environment signal representing an environment of the motor vehicle,
receiving a motor vehicle data signal representing motor vehicle data of the motor vehicle which are determined in the motor vehicle,
-ascertaining a real state of the motor vehicle on the basis of the ambient signal and the motor vehicle data signal,
simulating the motor vehicle on the basis of the ambient signal and a motor vehicle model of the motor vehicle in order to determine a simulated state of the motor vehicle,
-comparing the real state with the simulated state,
-evaluating at least one control command for controlling the motor vehicle with assistance from the infrastructure on the basis of the result of the comparison of the real state with the simulated state,
-outputting a control command signal representing the at least one control command sought.
According to a second aspect, a device, in particular an RSU, is provided, which is arranged for implementing all steps of the method according to the first aspect.
According to a third aspect, there is provided a computer program comprising instructions which, when the computer program is implemented by a computer, for example by an apparatus according to the second aspect, arrange the computer to carry out the method according to the first aspect.
According to a fourth aspect, a machine-readable storage medium is provided, on which a computer program according to the third aspect is stored.
The invention is based on and at the same time comprises the recognition that: the solution of the above task is: a motor vehicle model of the motor vehicle is used in order to simulate the motor vehicle, such that a simulated state of the motor vehicle is determined. The vehicle model abstracts the vehicle and, for example, the surroundings sensor system of the vehicle, which comprises one or more surroundings sensors, and/or, for example, the drive system and/or the steering system and/or the braking system of the vehicle. The motor vehicle model is described, for example, by motor vehicle-specific parameters (see below for illustration), in particular safety parameters and/or limit parameters, which have been checked, for example, in the context of motor vehicle registration and which have been approved by administrative authorities, for example by the federal motor vehicle administration in germany. Such security parameters are derived, for example, from the registration criteria of the administrative authority.
Since the motor vehicle is also simulated on the basis of the ambient signal, the motor vehicle is thus simulated in the particular current traffic situation.
The simulated condition is compared with the actual condition of the vehicle to detect one or more differences. In the case of a detected difference: the actual vehicle does not behave as in the simulation. For example, consider that: the detected difference can be a warning of a "malfunction of the motor vehicle, in particular of one or more components of the motor vehicle, for example the surroundings sensor system and/or the drive system and/or the steering system and/or the brake system". Consider, for example: the detected difference may be a cue for "the vehicle does not cope with the instantaneously present environment as it would have to do based on the vehicle model".
Based on this knowledge, one or more control commands can therefore be ascertained in an efficient manner for the infrastructure-assisted control of the motor vehicle in order to be effectively infrastructure-assisted controlled and thus assist or assist the motor vehicle.
Thus, the technical advantage is achieved: a solution for efficiently controlling a motor vehicle with assistance from an infrastructure is provided.
The expression "in an embodiment of the apparatus according to the first aspect" as used in this description includes the expression "in an embodiment of the apparatus according to the first aspect, wherein the embodiment comprises the respective feature of at least one of the embodiments as for example described in the description". This means that the individual features of the embodiments described in the description can be combined, for example, in any desired manner.
These features can be embodied in different vehicle-specific parameters of different functions of the motor vehicle and of the automation functions of the motor vehicle.
Thus, parameters are available with respect to the nominal function of the motor vehicle, for example steering. These parameters must be continuously monitored and checked, since the motor vehicle can only be operated in a defined parameter space.
Here, there are so-called safety parameters which are directly safety-relevant, for example, the timely adjustment of the necessary steering knuckle angle. There are also limit parameters such as the maximum settable steering knuckle angle. The system (here the steering system) is designed on the basis of such limit parameters, that is to say that the system can only be operated within the limit parameters. For example, if the maximum permissible knuckle angle is exceeded, the desired steering effect can no longer be achieved. Such parameters are also generally dynamic, that is to say they can be variable depending on the driving situation, the road situation, etc.
In one embodiment of the method, the vehicle data comprises one or more of the following: inertia measurement data of one or more inertia sensors and actuator measurement data of one or more actuator-specific sensors, wherein the simulation of the motor vehicle is performed on the basis of the inertia measurement data and/or on the basis of the actuator measurement data.
Thereby, for example, technical advantages are achieved: the simulation can be performed efficiently.
The inertial measurement data represents, for example, a measured yaw rate and/or a measured longitudinal acceleration and/or a measured lateral acceleration. The inertial sensor is, for example, a yaw rate sensor or an acceleration sensor, in particular a longitudinal acceleration sensor or a lateral acceleration sensor.
In one embodiment, it is provided that the simulation of the motor vehicle is carried out on the basis of motor vehicle data.
Thereby, technical advantages such as: the simulation can be performed efficiently.
In one embodiment of the method, it is provided that the at least one control instruction is an element from the following group of control instructions: the method comprises the steps of activating a vehicle function of the vehicle, in particular an at least partially automated driving function, deactivating a vehicle function of the vehicle, in particular an at least partially automated driving function, at least partially automatically controlling a longitudinal guidance of the vehicle, at least partially automatically controlling a transverse guidance of the vehicle.
Thereby, technical advantages such as: particularly suitable control instructions may be used.
An exemplary motor vehicle function may be a continuously active steering function of the motor vehicle. In contrast, for example, vehicle functions such as braking and driving are active as required (the vehicle should accelerate or brake).
In one embodiment of the method, it is provided that, if the result indicates: if there is a deviation between the real state and the simulated state, at least one control command is determined on the basis of the deviation.
Thereby, technical advantages such as: at least one control instruction can be efficiently evaluated.
According to one specific embodiment of the method, it is provided that the at least partially automated driving function is an at least partially automated lane change function, wherein the at least one control command is to deactivate the lane change function if the deviation is greater than or equal to a predetermined deviation threshold value, wherein the at least one control command is to activate the lane change function if the deviation is less than or equal to a predetermined deviation threshold value.
Thereby, technical advantages such as: it is possible to effectively control when the at least partially automated lane change function is activated or deactivated.
In one embodiment of the method, it is provided that the real and/or simulated state represents one or more of the following information: the position of the motor vehicle, the speed of the motor vehicle, the acceleration of the motor vehicle, the system state of the motor vehicle, in particular of the drive system, of the steering system, of the braking system, for example the lane keeping of the steering system, a prediction about the effective power of the braking device, in particular also associated with the regenerative brake.
Thereby, technical advantages such as: the real and/or simulated state gives particularly suitable information, with which, for example, a motor vehicle model in a simulation can be verified.
In one embodiment of the method, it is provided that a motor vehicle model is configured on the basis of the motor vehicle data signals, wherein the motor vehicle is simulated on the basis of the configured motor vehicle model.
Thereby, such technical advantages are achieved: the vehicle model can be configured efficiently so that the vehicle can be simulated efficiently thereby. The motor vehicle model generated for the simulation can be verified, for example, by means of information from the "real world". The function has been verified in advance with respect to the respective parameters. The evaluation of the individual parameters is thus based on well-documented operational data.
In one embodiment of the method according to the first aspect, the method is a computer-implementable method.
The technical function of the method according to the first aspect is derived from the respective technical functions of the device according to the second aspect and vice versa.
This means that method features are derived from device features and vice versa.
In one embodiment of the method, the method is performed by means of the apparatus according to the second aspect.
The expression "also or" includes the expression "and/or".
In one embodiment, it is provided that the motor vehicle is a motor vehicle which is guided at least partially automatically.
The expression "at least partially automated guidance" includes one or more of the following: assisted guidance, partially automated guidance, highly automated guidance, fully automated guidance.
Assisted guidance (automation level 1) may mean that the driver of the motor vehicle continuously carries out lateral guidance or longitudinal guidance of the motor vehicle. A corresponding further driving task (i.e. controlling the longitudinal guidance or the transverse guidance of the motor vehicle) is automatically performed. This means that either the transverse guidance or the longitudinal guidance is automatically controlled when the motor vehicle is guided in an assisted manner, wherein the driver always remains in charge of guiding the vehicle when driving in an assisted manner.
Partially automated guidance (automation level 2) may mean that the longitudinal guidance and the transverse guidance of the motor vehicle are automatically controlled under certain conditions (e.g. driving on a highway, driving in a parking lot, passing an object, driving in a lane determined by a lane marking) and/or for a certain period of time. The driver of the motor vehicle does not have to manually control the longitudinal guidance and the transverse guidance of the motor vehicle by himself. The driver must constantly monitor the automatic control of the longitudinal guidance and the transverse guidance in order to be able to intervene manually if necessary. The driver must be ready to fully take over the motor vehicle guidance at any time. The driver is always generally considered as a backup floor in the event of an abnormal event or in the event of damage.
Highly automated guidance (automation level 3) may mean that the longitudinal guidance and the transverse guidance of the motor vehicle are automatically controlled for a specific period of time under specific conditions (e.g. driving on a motorway, driving in a parking lot, passing an object, driving in a lane determined by a lane marking). The driver of the motor vehicle does not have to manually control the longitudinal guidance and the transverse guidance of the motor vehicle by himself. The driver does not have to constantly monitor the automatic control of the longitudinal guidance and the transverse guidance in order to be able to intervene manually if necessary. If necessary, a request for taking over is automatically output to the driver in order to take over the control of the longitudinal guidance and the transverse guidance, in particular with a sufficient time margin. Thus, the driver must potentially be able to take over control of longitudinal guidance and lateral guidance. The limits of the automatic control of the transverse guidance and the longitudinal guidance are automatically identified. With highly automated guidance, it is not possible to automatically reach a state of minimal risk in various initial situations.
Fully automated guidance (automation level 4) means that the longitudinal guidance and the transverse guidance of the motor vehicle are automatically controlled under certain conditions (e.g. driving on a highway, driving in a parking lot, passing an object, driving in a lane determined by lane markings). The driver of the motor vehicle does not have to manually control the longitudinal guidance and the transverse guidance of the motor vehicle by himself. The driver does not have to monitor the automatic control of the longitudinal guidance and the transverse guidance, so that manual intervention can be carried out if necessary. Before the automatic control of the transverse guidance and the longitudinal guidance is finished, a request is automatically made to the driver to take over the driving task (control of the transverse guidance and the longitudinal guidance of the motor vehicle), in particular with a sufficient time margin. If the driver does not take over the driving task, it is automatically returned to the state of minimum risk. The limits of the automatic control of the transverse guidance and the longitudinal guidance are automatically identified. In all cases it is possible to automatically return to the system state with the least risk. It is possible here for a specific route section to no longer have a driver in the vehicle or for the driver to temporarily be only a passenger in his vehicle.
In one embodiment of the method, it is provided that the motor vehicle is manually guided by the driver (automation level 0).
An environmental sensor in the sense of the present description is for example one of the following environmental sensors: radar sensors, lidar sensors, ultrasonic sensors, video sensors, magnetic field sensors, and infrared sensors. The environmental sensor is, for example, an environmental sensor of the motor vehicle, i.e., an environmental sensor of the motor vehicle itself. The environmental sensor is, for example, an infrastructure environmental sensor, i.e., an infrastructure environmental sensor. In the case of a plurality of environmental sensors, for example, at least one environmental sensor is the environmental sensor of the motor vehicle itself and/or at least one environmental sensor is an infrastructure environmental sensor, for example.
The infrastructure environment sensors are, for example, spatially distributed.
The expression "at least one" means "one or more".
In one embodiment of the method, it is provided that individual vehicle data and/or individual simulation states and/or individual real states are evaluated and verified by a plurality of vehicles in order to allow, for example, additional driving functions.
In one embodiment of the method, it is provided that the at least partially automated driving function is tested for a first degree of automation on the basis of the real state and/or the simulated state and/or the comparison in order to verify the at least partially automated driving function for a second degree of automation, wherein the second degree of automation is greater than the first degree of automation, wherein the at least one control instruction comprises the activation or the unlocking of the at least partially automated driving function for the second degree of automation when the at least partially automated driving function is verified for the second degree of automation.
In this embodiment, for example, certain functions can be tested and verified at a low level of automation or degree of automation (e.g., partially automated driving) and can be unlocked accordingly for a higher level of automation in terms of safety.
An example of this is the assistance of lane changes, where the driver also participates in the assistance function. The system can already comprehend all aspects of a fully automatic lane change and may also be able to implement this function independently. If the driver performs this function safely in the case of different driving situations, traffic conditions, weather conditions, etc., the system behavior in the event of a fault can also be simulated in the model. If the system can then control all the risks, this function can be considered as: are also well tested and validated for a high degree of automation. For example, the data set may be submitted to an administrative authority for review and approval.
Drawings
Embodiments of the present invention are shown in the drawings and are explained in more detail in the description that follows. The figures show:
figure 1 shows a method for infrastructure-assisted control of a motor vehicle,
figure 2 shows a device of the type described above,
FIG. 3 shows a machine-readable storage medium, an
Fig. 4 shows a block diagram.
Detailed Description
Fig. 1 shows a flow chart of a method for infrastructure-assisted control of a motor vehicle, comprising the following steps:
-receiving 101 an ambient signal, the ambient signal representing an environment of the motor vehicle,
receiving 103 a motor vehicle data signal representing motor vehicle data of a motor vehicle determined in the motor vehicle,
determining 105 a real state of the vehicle on the basis of the ambient signal and the vehicle data signal,
simulating 107 the motor vehicle on the basis of the ambient signal and a motor vehicle model of the motor vehicle in order to determine a simulated state of the motor vehicle,
-comparing 109 the real state with the simulated state,
-evaluating 111 at least one control command for controlling the motor vehicle with assistance from the infrastructure on the basis of the result of the comparison of the real state with the simulated state,
-outputting 113 a control command signal representing at least one sought control command.
Fig. 2 shows a device 201.
The apparatus 201 is arranged for carrying out all the steps of the method according to the first aspect.
Fig. 3 illustrates a machine-readable storage medium 301.
A computer program 303 is stored on a machine-readable storage medium 301, said computer program comprising instructions which, when the computer program 303 is implemented by a computer, arrange the computer to carry out the method according to the first aspect.
Fig. 4 shows a block diagram 401, which schematically illustrates the solution described here for infrastructure-assisted control of a motor vehicle.
According to block diagram 401, a motor vehicle 403 is shown, which is traveling on road 405. In front of the motor vehicle 403 in the direction of travel, a pedestrian 407 is on the road 405.
The motor vehicle 403 comprises a first environment sensor 409 and a second environment sensor 411, which each sense the environment of the motor vehicle 403 and provide environment sensor data corresponding to this sensing, which is symbolically illustrated by an arrow with the reference number 413.
In this regard, two environmental sensors 409, 411 sense the pedestrian 407.
In the surroundings of the roadway 405, a first infrastructure environment sensor 415 and a second infrastructure environment sensor 417 are arranged, which each sense the environment of the motor vehicle 403 and provide environment data corresponding to the sensing, which is symbolically illustrated by an arrow with reference number 416.
Thus, environmental data or environmental signals corresponding to the respective sensing are provided.
According to block diagram 401, device 201 of fig. 2 is provided, which receives an ambient signal representing the environment of motor vehicle 403.
Not shown: such as further infrastructure environment sensors, which may include, for example, weather sensors, to sense weather in the environment of the motor vehicle 403. The corresponding weather data likewise represents the environment of the motor vehicle and can therefore be included under the concept "environmental signal". For example, illustratively, draw: the sun 421 is shining and a rain zone marked with a cloud with reference 423 is close to the road 405. Thus, the weather conditions are known, for example.
According to block 419, device 201 receives these environment signals and receives motor vehicle data signals, which represent the motor vehicle data of motor vehicle 403 which are ascertained in the motor vehicle interior. According to block 419, it is further provided that the actual state of the motor vehicle is ascertained by means of the device 201 on the basis of the ambient signal and the motor vehicle data signal.
According to the block 425, the device 201 simulates the behavior of the motor vehicle 403 on the road 405 on the basis of a motor vehicle model (not shown) of the motor vehicle 403 and on the basis of the environmental signal in order to determine a simulated state 427. The simulated state 427 is compared with the real state 431 by means of the device 201, according to the functional block 429 settings. The device 201 determines at least one control command 433 for the infrastructure-assisted control of the motor vehicle based on the comparison or on the result of the comparison. The ascertained control command is output or a corresponding control command signal is output, which is symbolically indicated by an arrow with reference numeral 434.
For example, the control commands 433 relate to control commands for controlling a steering system of the motor vehicle 403, which steering system is symbolically illustrated by a steering wheel with the reference numeral 435. The control commands 433 can be, for example, control commands for a drive train of the motor vehicle 403, which is symbolically illustrated by a schematically illustrated drive motor 437. The control commands 433 can be, for example, control commands for a brake system of the motor vehicle 403, which brake system is symbolically illustrated by two wheels connected by an axle and having the reference numeral 439.
The function block 425 may be fed or provided with, for example, a vehicle data signal to configure a vehicle based on the vehicle data signal, wherein the vehicle 403 is then simulated based on the configured vehicle model.
If the motor vehicle for example exhibits a very precise and accurate lane keeping (because there is just no wind either the road is clean and very flat, or because the steering in the corresponding vehicle is very precise), the motor vehicle controlled by the infrastructure can travel through the intersection more quickly. If the simulation state indicates that the steering is very unstable, this information can be provided, for example, to a repair station. If the respective simulation state of a plurality of motor vehicles gives very stable data for a specific road section and the road section is visible from a very great distance, it is provided, for example, that a specific driving function, for example an at least partially automated lane change function, is enabled. In the case of rain and/or heavy traffic, this function is not unlocked, i.e. not activated. For example, such data and parameters may be considered as stable data: the data and parameters provide insights about the system design and furthermore provide a message about which robustness margins prevail for the respective driving situation under the respective weather conditions.
Claims (10)
1. Method for infrastructure-assisted control of a motor vehicle (403), comprising the following steps:
-receiving (101) an ambient signal representing an environment of the motor vehicle (403),
-receiving (103) a vehicle data signal representing vehicle data of the vehicle (403) determined in the vehicle interior,
-ascertaining (105) a true state (431) of the motor vehicle on the basis of the environment signal and the motor vehicle data signal,
-simulating (107) the motor vehicle on the basis of the ambient signal and a motor vehicle model of the motor vehicle (403) in order to determine a simulated state (427) of the motor vehicle (403),
-comparing (109) the real state (431) with the simulated state (427),
-deriving (111) at least one control instruction (433) for controlling the motor vehicle assisted by an infrastructure based on a result of the comparison of the real state with the simulated state,
-outputting (113) a control instruction signal representing the sought at least one control instruction (433).
2. The method of claim 1, wherein the at least one control instruction (433) is an element selected from the following group of control instructions (433): activating a vehicle function of the vehicle (403), in particular an at least partially automated driving function, deactivating a vehicle function of the vehicle (403), in particular an at least partially automated driving function, at least partially automatically controlling a longitudinal guidance of the vehicle (403), at least partially automatically controlling a transverse guidance of the vehicle (403).
3. The method according to claim 1 or 2, wherein if the result indicates that there is a deviation of the real state (431) from the simulated state (427), the at least one control instruction (433) is derived based on the deviation.
4. Method according to claims 2 and 3, wherein the at least partially automated driving function is an at least partially automated lane change function, wherein the at least one control instruction (433) is to deactivate the lane change function if the deviation is greater than or equal to or greater than a predetermined deviation threshold, wherein the at least one control instruction (433) is to activate the lane change function if the deviation is less than or equal to or less than the predetermined deviation threshold.
5. The method according to any of the preceding claims, wherein the real and/or simulated state (431, 427) gives one or more of the following information: a position of the motor vehicle (403), a speed of the motor vehicle (403), an acceleration of the motor vehicle (403), a system state of a system of the motor vehicle (403), in particular of a drive system (437), of a steering system (435), of a brake system (439), for example a lane keeping of the steering system (435).
6. The method according to any one of the preceding claims, wherein the motor vehicle model is configured based on the motor vehicle data signal, wherein the motor vehicle (403) is simulated based on the configured motor vehicle model.
7. Method according to any of the preceding claims referring back to claim 2, wherein the at least partially automated driving function is tested for a first degree of automation based on the real state and/or simulated state and/or comparison in order to verify the at least partially automated driving function for a second degree of automation, wherein the second degree of automation is greater than the first degree of automation, wherein the at least one control instruction comprises activating or unlocking the at least partially automated driving function for the second degree of automation when verifying the at least partially automated driving function for the second degree of automation.
8. Apparatus (201) arranged to perform all the steps of the method according to any one of the preceding claims.
9. Computer program (303), comprising instructions, which, when the computer program (303) is implemented by a computer, arrange the computer to carry out the method according to any one of claims 1 to 7.
10. A machine-readable storage medium (301) on which the computer program (303) according to claim 7 is stored.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| DE102021202151.4 | 2021-03-05 | ||
| DE102021202151.4A DE102021202151A1 (en) | 2021-03-05 | 2021-03-05 | Method for infrastructure-supported control of a motor vehicle |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115009290A true CN115009290A (en) | 2022-09-06 |
Family
ID=82898141
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210214618.2A Pending CN115009290A (en) | 2021-03-05 | 2022-03-07 | Method, device and storage medium for infrastructure-assisted control of a motor vehicle |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN115009290A (en) |
| DE (1) | DE102021202151A1 (en) |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102013206746B4 (en) | 2013-04-16 | 2016-08-11 | Ford Global Technologies, Llc | Method and device for modifying the configuration of a driver assistance system of a motor vehicle |
| US9507346B1 (en) | 2015-11-04 | 2016-11-29 | Zoox, Inc. | Teleoperation system and method for trajectory modification of autonomous vehicles |
| DE102017213217A1 (en) | 2017-08-01 | 2019-02-07 | Ford Global Technologies, Llc | Test scenario database system for realistic virtual test driving scenarios |
| US10755007B2 (en) | 2018-05-17 | 2020-08-25 | Toyota Jidosha Kabushiki Kaisha | Mixed reality simulation system for testing vehicle control system designs |
-
2021
- 2021-03-05 DE DE102021202151.4A patent/DE102021202151A1/en active Pending
-
2022
- 2022-03-07 CN CN202210214618.2A patent/CN115009290A/en active Pending
Also Published As
| Publication number | Publication date |
|---|---|
| DE102021202151A1 (en) | 2022-09-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11983972B1 (en) | Simulating virtual objects | |
| US11170588B2 (en) | Autonomous system validation | |
| CN107867292B (en) | Safety critical system control in autonomous vehicles | |
| CN109421738A (en) | Method and apparatus for monitoring autonomous vehicle | |
| US10793123B2 (en) | Emergency braking for autonomous vehicles | |
| CN105984342A (en) | Driving control device | |
| US20190111922A1 (en) | In-vehicle traffic assist | |
| CN109421743A (en) | Method and apparatus for monitoring autonomous vehicle | |
| CN109421740A (en) | Method and apparatus for monitoring autonomous vehicle | |
| CN110471406A (en) | Automated driving system | |
| CN111795832A (en) | Intelligent driving vehicle testing method, device and equipment | |
| WO2018220829A1 (en) | Policy generation device and vehicle | |
| US20220306113A1 (en) | Customization of autonomous-driving lane changes of motor vehicles based on drivers' driving behaviours | |
| Gietelink et al. | Development of a driver information and warning system with vehicle hardware-in-the-loop simulations | |
| CN110562233A (en) | driver assistance | |
| CN111984018A (en) | Automatic driving method and device | |
| CN107089205A (en) | The system and method alleviated for vehicle subsystem fault | |
| WO2022072172A1 (en) | Systems and methods for imminent collision avoidance | |
| CN112141073A (en) | Method for controlling a vehicle | |
| US20220176949A1 (en) | Method for controlling a vehicle | |
| US11605306B2 (en) | Systems and methods for driver training during operation of automated vehicle systems | |
| CN105946578A (en) | Accelerator pedal control method and device and vehicle | |
| Hong et al. | Development of a driver's intention determining algorithm for a steering system based collision avoidance system | |
| CN115009290A (en) | Method, device and storage medium for infrastructure-assisted control of a motor vehicle | |
| US20230073065A1 (en) | Limp home mode for an autonomous vehicle using a secondary autonomous sensor system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |