CN116853269A - Active control method and device for preventing abnormal running speed of vehicle, equipment and medium - Google Patents

Active control method and device for preventing abnormal running speed of vehicle, equipment and medium Download PDF

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Publication number
CN116853269A
CN116853269A CN202310959872.XA CN202310959872A CN116853269A CN 116853269 A CN116853269 A CN 116853269A CN 202310959872 A CN202310959872 A CN 202310959872A CN 116853269 A CN116853269 A CN 116853269A
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CN
China
Prior art keywords
vehicle
road section
running speed
information
speed
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Pending
Application number
CN202310959872.XA
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Chinese (zh)
Inventor
刘涛
谭侃伦
刘同文
龙杨东
张开瑞
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Chongqing Changan Automobile Co Ltd
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Chongqing Changan Automobile Co Ltd
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Priority to CN202310959872.XA priority Critical patent/CN116853269A/en
Publication of CN116853269A publication Critical patent/CN116853269A/en
Pending legal-status Critical Current

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Classifications

    • 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
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
    • B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
    • B60W30/09—Taking automatic action to avoid collision, e.g. braking and steering
    • 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
    • B60W2552/00—Input parameters relating to infrastructure
    • 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
    • B60W2555/00—Input parameters relating to exterior conditions, not covered by groups B60W2552/00, B60W2554/00
    • B60W2555/20—Ambient conditions, e.g. wind or rain

Abstract

The scheme provides an active control method, device, vehicle, equipment and medium for preventing abnormal running speed of a vehicle, which are used for detecting abnormal running speed of the vehicle in the driving process of a driver and giving corresponding emergency braking measures by combining the current running speed of the vehicle and the type of the vehicle. The method comprises the following steps: acquiring facial expression classification of a driver, driving parameters of a vehicle, weather information of the position of the vehicle and the position of a road section based on the position of the vehicle; obtaining road section related information and road section surrounding environment related information based on the road section position of the vehicle; obtaining a vehicle running speed state based on the facial expression classification of a driver, the running parameters of the vehicle, the position of a road section where the vehicle is located, weather information of the position of the vehicle, road section related information and road section surrounding environment related information; when it is determined that the vehicle running speed is abnormal based on the vehicle running speed state, active safety control is performed.

Description

Active control method and device for preventing abnormal running speed of vehicle, equipment and medium
Technical Field
The invention relates to the technical field of active safety of vehicles, in particular to an active control method, an active control device, a vehicle, equipment and a medium for preventing abnormal running speed of a vehicle.
Background
With the continuous development of economy and technology, people have higher and higher attention to vehicle driving safety, and the attention direction of people in the general industry and the vehicle industry is changed from the traditional passive safety field to the active safety field, so that the requirements of people on vehicle safety are changed from reducing the damage degree caused by the accident of the vehicle to reducing the possibility of the accident.
Active safety technologies such as an Antilock Braking System (ABS), an Electronic Stability Program (ESP) of a vehicle, a Lane Departure Warning System (LDWS), a forward collision avoidance system (FCA), a collision approximation steering system (CIS), a pedestrian detection system (PD) and the like have been developed in recent years and widely used. Traditional active safety technologies (such as application publication numbers US9014915B2 and CN111559380B, US9090265B 2) mainly finish the perception and information collection of the vehicle to the state of the driver and the surrounding environment according to vehicle-mounted radar, cameras, vehicle-mounted GPS and other devices, so that the vehicle can prevent the upcoming danger. The conventional active safety technology has a great number of preventive measures for the upcoming danger of the vehicle, but the conventional active safety technology has yet to be further improved for preventing abnormal running speed of the vehicle caused by situations of abrupt misoperation of the driver, failure, abnormality and the like of the vehicle control equipment in the normal running process of the vehicle.
Although the existing active safety technology has been still further perfected for the research on abnormal vehicle running speed, the existing scholars have plentiful research on abnormal vehicle running speed identification, for example, application publication number CN110533880a proposes a method for detecting whether the driving state of a driver is abnormal or not based on electrocardiosignals, the invention considers that the abnormal driving state occurrence time of the driver is usually higher than the abnormal running state occurrence time of the vehicle, and proposes the invention based on the concept; the application publication number CN112071069B provides a method for detecting braking failure of a freight vehicle on a long downhill road section, wherein the method is characterized in that a monitoring point is set up on a vehicle-mounted GPS and a road side, a video sensor and a radar sensor are installed, and abnormal high-speed running and continuous acceleration behavior detection of the freight vehicle are realized by utilizing information such as track information, vehicle scratch and the like; the application publication number US9881498B2 provides a method of evaluating a vehicle traveling abnormality without using an on-vehicle camera and image processing calculation, the core of which is to judge whether the vehicle traveling is abnormal behavior by information of the position, traveling, etc. of the vehicle and its neighboring vehicles. In addition, the road surface related data is obtained through the devices such as a NOA system which is proposed by Tesla 2019, a NOP system which is proposed by a future automobile 2020 and an NGP system which is proposed by a Minpeng automobile 2021, so that the management function of the running speed of the vehicle is realized.
In summary, the related researches on the abnormal driving condition of the vehicle need to collect data by means of a large amount of expensive and complex equipment pavement environments and drivers, and part of the researches on the detection method of the abnormal driving condition of the vehicle either enable the drivers to wear complex equipment, or require a large amount of equipment for related detection, communication and the like, or require a large number of vehicles on the road section to have higher detection, communication and the like. Thus, there is a need in the industry for an active safety technique for preventing vehicle driving anomalies that is low cost, has technical requirements within acceptable limits and has no wearable device requirements for the driver.
Disclosure of Invention
In view of the above, the present application aims to provide an active control method, device, vehicle, device and medium for preventing abnormal running speed of a vehicle, which aim to detect abnormal running speed of the vehicle in the driving process of the driver on the premise of lower cost, low technical requirement and no requirement on wearing equipment of the driver, and provide corresponding emergency braking measures in combination with the current running speed of the vehicle and the type of the vehicle, so as to avoid occurrence of vehicle accidents.
In order to achieve the above purpose, the specific technical scheme of the invention is as follows:
the invention provides an active control method for preventing abnormal running speed of a vehicle, which comprises the following steps:
acquiring facial expression classification of a driver, driving parameters of a vehicle, weather information of the position of the vehicle and the position of a road section based on the position of the vehicle;
obtaining road section related information and road section surrounding environment related information based on the road section position of the vehicle;
obtaining a vehicle running speed state based on the facial expression classification of a driver, the running parameters of the vehicle, the position of a road section where the vehicle is located, weather information of the position of the vehicle, road section related information and road section surrounding environment related information;
when it is determined that the vehicle running speed is abnormal based on the vehicle running speed state, active safety control is performed.
Preferably, the obtaining the vehicle running speed state based on the facial expression classification of the driver, the running parameters of the vehicle, the road section position of the vehicle, weather information of the position of the vehicle, road section related information and road section surrounding environment related information includes:
predicting the highest safe driving speed of the road section where the vehicle is located based on the road section position where the vehicle is located, weather information of the position where the vehicle is located, road section related information and road section surrounding environment related information;
And obtaining the vehicle running speed state based on the facial expression classification of the driver, the running parameters of the vehicle, the road section related information and the highest safe running speed of the road section where the vehicle is located.
Preferably, predicting the highest safe driving speed of the road section where the vehicle is located based on the road section location where the vehicle is located, weather information of the vehicle location, road section related information, and road section surrounding environment related information, includes:
and inputting the road section position of the vehicle, weather information of the position of the vehicle, road section related information and road section surrounding environment related information into a pre-trained road section highest safe running speed prediction model, and outputting the highest safe running speed of the road section of the vehicle.
Preferably, the obtaining the vehicle running speed state based on the facial expression classification of the driver, the running parameters of the vehicle, the road section related information and the highest safe running speed of the road section where the vehicle is located includes:
the facial expression classification of the driver, the driving parameters of the vehicle, the road section related information and the highest safe driving speed of the road section where the vehicle is located are input into a pre-trained abnormal driving speed condition identification model of the vehicle, and the driving speed state of the vehicle is output.
Preferably, the link related information includes: road segment function grade information, road segment congestion state information, road segment speed limit information and road segment long downhill slope information, and road segment surrounding environment related information comprises: road section intersection information, road section signal lamp information and road section surrounding building information;
Inputting the position of the road section where the vehicle is located, weather information of the position of the vehicle, information related to the road section and information related to the surrounding environment of the road section into a pre-trained prediction model of the highest safe running speed of the road section, and outputting the highest safe running speed of the road section where the vehicle is located, wherein the method comprises the following steps:
the method comprises the steps of inputting the road section position of a vehicle, weather information of the position of the vehicle, road section function grade information, road section congestion state information, road section speed limit information, road section long downhill slope information, road section intersection information, road section signal lamp information and road section surrounding building information into a pre-trained road section highest safe running speed prediction model, and outputting the highest safe running speed of the road section of the vehicle.
Preferably, the driving parameters of the vehicle include: the vehicle running speed, current accelerator pedal information of the vehicle, and current brake pedal information of the vehicle, and the link-related information includes: the long downhill road section information comprises a long downhill road section starting point distance and a long downhill road section ending point distance;
inputting the facial expression classification of the driver, the driving parameters of the vehicle, the road section related information and the highest safe driving speed of the road section where the vehicle is located into a pre-trained abnormal driving speed condition identification model of the vehicle, and outputting the driving speed state of the vehicle, wherein the method comprises the following steps:
The facial expression classification of the driver, the running speed of the vehicle, the current accelerator pedal information of the vehicle, the current brake pedal information of the vehicle, the starting distance of the long downhill road section, the finishing distance of the long downhill road section and the highest safe running speed of the road section where the vehicle is located are input into a pre-trained abnormal running speed condition identification model of the vehicle, and the running speed state of the vehicle is output.
Preferably, when determining that the vehicle running speed is abnormal based on the vehicle running speed state, the active safety control is performed, including:
if the running speed of the vehicle is abnormal and exceeds a first preset speed, immediately starting emergency braking, and prompting a user whether to stop the emergency braking; before the user does not input an instruction to stop the emergency braking, continuing the emergency braking until the vehicle speed is reduced to 0;
if the running speed of the vehicle is abnormal and is between the second preset vehicle speed and the first preset vehicle speed, prompting a user whether to stop emergency braking in a first time period; if the user does not input an instruction for stopping the emergency braking in the first time period, starting the emergency braking; before the user does not input an instruction to stop the emergency braking, continuing the emergency braking until the vehicle speed is reduced to 0;
If the running speed of the vehicle is abnormal and the running speed of the vehicle is lower than a second preset vehicle speed, prompting a user whether to stop emergency braking in a second time period; if the user does not input an instruction for stopping the emergency braking in the second time period, starting the emergency braking; before the user does not input an instruction to stop the emergency braking, continuing the emergency braking until the vehicle speed is reduced to 0;
the first preset vehicle speed is greater than the second preset vehicle speed, and the second time period is longer than the first time period.
Preferably, when the vehicle is a fuel vehicle, the emergency braking is performed by:
firstly, simultaneously executing a hazard warning flash lamp for starting a vehicle and disconnecting an accelerator pedal signal, and then executing service braking; if the vehicle does not decelerate to the highest safe running speed of the road section where the vehicle is located within the first preset time, starting a parking brake; after the parking braking is executed, if the vehicle is not decelerated to the highest safe running speed of the road section where the vehicle is located within a second preset time period, prompting a driver to avoid danger of an emergency lane and adopting a roadside object to scratch to execute deceleration;
when the vehicle is a new energy vehicle, the emergency braking measures are as follows:
firstly, simultaneously executing the starting of a hazard warning flash lamp of the vehicle and the disconnection of an accelerator pedal signal, and then using the service brake and the energy recovery brake of the vehicle; if the vehicle does not decelerate to the highest safe running speed of the road section where the vehicle is located within the first preset time, starting a parking brake; after the parking braking is executed, if the vehicle is not decelerated to the highest safe running speed of the road section where the vehicle is located within a second preset time period, prompting a driver to avoid danger of an emergency lane and adopting a roadside object to scratch to execute deceleration.
Preferably, the driver facial expression classification is obtained by inputting the acquired driver facial expression into a pre-trained facial expression recognition model.
The invention also provides a vehicle semi-active suspension control device based on the map navigation path, which comprises:
the acquisition module is used for acquiring facial expression classification of a driver, driving parameters of a vehicle, weather information of the position of the vehicle and the position of a road section based on the position of the vehicle;
the first determining module is used for obtaining the relevant information of the road section and the relevant information of the surrounding environment of the road section based on the position of the road section where the vehicle is located;
the second determining module is used for obtaining a vehicle running speed state based on facial expression classification of a driver, running parameters of the vehicle, the position of a road section where the vehicle is located, weather information of the position of the vehicle, road section related information and road section surrounding environment related information;
and the control module is used for executing active safety control when the running speed of the vehicle is abnormal based on the running speed state of the vehicle.
Preferably, the second determining module includes:
a first determining unit for predicting a highest safe running speed of a road section where the vehicle is located based on a road section location where the vehicle is located, weather information of the vehicle location, road section related information, and road section surrounding environment related information;
And the second determining unit is used for obtaining the vehicle running speed state based on the facial expression classification of the driver, the running parameters of the vehicle, the road section related information and the highest safe running speed of the road section where the vehicle is located.
Preferably, the first determination unit includes:
the first determination subunit is used for inputting the road section position where the vehicle is located, weather information of the position where the vehicle is located, road section related information and road section surrounding environment related information into a pre-trained road section highest safe running speed prediction model and outputting the highest safe running speed of the road section where the vehicle is located.
Preferably, the second determining unit includes:
and the second determination subunit is used for inputting the facial expression classification of the driver, the driving parameters of the vehicle, the road section related information and the highest safe driving speed of the road section where the vehicle is positioned into a pre-trained abnormal vehicle driving speed condition identification model and outputting the driving speed state of the vehicle.
Preferably, the link related information includes: road segment function grade information, road segment congestion state information, road segment speed limit information and road segment long downhill slope information, and road segment surrounding environment related information comprises: road section intersection information, road section signal lamp information and road section surrounding building information;
The first determination subunit is configured to:
the method comprises the steps of inputting the road section position of a vehicle, weather information of the position of the vehicle, road section function grade information, road section congestion state information, road section speed limit information, road section long downhill slope information, road section intersection information, road section signal lamp information and road section surrounding building information into a pre-trained road section highest safe running speed prediction model, and outputting the highest safe running speed of the road section of the vehicle.
Preferably, the driving parameters of the vehicle include: the vehicle running speed, current accelerator pedal information of the vehicle, and current brake pedal information of the vehicle, and the link-related information includes: the long downhill road section information comprises a long downhill road section starting point distance and a long downhill road section ending point distance;
the second determination subunit is configured to:
the facial expression classification of the driver, the running speed of the vehicle, the current accelerator pedal information of the vehicle, the current brake pedal information of the vehicle, the starting distance of the long downhill road section, the finishing distance of the long downhill road section and the highest safe running speed of the road section where the vehicle is located are input into a pre-trained abnormal running speed condition identification model of the vehicle, and the running speed state of the vehicle is output.
Preferably, the control module comprises:
The first control unit is used for immediately starting emergency braking and prompting a user whether to stop the emergency braking if the running speed of the vehicle is abnormal and exceeds a first preset vehicle speed; before the user does not input an instruction to stop the emergency braking, continuing the emergency braking until the vehicle speed is reduced to 0;
the second control unit is used for prompting a user whether to stop emergency braking in a first time period if the running speed of the vehicle is abnormal and is between a second preset vehicle speed and a first preset vehicle speed; if the user does not input an instruction for stopping the emergency braking in the first time period, starting the emergency braking; before the user does not input an instruction to stop the emergency braking, continuing the emergency braking until the vehicle speed is reduced to 0;
the third control unit is used for prompting a user whether to stop emergency braking in a second time period if the running speed of the vehicle is abnormal and the running speed of the vehicle is lower than a second preset vehicle speed; if the user does not input an instruction for stopping the emergency braking in the second time period, starting the emergency braking; before the user does not input an instruction to stop the emergency braking, continuing the emergency braking until the vehicle speed is reduced to 0;
the first preset vehicle speed is greater than the second preset vehicle speed, and the second time period is longer than the first time period.
Preferably, when the vehicle is a fuel vehicle, the emergency braking is performed by:
firstly, simultaneously executing a hazard warning flash lamp for starting a vehicle and disconnecting an accelerator pedal signal, and then executing service braking; if the vehicle does not decelerate to the highest safe running speed of the road section where the vehicle is located within the first preset time, starting a parking brake; after the parking braking is executed, if the vehicle is not decelerated to the highest safe running speed of the road section where the vehicle is located within a second preset time period, prompting a driver to avoid danger of an emergency lane and adopting a roadside object to scratch to execute deceleration;
when the vehicle is a new energy vehicle, the emergency braking measures are as follows:
firstly, simultaneously executing the starting of a hazard warning flash lamp of the vehicle and the disconnection of an accelerator pedal signal, and then using the service brake and the energy recovery brake of the vehicle; if the vehicle does not decelerate to the highest safe running speed of the road section where the vehicle is located within the first preset time, starting a parking brake; after the parking braking is executed, if the vehicle is not decelerated to the highest safe running speed of the road section where the vehicle is located within a second preset time period, prompting a driver to avoid danger of an emergency lane and adopting a roadside object to scratch to execute deceleration.
Preferably, the driver facial expression classification is obtained by inputting the acquired driver facial expression into a pre-trained facial expression recognition model.
The invention also provides a vehicle, which comprises the active control method device for preventing the abnormal running speed of the vehicle.
The invention also provides a control device comprising a processor, a memory and a program or instruction stored in the memory and capable of running on the processor, wherein the program or instruction realizes the steps of the active control method for preventing the abnormal running speed of the vehicle when being executed by the processor.
The present invention also provides a readable storage medium having stored thereon a program or instructions which, when executed by a processor, implement the steps of the active control method for preventing a vehicle running speed abnormality as described above.
Compared with the existing method, the invention has the beneficial effects that:
1. compared with the prior art, the vehicle-mounted equipment is less, and the function of judging the abnormal running speed of the vehicle under low cost is realized;
2. the active safety protection system mainly aims to prevent a vehicle accident from occurring due to abnormal running speed caused by misoperation of a driver or equipment problems of the vehicle in the running process of the vehicle, and the active safety protection system is realized by combining abnormal running speed state judgment of the vehicle with different emergency braking schemes of the vehicle;
3. The electronic online map is mainly used for acquiring the surrounding environment information of the vehicle, the requirement on the vehicle to sense the surrounding environment information is low, and various information provided by other vehicles and road side equipment is not required to be used for sensing the surrounding environment information of the vehicle, so that the related acquired information has low requirement on the quantity of vehicle-mounted equipment and the cost of the vehicle-mounted equipment;
4. the highest safe driving speed of the road section where the vehicle is located can be predicted, the vehicle surrounding information is not required to be acquired by advanced sensors such as vehicle-mounted radars and vehicle-mounted cameras, the number of the vehicle sensors is low, the highest safe speed of the road section is predicted based on various types of data, and the obtained result is more reliable;
5. the abnormal running speed of the vehicle on the road section can be judged, no requirement is required for the driver, no additional vehicle-mounted sensor is needed, the model not only takes the highest safe running speed of the road section as a judgment standard, but also is combined with the facial expression of the driver, the operation of the accelerator pedal and the brake pedal by the driver and other related information to judge the abnormal running speed of the vehicle, so that the obtained result is more in line with the actual situation;
6. when the vehicle speed is different, different emergency braking starting processes are adopted, and the system protects personnel in the vehicle more thoroughly under different speed scenes;
7. The proposed emergency braking scheme can select different braking schemes according to the type of the vehicle, and the scheme considers emergency braking measures of the vehicle after one or more braking methods are failed, so that the scheme is more comprehensive for protecting personnel in the vehicle;
8. the invention has wide application range, can be used in cities or outside cities, does not need drivers to input driving paths in the electronic map in advance, and has less vehicle-mounted equipment which is required to be added, and the required technical level is relatively simple.
Drawings
FIG. 1 shows a schematic flow chart of a method in an embodiment of the invention;
FIG. 2 shows a schematic diagram of on-board equipment and on-board software required for relevant information of a road section where a vehicle is located;
FIG. 3 shows a schematic diagram of input parameters and output parameters of a road segment highest safe driving speed prediction model;
FIG. 4 shows a schematic diagram of input parameters and output parameters of a vehicle travel speed anomaly identification model;
FIG. 5 shows a schematic of an emergency braking flow;
fig. 6 shows a schematic flow chart of an emergency braking scheme.
Detailed Description
The following description of the embodiments of the present invention will be made more clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present invention. All other embodiments, which can be made by a person skilled in the art without any inventive effort, are intended to be within the scope of the present invention based on the embodiments of the present invention.
Referring to fig. 1, fig. 1 shows a flowchart of an active safety system for preventing abnormal running speed of a vehicle according to the present application, where the flowchart includes: the first step is to obtain facial expression classification of a driver, driving parameters of the vehicle, a vehicle position and a road section position 101 where the vehicle is located, the second step is to obtain weather information 102 of the vehicle position based on the vehicle position, the third step is to obtain road section related information and road section surrounding environment related information 103 based on the road section position where the vehicle is located, the fourth step is to obtain vehicle driving speed state 104 based on the facial expression classification of the driver, the driving parameters of the vehicle, the road section position where the vehicle is located, the weather information of the vehicle position, the road section related information and the road section surrounding environment related information, and the fifth step is to execute active safety control 105 when determining that the vehicle driving speed is abnormal based on the vehicle driving speed state.
An active control method for preventing abnormal running speed of a vehicle disclosed in the example of the present application will be described in detail.
Referring to fig. 2, fig. 1 shows that the relevant information of the road section where the vehicle is located in the present application is a general term for information obtained by integrating and extracting three aspects of vehicle GPS positioning, an online electronic map and road section weather conditions. The vehicle GPS positioning information is provided by a vehicle-mounted GPS, the online electronic map is provided by vehicle-mounted map software, and the road section weather condition is provided by vehicle-mounted weather software. It should be understood that in actual situations, the vehicle-mounted map software and the vehicle-mounted weather software shown in the embodiments of the present application are not limited to a specific software, and other software meeting requirements also exist, and the vehicle-mounted map software and the vehicle-mounted weather software adopted by the driver are not specifically limited herein.
Referring to fig. 3, fig. 3 shows a process of inputting and outputting a prediction model of a highest safe driving speed of a road segment, the process including the steps of:
step 201: and collecting the relevant information of the road section where the vehicle is located.
Acquiring road section position information I based on vehicle-mounted GPS, vehicle-mounted map software and vehicle-mounted weather software p Road segment function class information I r Road segment congestion status information I c Speed limit information I of road section vehicle v Intersection information I of road section cr Signal lamp information I of road section sg Building information around road section I b Information I of long downhill path sl Road segment weather condition information I w . Notably, I p The road section position information of the road section is whether the position of the road section is in the city; when I r When the road section is a city road section, the corresponding road section function grade can be divided into a expressway, a main road, a secondary road and a branch road from high to low in sequence, and when I r The road section is a highway section, and the corresponding road section function grade can be divided into expressways, primary roads, secondary roads, tertiary roads and quaternary roads from high to low in sequence; i c Road congestion of road segments of (a)The states can be divided into smooth, basically smooth, slight congestion, medium congestion and serious congestion from low to high in sequence; i v The road section vehicle speed limit information of (1) is the highest running speed and the lowest running speed regulated according to the related country, and if the lowest speed is not regulated, the default lowest speed is 0; i cr The road section intersection information of the road section comprises three kinds of information including whether the road section has an intersection, the type of the intersection and the distance between the vehicle and the intersection; i sg The road section signal lamp information of the road section signal lamp comprises three kinds of information including whether a signal lamp exists, the distance between a vehicle and the signal lamp, the current lamp color of the signal lamp and the residual time length; i b The road section surrounding building information of (a) refers to buildings on two sides of a road section, and specifically comprises a residence land building, a public facility land building, an industrial land building, a business land building, a logistics storage land building, a traffic facility land building, a municipal public facility land building and a special land building; i sl The long downhill road section information of the road section comprises two kinds of information, namely the position of the long downhill road section and the road section gradient of the long downhill road section, and if the long downhill road section does not exist, the position information and the road section gradient information are output to be 0; i w The road section weather condition information of the vehicle comprises three kinds of information, namely weather of the position of the vehicle, temperature of the position of the vehicle and wind speed of the position of the vehicle.
In specific implementation, vehicle GPS positioning information is input into vehicle-mounted map software and weather software, the vehicle-mounted map software acquires a real-time online electronic map under the condition of connecting with the Internet, the vehicle GPS positioning information is input into the online electronic map, and the API interface provided by the vehicle-mounted map software is used for acquiring the position information I of a road section p Road segment function class information I r Road congestion status information I c Speed limit information I of road section vehicle v Intersection information I of road section cr Signal lamp information I of road section sg Building information around road section I b Information I of long downhill path sl The method comprises the steps of carrying out a first treatment on the surface of the And the vehicle-mounted weather software acquires weather, temperature and wind speed information of a road section where the vehicle is positioned by using the GPS positioning information of the vehicle on the basis of connecting the Internet.
Step 202: the XGBoost model predicts the highest safe driving speed of the road section.
The position information I of the road section prepared in advance is firstly used p Road segment function class information I r Road congestion status information I c Speed limit information I of road section vehicle v Intersection information I of road section cr Signal lamp information I of road section sg Building information around road section I b Information I of long downhill path sl Road segment weather condition information I w As input parameter, the highest safe driving speed V of the road section prepared in advance is used hp And (3) inputting the input parameters and the output parameters into the XGBoost model as output parameters for training, and loading the trained XGBoost prediction model into the vehicle. The XGBoost model formula is as follows:
model predictive value:
model objective function:
model regularization term:
in the formulas (1) - (3),the predicted value after the t time of iteration; f (f) t (x i ) Is the predicted value at the t time; x is x i Is input data; />For the actual value y i And predictive value->A deviation value between the two; omega (f) k ) Representing the sum of the complexity of k trees as a regular term, preventing overfitting; t (T) (i) The number of leaf nodes of the ith model; omega (i) The output value of the leaf node of the ith model; gamma and lambda are weight coefficients.
In particular, the input parameters (the road section position information I of the current vehicle p Road segment function class information I r Road congestion status information I c Speed limit information I of road section vehicle v Intersection information I of road section cr Signal lamp information I of road section sg Building information around road section I b Information I of long downhill path sl Road segment weather condition information I w ) And output parameters (road section highest safe driving speed I) sv ) And (3) inputting the model into the XGBoost model together for iterative training 10000 times, and searching out the model with the lowest error as a trained model.
Step 203: obtaining the highest safe driving speed V of the road section hp 。
Predicting the highest safe driving speed of the road section by using the trained XGBoost model to obtain the highest safe driving speed V of the current road section hp . Namely, the position of the road section where the vehicle is located, weather information of the position where the vehicle is located, road section function grade information, road section congestion state information, road section speed limit information, road section long downhill slope information, road section intersection information, road section signal lamp information and road section surrounding building information are input into a pre-trained road section highest safe running speed prediction model, and the highest safe running speed of the road section where the vehicle is located is output.
In specific implementation, input parameters required by the model are acquired from the vehicle-mounted equipment of the current vehicle and are input into the XGBoost model loaded on the vehicle to predict and obtain the highest safe running speed V of the current road section hp 。
Referring to fig. 4, fig. 4 shows a schematic diagram of a fourth step and a fifth step, which are a process of selecting a BP neural network to identify a vehicle driving speed anomaly model, based on the flowchart of fig. 1, wherein the fourth step and the fifth step include the following steps:
step 301: and selecting the vehicle-mounted equipment from which the required parameters are derived.
The vehicle-mounted device required in the fourth step includes: vehicle-mounted ESC, in-vehicle camera, accelerator pedal, brake pedal and electronic online map.
Step 302: the input parameters of the BP neural network are selected.
Acquiring vehicle running speed V from vehicle-mounted ESC and acquiring facial expression information I of driver from vehicle-mounted camera face Acquisition of accelerator pedal signal I from accelerator pedal acc Acquisition of brake pedal signal I from brake pedal break Obtaining the starting point distance I of a long downhill road section from an electronic online map lenstart Obtaining the end point distance I of a long downhill road section from an electronic online map lenend Maximum travel speed V of road section hp Seven parameters are taken as input parameters of the BP neural network.
Step 303: the BP neural network identifies a vehicle travel speed state.
Input parameter data (vehicle running speed V, driver facial expression image I) face Accelerator pedal signal I acc Brake pedal signal I break Distance I between start points of long downhill road section lenstart End distance I of long downhill road section lenend Maximum travel speed V of road section hp ) And outputting the parameter data (vehicle running speed state I 0-1 ) And (3) inputting the model into the BP neural network model together for iterative training 10000 times, and searching out a model with the lowest error as a trained model.
The BP neural network model formula is as follows:
number of hidden layers:
activation function:
forward propagation formula:
net＝W T X+b (6)
the error calculation formula:
implicit layer and output layer weights and threshold update formulas:
in the formulas (4) - (10), h is the number of neurons in an hidden layer; m is the number of neurons of the input layer; n is the number of neurons of the output layer; a is a constant, typically between 1 and 10; g (x) is the activation function of the neuron; net is the net activation value; w is a weight value; x is an input parameter; b is a threshold;is a predicted value; e is the model prediction total error; y is i Is the i-th actual value; />Is the i-th predicted value; n is the number of neurons of the output layer; w (W) (k) The weight value after the kth iteration; b (k) Is the threshold value after the kth iteration; η is the learning rate.
In concrete implementation, the calculated current vehicle running speed V and the facial expression image I of the driver face Accelerator pedal signal I acc Brake pedal signal I break Length of the pipeDistance I of starting point of downhill road section lenstart End distance I of long downhill road section lenend Maximum travel speed V of road section hp As an input BP neural network model, the trained BP neural network model is utilized to control the running speed state I of the vehicle 0-1 Predicting to obtain the running speed state I of the vehicle 0-1 。
Step 304: a vehicle travel speed state is acquired.
The trained BP neural network model is utilized to control the running speed state I of the vehicle 0-1 Predicting to obtain the current running speed state I of the vehicle 0-1 . That is, the driver facial expression classification, the vehicle running speed, the current accelerator pedal information of the vehicle, the current brake pedal information of the vehicle, the long downhill road section start-point distance, the long downhill road section end-point distance, and the highest safe running speed of the road section where the vehicle is located are input into a pre-trained vehicle running speed abnormal condition recognition model, and the vehicle running speed state is output.
In specific implementation, input parameters required by the model are acquired from the vehicle-mounted equipment of the current vehicle and are input into the BP neural network model loaded into the vehicle to predict and obtain the running speed state I of the current vehicle 0-1 。
Referring to fig. 5, fig. 5 shows the emergency brake start procedure, and the whole emergency brake start procedure method includes the following three cases:
when the running speed of the vehicle is greater than or equal to 120km/h (first preset vehicle speed) and the model judges that the running speed of the vehicle is abnormal, if the running speed of the vehicle is abnormal and exceeds the first preset vehicle speed, immediately starting emergency braking, and prompting a user whether to stop the emergency braking; the emergency braking is continued until the vehicle speed drops to 0, before the user does not input an instruction to stop the emergency braking. Specifically, considering that the irrecoverable loss can be caused by the too high running speed of the vehicle, the emergency brake is selected to be started immediately, then the voice in the vehicle is started to inquire whether the driver prohibits the emergency brake, and simultaneously, the character appears on the central control screen in the vehicle to inquire whether the driver prohibits the emergency brake; if the microphone and the central control screen in the vehicle do not receive the instruction for stopping the emergency braking during the emergency braking, the emergency braking is continuously executed, otherwise, the emergency braking is stopped; if the emergency braking is continuously executed until the running speed of the vehicle is smaller than the highest safe speed of the road section, inquiring whether the emergency braking is closed or not by personnel in the vehicle through the voice in the vehicle and the central control screen; if the microphone or the central control screen in the vehicle does not receive an explicit closing instruction, the emergency braking is continuously executed until the vehicle is completely stopped, and otherwise, the emergency braking is closed.
Prompting a user whether to stop emergency braking in a first time period when the vehicle running speed is greater than or equal to 80km/h (second preset vehicle speed) and the vehicle running speed is less than 120 km/h; if the user does not input an instruction for stopping the emergency braking in the first time period, starting the emergency braking; the emergency braking is continued until the vehicle speed drops to 0, before the user does not input an instruction to stop the emergency braking. Specifically, when the model judges that the running speed of the vehicle is abnormal, firstly starting the voice in the vehicle to inquire whether the driver prohibits the use of the emergency brake, and inquiring whether the driver prohibits the use of the emergency brake by the personnel in the vehicle when characters appear on a central control screen in the vehicle; considering that the running speed of the vehicle is high, if the microphone and the central control screen in the vehicle do not receive the instruction for stopping the emergency braking within 3 seconds (the first time period), the emergency braking is continuously executed, and otherwise, the emergency braking is stopped; if the emergency braking is executed until the running speed of the vehicle is smaller than the highest safe speed of the road section, inquiring whether the emergency braking is closed or not by personnel in the vehicle through the voice and the central control screen in the vehicle; if the microphone or the central control screen in the vehicle does not receive an explicit closing instruction, the emergency braking is continuously executed until the vehicle is completely stopped, and otherwise, the emergency braking is closed.
Prompting a user whether to stop emergency braking in a second time period when the running speed of the vehicle is less than 80km/h and the model judges that the running speed of the vehicle is abnormal; if the user does not input an instruction for stopping the emergency braking in the second time period, starting the emergency braking; the emergency braking is continued until the vehicle speed drops to 0, before the user does not input an instruction to stop the emergency braking. Specifically, firstly, starting the voice in the vehicle to inquire whether a driver prohibits the use of emergency braking, and inquiring whether a person in the vehicle prohibits the use of emergency braking by the words on a central control screen in the vehicle; considering that the running speed of the vehicle is not high, setting that the microphone and the central control screen in the vehicle do not receive the instruction for stopping the emergency braking within 5 seconds (second time period), and continuously executing the emergency braking, otherwise stopping the emergency braking; if the emergency braking is executed until the running speed of the vehicle is smaller than the highest safe speed of the road section, inquiring whether the emergency braking is closed or not by personnel in the vehicle through the voice and the central control screen in the vehicle; if the microphone or the central control screen in the vehicle does not receive an explicit closing instruction, the emergency braking is continuously executed until the vehicle is completely stopped, and otherwise, the emergency braking is closed.
Referring to fig. 6, fig. 6 shows a flow of emergency braking performed by the vehicle, which is as follows:
When the vehicle is a fuel-fired vehicle, firstly starting a hazard warning flash lamp of the vehicle, and simultaneously disconnecting an accelerator pedal signal to prevent misoperation of a driver; then using the service brake of the vehicle, and starting the parking brake if the vehicle does not reach the preset deceleration requirement within the preset time; if the parking brake is started and the set deceleration requirement is not met within the set time, the driver is informed of whether the emergency escape lane exists recently through the voice in the vehicle and is informed of taking deceleration measures such as scraping roadside objects and the like, and the central control screen displays a navigation path reaching the nearest emergency escape lane and a schematic diagram or video of operation of the corresponding deceleration measures.
When the vehicle is a new energy vehicle, firstly starting a hazard alarm flash lamp of the vehicle and disconnecting an accelerator pedal signal to prevent misoperation of a driver; then using the service brake and the energy recovery brake of the vehicle, and starting the parking brake if the vehicle does not reach the preset deceleration requirement within the preset time; if the parking brake is started and the set deceleration requirement is not met within the set time, the driver is informed of whether the emergency escape lane exists recently through the voice in the vehicle and is informed of taking deceleration measures such as scraping roadside objects and the like, and the central control screen displays a navigation path reaching the nearest emergency escape lane and a schematic diagram or video of operation of the corresponding deceleration measures.
The invention also provides a vehicle semi-active suspension control device based on the map navigation path, which comprises:
the acquisition module is used for acquiring facial expression classification of a driver, driving parameters of a vehicle, weather information of the position of the vehicle and the position of a road section based on the position of the vehicle;
the first determining module is used for obtaining the relevant information of the road section and the relevant information of the surrounding environment of the road section based on the position of the road section where the vehicle is located;
the second determining module is used for obtaining a vehicle running speed state based on facial expression classification of a driver, running parameters of the vehicle, the position of a road section where the vehicle is located, weather information of the position of the vehicle, road section related information and road section surrounding environment related information;
and the control module is used for executing active safety control when the running speed of the vehicle is abnormal based on the running speed state of the vehicle.
Preferably, the second determining module includes:
a first determining unit for predicting a highest safe running speed of a road section where the vehicle is located based on a road section location where the vehicle is located, weather information of the vehicle location, road section related information, and road section surrounding environment related information;
and the second determining unit is used for obtaining the vehicle running speed state based on the facial expression classification of the driver, the running parameters of the vehicle, the road section related information and the highest safe running speed of the road section where the vehicle is located.
Preferably, the first determination unit includes:
the first determination subunit is used for inputting the road section position where the vehicle is located, weather information of the position where the vehicle is located, road section related information and road section surrounding environment related information into a pre-trained road section highest safe running speed prediction model and outputting the highest safe running speed of the road section where the vehicle is located.
Preferably, the second determining unit includes:
and the second determination subunit is used for inputting the facial expression classification of the driver, the driving parameters of the vehicle, the road section related information and the highest safe driving speed of the road section where the vehicle is positioned into a pre-trained abnormal vehicle driving speed condition identification model and outputting the driving speed state of the vehicle.
Preferably, the link related information includes: road segment function grade information, road segment congestion state information, road segment speed limit information and road segment long downhill slope information, and road segment surrounding environment related information comprises: road section intersection information, road section signal lamp information and road section surrounding building information;
the first determination subunit is configured to:
the method comprises the steps of inputting the road section position of a vehicle, weather information of the position of the vehicle, road section function grade information, road section congestion state information, road section speed limit information, road section long downhill slope information, road section intersection information, road section signal lamp information and road section surrounding building information into a pre-trained road section highest safe running speed prediction model, and outputting the highest safe running speed of the road section of the vehicle.
Preferably, the driving parameters of the vehicle include: the vehicle running speed, current accelerator pedal information of the vehicle, and current brake pedal information of the vehicle, and the link-related information includes: the long downhill road section information comprises a long downhill road section starting point distance and a long downhill road section ending point distance;
the second determination subunit is configured to:
the facial expression classification of the driver, the running speed of the vehicle, the current accelerator pedal information of the vehicle, the current brake pedal information of the vehicle, the starting distance of the long downhill road section, the finishing distance of the long downhill road section and the highest safe running speed of the road section where the vehicle is located are input into a pre-trained abnormal running speed condition identification model of the vehicle, and the running speed state of the vehicle is output.
Preferably, the control module comprises:
the first control unit is used for immediately starting emergency braking and prompting a user whether to stop the emergency braking if the running speed of the vehicle is abnormal and exceeds a first preset vehicle speed; before the user does not input an instruction to stop the emergency braking, continuing the emergency braking until the vehicle speed is reduced to 0;
the second control unit is used for prompting a user whether to stop emergency braking in a first time period if the running speed of the vehicle is abnormal and is between a second preset vehicle speed and a first preset vehicle speed; if the user does not input an instruction for stopping the emergency braking in the first time period, starting the emergency braking; before the user does not input an instruction to stop the emergency braking, continuing the emergency braking until the vehicle speed is reduced to 0;
The third control unit is used for prompting a user whether to stop emergency braking in a second time period if the running speed of the vehicle is abnormal and the running speed of the vehicle is lower than a second preset vehicle speed; if the user does not input an instruction for stopping the emergency braking in the second time period, starting the emergency braking; before the user does not input an instruction to stop the emergency braking, continuing the emergency braking until the vehicle speed is reduced to 0;
the first preset vehicle speed is greater than the second preset vehicle speed, and the second time period is longer than the first time period.
Preferably, when the vehicle is a fuel vehicle, the emergency braking is performed by:
firstly, simultaneously executing a hazard warning flash lamp for starting a vehicle and disconnecting an accelerator pedal signal, and then executing service braking; if the vehicle does not decelerate to the highest safe running speed of the road section where the vehicle is located within the first preset time, starting a parking brake; after the parking braking is executed, if the vehicle is not decelerated to the highest safe running speed of the road section where the vehicle is located within a second preset time period, prompting a driver to avoid danger of an emergency lane and adopting a roadside object to scratch to execute deceleration;
when the vehicle is a new energy vehicle, the emergency braking measures are as follows:
firstly, simultaneously executing the starting of a hazard warning flash lamp of the vehicle and the disconnection of an accelerator pedal signal, and then using the service brake and the energy recovery brake of the vehicle; if the vehicle does not decelerate to the highest safe running speed of the road section where the vehicle is located within the first preset time, starting a parking brake; after the parking braking is executed, if the vehicle is not decelerated to the highest safe running speed of the road section where the vehicle is located within a second preset time period, prompting a driver to avoid danger of an emergency lane and adopting a roadside object to scratch to execute deceleration.
Preferably, the driver facial expression classification is obtained by inputting the acquired driver facial expression into a pre-trained facial expression recognition model.
The invention also provides a vehicle, which comprises the active control method device for preventing the abnormal running speed of the vehicle.
The invention also provides a control device comprising a processor, a memory and a program or instruction stored in the memory and capable of running on the processor, wherein the program or instruction realizes the steps of the active control method for preventing the abnormal running speed of the vehicle when being executed by the processor.
The present invention also provides a readable storage medium having stored thereon a program or instructions which, when executed by a processor, implement the steps of the active control method for preventing a vehicle running speed abnormality as described above.
While the invention has been described in detail in connection with only a limited number of embodiments, it should be apparent that the invention is not limited to the precise embodiments set forth herein, but is well suited to the particular use contemplated. Further modifications, additions and substitutions will readily occur to those skilled in the art, and the invention is therefore not to be considered limited to the foregoing description without departing from the general concept as defined in the accompanying claims and the equivalents thereof.

Claims (13)

1. An active control method for preventing an abnormality in a running speed of a vehicle, comprising:
acquiring facial expression classification of a driver, driving parameters of a vehicle, weather information of the position of the vehicle and the position of a road section based on the position of the vehicle;
obtaining road section related information and road section surrounding environment related information based on the road section position of the vehicle;
obtaining a vehicle running speed state based on the facial expression classification of a driver, the running parameters of the vehicle, the position of a road section where the vehicle is located, weather information of the position of the vehicle, road section related information and road section surrounding environment related information;
when it is determined that the vehicle running speed is abnormal based on the vehicle running speed state, active safety control is performed.
2. The active control method for preventing abnormal vehicle running speed according to claim 1, wherein the obtaining of the vehicle running speed state based on the driver facial expression classification, the running parameters of the vehicle, the road segment position where the vehicle is located, the weather information of the position where the vehicle is located, the road segment related information, and the road segment surrounding environment related information, comprises:
predicting the highest safe driving speed of the road section where the vehicle is located based on the road section position where the vehicle is located, weather information of the position where the vehicle is located, road section related information and road section surrounding environment related information;
And obtaining the vehicle running speed state based on the facial expression classification of the driver, the running parameters of the vehicle, the road section related information and the highest safe running speed of the road section where the vehicle is located.
3. The active control method for preventing abnormal running speed of a vehicle according to claim 2, wherein predicting the highest safe running speed of the road segment in which the vehicle is located based on the position of the road segment in which the vehicle is located, weather information of the position in which the vehicle is located, road segment related information, and road segment surrounding environment related information, comprises:
and inputting the road section position of the vehicle, weather information of the position of the vehicle, road section related information and road section surrounding environment related information into a pre-trained road section highest safe running speed prediction model, and outputting the highest safe running speed of the road section of the vehicle.
4. The active control method for preventing abnormal running speed of a vehicle according to claim 2 or 3, wherein the obtaining of the running speed state of the vehicle based on the facial expression classification of the driver, the running parameters of the vehicle, the road section related information, and the highest safe running speed of the road section on which the vehicle is located, comprises:
the facial expression classification of the driver, the driving parameters of the vehicle, the road section related information and the highest safe driving speed of the road section where the vehicle is located are input into a pre-trained abnormal driving speed condition identification model of the vehicle, and the driving speed state of the vehicle is output.
5. The active control method for preventing a vehicle running speed abnormality according to claim 3, wherein the link-related information includes: road segment function grade information, road segment congestion state information, road segment speed limit information and road segment long downhill slope information, and road segment surrounding environment related information comprises: road section intersection information, road section signal lamp information and road section surrounding building information;
inputting the position of the road section where the vehicle is located, weather information of the position of the vehicle, information related to the road section and information related to the surrounding environment of the road section into a pre-trained prediction model of the highest safe running speed of the road section, and outputting the highest safe running speed of the road section where the vehicle is located, wherein the method comprises the following steps:
the method comprises the steps of inputting the road section position of a vehicle, weather information of the position of the vehicle, road section function grade information, road section congestion state information, road section speed limit information, road section long downhill slope information, road section intersection information, road section signal lamp information and road section surrounding building information into a pre-trained road section highest safe running speed prediction model, and outputting the highest safe running speed of the road section of the vehicle.
6. The active control method for preventing a vehicle running speed abnormality according to claim 4, characterized in that the running parameters of the vehicle include: the vehicle running speed, current accelerator pedal information of the vehicle, and current brake pedal information of the vehicle, and the link-related information includes: the long downhill road section information comprises a long downhill road section starting point distance and a long downhill road section ending point distance;
Inputting the facial expression classification of the driver, the driving parameters of the vehicle, the road section related information and the highest safe driving speed of the road section where the vehicle is located into a pre-trained abnormal driving speed condition identification model of the vehicle, and outputting the driving speed state of the vehicle, wherein the method comprises the following steps:
the facial expression classification of the driver, the running speed of the vehicle, the current accelerator pedal information of the vehicle, the current brake pedal information of the vehicle, the starting distance of the long downhill road section, the finishing distance of the long downhill road section and the highest safe running speed of the road section where the vehicle is located are input into a pre-trained abnormal running speed condition identification model of the vehicle, and the running speed state of the vehicle is output.
7. The active control method for preventing a vehicle running speed abnormality according to claim 1, characterized in that when determining a vehicle running speed abnormality based on a vehicle running speed state, active safety control is performed, comprising:
if the running speed of the vehicle is abnormal and exceeds a first preset speed, immediately starting emergency braking, and prompting a user whether to stop the emergency braking; before the user does not input an instruction to stop the emergency braking, continuing the emergency braking until the vehicle speed is reduced to 0;
if the running speed of the vehicle is abnormal and is between the second preset vehicle speed and the first preset vehicle speed, prompting a user whether to stop emergency braking in a first time period; if the user does not input an instruction for stopping the emergency braking in the first time period, starting the emergency braking; before the user does not input an instruction to stop the emergency braking, continuing the emergency braking until the vehicle speed is reduced to 0;
If the running speed of the vehicle is abnormal and the running speed of the vehicle is lower than a second preset vehicle speed, prompting a user whether to stop emergency braking in a second time period; if the user does not input an instruction for stopping the emergency braking in the second time period, starting the emergency braking; before the user does not input an instruction to stop the emergency braking, continuing the emergency braking until the vehicle speed is reduced to 0;
the first preset vehicle speed is greater than the second preset vehicle speed, and the second time period is longer than the first time period.
8. The active control method for preventing a vehicle running speed abnormality according to claim 7, characterized in that,
when the vehicle is a fuel vehicle, the measures for executing emergency braking are as follows:
firstly, simultaneously executing a hazard warning flash lamp for starting a vehicle and disconnecting an accelerator pedal signal, and then executing service braking; if the vehicle does not decelerate to the highest safe running speed of the road section where the vehicle is located within the first preset time, starting a parking brake; after the parking braking is executed, if the vehicle is not decelerated to the highest safe running speed of the road section where the vehicle is located within a second preset time period, prompting a driver to avoid danger of an emergency lane and adopting a roadside object to scratch to execute deceleration;
when the vehicle is a new energy vehicle, the emergency braking measures are as follows:
Firstly, simultaneously executing the starting of a hazard warning flash lamp of the vehicle and the disconnection of an accelerator pedal signal, and then using the service brake and the energy recovery brake of the vehicle; if the vehicle does not decelerate to the highest safe running speed of the road section where the vehicle is located within the first preset time, starting a parking brake; after the parking braking is executed, if the vehicle is not decelerated to the highest safe running speed of the road section where the vehicle is located within a second preset time period, prompting a driver to avoid danger of an emergency lane and adopting a roadside object to scratch to execute deceleration.
9. The active control method for preventing abnormal vehicle running speed according to any one of claims 1 to 8, wherein the driver facial expression classification is obtained by inputting the acquired driver facial expression into a pre-trained facial expression recognition model.
10. A vehicle semi-active suspension control device based on a map navigation path, characterized by comprising:
the acquisition module is used for acquiring facial expression classification of a driver, driving parameters of a vehicle, weather information of the position of the vehicle and the position of a road section based on the position of the vehicle;
the first determining module is used for obtaining the relevant information of the road section and the relevant information of the surrounding environment of the road section based on the position of the road section where the vehicle is located;
The second determining module is used for obtaining a vehicle running speed state based on facial expression classification of a driver, running parameters of the vehicle, the position of a road section where the vehicle is located, weather information of the position of the vehicle, road section related information and road section surrounding environment related information;
and the control module is used for executing active safety control when the running speed of the vehicle is abnormal based on the running speed state of the vehicle.
11. A vehicle comprising the active control method apparatus for preventing abnormality of running speed of a vehicle according to claim 10.
12. A control apparatus comprising a processor, a memory and a program or instruction stored on the memory and executable on the processor, the program or instruction when executed by the processor implementing the steps of the active control method for preventing an abnormality in the running speed of a vehicle as claimed in any one of claims 1 to 9.
13. A readable storage medium, wherein a program or instructions is stored on the readable storage medium, which when executed by a processor, implements the steps of the active control method for preventing a vehicle running speed abnormality according to any one of claims 1 to 9.
CN202310959872.XA 2023-07-28 2023-07-28 Active control method and device for preventing abnormal running speed of vehicle, equipment and medium Pending CN116853269A (en)

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