CN116461513B - Vehicle and collision early warning method thereof - Google Patents

Vehicle and collision early warning method thereof Download PDF

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Publication number
CN116461513B
CN116461513B CN202310714056.2A CN202310714056A CN116461513B CN 116461513 B CN116461513 B CN 116461513B CN 202310714056 A CN202310714056 A CN 202310714056A CN 116461513 B CN116461513 B CN 116461513B
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vehicle
early warning
test
speed
warning
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CN116461513A (en
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隋聪
杨阳
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FAW Group Corp
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FAW Group Corp
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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
    • B60W30/0953—Predicting travel path or likelihood of collision the prediction being responsive to vehicle dynamic parameters
    • 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
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
    • 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
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/105—Speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/0097—Predicting future conditions
    • 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
    • B60W2540/00—Input parameters relating to occupants
    • 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
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/40—Dynamic objects, e.g. animals, windblown objects
    • B60W2554/404—Characteristics
    • B60W2554/4042—Longitudinal speed
    • 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
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/80—Spatial relation or speed relative to objects
    • 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

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  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Human Computer Interaction (AREA)
  • Traffic Control Systems (AREA)

Abstract

The invention discloses a vehicle and a collision early warning method thereof. The method is applied to the field of intelligent automobiles, and comprises the following steps: acquiring first vehicle information of a first vehicle and second vehicle information of a second vehicle, wherein the first vehicle information comprises: the vehicle speed of the first vehicle and the age of the driver of the first vehicle, the second vehicle information includes: a speed of a second vehicle, the second vehicle being used to characterize a vehicle traveling in front of the first vehicle; acquiring a target early warning model corresponding to the age of a driver; processing the speed of the first vehicle and the speed of the second vehicle by using a target early warning model, and determining a target early warning distance of the first vehicle; and outputting the target early warning distance. The invention solves the technical problem of low adaptation degree of the early warning distance between the driver and the target.

Description

Vehicle and collision early warning method thereof
Technical Field
The invention relates to the field of intelligent automobiles, in particular to a vehicle and a collision early warning method thereof.
Background
At present, the vehicle driving safety problem is a hot topic in the current society, the traditional method for early warning the collision of vehicles only aims at the speed of the front and rear vehicles, so that the early warning distance is obtained, and early warning is carried out according to the early warning distance, so that danger is avoided, the number of objective factors in a real driving scene is large, the early warning distance determined according to the speed of the front and rear vehicles has a hysteresis problem, a driver cannot brake in time, and the adaptation degree of the early warning distance between the driver and a target is low.
In view of the above problems, no effective solution has been proposed at present.
Disclosure of Invention
The embodiment of the invention provides a vehicle and a collision early warning method thereof, which at least solve the technical problem of low adaptation degree of the early warning distance between a driver and a target.
According to an aspect of the embodiment of the present invention, there is provided a vehicle collision early warning method including: acquiring first vehicle information of a first vehicle and second vehicle information of a second vehicle, wherein the first vehicle information comprises: the vehicle speed of the first vehicle and the age of the driver of the first vehicle, the second vehicle information includes: a speed of a second vehicle, the second vehicle being used to characterize a vehicle traveling in front of the first vehicle; acquiring a target early warning model corresponding to the age of a driver; processing the speed of the first vehicle and the speed of the second vehicle by using a target early warning model, and determining a target early warning distance of the first vehicle; and outputting the target early warning distance.
Optionally, processing the speed of the first vehicle and the speed of the second vehicle by using the target early warning model, determining the target early warning distance of the first vehicle includes: processing the speed of the first vehicle and the speed of the second vehicle by using a first early warning model in the target early warning models, and determining a first early warning distance of the first vehicle, wherein the first early warning model is used for representing an early warning model corresponding to the emergency brake type; processing the speed of the first vehicle and the speed of the second vehicle by using a second early-warning model in the target early-warning models to determine a second early-warning distance of the first vehicle, wherein the second early-warning model is used for representing an early-warning model corresponding to the slow brake type; and summarizing the first early warning distance and the second early warning distance to obtain the target early warning distance.
Optionally, outputting the target early warning distance includes: determining an early warning level of the first vehicle based on the target early warning distance; and outputting the target early warning distance according to the output mode corresponding to the early warning level.
Optionally, determining the early warning level of the first vehicle based on the target early warning distance includes: determining the early warning level of the first vehicle as a first-level early warning in response to the target early warning distance being greater than the preset early warning distance; and determining the early warning level of the first vehicle as the secondary early warning in response to the target early warning distance being smaller than the preset early warning distance.
Optionally, acquiring a target early warning model corresponding to the age of the driver includes: classifying the ages of the drivers to obtain age categories of the drivers; and acquiring a preset early warning model corresponding to the age category of the driver from a plurality of preset early warning models to obtain a target early warning model, wherein the plurality of preset early warning models are used for representing early warning models corresponding to different age categories.
Optionally, the method further comprises: acquiring first test vehicle information of a first test vehicle and second test vehicle information of a second test vehicle, wherein the first test vehicle information comprises: the speed of the first test vehicle, the braking signal of the first test vehicle, the age of the test driver of the first test vehicle, the braking distance of the first test vehicle, and the second test vehicle information includes: the speed of the second test vehicle is used for representing the vehicle running in front of the first test vehicle; determining a brake category of the first test vehicle based on a speed of the first test vehicle and a brake signal of the first test vehicle; based on the brake category, a plurality of preset early warning models are generated for the speed of the first test vehicle, the brake distance of the first test vehicle, the speed of the second test vehicle and the age of the test driver.
Optionally, determining the braking category of the first test vehicle based on the speed of the first test vehicle and the braking signal of the first test vehicle includes: acquiring early warning time corresponding to a brake signal; determining an experimental deceleration of the first test vehicle based on the speed and the early warning time of the first test vehicle; responding to the experiment deceleration being smaller than the preset deceleration, and determining the brake type as the slow brake type by taking the brake signal as a first signal, wherein the first signal is used for representing a signal generated by stepping on a brake pedal when the response time of a first test vehicle driver is longer than the preset time; and responding to the experiment deceleration being greater than or equal to the preset deceleration, and determining the brake type as the sudden brake type by taking the brake signal as a second signal, wherein the second signal is used for representing a signal generated by stepping on a pedal when the response time of a driver of the first test vehicle is less than the preset time.
Optionally, based on the braking category, generating a plurality of preset early warning models including: classifying the ages of the test drivers, and determining the age class of the test drivers; dividing a brake category, a speed of a first test vehicle, a brake distance of the first test vehicle and a speed of a second test vehicle based on age classification of a test driver to obtain a plurality of vehicle data sets; based on the plurality of vehicle data sets, a plurality of preset early warning models are generated.
Optionally, generating a plurality of preset early warning models based on a plurality of vehicle data sets includes: dividing the vehicle data sets based on the brake categories in the vehicle data sets to obtain data sets corresponding to different brake categories; based on the data sets corresponding to different brake categories, generating early warning models corresponding to different brake categories; summarizing the pre-warning models corresponding to different brake categories to obtain a preset pre-warning model corresponding to the vehicle data set.
Optionally, based on the data sets corresponding to the different brake categories, generating the early warning model corresponding to the different brake categories includes: fitting the speed and the braking distance of a first test vehicle in the data set by using model training software, and determining a preset coefficient corresponding to the data set; determining a deceleration value corresponding to the data set based on a speed of a first test vehicle in the data set; and generating an early warning model corresponding to the data set based on the preset coefficient and the deceleration value.
Optionally, the vehicle collision early warning device includes: a first acquisition module configured to acquire first vehicle information of a first vehicle and second vehicle information of a second vehicle, wherein the first vehicle information includes: the vehicle speed of the first vehicle and the age of the driver of the first vehicle, the second vehicle information includes: a speed of a second vehicle, the second vehicle being used to characterize a vehicle traveling in front of the first vehicle; the second acquisition module is used for acquiring a target early warning model corresponding to the age of the driver; the determining module is used for processing the speed of the first vehicle and the speed of the second vehicle by utilizing the target early warning model and determining the target early warning distance of the first vehicle; and the output module is used for outputting the target early warning distance.
According to another aspect of an embodiment of the present invention, there is also provided a vehicle including: one or more processors; a storage means for storing one or more programs; the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the vehicle collision warning method of the above-described embodiment.
According to another aspect of an embodiment of the present invention, there is also provided a computer-readable storage medium including: the computer readable storage medium includes a stored program, wherein the device in which the computer readable storage medium is located is controlled to execute the vehicle collision warning method of the above embodiment when the program runs.
In the embodiment of the invention, first vehicle information of a first vehicle and second vehicle information of a second vehicle are acquired; acquiring a target early warning model corresponding to the age of the driver; processing the speed of the first vehicle and the speed of the second vehicle by using the target early warning model, and determining a target early warning distance of the first vehicle; and outputting the target early warning distance. It is noted that the target early-warning model is selected according to the age of the driver, and then the target early-warning distance is determined by using the target early-warning model, so that the target early-warning distance is closer to the early-warning distance which can be borne by the driver currently, the aim of improving the adaptation degree of the driver to the target early-warning distance is fulfilled, the technical effect of determining the corresponding target early-warning distance according to the age of the driver is realized, and the technical problem that the adaptation degree of the driver to the target early-warning distance is lower is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a flow chart of a vehicle collision warning method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of an alternative speed versus distance relationship according to an embodiment of the present application;
FIG. 3 is a flow chart of an alternative vehicle collision pre-warning method according to an embodiment of the application;
fig. 4 is a schematic view of a vehicle collision warning apparatus according to an embodiment of the present application.
Detailed Description
In order that those skilled in the art will better understand the present application, a technical solution in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
According to an embodiment of the present invention, there is provided an embodiment of a vehicle collision warning method, it being noted that the steps shown in the flowcharts of the drawings may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is shown in the flowcharts, in some cases the steps shown or described may be performed in an order different from that herein.
Fig. 1 is a flowchart of a vehicle collision warning method according to an embodiment of the present invention, as shown in fig. 1, the method including the steps of:
step S102, acquiring first vehicle information of a first vehicle and second vehicle information of a second vehicle, wherein the first vehicle information includes: the vehicle speed of the first vehicle and the age of the driver of the first vehicle, the second vehicle information includes: the speed of the second vehicle, which characterizes the vehicle traveling in front of the first vehicle.
The first vehicle may be a vehicle driven by a current driver, or may be a vehicle that needs to acquire an early warning distance. The second vehicle may be a vehicle that is to avoid collisions with the first vehicle.
The first vehicle information may be first vehicle-related information including, but not limited to: the speed of the first vehicle and the age of the driver driving the first vehicle. The second vehicle information may be second vehicle related information including, but not limited to: the speed of the second vehicle.
In an alternative embodiment, the driver of the first vehicle may input the age to the system via the control panel before driving the vehicle. The speed of the first vehicle and the speed of the second vehicle are detected in real time by the radar during the running process of the first vehicle.
In another alternative embodiment, a camera is installed in the first vehicle and matched to driver information by facial recognition of the driver, wherein the driver information includes, but is not limited to: driver name, age, sex, photograph. Meanwhile, the camera can detect the speed of the first vehicle and the speed of the second vehicle in real time and upload the speeds to the vehicle-mounted system.
Step S104, a target early warning model corresponding to the age of the driver is obtained.
The target early warning model may be a model for calculating the early warning distance of the first vehicle to achieve early warning in advance, so as to avoid the collision of the vehicle, including but not limited to: the system comprises a first early warning model and a second early warning model. The first early warning model may be an early warning model of a predetermined early warning distance when the vehicle is suddenly braked. The second early warning model may be an early warning model that predetermines an early warning distance when the vehicle is slowly braked.
In an alternative embodiment, after the vehicle-mounted system acquires the age of the driver, the vehicle-mounted system classifies the age of the driver, and determines the age category of the current driver, wherein the age category may be the age category of the driver, including but not limited to: young drivers, middle aged drivers. And matching the target early warning model to a corresponding target early warning model according to the age category of the driver. For example, when the driver is 28 years old, the driver may be classified to a young driver, and a target early warning model corresponding to the young driver may be obtained.
It should be noted that, multiple early warning models may be stored in the vehicle-mounted system, and the early warning models may be classified by age category, sex, or according to specific conditions, and generate corresponding early warning models. When danger is encountered, the response time of the human is related to not only age but also sex, and also objective factors such as fatigue degree of the driver at the time, so that the early warning model can be designed according to the needs.
And S106, processing the speed of the first vehicle and the speed of the second vehicle by using the target early warning model, and determining the target early warning distance of the first vehicle.
The target warning distance may be a warning distance for avoiding collision between the first vehicle and the second vehicle, including but not limited to: the first early warning distance and the second early warning distance. The first early warning distance can be an early warning distance required by the vehicle during sudden braking, and can also be a corresponding braking distance under the condition of sudden braking. The second early warning distance can be the early warning distance required by the vehicle for slow braking, and can also be the corresponding braking distance under the slow braking condition.
The brake type may be a brake type of a first vehicle, including but not limited to: the emergency braking type and the slow braking type. The sudden braking type can be a braking type in which the vehicle brakes in a sudden braking mode. The slow brake type may be a brake type in which the vehicle brakes in a slow brake manner.
In an alternative embodiment, the speed of the first vehicle and the speed of the second vehicle are input into a target early warning model, and the target early warning model calculates according to the speed of the first vehicle and the speed of the second vehicle to obtain a target early warning distance of the first vehicle.
In another alternative embodiment, the speed of the first vehicle and the speed of the second vehicle are input into a first early warning model of the target early warning model for calculation, and a first early warning distance of the vehicle is determined. And inputting the speed of the first vehicle and the speed of the second vehicle into a second early warning model of the target early warning model for calculation, and determining a second early warning distance of the vehicle.
Step S108, outputting the target early warning distance.
In an alternative embodiment, the obtained target pre-warning distance is compared with a preset pre-warning distance, so as to determine a corresponding pre-warning level, where the pre-warning level may be a level of anti-collision pre-warning for the vehicle according to the pre-warning distance, including but not limited to: primary early warning and secondary early warning. The first-level early warning can be an early warning level corresponding to the fact that the vehicle can brake in a slow braking mode. The second-level early warning can be an early warning level corresponding to braking of the vehicle when the vehicle runs with sudden braking. The preset pre-warning distance may be a pre-warning distance preset according to the situation, which is used for distinguishing the pre-warning level of the target pre-warning distance, and may be, but is not limited to: 10 meters, 7 meters and 5 meters. And determining an early warning output mode corresponding to the early warning level, and synchronously displaying the corresponding target early warning distance on the control panel when the vehicle carries out early warning according to different early warning levels. The corresponding target early warning distance can be output in real time according to specific conditions.
For example, in the running process of the vehicle, the first vehicle only needs to brake slowly at the moment, then the first early warning distance is acquired, the early warning level corresponding to the first early warning distance is the second early warning, the early warning mode of the second early warning can be voice light sound prompt, the first early warning distance is set to be green, and the first early warning distance is displayed on the control panel. However, when an emergency occurs, the first vehicle needs to perform emergency braking, the second warning distance is determined, the warning level corresponding to the second warning distance is first-level warning, the warning mode of the first-level warning can be voice circulation warning prompt, the second warning distance is set to be red, and the second warning distance is displayed on the control panel.
In another alternative embodiment, if the vehicle is in a driving process, danger can be avoided only by sudden braking or slow braking, then the obtained target early warning distance is compared with a preset early warning distance, and if the target early warning distance is greater than the preset early warning distance, the current early warning level is determined to be a first early warning; if the target early warning distance is smaller than the preset early warning distance, determining the current early warning level as the secondary early warning. And pre-warning is carried out according to the corresponding pre-warning level. For example, the current target early warning distance is greater than the preset early warning distance, the early warning level is primary early warning, and the current first vehicle only needs to be braked slowly.
By the steps, the first vehicle information of the first vehicle and the second vehicle information of the second vehicle can be acquired; acquiring a target early warning model corresponding to the age of the driver; processing the speed of the first vehicle and the speed of the second vehicle by using the target early warning model, and determining a target early warning distance of the first vehicle; and outputting the target early warning distance. It is noted that the target early-warning model is selected according to the age of the driver, and then the target early-warning distance is determined by using the target early-warning model, so that the target early-warning distance is closer to the early-warning distance which can be borne by the driver currently, the aim of improving the adaptation degree of the driver to the target early-warning distance is fulfilled, the technical effect of determining the corresponding target early-warning distance according to the age of the driver is realized, and the technical problem that the adaptation degree of the driver to the target early-warning distance is lower is solved.
Optionally, processing the speed of the first vehicle and the speed of the second vehicle by using the target early warning model, determining the target early warning distance of the first vehicle includes: processing the speed of the first vehicle and the speed of the second vehicle by using a first early warning model in the target early warning models, and determining a first early warning distance of the first vehicle, wherein the first early warning model is used for representing an early warning model corresponding to the emergency brake type; processing the speed of the first vehicle and the speed of the second vehicle by using a second early-warning model in the target early-warning models to determine a second early-warning distance of the first vehicle, wherein the second early-warning model is used for representing an early-warning model corresponding to the slow brake type; and summarizing the first early warning distance and the second early warning distance to obtain the target early warning distance.
In an alternative embodiment, after the speed of the first vehicle and the speed of the second vehicle are obtained, the speed of the first vehicle and the speed of the second vehicle are respectively brought into a first early warning model and a second early warning model in the target early warning model to calculate, a first early warning distance corresponding to the first early warning model and a second early warning distance corresponding to the second early warning model are determined, and the first early warning distance and the second early warning distance are determined to be the target early warning distance.
Optionally, outputting the target early warning distance includes: determining an early warning level of the first vehicle based on the target early warning distance; and outputting the target early warning distance according to the output mode corresponding to the early warning level.
In an alternative embodiment, the target early warning distance is compared with a preset early warning distance, early warning levels corresponding to the first early warning distance and the second early warning distance are determined, different early warning output modes are obtained according to different early warning levels, and the target early warning distance is output according to the current running state of the vehicle and the output mode of the current corresponding early warning level.
For example, when the target early warning distance is greater than the preset early warning distance, the early warning level is determined to be the first early warning, at this time, the driver only needs to slowly brake to avoid danger, the control panel only needs to pop up one bullet frame, the corresponding target early warning distance is displayed, and after the first vehicle is confirmed to successfully avoid danger, the bullet frame is automatically closed. If the target early warning distance is smaller than the preset early warning distance, the early warning level is determined to be the secondary early warning, at the moment, the driver needs to react rapidly and tread on the brake, so that the vehicle decelerates at a higher speed, at the moment, the control panel can display the shortest early warning distance in real time, the vehicle-mounted light control can also select a specific display lamp to flash red light, and meanwhile, the vehicle-mounted voice system can also prompt the driver to report the target early warning distance and report the distance between the first vehicle and the second vehicle in real time. If the early warning is the first-level early warning, the vehicle braking system can start the locking function to help the driver to brake together.
Optionally, determining the early warning level of the first vehicle based on the target early warning distance includes: determining the early warning level of the first vehicle as a first-level early warning in response to the target early warning distance being greater than the preset early warning distance; and determining the early warning level of the first vehicle as the secondary early warning in response to the target early warning distance being smaller than the preset early warning distance.
In an alternative embodiment, the first warning distance and the second warning distance in the target warning distance are respectively compared with the preset warning distance, the warning levels corresponding to different warning distances are determined, and if the second warning distance in the target warning distance is greater than the preset warning distance, the current warning level is determined to be a first-level warning; and if the first early warning distance in the target early warning distance is smaller than the preset early warning distance, determining the current early warning level as the secondary early warning.
Optionally, acquiring a target early warning model corresponding to the age of the driver includes: classifying the ages of the drivers to obtain age categories of the drivers; and acquiring a preset early warning model corresponding to the age category of the driver from a plurality of preset early warning models to obtain a target early warning model, wherein the plurality of preset early warning models are used for representing early warning models corresponding to different age categories.
The plurality of preset early warning models can be preset according to specific conditions, and can be classified according to age categories of drivers to obtain the plurality of early warning models. The target early warning model may be an early warning model determined based on the age of the current first vehicle driver.
In an alternative embodiment, the age of the driver is classified to obtain an age category of the driver, and a corresponding preset early-warning model is determined from a plurality of preset early-warning models according to the age category and is determined as the target early-warning model. For example, the current driver's age is 31 years, and the 31 years determine that the driver's age category is a young driver according to the age range corresponding to the age category stored in the system. And determining a preset early warning model related to the young driver in the plurality of preset early warning models as a target early warning model.
Optionally, the method further comprises: acquiring first test vehicle information of a first test vehicle and second test vehicle information of a second test vehicle, wherein the first test vehicle information comprises: the speed of the first test vehicle, the braking signal of the first test vehicle, the age of the test driver of the first test vehicle, the braking distance of the first test vehicle, and the second test vehicle information includes: the speed of the second test vehicle is used for representing the vehicle running in front of the first test vehicle; determining a brake category of the first test vehicle based on a speed of the first test vehicle and a brake signal of the first test vehicle; based on the brake category, a plurality of preset early warning models are generated for the speed of the first test vehicle, the brake distance of the first test vehicle, the speed of the second test vehicle and the age of the test driver.
The first test vehicle may be a vehicle that provides test data, and may be a small car that is equipped with IBOX (cloud box, an intelligent data processing center, capable of implementing information exchange between the vehicle and the outside). The first test vehicle information may be related information acquired by the first test vehicle during the test. The test driver can be a driver driving the first test vehicle, and drivers of different ages can be selected in advance according to requirements to conduct tests.
The second test vehicle may be a test vehicle that performs a collision test with the first test vehicle during a test, may perform uniform running at a set speed, may simulate a real road condition to perform acceleration and deceleration running, and may be a small car with IBOX.
The braking signal can be information of braking of the first test vehicle in the test process, can be calibrated manually according to the whole implementation process, and can also be a signal generated correspondingly when a driver steps on the brake, including but not limited to: the first signal and the second signal, wherein the first signal can be a signal corresponding to the brake being pressed down by a test driver under the condition of full reaction process. The second signal may be a signal corresponding to the brake being stamped when the test driver has to rapidly step on the brake to avoid the collision.
The braking distance may be a braking distance corresponding to the test vehicle during the test. The brake category may be a category of braking during test vehicle testing, including, but not limited to: the emergency braking type and the slow braking type.
In an alternative embodiment, a first test vehicle speed of a first test vehicle and a second test vehicle speed of a second test vehicle are obtained via IBOX. The test driver age may be determined from the registration list in advance. When a driver steps on the brake, a corresponding brake signal is obtained, and a subsequent technician can calibrate or obtain the brake signal according to the test condition. The technician can determine the corresponding braking distance according to the actual test condition. The braking category of the first test vehicle is further determined based on the vehicle speed and the braking signal of the first test vehicle. Classifying according to ages of test drivers, classifying different brake categories, vehicle speeds of first test vehicles, brake distances of first test vehicles and vehicle speeds of second test vehicles according to the ages, determining a plurality of data sets based on the ages, fitting the data sets, and generating a plurality of preset early warning models.
Optionally, determining the braking category of the first test vehicle based on the speed of the first test vehicle and the braking signal of the first test vehicle includes: acquiring early warning time corresponding to a brake signal; determining an experimental deceleration of the first test vehicle based on the speed and the early warning time of the first test vehicle; responding to the experiment deceleration being smaller than the preset deceleration, and determining the brake type as the slow brake type by taking the brake signal as a first signal, wherein the first signal is used for representing a signal generated by stepping on a brake pedal when the response time of a first test vehicle driver is longer than the preset time; and responding to the experiment deceleration being greater than or equal to the preset deceleration, and determining the brake type as the sudden brake type by taking the brake signal as a second signal, wherein the second signal is used for representing a signal generated by stepping on a pedal when the response time of a driver of the first test vehicle is less than the preset time.
The early warning time can be the time from the generation of the brake signal to the successful braking in the test process.
The above-mentioned experimental deceleration may be the deceleration corresponding to the first test vehicle during the test, or may be the deceleration generated during the braking of the first test vehicle. The preset deceleration may be a deceleration set in advance according to circumstances, and is used for distinguishing a brake signal corresponding to the experimental deceleration.
The first test vehicle driver reaction time may be a time from when the first test vehicle driver finds that the brake is needed to be applied to the brake. The preset time may be a time set in advance according to circumstances, and is used to determine a brake signal corresponding to the response time of the driver of the first test vehicle.
In an alternative embodiment, the early warning time corresponding to the brake signal is obtained according to the test result, and the speed and the early warning time of the first test vehicle are obtainedSubstitution formula->The experimental deceleration can be obtained, wherein the speed of the first test vehicle is the initial speed v corresponding to the early warning event 1 And end velocity v 2 . Comparing the experimental deceleration with the preset deceleration, and determining that the brake type is the slow brake type if the experimental deceleration is smaller than the preset deceleration and the brake signal is the first signal; if the experimental deceleration is greater than or equal to the preset deceleration and the braking signal is the second signal, determining that the braking type is the sudden braking type.
It should be noted that, the technician can refer to the testing process, mark the moment when the driver should step on the brake as the first signal, and mark the moment when the driver steps on the brake after the reaction time to locate the second signal.
FIG. 2 is a schematic illustration of an alternative speed versus distance relationship, as shown in FIG. 2, with the abscissa representing speed and the ordinate representing distance, v 1 Representing a first test vehicle speed, v 2 Representing the second test vehicle speed, d representing the distance between the first test vehicle and the second test vehicle. When the first test vehicle speed and the second test vehicle speed are equal, the distance between the two vehicles is the smallest, so that the second test vehicle speed can be selected from the last speed, and the early warning time can be the time from the corresponding initial speed to the last speed.
Optionally, based on the braking category, generating a plurality of preset early warning models including: classifying the ages of the test drivers, and determining the age class of the test drivers; dividing a brake category, a speed of a first test vehicle, a brake distance of the first test vehicle and a speed of a second test vehicle based on age classification of a test driver to obtain a plurality of vehicle data sets; based on the plurality of vehicle data sets, a plurality of preset early warning models are generated.
The plurality of vehicle data sets may be data sets determined by classifying data such as a brake type, a vehicle speed of the first vehicle, a brake distance of the first vehicle, and a vehicle speed of the second vehicle by classifying ages of the test drivers into a large class, and may be, but not limited to: a young driver corresponds to the vehicle data set and a middle-aged driver corresponds to the vehicle data set.
In an alternative embodiment, the ages of the test drivers are classified, the test drivers are classified into young drivers and middle-aged drivers, and the brake category of the young drivers, the speed of the first test vehicle, the brake distance of the first test vehicle, and the speed of the second test vehicle are determined as one data set. The brake category of the middle-aged driver, the speed of the first test vehicle, the brake distance of the first test vehicle, and the speed of the second test vehicle are determined as one data set. The data set of young drivers is shown in the following table one, and the data set of middle-aged drivers is shown in the following table two.
List one
Watch II
It should be noted that, the test adopts two methods to reduce the data error, the first method is to make several groups of tests more, reject the data with larger difference with the test requirement, and finally average the data. In the second method, in the experimental process, two testers record early warning time and speeds of a first test vehicle and a second test vehicle at the same time to carry out screenshot recording on videos, two groups of data are finally obtained, the two groups of data are compared, the data with larger difference are manually reviewed, unreasonable data are removed, and finally the data of the two testers are subjected to average calculation.
Optionally, generating a plurality of preset early warning models based on a plurality of vehicle data sets includes: dividing the vehicle data sets based on the brake categories in the vehicle data sets to obtain data sets corresponding to different brake categories; based on the data sets corresponding to different brake categories, generating early warning models corresponding to different brake categories; summarizing the pre-warning models corresponding to different brake categories to obtain a preset pre-warning model corresponding to the vehicle data set.
In an alternative embodiment, the data sets of the sudden braking of the young driver, the data set of the slow braking of the young driver, the data set of the sudden braking of the middle-aged driver and the data set of the slow braking of the middle-aged driver are determined according to the plurality of vehicle data sets by further dividing according to the braking categories. And generating a corresponding preset early warning model according to different data sets.
Optionally, based on the data sets corresponding to the different brake categories, generating the early warning model corresponding to the different brake categories includes: fitting the speed and the braking distance of a first test vehicle in the data set by using model training software, and determining a preset coefficient corresponding to the data set; determining a deceleration value corresponding to the data set based on a speed of a first test vehicle in the data set; and generating an early warning model corresponding to the data set based on the preset coefficient and the deceleration value.
The model training software described above may be software that is trained from data, including but not limited to: matrix laboratory (Matlab, matrix Laboratory).
The preset coefficient may be a coefficient determined according to the fitting result, and is used for determining a preset early warning model.
The deceleration values described above may be values for acceleration corresponding to different brake categories, including, but not limited to: sudden braking deceleration value and slow braking deceleration value.
In an alternative embodiment, the data of the first test vehicle are put into model training software for fitting according to different data sets, training is performed according to two different models respectively, and the data with the best training result is selected to generate a corresponding preset early warning model. Wherein, model 1:，/>is->Function of->Is a time constant, d is->Is>、/>Is a distance constant. Model 2:，/>is constant (I)>Is->Is a linear function of (a). Obtaining preset coefficients of young drivers and middle-aged drivers, and referring to related documents according to the speed of a first test vehicle in a data set to determine that the acceleration of sudden brake of a general driver is 7.2>The acceleration of the slow brake is 3.6 +.>Thereby generating a corresponding early warning model according to the preset coefficient and the deceleration value:
The early warning model of slow brake of young drivers is；
Early warning model of sudden brake of young driver is；
The early warning model of the slow brake of the middle-aged driver is that；
The early warning model of the sudden brake of the middle-aged driver is that。
It should be noted that the slow braking acceleration in the early warning model is 3.6The corresponding time is that the response time of the corresponding age group is added to the vehicle slow brake time, and the sudden brake acceleration is 7.2 +.>The corresponding time is the extreme reaction time of the corresponding age group.
FIG. 3 is a flow chart of an alternative vehicle collision warning method according to an embodiment of the invention, as shown in FIG. 3, comprising:
step S301 obtains a vehicle speed of a first vehicle, a vehicle speed of a second vehicle, and an age of a driver of the first vehicle.
Step S302, selecting a target early warning model according to the age of the driver.
Step S303, the speed of the first vehicle and the speed of the second vehicle are input into a target early warning model, and the target early warning distance is obtained.
Step S304, corresponding early warning levels are determined according to the target early warning distances.
Step S305, early warning is carried out according to the corresponding early warning level.
Example 2
According to another aspect of the embodiment of the present invention, a vehicle collision pre-warning device is provided, where the vehicle collision pre-warning method of the above embodiment may be executed, and a specific implementation method and a preferred application scenario are the same as those of the above embodiment, and are not described herein.
Fig. 4 is a schematic view of a vehicle collision warning apparatus according to an embodiment of the present invention, as shown in fig. 4, including the following parts: a first acquisition module 40, a second acquisition module 42, a determination module 44, an output module 46.
Wherein, the first obtaining module 40 is configured to obtain first vehicle information of a first vehicle and second vehicle information of a second vehicle, where the first vehicle information includes: the vehicle speed of the first vehicle and the age of the driver of the first vehicle, the second vehicle information includes: a speed of a second vehicle, the second vehicle being used to characterize a vehicle traveling in front of the first vehicle;
a second obtaining module 42, configured to obtain a target early warning model corresponding to the age of the driver;
a determining module 44, configured to process the speed of the first vehicle and the speed of the second vehicle by using the target early warning model, and determine a target early warning distance of the first vehicle;
and the output module 46 is used for outputting the target early warning distance.
Optionally, the determining module includes: the first determining unit is used for processing the speed of the first vehicle and the speed of the second vehicle by utilizing a first early warning model in the target early warning models to determine a first early warning distance of the first vehicle, wherein the first early warning model is used for representing an early warning model corresponding to the emergency brake type; the second determining unit is used for processing the speed of the first vehicle and the speed of the second vehicle by utilizing a second early-warning model in the target early-warning models to determine a second early-warning distance of the first vehicle, wherein the second early-warning model is used for representing an early-warning model corresponding to the slow brake type; and the summarizing unit is used for summarizing the first early warning distance and the second early warning distance to obtain the target early warning distance.
Optionally, the output module includes: the third determining unit is used for determining the early warning level of the first vehicle based on the target early warning distance; and the output unit is used for outputting the target early warning distance according to the output mode corresponding to the early warning level.
Optionally, the third determining unit includes: the first determining subunit is used for determining the early warning level of the first vehicle as a first-level early warning in response to the target early warning distance being greater than a preset early warning distance; and the second determining subunit is used for determining the early warning level of the first vehicle as the secondary early warning in response to the target early warning distance being smaller than the preset early warning distance.
Optionally, the second acquisition module includes: the classification unit is used for classifying the ages of the drivers to obtain age categories of the drivers; the first acquisition unit is used for acquiring preset early warning models corresponding to the age categories of the drivers from a plurality of preset early warning models to obtain target early warning models, wherein the plurality of preset early warning models are used for representing the early warning models corresponding to different age categories.
Optionally, the second acquisition module further includes: a second acquisition unit configured to acquire first test vehicle information of a first test vehicle and second test vehicle information of a second test vehicle, wherein the first test vehicle information includes: the speed of the first test vehicle, the braking signal of the first test vehicle, the age of the test driver of the first test vehicle, the braking distance of the first test vehicle, and the second test vehicle information includes: the speed of the second test vehicle is used for representing the vehicle running in front of the first test vehicle; a fourth determining unit configured to determine a brake category of the first test vehicle based on a vehicle speed of the first test vehicle and a brake signal of the first test vehicle; the generation unit is used for generating a plurality of preset early warning models based on the brake category, the speed of the first test vehicle, the brake distance of the first test vehicle, the speed of the second test vehicle and the age of the test driver.
Optionally, the fourth determining unit includes: the first acquisition subunit is used for acquiring the early warning time corresponding to the brake signal; the third determination subunit is used for determining the experimental deceleration of the first test vehicle based on the speed and the early warning time of the first test vehicle; the fourth determination subunit is used for responding to the situation that the experimental deceleration is smaller than the preset deceleration, the brake signal is a first signal, and the brake type is determined to be a slow brake type, wherein the first signal is used for representing a signal generated by stepping on a brake pedal when the response time of a first test vehicle driver is longer than the preset time; and the fifth determination subunit is used for responding to the fact that the experimental deceleration is greater than or equal to the preset deceleration, the braking signal is a second signal, and the braking category is determined to be the sudden braking type, wherein the second signal is used for representing a signal generated by stepping on a pedal when the response time of the driver of the first test vehicle is less than the preset time.
Optionally, the generating unit includes: the classifying subunit is used for classifying the ages of the test drivers and determining the age categories of the test drivers; the dividing subunit is used for dividing the braking category, the speed of the first test vehicle, the braking distance of the first test vehicle and the speed of the second test vehicle based on the age classification of the test driver to obtain a plurality of vehicle data sets; and the generation subunit is used for generating a plurality of preset early warning models based on a plurality of vehicle data sets.
Optionally, the generating subunit includes: dividing the vehicle data sets based on the brake categories in the vehicle data sets to obtain data sets corresponding to different brake categories; based on the data sets corresponding to different brake categories, generating early warning models corresponding to different brake categories; summarizing the pre-warning models corresponding to different brake categories to obtain a preset pre-warning model corresponding to the vehicle data set.
Optionally, the generating subunit further comprises: fitting the speed and the braking distance of a first test vehicle in the data set by using model training software, and determining a preset coefficient corresponding to the data set; determining a deceleration value corresponding to the data set based on a speed of a first test vehicle in the data set; and generating an early warning model corresponding to the data set based on the preset coefficient and the deceleration value.
Example 3
According to another aspect of an embodiment of the present invention, there is also provided a vehicle including: one or more processors; a storage means for storing one or more programs; the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the vehicle collision warning method of the above-described embodiment.
Example 4
According to another aspect of an embodiment of the present application, there is also provided a computer-readable storage medium including: the computer readable storage medium includes a stored program, wherein the device in which the computer readable storage medium is located is controlled to execute the vehicle collision warning method of the above embodiment when the program runs.
The foregoing embodiment numbers of the present application are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present application, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.

Claims (8)

1. A vehicle collision warning method, characterized by comprising:
acquiring first vehicle information of a first vehicle and second vehicle information of a second vehicle, wherein the first vehicle information comprises: the vehicle speed of the first vehicle and the age of the driver of the first vehicle, the second vehicle information including: a vehicle speed of the second vehicle, the second vehicle being used to characterize a vehicle traveling in front of the first vehicle;
acquiring a target early warning model corresponding to the age of the driver;
processing the speed of the first vehicle and the speed of the second vehicle by using the target early warning model, and determining a target early warning distance of the first vehicle;
outputting the target early warning distance;
the method comprises the steps of obtaining a target early warning model corresponding to the age of the driver, and further comprises the following steps:
acquiring first test vehicle information of a first test vehicle and second test vehicle information of a second test vehicle, wherein the first test vehicle information comprises: the speed of the first test vehicle, the braking signal of the first test vehicle, the age of the test driver of the first test vehicle, the braking distance of the first test vehicle, and the second test vehicle information includes: the speed of the second test vehicle is used for representing the vehicle running in front of the first test vehicle;
Determining a brake category of the first test vehicle based on a speed of the first test vehicle and a brake signal of the first test vehicle;
based on the braking category, generating a plurality of preset early warning models, wherein the plurality of preset early warning models are used for representing early warning models corresponding to different age categories;
wherein determining a brake category of the first test vehicle based on a speed of the first test vehicle and a brake signal of the first test vehicle comprises:
acquiring early warning time corresponding to the brake signal;
determining an experimental deceleration of the first test vehicle based on the speed and the early warning time of the first test vehicle;
responding to the experiment deceleration being smaller than the preset deceleration, and determining that the brake category is a slow brake type by taking the brake signal as a first signal, wherein the first signal is used for representing a signal generated by stepping on a brake pedal when the response time of a first test vehicle driver is longer than the preset time;
And responding to the experiment deceleration being greater than or equal to the preset deceleration, wherein the brake signal is a second signal, and determining that the brake type is a sudden brake type, wherein the second signal is used for representing a signal generated by stepping on a pedal when the response time of the first test vehicle driver is less than the preset time.
2. The vehicle collision warning method according to claim 1, wherein processing the vehicle speed of the first vehicle and the vehicle speed of the second vehicle using the target warning model, determining a target warning distance of the first vehicle, comprises:
processing the speed of the first vehicle and the speed of the second vehicle by using a first early-warning model in the target early-warning models, and determining a first early-warning distance of the first vehicle, wherein the first early-warning model is used for representing an early-warning model corresponding to an emergency brake type;
processing the speed of the first vehicle and the speed of the second vehicle by using a second early-warning model in the target early-warning models, and determining a second early-warning distance of the first vehicle, wherein the second early-warning model is used for representing an early-warning model corresponding to a slow brake type;
And summarizing the first early warning distance and the second early warning distance to obtain the target early warning distance.
3. The vehicle collision warning method according to claim 1, wherein outputting the target warning distance includes:
determining an early warning level of the first vehicle based on the target early warning distance;
and outputting the target early warning distance according to the output mode corresponding to the early warning level.
4. The vehicle collision warning method according to claim 3, wherein determining a warning level of the first vehicle based on the target warning distance includes:
determining the early warning level of the first vehicle as a primary early warning in response to the target early warning distance being greater than a preset early warning distance;
and determining the early warning level of the first vehicle as a secondary early warning in response to the target early warning distance being smaller than the preset early warning distance.
5. The vehicle collision warning method according to claim 1, wherein acquiring the target warning model corresponding to the age of the driver includes:
classifying the age of the driver to obtain the age class of the driver;
and acquiring a preset early warning model corresponding to the age category of the driver from the plurality of preset early warning models to obtain the target early warning model.
6. The vehicle collision warning method according to claim 1, wherein generating the plurality of preset warning models based on the braking category, the vehicle speed of the first test vehicle, the braking distance of the first test vehicle, the vehicle speed of the second test vehicle, and the age of the test driver, comprises:
classifying the ages of the test drivers, and determining the age classification of the test drivers;
dividing the braking category, the speed of the first test vehicle, the braking distance of the first test vehicle and the speed of the second test vehicle based on the age classification of the test driver to obtain a plurality of vehicle data sets;
and generating the plurality of preset early warning models based on the plurality of vehicle data sets.
7. The vehicle collision warning method of claim 6, wherein generating the plurality of preset warning models based on the plurality of vehicle data sets comprises:
dividing the vehicle data set based on the brake categories in the vehicle data set to obtain data sets corresponding to different brake categories;
generating early warning models corresponding to different brake categories based on the data sets corresponding to the different brake categories;
Summarizing the pre-warning models corresponding to the different brake categories to obtain the preset pre-warning model corresponding to the vehicle data set.
8. A vehicle, characterized by comprising:
one or more processors;
a storage means for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the vehicle collision warning method of any one of claims 1 to 7.
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