CN116572996A - Vehicle control method and device and unmanned vehicle - Google Patents
Vehicle control method and device and unmanned vehicle Download PDFInfo
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- CN116572996A CN116572996A CN202310847459.4A CN202310847459A CN116572996A CN 116572996 A CN116572996 A CN 116572996A CN 202310847459 A CN202310847459 A CN 202310847459A CN 116572996 A CN116572996 A CN 116572996A
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- 238000000034 method Methods 0.000 title claims abstract description 36
- 230000003068 static effect Effects 0.000 claims abstract description 129
- 238000004088 simulation Methods 0.000 claims abstract description 50
- 230000010485 coping Effects 0.000 claims abstract description 17
- 238000001514 detection method Methods 0.000 claims description 5
- 238000010408 sweeping Methods 0.000 claims description 4
- 238000004134 energy conservation Methods 0.000 claims description 3
- 239000011435 rock Substances 0.000 description 9
- 239000004575 stone Substances 0.000 description 8
- 230000000694 effects Effects 0.000 description 4
- 230000002411 adverse Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000001788 irregular Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
Classifications
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- 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
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
- B60W60/001—Planning or execution of driving tasks
- B60W60/0011—Planning or execution of driving tasks involving control alternatives for a single driving scenario, e.g. planning several paths to avoid obstacles
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- 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
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
- B60W30/095—Predicting travel path or likelihood of collision
- B60W30/0956—Predicting travel path or likelihood of collision the prediction being responsive to traffic or environmental parameters
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- 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
- B60W50/0098—Details of control systems ensuring comfort, safety or stability not otherwise provided for
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- 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
- B60W2552/00—Input parameters relating to infrastructure
- B60W2552/50—Barriers
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- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Human Computer Interaction (AREA)
- Traffic Control Systems (AREA)
Abstract
The disclosure relates to the technical field of automatic driving, and provides a vehicle control method and device and an unmanned vehicle, wherein the method comprises the following steps: detecting that an obstacle exists in front of the vehicle, and determining the type of the obstacle; under the condition that the type of the obstacle is a static obstacle, acquiring the position information and attribute information of the static obstacle; determining a first risk area, a second risk area and a third risk area in a preset distance range in front of a vehicle; under the condition that the static obstacle is positioned in the second risk area based on the position information, generating a simulation factor according to the position information and the attribute information of the static obstacle; simulating the dangerous degree generated when the vehicle collides with the static obstacle at the position of the static obstacle based on the simulation factor; and determining a coping strategy of the vehicle to the static obstacle according to the risk degree, and controlling the vehicle according to the coping strategy. The method and the device improve the accuracy of vehicle collision risk prediction and vehicle control.
Description
Technical Field
The disclosure belongs to the technical field of automatic driving, and particularly relates to a vehicle control method and device and an unmanned vehicle.
Background
In unmanned transportation operation of mines, unmanned mine cars usually run along a preset path. On the mine car travel path, obstacles such as falling rocks sometimes appear, and if the obstacles cannot be timely identified and proper solving measures are taken, the normal travel of the mine car is affected, so that the overall operation efficiency is affected.
At present, an unmanned mine car mainly carries out recognition of a front obstacle based on a sensor (such as a laser radar) and the like, but is limited by the problems of sensor accuracy and the like, transverse running deviation often exists when the unmanned mine car runs, and how to accurately recognize the collision risk of the obstacle in the transverse running deviation range and effectively control the vehicle based on the collision risk becomes a problem to be solved urgently in the field.
In the related art, the relative magnitude of the collision risk is generally determined according to the distance between an obstacle in the lateral driving deviation range and the center line of the vehicle body sweep area, so as to control the driving strategy of the vehicle. However, the collision prediction of the obstacle is inaccurate, so that the subsequent control of the vehicle is easy to cause potential safety hazard or cause low operation efficiency and other problems.
Disclosure of Invention
The disclosure aims to at least solve one of the technical problems in the prior art, and provides a vehicle control method and device and an unmanned vehicle.
According to one aspect of the present disclosure, a control method of a vehicle is disclosed, including: detecting that an obstacle exists in front of a vehicle in the process that the vehicle runs along a target path, and determining the type of the obstacle, wherein the vehicle is an automatic driving vehicle, and the type of the obstacle comprises a static obstacle and a dynamic obstacle; acquiring position information and attribute information of a static obstacle under the condition that the type of the obstacle is the static obstacle; determining a first risk area, a second risk area and a third risk area in a preset distance range in front of the vehicle, wherein the first risk area is a vehicle body sweeping range when the vehicle runs along the target path, the second risk area is close to the first risk area and has a width related to the running transverse deviation of the vehicle, and the third risk area is other areas far away from the first risk area; generating a simulation factor according to the position information and attribute information of the static obstacle under the condition that the static obstacle is positioned in the second risk area based on the position information; simulating the degree of danger generated when the vehicle collides with the static obstacle at the position where the static obstacle is located based on the simulation factor; and determining a coping strategy of the vehicle to the static obstacle according to the dangerous degree, and controlling the vehicle according to the coping strategy.
According to another aspect of the present disclosure, there is provided a control device of a vehicle, including: the obstacle detection module is used for detecting that an obstacle exists in front of a vehicle in the process that the vehicle runs along a target path, and determining the type of the obstacle, wherein the vehicle is an automatic driving vehicle, and the type of the obstacle comprises a static obstacle and a dynamic obstacle; the obstacle information acquisition module is used for acquiring the position information and the attribute information of the static obstacle under the condition that the type of the obstacle is the static obstacle; the risk area determining module is used for determining a first risk area, a second risk area and a third risk area in a preset distance range in front of the vehicle, wherein the first risk area is a vehicle body sweeping range when the vehicle runs according to the target path, the second risk area is close to the first risk area, the width of the second risk area is related to the running transverse deviation of the vehicle, and the third risk area is other areas far away from the first risk area; the simulator is used for generating a simulation factor according to the position information and the attribute information of the static obstacle and simulating the dangerous degree generated when the vehicle collides with the static obstacle at the position of the static obstacle under the condition that the static obstacle is positioned in the second risk area based on the position information; and the vehicle control module is used for determining the coping strategy of the vehicle to the static obstacle according to the dangerous degree and controlling the vehicle according to the coping strategy.
According to another aspect of the present disclosure, there is provided an unmanned vehicle including the control apparatus of any one of the above.
The technical scheme disclosed by the disclosure has the following beneficial technical effects:
according to the vehicle control method provided by the embodiment of the disclosure, the static obstacle in the second risk area (namely the area related to the lateral deviation of the vehicle running) in front of the unmanned vehicle is identified, the simulation factor is generated according to the position information and the attribute information of the static obstacle, and the dangerous degree of collision between the vehicle and the static obstacle is simulated and predicted, so that the countermeasure adopted for the vehicle is determined according to the dangerous degree, the accuracy of vehicle collision risk prediction and vehicle control is improved, and the potential safety hazard caused by inaccurate collision risk prediction and the adverse effect on the running efficiency are reduced.
Drawings
FIG. 1 is a schematic illustration of a control method of a vehicle according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of a risk area distribution of an embodiment of the present disclosure;
fig. 3 is a schematic structural view of a control device of a vehicle according to an embodiment of the present disclosure.
Detailed Description
For the purposes of promoting an understanding of the principles and advantages of the disclosure, reference will now be made to the drawings and specific language will be used to describe the same. It should be apparent that the described embodiments are only some of the embodiments of the present disclosure and not all of the embodiments of the present disclosure, and that the present disclosure is not limited by the example embodiments described herein.
As shown in fig. 1, according to an embodiment of the present invention, there is provided a vehicle control method, which may be applied to a vehicle end or a platform end, and the method includes the following steps:
step S102, detecting that an obstacle exists in front of the vehicle in the process of driving the vehicle according to a target path, and determining the type of the obstacle, wherein the vehicle is an automatic driving vehicle, and the type of the obstacle comprises a static obstacle and a dynamic obstacle.
In this step, the vehicle is an autonomous vehicle, and the target path travel of the vehicle may be a travel path previously planned for the autonomous vehicle. For example, the unmanned mining card performs a transportation operation according to a pre-planned travel route.
In this step, the presence of an obstacle in front may be detected according to a sensor provided at the vehicle end, and the type of the obstacle may be determined, wherein the type of the obstacle may be a static obstacle (for example, static falling rocks, ice cubes, other scattered objects, stopped other vehicles, etc.) or a dynamic obstacle (for example, a moving vehicle, a person, etc.). The sensor can be a laser radar, a millimeter wave radar, a camera or the like. Or, whether an obstacle exists in front of the vehicle can be identified through other vehicles around the vehicle, and the obstacle is transmitted to the vehicle through V2V or transmitted to the cloud platform. Alternatively, the road side device may detect whether an obstacle exists in front of the vehicle, and transmit the obstacle to the vehicle, or transmit the obstacle to the cloud platform. Optionally, in order to improve accuracy of obstacle recognition and reduce a dead zone of recognition (for example, the vehicle may only recognize one side of the obstacle and the other side of the obstacle is the dead zone of vehicle recognition), the obstacle may be recognized by combining the above at least two obstacle detection methods (may also be applied to the attribute information in the recognition step S104), and a final recognition result may be determined according to the at least two recognition results. For example, the sensor at the vehicle end, the drive test equipment and the sensors of other vehicles around the vehicle collect obstacle information at the same time and then gather the obstacle information to the vehicle end or the platform end so as to realize the detection of the obstacle.
Step S104, in the case that the type of the obstacle is a static obstacle, acquiring the position information and the attribute information of the static obstacle.
In this embodiment, attribute information of the static obstacle is used to characterize the self-inherent characteristics of the static obstacle, and is used for simulation. The attribute information includes, but is not limited to, size, type, shape, etc.
For the irregular obstacle, the size may refer to the maximum external dimension (length, width, height) of the static obstacle, or a minimum circular envelope block surrounding the irregular object may be determined, and the diameter or radius of the minimum circular envelope block may be used as the size of the static obstacle.
The static obstacle may be stone, ice, other vehicles (such as a accident vehicle), etc.
The shape of the static obstacle may be whether the obstacle has a sharp angle, a sharp angle orientation, or the like.
In this step, the position information and the attribute information of the static obstacle may be acquired by sensors provided at the vehicle end, the road side, or other vehicles around, in combination with positioning or the like. For example, attribute information of the static obstacle can be obtained by a laser radar set at the vehicle end. For another example, the position information of the static obstacle may be determined according to the position of the roadside device and by combining the images of the static obstacle captured by the roadside device.
Step S106, determining a first risk area, a second risk area and a third risk area within a preset distance range in front of the vehicle, where the first risk area is a vehicle body sweep range when the vehicle travels along the target path, the second risk area is adjacent to the first risk area and has a width related to a traveling lateral deviation of the vehicle, and the third risk area is other areas far away from the first risk area.
In this step, as shown in fig. 2, a schematic diagram of a risk area distribution is provided, wherein the first risk area S1 is a vehicle body sweep range when the vehicle (i.e., gray square) travels along the target path, that is, an envelope area swept by front, rear, left, and right boundaries of the vehicle. The second risk region S2 is a running lateral deviation region that is generated by the influence of the vehicle sensor accuracy, the vehicle positioning accuracy, and the like, and the width of the second risk region is related to the running lateral deviation of the vehicle, and is distributed on both sides of the first risk region. The third risk zone S3 is a zone that is distributed further outside the second risk zone and belongs to a (relative) safety zone for the running of the vehicle.
Step S108, when the static obstacle is determined to be located in the second risk area based on the position information, generating a simulation factor according to the position information and the attribute information of the static obstacle.
In this step, corresponding obstacle simulation data may be generated according to the position information and attribute information of the static obstacle, i.e., physical modeling and/or scene modeling of the obstacle may be performed. Optionally, the simulation factor includes one or more of: the weight of the static obstacle, the shape of the static obstacle, the size of the static obstacle, the distance between the static obstacle and the vehicle. Of course, speed information of the obstacle may also be included, and for a static obstacle, the speed is 0. Wherein the weight of the obstacle may be calculated or predicted based on attribute information of the obstacle. For example, the weight of the obstacle may be estimated based on the size, shape, and type of obstacle.
Alternatively, the position information and the attribute information of the static obstacle may be input into simulation software, and the physical modeling thereof in the corresponding driving environment may be output. Alternatively, the model elements pre-stored in the system may be matched according to the attribute information of the static obstacle, and the matched model elements or the adjustment results of the matched model elements may be used as simulation factors of the static obstacle. For example, model elements of different types of vehicles are prestored in the system, and if the type of the static obstacle is detected to be when the sprinkler is in a sprinkler, the model elements corresponding to the sprinkler can be directly searched according to the type, so that the simulation factors are obtained. For another example, a basic model of the falling stone may be pre-stored in the system, and when the type of the static obstacle is the falling stone, the basic model of the falling stone may be adaptively adjusted to obtain a corresponding simulation factor. Optionally, in order to improve the simulation efficiency, when the size of the falling stone is smaller than a preset size threshold, directly using a basic model of the falling stone; and when the size of the falling rocks is larger than or equal to a preset size threshold, carrying out fine adjustment on the basic model of the falling rocks based on other properties of the falling rocks to obtain a final simulation factor. Optionally, the preset size threshold is related to a wheel size of the vehicle. For example, the preset size threshold may be 15% of the wheel diameter, wherein the size of the falling stone is less than the preset size threshold, i.e. the maximum diameter of the falling stone is less than 15% of the wheel diameter. Optionally, fine tuning the basic model of the falling rock based on other properties of the falling rock may include: and determining a collision surface of the falling rocks and the vehicle, judging whether the falling rocks on the collision surface have sharp angles (determined according to the surface radian of the sensed obstacle), and if so, adjusting the basic model according to sharp angle information (sharp angle position, size and the like) and neglecting adjustment of other characteristics so as to improve the generation efficiency of the simulation factors. It should be noted that, the simulation factor may also be generated according to all or other part of attribute information of the static obstacle, which is not specifically limited in this disclosure.
Optionally, the generating the simulation factor according to the position information and the attribute information of the static obstacle includes: acquiring map data of an area where the static obstacle is located; and generating the simulation factors according to the position information, the attribute information and the map data of the static obstacle. In this embodiment, the scene simulation factor may be generated according to map data within a preset range of the area where the static obstacle is located, and the corresponding physical simulation factor may be generated in the scene. The map data may include information such as a retaining wall and a cliff around a static obstacle. Based on this, a more accurate simulation of the collision course and results of the vehicle and the static obstacle can be made.
Step S110, simulating the dangerous degree generated when the vehicle collides with the static obstacle at the position of the static obstacle based on the simulation factor.
In this step, the degree of risk of collision of the vehicle with the static obstacle can be simulated based on the physical simulation factor (and the scene simulation factor) obtained by the above simulation. Alternatively, the collision process and/or the collision result of the vehicle and the static obstacle may be simulated using the simulation factor, a pre-stored vehicle dynamics model, a momentum conservation formula, and a kinetic energy conservation formula, and the risk degree may be determined according to the collision process and/or the collision result.
The degree of risk may be classified into different classes according to the collision course, the collision result, or a combination of the collision course and the collision result. For example, determining the degree of risk based on the collision result may include: if the collision result is that the vehicle overturns, the dangerous degree is high, and if the vehicle stops, the dangerous degree is low when the vehicle rolls over the obstacle. In addition, the situation of vehicle damage can be considered, and the more serious the vehicle damage situation is, the higher the corresponding risk degree is. For another example, determining the degree of risk based on the collision process includes: determining whether the position of the vehicle deviates from the original running track too much or not in the collision process, wherein the greater the position is, the higher the dangerous degree is; or if there is a drop cliff, the risk is high.
Optionally, based on the simulation factor, simulating the risk degree generated when the vehicle collides with the static obstacle at the position where the static obstacle is located, and further including: and simulating the dangerous degree generated when the vehicle collides with the static obstacle at the position of the static obstacle according to the simulation factor and at least the current running speed of the vehicle. In this embodiment, the degree of risk generated when the vehicle collides with the static obstacle may be simulated based on the current running speed of the vehicle. In addition, since there may be an error in the vehicle speed control, in order to further improve the accuracy of the simulation, the degree of risk generated when the vehicle collides with the static obstacle at the position of the static obstacle may be simulated according to the simulation factor and a plurality of driving speeds including the current driving speed and at least one other target speed differing from the current driving speed by a specified speed difference. The specified speed difference may be as small as possible, so that the current running speed is relatively close to the other target speeds.
And step S112, determining a coping strategy of the vehicle to the static obstacle according to the risk degree, and controlling the vehicle according to the coping strategy.
In this step, the coping strategies may include deceleration, detour, riding, etc. Alternatively, the deceleration of the vehicle may be determined based on the risk level, the risk level being proportional to the absolute value of the deceleration; and performing deceleration control on the vehicle according to the deceleration. That is, in the case where the degree of danger is higher, a strategy of decelerating the vehicle more quickly is adopted to reduce the potential safety hazard.
According to the vehicle control method provided by the embodiment, the static obstacle in the second risk area (namely the area related to the lateral deviation of the vehicle running) in front of the unmanned vehicle is identified, the simulation factor is generated according to the position information and the attribute information of the static obstacle, and the dangerous degree of the collision between the vehicle and the static obstacle is simulated and predicted, so that the countermeasure adopted for the vehicle is determined according to the dangerous degree, the accuracy of the vehicle collision risk prediction and the vehicle control is improved, and the potential safety hazard caused by inaccurate collision risk prediction and the adverse effect on the running efficiency are reduced.
Fig. 3 is a control device of a vehicle according to an embodiment of the present disclosure, for implementing the above-described control method of the vehicle. The device comprises:
the obstacle detection module 30 is configured to detect that an obstacle exists in front of a vehicle during a driving process of the vehicle according to a target path, and determine a type of the obstacle, where the vehicle is an autonomous vehicle, and the type of the obstacle includes a static obstacle and a dynamic obstacle.
An obstacle information obtaining module 32, configured to obtain, in a case where the type of the obstacle is a static obstacle, position information and attribute information of the static obstacle.
The risk area determining module 34 is configured to determine a first risk area, a second risk area and a third risk area within a preset distance range in front of the vehicle, where the first risk area is a vehicle body sweep range when the vehicle travels along the target path, the second risk area is adjacent to the first risk area and has a width related to a lateral deviation of travel of the vehicle, and the third risk area is another area far away from the first risk area.
And a simulator 36, configured to generate a simulation factor according to the position information and the attribute information of the static obstacle, and simulate a degree of risk generated when the vehicle collides with the static obstacle at the position of the static obstacle, when the static obstacle is determined to be located in the second risk area based on the position information.
A vehicle control module 38 for determining a coping strategy of the vehicle with respect to the static obstacle according to the risk degree, and controlling the vehicle according to the coping strategy.
Optionally, the simulator is further configured to: acquiring map data of an area where the static obstacle is located; and generating the simulation factors according to the position information, the attribute information and the map data of the static obstacle.
Optionally, the simulator is further configured to: and simulating a collision process and/or a collision result of the vehicle and the static obstacle by using the simulation factors, a pre-stored vehicle dynamics model, a momentum conservation formula and a kinetic energy conservation formula, and determining the risk degree according to the collision process and/or the collision result.
Optionally, the vehicle control module is further configured to: determining a deceleration of the vehicle based on the risk level, the risk level being proportional to an absolute value of the deceleration; and performing deceleration control on the vehicle according to the deceleration.
Optionally, the simulator is further configured to: and simulating the dangerous degree generated when the vehicle collides with the static obstacle at the position of the static obstacle according to the simulation factor and at least the current running speed of the vehicle.
Optionally, the simulator is further configured to: and simulating the dangerous degree generated when the vehicle collides with the static obstacle at the position of the static obstacle according to the simulation factors and a plurality of running speeds, wherein the plurality of running speeds comprise the current running speed and at least one speed differing from the current running speed by a specified speed difference value.
According to the control device for the vehicle, provided by the embodiment of the disclosure, the static obstacle in the second risk area (namely the area related to the lateral deviation of the vehicle running) in front of the unmanned vehicle is identified, the simulation factor is generated according to the position information and the attribute information of the static obstacle, and the dangerous degree of collision between the vehicle and the static obstacle is simulated and predicted, so that the countermeasure adopted for the vehicle is determined according to the dangerous degree, the accuracy of the collision risk prediction and the vehicle control of the vehicle is improved, and the potential safety hazard caused by inaccurate collision risk prediction and the adverse effect on the running efficiency are reduced.
According to another aspect of the present disclosure, there is provided an unmanned vehicle including the control apparatus of any one of the above. The device is described in the above embodiments, and will not be described in detail herein.
In the description of the present disclosure, it is to be noted that, unless otherwise indicated, the meaning of "plurality" is two or more; the terms "upper," "lower," "left," "right," "inner," "outer," and the like indicate an orientation or positional relationship merely for convenience of describing the present disclosure and simplifying the description, and do not indicate or imply that the devices or elements being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus are not to be construed as limiting the present disclosure. Furthermore, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The "vertical" is not strictly vertical but is within the allowable error range. "parallel" is not strictly parallel but is within the tolerance of the error.
In the description of the present disclosure, it should also be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be directly connected or indirectly connected through an intermediate medium. The specific meaning of the terms in the present disclosure may be understood as appropriate by those of ordinary skill in the art.
The embodiments of the present disclosure have been described above with reference to the accompanying drawings, but the present disclosure is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those of ordinary skill in the art without departing from the spirit of the disclosure and the scope of the claims, which are all within the protection of the present disclosure.
Claims (10)
1. A control method of a vehicle, characterized by comprising:
detecting that an obstacle exists in front of a vehicle in the process that the vehicle runs along a target path, and determining the type of the obstacle, wherein the vehicle is an automatic driving vehicle, and the type of the obstacle comprises a static obstacle and a dynamic obstacle;
acquiring position information and attribute information of a static obstacle under the condition that the type of the obstacle is the static obstacle;
determining a first risk area, a second risk area and a third risk area in a preset distance range in front of the vehicle, wherein the first risk area is a vehicle body sweeping range when the vehicle runs along the target path, the second risk area is close to the first risk area and has a width related to the running transverse deviation of the vehicle, and the third risk area is other areas far away from the first risk area;
generating a simulation factor according to the position information and attribute information of the static obstacle under the condition that the static obstacle is positioned in the second risk area based on the position information;
simulating the degree of danger generated when the vehicle collides with the static obstacle at the position where the static obstacle is located based on the simulation factor;
and determining a coping strategy of the vehicle to the static obstacle according to the dangerous degree, and controlling the vehicle according to the coping strategy.
2. The method of claim 1, wherein generating a simulation factor based on the location information and the attribute information of the static obstacle comprises:
acquiring map data of an area where the static obstacle is located;
and generating the simulation factors according to the position information, the attribute information and the map data of the static obstacle.
3. The method according to claim 1 or 2, wherein simulating the degree of risk of the vehicle colliding with the static obstacle at the location of the static obstacle based on the simulation factor comprises:
and simulating a collision process and/or a collision result of the vehicle and the static obstacle by using the simulation factors, a pre-stored vehicle dynamics model, a momentum conservation formula and a kinetic energy conservation formula, and determining the risk degree according to the collision process and/or the collision result.
4. The method of claim 1, wherein determining a coping strategy of the vehicle with respect to the static obstacle according to the degree of risk, and controlling the vehicle according to the coping strategy comprises:
determining a deceleration of the vehicle based on the risk level, the risk level being proportional to an absolute value of the deceleration;
and performing deceleration control on the vehicle according to the deceleration.
5. The method of claim 1, wherein simulating the degree of risk of the vehicle colliding with the static obstacle at the location of the static obstacle based on the simulation factor comprises:
and simulating the dangerous degree generated when the vehicle collides with the static obstacle at the position of the static obstacle according to the simulation factor and at least the current running speed of the vehicle.
6. The method according to claim 5, wherein simulating the degree of risk of the vehicle colliding with the static obstacle in the place of the static obstacle according to the simulation factor and at least the current running speed of the vehicle comprises:
and simulating the dangerous degree generated when the vehicle collides with the static obstacle at the position of the static obstacle according to the simulation factors and a plurality of running speeds, wherein the plurality of running speeds comprise the current running speed and at least one speed differing from the current running speed by a specified speed difference value.
7. The method of claim 1, wherein the simulation factor comprises one or more of: the weight of the static obstacle, the shape of the static obstacle, the size of the static obstacle, the distance between the static obstacle and the vehicle.
8. The method of claim 1, wherein the attribute information includes one or more of: size, type, shape.
9. A control device for a vehicle, comprising:
the obstacle detection module is used for detecting that an obstacle exists in front of a vehicle in the process that the vehicle runs along a target path, and determining the type of the obstacle, wherein the vehicle is an automatic driving vehicle, and the type of the obstacle comprises a static obstacle and a dynamic obstacle;
the obstacle information acquisition module is used for acquiring the position information and the attribute information of the static obstacle under the condition that the type of the obstacle is the static obstacle;
the risk area determining module is used for determining a first risk area, a second risk area and a third risk area in a preset distance range in front of the vehicle, wherein the first risk area is a vehicle body sweeping range when the vehicle runs according to the target path, the second risk area is close to the first risk area, the width of the second risk area is related to the running transverse deviation of the vehicle, and the third risk area is other areas far away from the first risk area;
the simulator is used for generating a simulation factor according to the position information and the attribute information of the static obstacle and simulating the dangerous degree generated when the vehicle collides with the static obstacle at the position of the static obstacle under the condition that the static obstacle is positioned in the second risk area based on the position information;
and the vehicle control module is used for determining the coping strategy of the vehicle to the static obstacle according to the dangerous degree and controlling the vehicle according to the coping strategy.
10. An unmanned vehicle comprising the control apparatus of claim 9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310847459.4A CN116572996B (en) | 2023-07-12 | 2023-07-12 | Vehicle control method and device and unmanned vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310847459.4A CN116572996B (en) | 2023-07-12 | 2023-07-12 | Vehicle control method and device and unmanned vehicle |
Publications (2)
| Publication Number | Publication Date |
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| CN116572996A true CN116572996A (en) | 2023-08-11 |
| CN116572996B CN116572996B (en) | 2023-09-12 |
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| CN202310847459.4A Active CN116572996B (en) | 2023-07-12 | 2023-07-12 | Vehicle control method and device and unmanned vehicle |
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| US20120161951A1 (en) * | 2010-12-23 | 2012-06-28 | Denso Corporation | Vehicular obstacle notification apparatus |
| US20210181741A1 (en) * | 2019-12-11 | 2021-06-17 | Baidu Usa Llc | Method for determining passable area in planning a path of autonomous driving vehicles |
| CN113147752A (en) * | 2021-03-02 | 2021-07-23 | 浙江亚太智能网联汽车创新中心有限公司 | Unmanned driving method and system |
| WO2022133684A1 (en) * | 2020-12-21 | 2022-06-30 | 华为技术有限公司 | Control method, related device, and computer-readable storage medium |
| CN115107809A (en) * | 2022-07-28 | 2022-09-27 | 北京京深深向科技有限公司 | Automatic driving decision method and device, electronic equipment and storage medium |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120161951A1 (en) * | 2010-12-23 | 2012-06-28 | Denso Corporation | Vehicular obstacle notification apparatus |
| US20210181741A1 (en) * | 2019-12-11 | 2021-06-17 | Baidu Usa Llc | Method for determining passable area in planning a path of autonomous driving vehicles |
| WO2022133684A1 (en) * | 2020-12-21 | 2022-06-30 | 华为技术有限公司 | Control method, related device, and computer-readable storage medium |
| CN113147752A (en) * | 2021-03-02 | 2021-07-23 | 浙江亚太智能网联汽车创新中心有限公司 | Unmanned driving method and system |
| CN115107809A (en) * | 2022-07-28 | 2022-09-27 | 北京京深深向科技有限公司 | Automatic driving decision method and device, electronic equipment and storage medium |
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