CN116343524A - Method for predicting vehicle collision, controller and storage medium - Google Patents
Method for predicting vehicle collision, controller and storage medium Download PDFInfo
- Publication number
- CN116343524A CN116343524A CN202310332673.6A CN202310332673A CN116343524A CN 116343524 A CN116343524 A CN 116343524A CN 202310332673 A CN202310332673 A CN 202310332673A CN 116343524 A CN116343524 A CN 116343524A
- Authority
- CN
- China
- Prior art keywords
- target pedestrian
- vehicle
- collision
- collision time
- determining
- 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 50
- 230000033001 locomotion Effects 0.000 claims abstract description 58
- 230000011664 signaling Effects 0.000 claims abstract description 8
- 230000004927 fusion Effects 0.000 claims description 6
- 238000010586 diagram Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 8
- 238000004590 computer program Methods 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 238000013528 artificial neural network Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 1
- 230000008094 contradictory effect Effects 0.000 description 1
- 231100001261 hazardous Toxicity 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/166—Anti-collision systems for active traffic, e.g. moving vehicles, pedestrians, bikes
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Traffic Control Systems (AREA)
Abstract
The application discloses a prediction method, a controller and a storage medium for vehicle collision. The method comprises the following steps: acquiring motion information of a target pedestrian and the speed of a vehicle, wherein the motion information of the target pedestrian comprises the longitudinal position of the target pedestrian; predicting collision time according to the motion information of the target pedestrian and the speed of the vehicle; based on the collision time, determining the transverse position of the target pedestrian after the collision time and the position information of the vehicle after the collision time; determining a dangerous area according to the longitudinal position of the target pedestrian and the position information of the vehicle after the collision time; judging whether the transverse position of the target pedestrian after the collision time is in a dangerous area or not; and sending an alarm signal and controlling the vehicle to brake under the condition that the transverse position of the target pedestrian after the collision time is in the dangerous area. According to the method and the device for determining the dangerous area, the dangerous area is determined according to the longitudinal position of the target pedestrian and the position information of the vehicle after the collision time, the range of the dangerous area can be adjusted in real time, and the method and the device are suitable for complex and changeable vehicle driving scenes.
Description
Technical Field
The present application relates to the field of vehicle driving assistance, and in particular, to a vehicle collision prediction method, a controller, and a storage medium.
Background
In order to reduce the occurrence of collision accidents of automobiles and improve traffic safety, automobile host factories and suppliers increasingly pay attention to passive safety technology and active safety technology of automobiles. At present, domestic researchers have many researches on safe distance models and control algorithms. For example, a neural network method is used for researching an automobile collision avoidance algorithm, namely a cooperative early warning algorithm based on a fuzzy neural network, so that the method is applied to a front collision early warning system. However, the neural network method is adopted to perform front collision early warning, so that the robustness is poor. In addition, in the prior art, a dangerous area needs to be preset, the size of the dangerous area range is usually fixed, and the dangerous area range cannot adapt to complex and changeable vehicle driving scenes. Therefore, the prior art has the problems of poor robustness and incapability of adapting to complex and changeable vehicle driving scenes.
Disclosure of Invention
An object of the embodiments of the present application is to provide a method, a controller and a storage medium for predicting a vehicle collision, which are used for solving the problems in the prior art that robustness is poor and a complex and variable vehicle driving scene cannot be adapted.
In order to achieve the above object, a first aspect of the present application provides a method for predicting a vehicle collision, including:
acquiring motion information of a target pedestrian and the speed of a vehicle, wherein the motion information of the target pedestrian comprises the longitudinal position of the target pedestrian;
predicting collision time according to the motion information of the target pedestrian and the speed of the vehicle;
based on the collision time, determining the transverse position of the target pedestrian after the collision time and the position information of the vehicle after the collision time;
determining a dangerous area according to the longitudinal position of the target pedestrian and the position information of the vehicle after the collision time;
judging whether the transverse position of the target pedestrian after the collision time is in a dangerous area or not;
and sending an alarm signal and controlling the vehicle to brake under the condition that the transverse position of the target pedestrian after the collision time is in the dangerous area.
In the embodiment of the application, determining the dangerous area according to the longitudinal position of the target pedestrian and the position information of the vehicle after the collision time comprises the following steps:
determining a position interval in which the longitudinal position of the target pedestrian is located;
and determining a dangerous area according to the position interval of the longitudinal position of the target pedestrian and the position information of the vehicle after the collision time.
In this embodiment of the present application, the motion information of the target pedestrian further includes a longitudinal speed of the target pedestrian, and predicting the collision time according to the motion information of the target pedestrian and the speed of the vehicle includes:
the collision time is predicted from the longitudinal position of the target pedestrian, the longitudinal speed of the target pedestrian, and the speed of the vehicle.
In this embodiment of the present application, the motion information of the target pedestrian includes a lateral speed of the target pedestrian and a lateral position of the target pedestrian, the position information of the vehicle after the collision time includes a lateral position of the vehicle after the collision time, and determining the lateral position of the target pedestrian after the collision time includes:
determining a position deviation value, wherein the position deviation value is a deviation value of the lateral position of the vehicle after the collision time and the lateral position of the target pedestrian;
and determining the transverse position of the target pedestrian after the collision time according to the position deviation value, the transverse speed of the target pedestrian and the collision time.
In the embodiment of the present application, the lateral position of the target pedestrian after the collision time satisfies the formula (1):
y pre ＝dy-v y t； (1)
wherein y is pre For the lateral position of the target pedestrian after the collision time, dy is the position deviation value, v y The lateral speed of the target pedestrian is given, and t is the collision time.
In an embodiment of the present application, determining the position deviation value includes:
the position deviation value is determined based on the lateral position of the target pedestrian, the longitudinal position of the target pedestrian, and the vehicle turning radius.
In the embodiment of the present application, the positional deviation value satisfies the formula (2):
where dy is the position deviation value, y is the lateral position of the target pedestrian, R is the turning radius of the vehicle, and x is the longitudinal position of the target pedestrian.
In the embodiment of the present application, acquiring the motion information of the target pedestrian includes:
and acquiring the motion information of the target pedestrian through information fusion.
A second aspect of the present application provides a controller comprising:
a memory configured to store instructions; and
and a processor configured to recall the instructions from the memory and to enable the above-described method of predicting a vehicle collision when executing the instructions.
A third aspect of the present application provides a machine-readable storage medium having stored thereon instructions for causing a machine to perform the above-described method of predicting a vehicle collision.
Through the technical scheme, the motion information of the target pedestrian and the speed of the vehicle are firstly obtained, and the motion information of the target pedestrian comprises the longitudinal position of the target pedestrian. And predicting the collision time according to the motion information of the target pedestrian and the speed of the vehicle. Subsequently, based on the collision time, the lateral position of the target pedestrian after the collision time and the positional information of the vehicle after the collision time are determined. And then determining a dangerous area according to the longitudinal position of the target pedestrian and the position information of the vehicle after the collision time, and further judging whether the transverse position of the target pedestrian after the collision time is in the dangerous area. And finally, sending an alarm signal and controlling the vehicle to brake under the condition that the transverse position of the target pedestrian after the collision time is in the dangerous area. According to the method and the device for determining the dangerous area, the dangerous area is determined according to the longitudinal position of the target pedestrian and the position information of the vehicle after the collision time, the range of the dangerous area can be adjusted in real time, the method and the device are suitable for complex and changeable vehicle driving scenes, and the robustness is improved.
Additional features and advantages of embodiments of the present application will be set forth in the detailed description that follows.
Drawings
The accompanying drawings are included to provide a further understanding of embodiments of the present application and are incorporated in and constitute a part of this specification, illustrate embodiments of the present application and together with the description serve to explain, without limitation, the embodiments of the present application. In the drawings:
FIG. 1 schematically illustrates a flow chart of a method of predicting a vehicle collision according to an embodiment of the present application;
FIG. 2 schematically illustrates a schematic view of collision prediction in a vehicle left turn scenario according to an embodiment of the present application;
fig. 3 schematically shows a block diagram of a controller according to an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it should be understood that the specific implementations described herein are only for illustrating and explaining the embodiments of the present application, and are not intended to limit the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present application based on the embodiments herein.
It should be noted that, in the embodiment of the present application, directional indications (such as up, down, left, right, front, and rear … …) are referred to, and the directional indications are merely used to explain the relative positional relationship, movement conditions, and the like between the components in a specific posture (as shown in the drawings), and if the specific posture is changed, the directional indications are correspondingly changed.
In addition, if there is a description of "first", "second", etc. in the embodiments of the present application, the description of "first", "second", etc. is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be regarded as not exist and not within the protection scope of the present application.
Fig. 1 schematically shows a flow chart of a method of predicting a vehicle collision according to an embodiment of the present application. As shown in fig. 1, an embodiment of the present application provides a method for predicting a vehicle collision, which may include the steps of:
step 103, determining the transverse position of the target pedestrian after the collision time and the position information of the vehicle after the collision time based on the collision time;
104, determining a dangerous area according to the longitudinal position of the target pedestrian and the position information of the vehicle after the collision time;
and 106, sending an alarm signal and controlling the vehicle to brake under the condition that the transverse position of the target pedestrian after the collision time is in the dangerous area.
The prediction method is applied to a vehicle turning scene, and can be used for predicting whether the vehicle collides with a pedestrian in the turning process.
In the embodiment of the present application, first, the controller may determine a target pedestrian and acquire motion information of the target pedestrian. The target pedestrian is a pedestrian that may collide with the vehicle, and may be determined based on the movement information of the pedestrian, the traveling direction of the vehicle, and the speed of the vehicle. The movement information of the target pedestrian includes a lateral speed, a lateral position, a longitudinal speed, and a longitudinal position of the target pedestrian. Meanwhile, the controller can also acquire the current speed of the vehicle. Based on the movement information of the target pedestrian and the speed of the vehicle, the controller can predict the time of collision, i.e., how long later the vehicle and the target pedestrian may collide. After determining the time of collision, the controller may determine a lateral position of the target pedestrian after the time of collision based on the lateral speed and the time of collision of the target pedestrian, and determine position information of the vehicle after the time of collision based on the speed and the time of collision of the vehicle. Then, the controller may determine a dangerous area according to the longitudinal position of the target pedestrian and the position information of the vehicle after the collision time, and determine whether the lateral position of the target pedestrian after the collision time is within the dangerous area. And under the condition that the transverse position of the target pedestrian after the collision time is in the dangerous area, the controller sends an alarm signal and controls the vehicle to brake. Under the condition that the transverse position of the target pedestrian after the collision time is not in the dangerous area, the controller does not send an alarm signal, and the vehicle runs normally. Therefore, the controller can predict whether the vehicle collides with the target pedestrian in the turning process, the possibility of accident is reduced, and the safety of the vehicle and the target pedestrian is ensured.
Through the technical scheme, the motion information of the target pedestrian and the speed of the vehicle are firstly obtained, and the motion information of the target pedestrian comprises the longitudinal position of the target pedestrian. And predicting the collision time according to the motion information of the target pedestrian and the speed of the vehicle. Subsequently, based on the collision time, the lateral position of the target pedestrian after the collision time and the positional information of the vehicle after the collision time are determined. And then determining a dangerous area according to the longitudinal position of the target pedestrian and the position information of the vehicle after the collision time, and further judging whether the transverse position of the target pedestrian after the collision time is in the dangerous area. And finally, sending an alarm signal and controlling the vehicle to brake under the condition that the transverse position of the target pedestrian after the collision time is in the dangerous area. According to the method and the device for determining the dangerous area, the dangerous area is determined according to the longitudinal position of the target pedestrian and the position information of the vehicle after the collision time, the range of the dangerous area can be adjusted in real time, the method and the device are suitable for complex and changeable vehicle driving scenes, and the robustness is improved.
In the embodiment of the present application, acquiring the motion information of the target pedestrian may include:
and acquiring the motion information of the target pedestrian through information fusion.
Specifically, the information fusion refers to an information processing process of analyzing and comprehensively processing the obtained plurality of sensor data, thereby determining the movement information of the target pedestrian. In the prediction process of the vehicle collision, the controller may determine the movement information of the target pedestrian through information fusion based on the received plurality of sensor data.
Fig. 2 schematically illustrates a schematic view of collision prediction in a left turn scenario of a vehicle according to an embodiment of the present application. Taking a left turn scene of a vehicle as an example, a coordinate system is established by taking the forward direction of the vehicle as a longitudinal positive direction and the left side of the vehicle as a transverse positive direction as shown in fig. 2, x is the longitudinal position of a target pedestrian, y is the transverse position of the target pedestrian, v y V is the speed of the vehicle, and dy is the position deviation value, which is the lateral speed of the target pedestrian. In this embodiment of the present application, the motion information of the target pedestrian further includes a longitudinal speed of the target pedestrian, and step 102 of predicting the collision time according to the motion information of the target pedestrian and the speed of the vehicle may include:
the collision time is predicted from the longitudinal position of the target pedestrian, the longitudinal speed of the target pedestrian, and the speed of the vehicle.
Specifically, the controller may predict the collision time based on the motion information of the target pedestrian and the speed of the vehicle. The movement information of the target pedestrian includes a longitudinal speed of the target pedestrian. The controller may predict the collision time based on the longitudinal position of the target pedestrian, the longitudinal speed of the target pedestrian, and the speed of the vehicle. The collision time satisfies the formula (3):
wherein t is the collision time, x is the longitudinal position of the target pedestrian, v is the speed of the vehicle, v x Is the longitudinal speed of the target pedestrian. In this way, the controller can predict the collision time at which the vehicle and the target pedestrian may collide.
In the embodiment of the present application, step 104 of determining the dangerous area according to the longitudinal position of the target pedestrian and the position information of the vehicle after the collision time may include:
determining a position interval in which the longitudinal position of the target pedestrian is located;
and determining a dangerous area according to the position interval of the longitudinal position of the target pedestrian and the position information of the vehicle after the collision time.
Specifically, the closer the longitudinal position of the target pedestrian is to the vehicle, the greater the possibility of collision can be considered, and thus a greater risk area is set. The farther the longitudinal position of the target pedestrian is from the vehicle, the less likely it is to collide, and thus a smaller risk area is set. Therefore, the controller can determine the position section where the longitudinal position of the target pedestrian is located, and the position section can be set according to the actual situation. The controller may determine the width of the hazard zone based on the location interval in which the longitudinal location of the target pedestrian is located. Further, since the position of the dangerous area is updated as the vehicle moves, it is necessary to determine the specific position of the dangerous area in combination with the position information of the vehicle after the time of collision. In one example, a plurality of location intervals, such as a first location interval, a second location interval, and a second location interval, may be predetermined, wherein the first location interval is 1 meter to 2 meters. When it is detected that the distance between the longitudinal position of the target pedestrian and the vehicle is 1.5 m, the position zone in which the longitudinal position of the target pedestrian is located may be regarded as the first position zone, and at this time, the width of the dangerous area may be set to 2 m. In this way, the controller can determine the width and location of the hazardous area.
As shown in fig. 2, in an embodiment of the present application, determining the position deviation value may include:
the position deviation value is determined based on the lateral position of the target pedestrian, the longitudinal position of the target pedestrian, and the vehicle turning radius.
Specifically, the controller may determine the position deviation value. The controller may acquire the movement information of the target pedestrian, wherein the movement information of the target pedestrian includes a lateral position of the target pedestrian and a longitudinal position of the target pedestrian. And, the controller may acquire the vehicle turning radius. The position deviation value may be further determined in combination with the lateral position of the target pedestrian, the longitudinal position of the target pedestrian, and the vehicle turning radius.
In the embodiment of the present application, the positional deviation value may satisfy formula (2):
where dy is the position deviation value, y is the lateral position of the target pedestrian, R is the turning radius of the vehicle, and x is the longitudinal position of the target pedestrian.
Specifically, the controller may determine the position deviation value. The controller may acquire the movement information of the target pedestrian, wherein the movement information of the target pedestrian includes a lateral position of the target pedestrian and a longitudinal position of the target pedestrian. And, the controller may acquire the vehicle turning radius. The position deviation value may be further determined in combination with the lateral position of the target pedestrian, the longitudinal position of the target pedestrian, and the vehicle turning radius. Wherein the vehicle turning radius satisfies the formula (4):
where R is the vehicle turning radius, v is the vehicle speed, and ω is the yaw rate.
As shown in fig. 2, in the embodiment of the present application, the movement information of the target pedestrian includes a lateral speed of the target pedestrian and a lateral position of the target pedestrian, the position information of the vehicle after the collision time includes a lateral position of the vehicle after the collision time, and determining the lateral position of the target pedestrian after the collision time may include:
determining a position deviation value, wherein the position deviation value is a deviation value of the lateral position of the vehicle after the collision time and the lateral position of the target pedestrian;
and determining the transverse position of the target pedestrian after the collision time according to the position deviation value, the transverse speed of the target pedestrian and the collision time.
Specifically, the controller may predict the lateral position of the target pedestrian after the time of collision. The controller needs to first determine the position deviation value. The position deviation value is a deviation value of the lateral position of the vehicle after the collision time from the lateral position of the current target pedestrian. And then the transverse position of the target pedestrian after the collision time can be determined according to the position deviation value, the transverse speed of the target pedestrian and the collision time. In this way, the lateral position of the target pedestrian after the time of collision can be determined so that the subsequent controller can determine whether the lateral position of the target pedestrian after the time of collision is within the hazard area.
In the embodiment of the present application, the lateral position of the target pedestrian after the collision time may satisfy the formula (1):
y pre ＝dy-v y t； (1)
wherein y is pre For the lateral position of the target pedestrian after the collision time, dy is the position deviation value, v y The lateral speed of the target pedestrian is given, and t is the collision time.
Specifically, the controller may determine the lateral position of the target pedestrian after the collision time based on the position deviation value, the lateral speed of the target pedestrian, and the collision time. In this way, the subsequent controller is made to determine whether the lateral position of the target pedestrian after the time of collision is within the hazard area.
In addition, the controller may also determine a motion scene of the target pedestrian according to the motion information of the target pedestrian. Table 1 schematically shows a motion scenario of a target pedestrian according to a specific embodiment of the present application. As shown in Table 1, according to the lateral velocity v of the target pedestrian y Lateral position y, longitudinal velocity v x And the longitudinal position x can distinguish the motion scene of the target pedestrian. In this way, the alarm signal can be more accurate.
TABLE 1
| Location of target pedestrian | Speed of target pedestrian | Motion scene of target pedestrian |
| x>0，y<0 | v y >0，v x ＝0 | Right side cross |
| x>0，y>0 | v y <0，v x ＝0 | Left side cross |
| x>0，y<0 | v y <0，v x ＝0 | Far to the right |
| x>0，y>0 | v y >0，v x ＝0 | Left side is far away from |
| x>0 | v y ＝0，v x ＝0 | Rest |
| x>0，y<0 | v y >0，v x >0 | Right side facing away from the vehicle |
| x>0，y>0 | v y <0，v x >0 | Left side back vehicle crossing |
| x>0，y<0 | v y >0，v x <0 | Right side facing the vehicle |
| x>0，y>0 | v y <0，v x <0 | Left side facing the vehicle |
Fig. 3 schematically shows a block diagram of a controller according to an embodiment of the present application. As shown in fig. 3, an embodiment of the present application provides a controller, which may include:
a memory 310 configured to store instructions; and
the processor 320 is configured to recall instructions from the memory 310 and when executing the instructions, to enable the vehicle collision prediction method described above.
Specifically, in embodiments of the present application, processor 320 may be configured to:
acquiring motion information of a target pedestrian and the speed of a vehicle, wherein the motion information of the target pedestrian comprises the longitudinal position of the target pedestrian;
predicting collision time according to the motion information of the target pedestrian and the speed of the vehicle;
based on the collision time, determining the transverse position of the target pedestrian after the collision time and the position information of the vehicle after the collision time;
determining a dangerous area according to the longitudinal position of the target pedestrian and the position information of the vehicle after the collision time;
judging whether the transverse position of the target pedestrian after the collision time is in a dangerous area or not;
and sending an alarm signal and controlling the vehicle to brake under the condition that the transverse position of the target pedestrian after the collision time is in the dangerous area.
Further, the processor 320 may be further configured to:
determining a position interval in which the longitudinal position of the target pedestrian is located;
and determining a dangerous area according to the position interval of the longitudinal position of the target pedestrian and the position information of the vehicle after the collision time.
Further, the processor 320 may be further configured to:
the collision time is predicted from the longitudinal position of the target pedestrian, the longitudinal speed of the target pedestrian, and the speed of the vehicle.
Further, the processor 320 may be further configured to:
determining a position deviation value, wherein the position deviation value is a deviation value of the lateral position of the vehicle after the collision time and the lateral position of the target pedestrian;
and determining the transverse position of the target pedestrian after the collision time according to the position deviation value, the transverse speed of the target pedestrian and the collision time.
In the embodiment of the present application, the lateral position of the target pedestrian after the collision time satisfies the formula (1):
y pre ＝dy-v y t； (1)
wherein y is pre For the lateral position of the target pedestrian after the collision time, dy is the position deviation value, v y The lateral speed of the target pedestrian is given, and t is the collision time.
Further, the processor 320 may be further configured to:
the position deviation value is determined based on the lateral position of the target pedestrian, the longitudinal position of the target pedestrian, and the vehicle turning radius.
In the embodiment of the present application, the positional deviation value satisfies the formula (2):
where dy is the position deviation value, y is the lateral position of the target pedestrian, R is the turning radius of the vehicle, and x is the longitudinal position of the target pedestrian.
Further, the processor 320 may be further configured to:
and acquiring the motion information of the target pedestrian through information fusion.
Through the technical scheme, the motion information of the target pedestrian and the speed of the vehicle are firstly obtained, and the motion information of the target pedestrian comprises the longitudinal position of the target pedestrian. And predicting the collision time according to the motion information of the target pedestrian and the speed of the vehicle. Subsequently, based on the collision time, the lateral position of the target pedestrian after the collision time and the positional information of the vehicle after the collision time are determined. And then determining a dangerous area according to the longitudinal position of the target pedestrian and the position information of the vehicle after the collision time, and further judging whether the transverse position of the target pedestrian after the collision time is in the dangerous area. And finally, sending an alarm signal and controlling the vehicle to brake under the condition that the transverse position of the target pedestrian after the collision time is in the dangerous area. According to the method and the device for determining the dangerous area, the dangerous area is determined according to the longitudinal position of the target pedestrian and the position information of the vehicle after the collision time, the range of the dangerous area can be adjusted in real time, the method and the device are suitable for complex and changeable vehicle driving scenes, and the robustness is improved.
The embodiment of the application also provides a machine-readable storage medium, wherein the machine-readable storage medium is stored with instructions for causing a machine to execute the method for predicting the vehicle collision.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (10)
1. A method of predicting a vehicle collision, comprising:
acquiring motion information of a target pedestrian and the speed of a vehicle, wherein the motion information of the target pedestrian comprises the longitudinal position of the target pedestrian;
predicting collision time according to the motion information of the target pedestrian and the speed of the vehicle;
determining the transverse position of the target pedestrian after the collision time and the position information of the vehicle after the collision time based on the collision time;
determining a dangerous area according to the longitudinal position of the target pedestrian and the position information of the vehicle after the collision time;
judging whether the transverse position of the target pedestrian after the collision time is in the dangerous area or not;
and sending an alarm signal and controlling the vehicle to brake under the condition that the transverse position of the target pedestrian after the collision time is in the dangerous area.
2. The prediction method according to claim 1, wherein the determining a dangerous area from the longitudinal position of the target pedestrian and the positional information of the vehicle after the time of collision includes:
determining a position interval in which the longitudinal position of the target pedestrian is located;
and determining a dangerous area according to the position interval of the longitudinal position of the target pedestrian and the position information of the vehicle after the collision time.
3. The prediction method according to claim 1, wherein the movement information of the target pedestrian further includes a longitudinal speed of the target pedestrian, the predicting the collision time based on the movement information of the target pedestrian and the speed of the vehicle includes:
and predicting the collision time according to the longitudinal position of the target pedestrian, the longitudinal speed of the target pedestrian and the speed of the vehicle.
4. The prediction method according to claim 1, wherein the movement information of the target pedestrian includes a lateral speed of the target pedestrian and a lateral position of the target pedestrian, the position information of the vehicle after the time of collision includes a lateral position of the vehicle after the time of collision, and determining the lateral position of the target pedestrian after the time of collision includes:
determining a position deviation value, which is a deviation value of a lateral position of the vehicle after a collision time from a lateral position of the target pedestrian;
and determining the transverse position of the target pedestrian after the collision time according to the position deviation value, the transverse speed of the target pedestrian and the collision time.
5. The prediction method according to claim 4, wherein the lateral position of the target pedestrian after the time of collision satisfies formula (1):
y pre ＝dy-v y t； (1)
wherein y is pre For the lateral position of the target pedestrian after the collision time, dy is the position deviation value, v y And t is the collision time, and is the transverse speed of the target pedestrian.
6. The method of predicting as set forth in claim 4, wherein the determining the position deviation value includes:
and determining the position deviation value according to the transverse position of the target pedestrian, the longitudinal position of the target pedestrian and the turning radius of the vehicle.
7. The prediction method according to claim 6, wherein the positional deviation value satisfies formula (2):
where dy is the position deviation value, y is the lateral position of the target pedestrian, R is the turning radius of the vehicle, and x is the longitudinal position of the target pedestrian.
8. The prediction method according to claim 1, wherein the acquiring the motion information of the target pedestrian includes:
and acquiring the motion information of the target pedestrian through information fusion.
9. A controller, comprising:
a memory configured to store instructions; and
a processor configured to invoke the instructions from the memory and when executing the instructions is capable of implementing the method of predicting a vehicle collision according to any one of claims 1 to 8.
10. A machine-readable storage medium having stored thereon instructions for causing a machine to perform the method of predicting a vehicle collision according to any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310332673.6A CN116343524A (en) | 2023-03-30 | 2023-03-30 | Method for predicting vehicle collision, controller and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310332673.6A CN116343524A (en) | 2023-03-30 | 2023-03-30 | Method for predicting vehicle collision, controller and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116343524A true CN116343524A (en) | 2023-06-27 |
Family
ID=86877099
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310332673.6A Pending CN116343524A (en) | 2023-03-30 | 2023-03-30 | Method for predicting vehicle collision, controller and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116343524A (en) |
-
2023
- 2023-03-30 CN CN202310332673.6A patent/CN116343524A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10446033B2 (en) | Vehicle detection and avoidance | |
| JP6404634B2 (en) | Consistent behavior generation of predictive advanced driver assistance systems | |
| JP6441959B2 (en) | Reduce false alarms using location data | |
| KR20190124130A (en) | Apparatus and method for providing safety strategy of vehicle | |
| US20150210280A1 (en) | Method for minimizing automatic braking intrusion based on collision confidence | |
| US20200086854A1 (en) | Vehicle collision avoidance | |
| CN104554258A (en) | Path planning for evasive steering manuever employing virtual potential field technique | |
| CN104554272A (en) | Path planning for evasive steering maneuver in presence of target vehicle and surrounding objects | |
| JP2017503268A (en) | Method and system for vehicle driver assistance | |
| US20200114921A1 (en) | Sensor-limited lane changing | |
| US11003182B2 (en) | Vehicle monitoring and control infrastructure | |
| EP2786173A2 (en) | Vehicle control system, specific object determination device, specific object determination method, and non-transitory storage medium storing specific object determination program | |
| US11673555B2 (en) | Vehicle threat detection and response | |
| CN113096424B (en) | Automatic emergency braking method and system for pedestrian crossing vehicle | |
| KR20220004747A (en) | Automatic emergency braking system and method | |
| US11731661B2 (en) | Systems and methods for imminent collision avoidance | |
| CN111188549A (en) | Anti-collision method and device applied to vehicle | |
| CN113453969A (en) | Method for protecting a vehicle | |
| JP6095197B2 (en) | Vehicle object detection device | |
| US11273806B2 (en) | Enhanced collision mitigation | |
| KR102592199B1 (en) | Method for setting alerting area of vehicle and bind spot detection system for making the same | |
| KR20210150926A (en) | Apparatus for controlling automotive driving detecting low speed cut in vehicle in crowed road, system having the same and method thereof | |
| CN116343524A (en) | Method for predicting vehicle collision, controller and storage medium | |
| JP2018112461A (en) | Probability calculation device and probability calculation method | |
| CN111145570A (en) | Vehicle control method, control device, storage medium, and processor |
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 |