CN113386738A - Risk early warning system, method and storage medium - Google Patents
Risk early warning system, method and storage medium Download PDFInfo
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- CN113386738A CN113386738A CN202010177712.6A CN202010177712A CN113386738A CN 113386738 A CN113386738 A CN 113386738A CN 202010177712 A CN202010177712 A CN 202010177712A CN 113386738 A CN113386738 A CN 113386738A
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- 238000011156 evaluation Methods 0.000 claims abstract description 16
- 230000000694 effects Effects 0.000 claims abstract description 15
- 230000010365 information processing Effects 0.000 claims abstract description 13
- 230000006399 behavior Effects 0.000 claims description 106
- 230000033001 locomotion Effects 0.000 claims description 22
- 230000006855 networking Effects 0.000 claims description 15
- 238000004590 computer program Methods 0.000 claims description 14
- 230000008859 change Effects 0.000 claims description 9
- 238000012549 training Methods 0.000 claims description 8
- 238000010295 mobile communication Methods 0.000 claims description 7
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- 238000010586 diagram Methods 0.000 description 8
- 230000003542 behavioural effect Effects 0.000 description 3
- 238000012502 risk assessment Methods 0.000 description 3
- 206010039203 Road traffic accident Diseases 0.000 description 2
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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
- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
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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
- 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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- 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
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Abstract
Provided are a risk early warning system, a method and a storage medium, the system including: an information acquisition unit configured to acquire position information and behavior information of a target object in front of a vehicle; an information processing unit configured to determine a behavior type of the target object according to the behavior information of the target object, and determine an activity area of the target object within a next set period of time based on the position information and the behavior type of the target object; the vehicle collision warning system comprises an evaluation unit and a control unit, wherein the evaluation unit is used for evaluating a risk value that the target object has collision risk with the vehicle and/or other vehicles around the vehicle in a next set time period at least based on an overlapping area between an activity area of the target object and a road, and the control unit is used for judging a risk level corresponding to the vehicle and/or other vehicles around the vehicle according to the magnitude of the risk value, sending corresponding warning information to the vehicle and/or other vehicles around the vehicle, and/or controlling lane changing or deceleration of the vehicle.
Description
Technical Field
The present invention relates to the field of vehicle-related technologies, and more particularly, to a risk early warning system for a vehicle, a risk early warning method, a corresponding computer device, and a computer-readable storage medium.
Background
During vehicle operation, the vehicle typically uses various sensors, such as cameras, to detect pedestrians or other non-motorized vehicles in front of the vehicle. However, since most sensors have difficulty reliably detecting pedestrians or other non-motorized vehicles located at a large distance, the driver of a human-driven vehicle or an autonomous vehicle cannot reliably know the position of a target object and its possible moving direction or trajectory. Therefore, it may be difficult to control the braking or lane change of the vehicle in a short time, so that traffic accidents are easily caused.
Therefore, there is a need to provide an improved solution for predicting a risk value of a target object in front of a vehicle relative to the vehicle.
Disclosure of Invention
In order to solve at least the above-described problems, the present invention has been made in a number of ways as described below.
Specifically, according to a first aspect of the present invention, there is provided a risk pre-warning system, comprising:
an information acquisition unit configured to acquire position information and behavior information of a target object in front of a vehicle;
an information processing unit configured to determine a behavior type of the target object according to the behavior information of the target object, and determine an activity area of the target object in a next set time period based on the position information and the behavior type of the target object;
an evaluation unit configured to evaluate a risk value that the target object has a collision risk with the vehicle and/or other vehicles around the vehicle within a next set period of time, based on at least an overlapping area between the active area of the target object and a road; and
the control unit is configured to judge the risk level corresponding to the vehicle and/or other vehicles around the vehicle according to the risk value, so as to send corresponding warning information to the vehicle and/or other vehicles around the vehicle, and/or control the vehicle and/or other vehicles around the vehicle to change lanes or decelerate.
In one embodiment, the information obtaining unit is further configured to: and acquiring the position information of the target object through a positioning unit of the mobile terminal equipment of the target object, a mobile communication unit of the mobile terminal equipment and/or other networking devices around the target object.
In one embodiment, the information obtaining unit is further configured to: and acquiring the behavior information of the target object through a motion sensor unit of the mobile terminal equipment of the target object, one or more application programs of the mobile terminal equipment and/or other networking devices around the target object.
In one embodiment, the information processing unit is further configured to:
determining a current task of the target object according to the behavior information of the target object, and determining a behavior type of the target object based on a preset rule according to the current task; and/or
And determining the behavior type of the target object according to the matching result of the behavior information and the at least one behavior model by inputting the behavior information of the target object into the at least one behavior model obtained by training in a machine learning mode.
In one embodiment, the evaluation unit is further configured to:
evaluating a risk value of the target object having a collision risk with the vehicle and/or other vehicles around the vehicle in a next set time period according to an area of an overlapping region between an active region of the target object and a lane on a road, wherein the larger the area of the overlapping region, the higher the risk value; and/or
Evaluating a risk value of the target object having a collision risk with the vehicle and/or other vehicles around the vehicle in a next set time period according to the type and number of lanes covered by the active area of the target object and the driving lanes of the vehicle and/or other vehicles around the vehicle, wherein the risk value is higher the more and/or closer the number of covered lanes is to the driving lanes of the vehicle and/or other vehicles around the vehicle.
Specifically, according to another aspect of the present invention, there is provided a risk early warning method, including:
acquiring position information and behavior information of a target object in front of a vehicle;
determining the behavior type of the target object according to the behavior information of the target object, and determining the activity area of the target object in the next set time period based on the position information and the behavior type of the target object;
evaluating a risk value that the target object has a risk of collision with the vehicle and/or other vehicles around the vehicle within a next set period of time based at least on an overlap area between the active area of the target object and a road; and
and judging the risk level corresponding to the vehicle and/or other vehicles around the vehicle according to the risk value, so as to send corresponding warning information to the vehicle and/or other vehicles around the vehicle, and/or control the vehicle and/or other vehicles around the vehicle to change lanes or decelerate.
In one embodiment, the acquiring the position information of the target object in front of the vehicle further comprises:
and acquiring the position information of the target object through a positioning unit of the mobile terminal equipment of the target object, a mobile communication unit of the mobile terminal equipment and/or other networking devices around the target object.
In one embodiment, the acquiring behavior information of the target object in front of the vehicle further includes:
and acquiring the behavior information of the target object through a motion sensor unit of the mobile terminal equipment of the target object, one or more application programs of the mobile terminal equipment and/or other networking devices around the target object.
In one embodiment, the determining the behavior type of the target object according to the behavior information of the target object further comprises:
determining a current task of the target object according to the behavior information of the target object, and determining a behavior type of the target object based on a preset rule according to the current task; and/or
And determining the behavior type of the target object according to the matching result of the behavior information and the at least one behavior model by inputting the behavior information of the target object into the at least one behavior model obtained by training in a machine learning mode.
In one embodiment, the evaluating the risk value that the target object has a collision risk with the vehicle and/or other vehicles around the vehicle within a next set time period based on at least an overlapping area between the active area of the target object and a road further comprises:
evaluating a risk value of the target object having a collision risk with the vehicle and/or other vehicles around the vehicle in a next set time period according to an area of an overlapping region between an active region of the target object and a lane on a road, wherein the larger the area of the overlapping region, the higher the risk value; and/or
Evaluating a risk value of the target object having a collision risk with the vehicle and/or other vehicles around the vehicle in a next set time period according to the type and number of lanes covered by the active area of the target object and the driving lanes of the vehicle and/or other vehicles around the vehicle, wherein the risk value is higher the more and/or closer the number of covered lanes is to the driving lanes of the vehicle and/or other vehicles around the vehicle.
According to still another aspect of the present invention, there is provided a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the risk pre-warning method according to any one of the above items when executing the computer program.
According to a further aspect of the present invention, there is provided a computer storage medium having stored thereon a computer program which, when executed by a processor, implements the risk pre-warning method of any of the above.
By using the scheme of the invention, the position information and the behavior information of the target object in front of the vehicle are obtained, so that the activity area of the target object is determined, and then the risk value of the target object, which can cause risks to the vehicle and/or other vehicles around the vehicle in the next set time period, can be evaluated according to the overlapping area between the activity area of the target object and the road. Therefore, under the condition that the target object is far away from the vehicle, whether the target object can cause risks to the vehicle and/or other vehicles around the vehicle can be predicted in advance, and therefore corresponding operations are warned or controlled in advance, and traffic accidents are avoided.
Drawings
Non-limiting and non-exhaustive embodiments of the present invention are described, by way of example, with reference to the following drawings, in which:
fig. 1 shows a schematic diagram of a risk pre-warning system according to an embodiment of the invention;
fig. 2 shows a schematic diagram of a risk pre-warning system according to another embodiment of the invention;
fig. 3 illustrates a scenario application diagram of a risk pre-warning system according to an embodiment of the present invention;
fig. 4 shows a flowchart of a risk early warning method for a vehicle according to an embodiment of the present invention.
Detailed Description
In order to make the above and other features and advantages of the present invention more apparent, the present invention is further described below with reference to the accompanying drawings. It is to be understood that the specific embodiments described herein are for purposes of illustration only and are not to be construed as limiting, for those of ordinary skill in the art.
Fig. 1 shows a schematic diagram of a risk early warning system for a vehicle according to an embodiment of the present invention.
As shown in fig. 1, the risk early warning system 100 includes: an information acquisition unit 101, an information processing unit 102, an evaluation unit 103, and a control unit 104. The risk early warning system provided by the invention can be positioned at a remote server side or a vehicle side and is used for providing risk assessment for vehicles running on a highway, an urban expressway, an urban main road, an urban secondary main road and the like.
The information acquisition unit 101 may be configured to acquire position information and behavior information of a target object in front of the vehicle. It should be noted that the term "target object" herein may refer not only to pedestrians, other non-motorized vehicles (e.g., bicycles, motorcycles, tricycles, etc.) on the road, but also to other autonomous or non-autonomous vehicles on the road. For example, in a case where the risk early warning system is provided on the remote server side, the information acquisition unit 101 may acquire the position information and behavior information of the target object through a mobile terminal device of the target object and/or other networked devices around the target object in response to receiving an evaluation request signal for performing risk evaluation on the target object in front of the vehicle, which is transmitted by at least one vehicle (such as any one of the vehicles a100-a400 in fig. 3, etc.). Alternatively, in an alternative embodiment in which the risk early warning system 100 is provided on the vehicle side, when the current vehicle detects that a target object exists ahead, the information acquisition unit 101 may actively acquire the position information and behavior information of the target object through the mobile terminal device of the target object and/or other networked devices around the target object.
The information processing unit 102 may be configured to determine a behavior type of the target object according to the behavior information of the target object, and determine an activity area of the target object in a next set time period based on the position information and the behavior type of the target object. For example, it is assumed that the acquired behavior information of the target object is that the target object is running forward regularly, or that the taxi calling software of its mobile terminal device is in an on state although the position of the target object is stationary. Thus, the information processing unit 102 may determine that the behavior type of the target object is running or waiting for a taxi, respectively, and further determine the activity area of the target object according to the position information and the behavior type of the target object.
The evaluation unit 103 may be configured to evaluate a risk value that the target object has a risk of collision with the vehicle and/or other vehicles around the vehicle within a next set period of time based on at least an overlapping area between the active area of the target object and a road.
The control unit 104 may be configured to determine a risk level corresponding to the vehicle and/or other vehicles around the vehicle according to the magnitude of the risk value, to send corresponding warning information to the vehicle and/or other vehicles around the vehicle, and/or to control the vehicle and/or other vehicles around the vehicle to change lanes or decelerate.
Specifically, the control unit 104 may receive the risk values of the target objects relative to the vehicles a100-a400 sent by the evaluation unit 103, and if the target object causes the risk value relative to any vehicle to exceed a set threshold or reach a corresponding risk level, the control unit may send a warning message for the specific vehicle or take a corresponding risk avoidance measure according to the risk level. For example, assuming that the risk levels are classified into five levels (first level to fifth level), if the risk value of the target object to the vehicle a100 corresponds to the fifth level (i.e., the most dangerous level), red warning information is transmitted to the vehicle a100 or control information for controlling lane change or deceleration of the vehicle a100 is transmitted, and for example, it is possible to display that the target object ahead is an emergency dangerous object on a vehicle display screen of the vehicle a100 or to control lane change or deceleration of the vehicle a100 through a steering, transmission, or braking system of the vehicle a 100.
Fig. 2 shows a schematic diagram of a risk pre-warning system according to another embodiment of the present invention.
As shown in fig. 2, in this embodiment, the risk early warning system 100 is provided on the remote server side, the input side of the risk early warning system 100 communicates with the mobile terminal device of the target object, other networked devices around the target object, and the output side of the risk early warning system 100 communicates with the vehicle, for example, with the vehicle that transmits the evaluation request signal to the risk early warning system 100 or other vehicles around the vehicle.
Preferably, the information obtaining unit 101 may be further configured to: and acquiring the position information of the target object through a positioning unit of the mobile terminal equipment of the target object, a mobile communication unit of the mobile terminal equipment and/or other networking devices around the target object. Moreover, the position information acquired through various ways can also be fused to obtain more accurate position information about the target object, thereby improving the accuracy of the position information of the target object and overcoming the problem that the target object cannot be accurately detected only by a sensor of the current vehicle due to being far away from the target object.
The information acquisition unit 101 may search for the mobile terminal device of the target object and other networked devices around the target object through a remote server or a nearby base station, and establish a communication connection with the mobile terminal device of the target object and/or other networked devices around the target object, and then acquire the position coordinates of the target object. The mobile terminal device of the target object may comprise, for example, a mobile phone, a tablet computer, a wearable device, a game terminal device, a vehicle-mounted computer, etc. The position coordinates of the target object (such as the latitude and longitude of the target object) may be determined by a positioning unit (e.g., Global Navigation Satellite System (GNSS)) and/or a mobile communication unit (e.g., Wi-Fi, 4G, or 5G communication module, etc.) of the mobile terminal device. Such as a networked camera mounted on a nearby road or other networked vehicle traveling past or towards the target object, etc.
Preferably, the information obtaining unit 101 may further obtain the behavior information of the target object through a motion sensor unit of a mobile terminal device of the target object, one or more applications of the mobile terminal device, and/or other networking devices around the target object.
Specifically, as shown in fig. 2, the movement data of the target object (e.g., the movement speed, the movement direction, the step parameter, the movement range or the movement trajectory of the target object, etc.) may be determined by the motion sensor unit of the mobile terminal device of the target object. For example, parameters such as time of movement, speed of movement, or energy expended by movement of the target object may be recorded using the wearable motion sensor, and movement data (including determining current movement data and predicting future movement data) of the target object may be determined by analyzing the parameters recorded by the wearable motion sensor.
In addition, the task of the target object may be analyzed by acquiring the state of one or more applications of the mobile terminal device being used by the target object. For example, it may be analyzed that the target object is calling a phone, calling a car, playing a game, etc. by acquiring an application program currently running or in an execution state of the mobile terminal device of the target object. Assuming that the target object is taking a car by using the taxi taking software on the mobile phone, the current taxi taking starting point of the target object, the planned arrival time of the vehicle providing taxi taking service, the distance between the current position of the target object and the taxi taking starting point and other data information can be obtained by analyzing the running data of the taxi taking software, so that the task and the movement data of the target object can be determined according to the data information.
Of course, various specific behavior information of the target object may also be acquired by various sensors of other networked devices around the target object. The behavior information of the target object (such as behaviors of pedestrians calling a mobile phone, calling out a car rental, running or playing games and the like) is acquired through the mobile terminal device of the target object and/or other networking devices around the target object, so that the accuracy of the acquired behavior information of the target object can be improved, the behavior type of the target object can be accurately judged, and the determination of the activity area of the target object in the next set time period is facilitated.
In an embodiment, the information processing unit 102 is further configured to: and determining the current task of the target object according to the behavior information of the target object, and determining the behavior type of the target object based on a preset rule according to the current task. For example, image information of the target object may be obtained by other networked devices around the target object, from which the current task of the target object may be analyzed. For example: assuming that the target object is shown in the image as either waving a hand to take a taxi or walking towards a taxi, it can be determined that the target object is ready to take a taxi, knowing that the type of behavior of the target object is taking a taxi. Based on this behavior type of the target object, it may be determined that the active region of the target object is a region from the current location to a taxi parking location (determined or not determined). Other behaviors of the target object, such as running, taking a bus, crossing roads, changing routes, etc., may be determined in a similar manner.
Alternatively, the information processing unit 102 may be further configured to: and determining the behavior type of the target object according to the matching result of the behavior information and the at least one behavior model by inputting the behavior information of the target object into the at least one behavior model obtained by training in a machine learning mode. For example, the behavioral model may be formed for a single behavioral training using various behavioral information data of a large number of users. The behavior model formed by training can be adopted to judge whether the behavior information of the current target object is matched with the behavior model so as to determine whether the current behavior type of the target object belongs to the behavior type corresponding to the behavior model. According to the determined behavior type, the activity area of the target object in the next set time period can be determined.
Fig. 3 is a diagram illustrating a scene application of a risk early warning system according to an embodiment of the present invention.
Specifically, the target object and the active region (e.g., black shaded portion in fig. 3) of the target object determined by the information processing unit 102 are shown in fig. 3. The two bold black lines in fig. 3 represent the movement locus of the vehicle a100 in the next set period. The evaluation unit 103 may be configured to evaluate a risk value of collision of the target object with any of the vehicles a100-a400 based on at least an area size of an overlapping area of the active area and the lane, and/or a type and number of lanes covered by the active area. It can be seen from fig. 3 that the two black bold straight lines may overlap and intersect the black shaded portion, which means that the target object may collide with the vehicle a100, and the risk value is large.
Preferably, the evaluation unit 103 may be further configured to: evaluating a risk value of the target object having a risk of collision with the vehicle and/or other vehicles around the vehicle in a next set period of time according to an area of an overlapping region between an active region of the target object and a lane on a road, wherein the larger the area of the overlapping region, the higher the risk value.
Specifically, as shown in fig. 3, if the area of the black shading of the overlapping area between the active area of the target object and the lane on the road is larger, it indicates that the target object is more likely to be collided with by any one of the vehicle a100, the vehicle a200, the vehicle a300, the vehicle a400, and the like, as the risk of the target object to the vehicle a100, the vehicle a200, the vehicle a300, the vehicle a400, and the like is higher.
Additionally, or alternatively, the evaluation unit 103 may be further configured to: evaluating a risk value of the target object having a collision risk with the vehicle and/or other vehicles around the vehicle in a next set time period according to the type and number of lanes covered by the active area of the target object and the driving lanes of the vehicle and/or other vehicles around the vehicle, wherein the risk value is higher the more and/or closer the number of covered lanes is to the driving lanes of the vehicle and/or other vehicles around the vehicle.
As shown in fig. 3, the active area covers a lane in which the vehicle a100 is located, indicating that there is a high risk of collision of the target object with the vehicle a100, but there is less risk to the vehicle a200, the vehicle a300, the vehicle a400, and the like. If the active area covers 3 lanes in which the vehicles a100-a400 are located, it indicates that the target object poses a greater risk to the vehicles a100-a400, etc. For example, the risk value may be progressively larger from small to large for the following cases: the active area does not cover the lane; the active area only covers the emergency lane; the active area covers the first lane on the right side; the active area covers the second right lane.
In one embodiment, after any one of the vehicles a100-a400 finds a target object, the position information and the behavior information of the target object may be acquired by the information acquiring unit 101 in the risk early warning system 100 after the risk early warning system 100 receives a request signal for performing risk assessment on the target object in front by sending the request signal for performing risk assessment on the target object in front to the risk early warning system 100 disposed on a remote server. The information processing unit 102 determines a behavior type of the target object from the behavior information of the target object, and determines an activity area of the target object within a next set period of time based on the position information and the behavior type of the target object, and evaluates a risk value that the target object poses a risk to at least one of the vehicles a100-a400 within the next set period of time based on at least an overlap area between the activity area of the target object and a road by the evaluation unit 103. After the risk value is determined, the risk early warning system 100 feeds back the corresponding risk value to the vehicle a100-a400 and/or the target object and the like, so as to warn the vehicle with the risk value exceeding the set threshold value in the vehicle a100-a400 to take risk avoiding operation, or warn the target object to carry out risk avoiding.
Fig. 4 shows a flowchart of a risk early warning method for a vehicle according to an embodiment of the present invention.
As shown in fig. 4, the risk early warning method S100 for a vehicle includes:
s101: acquiring position information and behavior information of a target object in front of a vehicle;
s102: determining the behavior type of the target object according to the behavior information of the target object, and determining the activity area of the target object in the next set time period based on the position information and the behavior type of the target object; and
s103: evaluating a risk value that the target object has a risk of collision with the vehicle and/or other vehicles around the vehicle within a next set period of time based at least on an overlap area between the active area of the target object and a road; and
s104: and judging the risk level corresponding to the vehicle and/or other vehicles around the vehicle according to the risk value, so as to send corresponding warning information to the vehicle and/or other vehicles around the vehicle, and/or control the vehicle and/or other vehicles around the vehicle to change lanes or decelerate.
Preferably, the acquiring of the position information of the target object in front of the vehicle further includes:
and acquiring the position information of the target object through a positioning unit of the mobile terminal equipment of the target object, a mobile communication unit of the mobile terminal equipment and/or networking devices around the target object or sensors of any networking vehicles.
Preferably, the acquiring behavior information of the target object in front of the vehicle further includes:
and acquiring the behavior information of the target object through a motion sensor unit of the mobile terminal equipment of the target object, one or more application programs of the mobile terminal equipment and/or networking devices around the target object or sensors of any networking vehicles.
Preferably, the determining the behavior type of the target object according to the behavior information of the target object further includes:
determining a current task of the target object according to the behavior information of the target object, and determining a behavior type of the target object based on a preset rule according to the current task; and/or
And determining the behavior type of the target object according to the matching result of the behavior information and the at least one behavior model by inputting the behavior information of the target object into the at least one behavior model obtained by training in a machine learning mode.
Preferably, the evaluating the risk value that the target object has collision risk with the vehicle and/or other vehicles around the vehicle in the next set time period based on at least the overlapping area between the active area of the target object and the road further comprises:
evaluating a risk value of the target object having a collision risk with the vehicle and/or other vehicles around the vehicle in a next set time period according to an area of an overlapping region between an active region of the target object and a lane on a road, wherein the larger the area of the overlapping region, the higher the risk value; and/or
Evaluating a risk value of the target object having a collision risk with the vehicle and/or other vehicles around the vehicle in a next set time period according to the type and number of lanes covered by the active area of the target object and the driving lanes of the vehicle and/or other vehicles around the vehicle, wherein the risk value is higher the more and/or closer the number of covered lanes is to the driving lanes of the vehicle and/or other vehicles around the vehicle.
As for a more specific aspect of the risk early warning method for a vehicle of the present invention, reference may be made to the above description of the risk early warning system for a vehicle of the present invention, and details thereof are not repeated herein.
In addition, it should be understood that the information acquiring unit 101, the information processing unit 102, the evaluating unit 103 and the control unit 104 in the risk early warning system 100 may be implemented wholly or partially by software, hardware and a combination thereof, for example, may be embedded in a native processor in a computer device in a hardware form, or may be stored in a memory in the computer device in a software form, so that the processor can call and execute the corresponding operations described above.
In one embodiment, the invention also provides a vehicle provided with the risk early warning system of any one of the above embodiments.
In another embodiment, a computer device is provided, which includes a memory and a processor, the memory stores a computer program that can be executed on the processor, and the processor executes the computer program to implement the steps of the risk pre-warning method in any one of the above embodiments. The computer device can be a server, an on-vehicle system device or a mobile terminal device of a vehicle user according to different positions of the risk early warning system. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, is capable of implementing any of the above-described risk pre-warning methods of the present invention.
Those skilled in the art will appreciate that the schematic diagram of the risk early warning system 100 shown in fig. 1 is only a block diagram of a part of the structure related to the present application, and does not constitute a limitation to the computer device to which the present application is applied, and a specific computer device may include more or less components than those shown in the figure, or combine some components, or have a different arrangement of components.
Another aspect of the present invention further provides a computer-readable storage medium, on which a computer program is stored, the computer program, when being executed by a processor, implementing the risk pre-warning method according to any one of the above embodiments.
It will be understood by those skilled in the art that all or part of the steps in the method according to the above embodiments of the present invention may be indicated by the relevant hardware to be completed by a computer program, which may be stored in a non-volatile computer-readable storage medium, and which, when executed, may implement the steps of the above embodiments of the method. Any reference to memory, storage, database, or other medium used in the embodiments provided herein can include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory.
The features of the above embodiments may be arbitrarily combined, and for the sake of brevity, all possible combinations of the features in the above embodiments are not described, but should be construed as being within the scope of the present disclosure as long as there is no contradiction between the combinations of the features.
While the invention has been described in connection with the embodiments, it is to be understood by those skilled in the art that the foregoing description and drawings are merely illustrative and not restrictive of the broad invention, and that this invention not be limited to the disclosed embodiments. Various modifications and variations are possible without departing from the spirit of the invention.
Claims (13)
1. A risk pre-warning system, the risk pre-warning system comprising:
an information acquisition unit configured to acquire position information and behavior information of a target object in front of a vehicle;
an information processing unit configured to determine a behavior type of the target object according to the behavior information of the target object, and determine an activity area of the target object in a next set time period based on the position information and the behavior type of the target object;
an evaluation unit configured to evaluate a risk value that the target object has a collision risk with the vehicle and/or other vehicles around the vehicle within a next set period of time, based on at least an overlapping area between the active area of the target object and a road; and
the control unit is configured to judge the risk level corresponding to the vehicle and/or other vehicles around the vehicle according to the risk value, so as to send corresponding warning information to the vehicle and/or other vehicles around the vehicle, and/or control the vehicle and/or other vehicles around the vehicle to change lanes or decelerate.
2. The risk pre-warning system of claim 1, wherein the information acquisition unit is further configured to: and acquiring the position information of the target object through a positioning unit of the mobile terminal equipment of the target object, a mobile communication unit of the mobile terminal equipment and/or other networking devices around the target object.
3. The risk pre-warning system of claim 1, wherein the information acquisition unit is further configured to: and acquiring the behavior information of the target object through a motion sensor unit of the mobile terminal equipment of the target object, one or more application programs of the mobile terminal equipment and/or other networking devices around the target object.
4. The risk pre-warning system according to any one of claims 1 to 3, wherein the information processing unit is further configured to:
determining a current task of the target object according to the behavior information of the target object, and determining a behavior type of the target object based on a preset rule according to the current task; and/or
And determining the behavior type of the target object according to the matching result of the behavior information and the at least one behavior model by inputting the behavior information of the target object into the at least one behavior model obtained by training in a machine learning mode.
5. The risk pre-warning system according to any one of claims 1 to 3, wherein the evaluation unit is further configured to:
evaluating a risk value of the target object having a collision risk with the vehicle and/or other vehicles around the vehicle in a next set time period according to an area of an overlapping region between an active region of the target object and a lane on a road, wherein the larger the area of the overlapping region, the higher the risk value; and/or
Evaluating a risk value of the target object having a collision risk with the vehicle and/or other vehicles around the vehicle in a next set time period according to the type and number of lanes covered by the active area of the target object and the driving lanes of the vehicle and/or other vehicles around the vehicle, wherein the risk value is higher the more and/or closer the number of covered lanes is to the driving lanes of the vehicle and/or other vehicles around the vehicle.
6. A vehicle provided with a risk pre-warning system according to any one of claims 1-5.
7. A risk pre-warning method, comprising:
acquiring position information and behavior information of a target object in front of a vehicle;
determining the behavior type of the target object according to the behavior information of the target object, and determining the activity area of the target object in the next set time period based on the position information and the behavior type of the target object;
evaluating a risk value that the target object has a risk of collision with the vehicle and/or other vehicles around the vehicle within a next set period of time based at least on an overlap area between the active area of the target object and a road; and
and judging the risk level corresponding to the vehicle and/or other vehicles around the vehicle according to the risk value, so as to send corresponding warning information to the vehicle and/or other vehicles around the vehicle, and/or control the vehicle and/or other vehicles around the vehicle to change lanes or decelerate.
8. The risk pre-warning method according to claim 7, wherein the acquiring of the position information of the target object in front of the vehicle further comprises:
and acquiring the position information of the target object through a positioning unit of the mobile terminal equipment of the target object, a mobile communication unit of the mobile terminal equipment and/or other networking devices around the target object.
9. The risk pre-warning method according to claim 7, wherein the acquiring behavior information of the target object in front of the vehicle further comprises:
and acquiring the behavior information of the target object through a motion sensor unit of the mobile terminal equipment of the target object, one or more application programs of the mobile terminal equipment and/or other networking devices around the target object.
10. The risk pre-warning method according to any one of claims 7 to 9, wherein the determining the behavior type of the target object according to the behavior information of the target object further comprises:
determining a current task of the target object according to the behavior information of the target object, and determining a behavior type of the target object based on a preset rule according to the current task; and/or
And determining the behavior type of the target object according to the matching result of the behavior information and the at least one behavior model by inputting the behavior information of the target object into the at least one behavior model obtained by training in a machine learning mode.
11. The risk pre-warning method according to any one of claims 7 to 9, wherein the evaluating a risk value that the target object has a risk of collision with the vehicle and/or other vehicles around the vehicle within a next set time period based on at least an overlapping area between the active area of the target object and a road further comprises:
evaluating a risk value of the target object having a collision risk with the vehicle and/or other vehicles around the vehicle in a next set time period according to an area of an overlapping region between an active region of the target object and a lane on a road, wherein the larger the area of the overlapping region, the higher the risk value; and/or
Evaluating a risk value of the target object having a collision risk with the vehicle and/or other vehicles around the vehicle in a next set time period according to the type and number of lanes covered by the active area of the target object and the driving lanes of the vehicle and/or other vehicles around the vehicle, wherein the risk value is higher the more and/or closer the number of covered lanes is to the driving lanes of the vehicle and/or other vehicles around the vehicle.
12. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor realizes the steps of the method according to any of claims 7-11 when executing the computer program.
13. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any one of claims 7 to 11.
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| CN202010177712.6A CN113386738A (en) | 2020-03-13 | 2020-03-13 | Risk early warning system, method and storage medium |
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