CN114954307A - Driving assistance system based on artificial intelligence - Google Patents

Driving assistance system based on artificial intelligence Download PDF

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Publication number
CN114954307A
CN114954307A CN202210848230.8A CN202210848230A CN114954307A CN 114954307 A CN114954307 A CN 114954307A CN 202210848230 A CN202210848230 A CN 202210848230A CN 114954307 A CN114954307 A CN 114954307A
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China
Prior art keywords
module
vehicle
central control
control module
monitoring
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CN202210848230.8A
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Chinese (zh)
Inventor
张鸿
王谆
吉泉仲
黄镱
欧阳文凯
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Hohai University HHU
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Hohai University HHU
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Priority to CN202210848230.8A priority Critical patent/CN114954307A/en
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R16/00—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for
    • B60R16/02—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements
    • B60R16/023—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements for transmission of signals between vehicle parts or subsystems
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60H—ARRANGEMENTS OF HEATING, COOLING, VENTILATING OR OTHER AIR-TREATING DEVICES SPECIALLY ADAPTED FOR PASSENGER OR GOODS SPACES OF VEHICLES
    • B60H3/00—Other air-treating devices
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60Q—ARRANGEMENT OF SIGNALLING OR LIGHTING DEVICES, THE MOUNTING OR SUPPORTING THEREOF OR CIRCUITS THEREFOR, FOR VEHICLES IN GENERAL
    • B60Q1/00—Arrangement of optical signalling or lighting devices, the mounting or supporting thereof or circuits therefor
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60Q—ARRANGEMENT OF SIGNALLING OR LIGHTING DEVICES, THE MOUNTING OR SUPPORTING THEREOF OR CIRCUITS THEREFOR, FOR VEHICLES IN GENERAL
    • B60Q9/00—Arrangement or adaptation of signal devices not provided for in one of main groups B60Q1/00 - B60Q7/00, e.g. haptic signalling
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60Q—ARRANGEMENT OF SIGNALLING OR LIGHTING DEVICES, THE MOUNTING OR SUPPORTING THEREOF OR CIRCUITS THEREFOR, FOR VEHICLES IN GENERAL
    • B60Q9/00—Arrangement or adaptation of signal devices not provided for in one of main groups B60Q1/00 - B60Q7/00, e.g. haptic signalling
    • B60Q9/008—Arrangement or adaptation of signal devices not provided for in one of main groups B60Q1/00 - B60Q7/00, e.g. haptic signalling for anti-collision purposes
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R25/00—Fittings or systems for preventing or indicating unauthorised use or theft of vehicles
    • B60R25/20—Means to switch the anti-theft system on or off
    • B60R25/2018—Central base unlocks or authorises unlocking
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60S—SERVICING, CLEANING, REPAIRING, SUPPORTING, LIFTING, OR MANOEUVRING OF VEHICLES, NOT OTHERWISE PROVIDED FOR
    • B60S1/00—Cleaning of vehicles
    • B60S1/02—Cleaning windscreens, windows or optical devices
    • B60S1/04—Wipers or the like, e.g. scrapers
    • B60S1/06—Wipers or the like, e.g. scrapers characterised by the drive
    • B60S1/08—Wipers or the like, e.g. scrapers characterised by the drive electrically driven
    • B60S1/0818—Wipers or the like, e.g. scrapers characterised by the drive electrically driven including control systems responsive to external conditions, e.g. by detection of moisture, dirt or the like
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60Q—ARRANGEMENT OF SIGNALLING OR LIGHTING DEVICES, THE MOUNTING OR SUPPORTING THEREOF OR CIRCUITS THEREFOR, FOR VEHICLES IN GENERAL
    • B60Q2300/00—Indexing codes for automatically adjustable headlamps or automatically dimmable headlamps
    • B60Q2300/30—Indexing codes relating to the vehicle environment
    • B60Q2300/31—Atmospheric conditions
    • B60Q2300/314—Ambient light

Abstract

The invention discloses an auxiliary driving system based on artificial intelligence, which comprises a central control module; the surrounding environment monitoring module is used for monitoring the surrounding environment condition of the vehicle and feeding back the surrounding environment condition to the central control module; the vehicle internal environment monitoring module is used for monitoring the environment condition inside the vehicle and feeding back the environment condition to the central control module; the vehicle state monitoring module is used for monitoring the running condition of the vehicle body and feeding back the running condition to the central control module; the driver state monitoring module is used for monitoring the fatigue state of the current driver and feeding the fatigue state back to the central control module; the driving auxiliary module is used for realizing the functions of vehicle speed adjustment, lane adjustment, overtaking, braking and parking under the control of the central control module so as to realize the driving assistance of a driver to the vehicle in the driving process; the safety driving method has the advantages that the safety driving coefficient can be improved, a driver is reminded to operate normally, safety risks of a safety road are avoided, safety of personnel and vehicles is guaranteed, and traffic accidents are reduced.

Description

Driving assistance system based on artificial intelligence
Technical Field
The invention relates to the field, in particular to an auxiliary driving system based on artificial intelligence.
Background
With the development of social economy and the improvement of living standard of people, people use more and more automobiles, the automobiles are gradually merged into the lives of people, the problem of safe driving becomes more important, and the artificial intelligent driving assistance technology becomes a hot spot of social research. The existing driving assistance systems are generally based on the technologies of cameras and radars, give a warning to a driver aiming at an impending risk by providing environmental data in front of, at the side of and behind a vehicle and taking corresponding operations, provide assistance for the driver, and can also provide intuitive prompts or operations such as braking and steering input and the like, help to guide the driver to keep driving in a lane or provide convenient functions such as an adaptive cruise control system and the like.
However, the traditional assistant driving system has limited functions and does not have functions of monitoring the environment in the vehicle and monitoring and analyzing the driver, particularly in recent years, the traffic accident rate is higher and higher, and most traffic accidents are caused by human factors, such as fatigue driving, drunk driving and the like of the driver; data indicate that inattentive driving is one of the important factors leading to car accidents; therefore, it is urgently needed to add a monitoring function for a driver to an existing auxiliary driving system to improve a safe driving coefficient, remind the driver of normative operation, avoid safety risks of a safe road, guarantee safety of personnel and vehicles, and reduce traffic accidents.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides an auxiliary driving system based on artificial intelligence, which can improve the safe driving coefficient, remind a driver of normative operation, avoid the safety risk of a safe road, guarantee the safety of personnel and vehicles and reduce the occurrence of traffic accidents.
The technical scheme adopted by the invention for solving the technical problems is as follows: an assistant driving system based on artificial intelligence comprises
A central control module;
the surrounding environment monitoring module is used for monitoring the surrounding environment condition of the vehicle and feeding back the surrounding environment condition to the central control module;
the vehicle internal environment monitoring module is used for monitoring the environment condition in the vehicle and feeding back the environment condition to the central control module;
the vehicle state monitoring module is used for monitoring the running condition of the vehicle body and feeding back the running condition to the central control module;
the driver state monitoring module is used for monitoring the fatigue state of the current driver and feeding back the fatigue state to the central control module;
the driving auxiliary module is used for realizing the functions of vehicle speed adjustment, lane adjustment, overtaking, braking and parking under the control of the central control module so as to realize the driving assistance of a driver to the vehicle in the driving process;
the warning module is used for realizing alarm reminding under the control of the central control module;
the communication module is used for the communication between the central control module and the remote cloud;
and the terminal module is electrically connected with the remote cloud end and used for realizing the interactive communication between the terminal module and the remote cloud end.
Preferably, the surrounding environment monitoring module includes an atmosphere monitoring unit, a millimeter wave radar module, a camera module and a light supplement lamp, the atmosphere monitoring unit is arranged on the vehicle and used for monitoring the current atmospheric environment condition and feeding back to the central control module, the millimeter wave radar module is arranged at the top of the vehicle and used for monitoring the distance between an obstacle and the vehicle in real time, the millimeter wave radar module is electrically connected with the central control module, the camera module is used for detecting the road condition around the vehicle and feeding back to the central control module, and the light supplement lamp is arranged on the vehicle and electrically connected with the central control module to improve the brightness of a target area. In the structure, the atmosphere monitoring unit is used for monitoring the atmosphere environment of the current environment where the vehicle is located, wherein the atmosphere environment comprises air temperature and humidity, illumination intensity, rainfall information, PM2.5 and the like, so that equipment in the vehicle is better controlled through the central control module; thereby millimeter wave radar module and camera module cooperation are used can real-time identification vehicle week side road conditions the image information such as pedestrian, road conditions, target vehicle and barrier to the cooperation drives auxiliary module, makes when driving, makes the regulation according to the site conditions pertinence, and can also play the real-time road conditions information and the alarm information of receiving, reminds the driver in real time, sends the warning to detecting nearer target distance, further improves the security of driving.
Preferably, when the atmosphere monitoring unit monitors that the current vehicle is in a dark environment, a first instruction is sent to the central control module, the central control module generates a light supplement instruction after receiving the first instruction and sends the light supplement instruction to the light supplement lamp, and the light supplement lamp performs light supplement processing on a target area after receiving the light supplement instruction. In this structure, under the not enough condition of light, central control module sends the light filling instruction and starts the light filling lamp and improve the luminance to the target area and carry out the light filling to keep other regional original luminance, can consequently at night, under the not good condition of lighting conditions such as sleet haze, the fuzzy not enough of image is gathered in effectual improvement, through the regional light filling of target, can improve imaging quality and detection distance, reduce weather environment to driver's influence, reduce the emergence of accident.
Preferably, the atmosphere monitoring unit comprises a rainfall sensor, an illumination sensor, a PM2.5 sensor, a wind speed sensor and a temperature and humidity sensor, and the rainfall sensor, the illumination sensor, the PM2.5 sensor, the wind speed sensor and the temperature and humidity sensor are respectively and electrically connected with the central control module. In the structure, the rainfall sensor is used for detecting the rainfall, and once the rainfall exceeds a threshold value, the windscreen wiper is started; the illumination sensor is used for detecting the illumination intensity of the current environment, and once the illumination intensity exceeds a threshold value, the light supplement lamp and the searchlight are started; the PM2.5 sensor is used for detecting a PM2.5 value of the current environment, and once the PM2.5 value exceeds a threshold value, the air purification equipment is started to ensure the health of people in the vehicle; the wind speed sensor is used for detecting the wind volume of the current environment, and once the wind volume exceeds a threshold value, the wind speed sensor can intervene in the driving assistance module to enhance the driving safety; the temperature and humidity sensor is used for detecting the temperature and humidity of the current environment so as to be beneficial to the adjustment of the air temperature in the vehicle.
Preferably, the in-vehicle environment monitoring module comprises a high-definition camera, the high-definition camera is arranged in the vehicle and is electrically connected with the central control module, so that when an emergency occurs in the vehicle, the central control module controls the warning module to send out an alarm prompt, and the specific steps are as follows,
s1: pushing an instruction whether to open the high-definition camera to a user, if the user selects to open the high-definition camera, entering S2, and if the user does not agree, enabling the high-definition camera to be in a silent state;
s2: the high-definition camera starts to work, shoots the conditions in the vehicle in real time, and sends the collected video information to the image processing module for storage;
s3: the image processing module is used for storing video information sent by the high-definition camera and forwarding the video information to the deep learning judgment module for behavior analysis;
s4: the deep learning judgment module is pre-trained with an abnormal behavior judgment model, performs behavior judgment on the received video information according to the abnormal behavior judgment model, and feeds back a judgment result to the central control module;
s5: if the judgment result is abnormal, the central control module controls the warning module to send out an alarm prompt, reports the alarm prompt to a remote cloud end through the communication module, and simultaneously transmits video information to a vehicle-mounted storage medium for backup; and if the judgment result is normal, directly inputting the video information into the vehicle-mounted storage medium for backup.
In the structure, when camera shooting is carried out in the vehicle, user consent can be obtained to protect the privacy of people in the vehicle, the identification method is accurate in motion identification, human motion detection is carried out by utilizing the deep learning judgment module, the identification method has strong generalization capability, and the motion model is analyzed and modeled through offline data, so that the specific rule and the motion range of motion are captured very accurately, the motion of the people can be matched accurately, and once abnormal behaviors such as robbery, falling into water, traffic accidents and driver unconsciousness occur, the alarm can be given rapidly, and meanwhile, the driving auxiliary module intervenes in the work.
Preferably, the vehicle state monitoring module comprises a vehicle speed monitoring unit, a tire pressure monitoring unit, a water temperature monitoring unit, an oil amount monitoring unit and a circuit monitoring unit, and the vehicle speed monitoring unit, the tire pressure monitoring unit, the water temperature monitoring unit, the oil amount monitoring unit and the circuit monitoring unit are respectively electrically connected with the central control module.
Preferably, the driver state monitoring module is realized by a face collecting unit arranged in the vehicle, and comprises the following specific steps,
the method comprises the following steps: the method comprises the steps that a face image of a driver is collected through a face collecting unit at regular time;
step two: normalizing and graying the acquired face image of the driver to acquire a face area;
step three: positioning a pupil mass center and a pupil area of a human eye in the human face area, acquiring human eye state parameters and tracking the human eye;
step four: putting the human eye state parameters into a pre-trained fatigue characteristic state judgment model, judging whether the driver is in a fatigue state, and if so, executing a fifth step; otherwise, returning to execute the first step;
step five: the central control module controls the warning module to send out warning reminding, and reports to the remote cloud through the communication module.
In this structure, need not with driver direct contact to carry out the interference of effectively getting rid of environmental noise through driver face image, and possess high real-time, combine people's eye pupil barycenter and pupil region, improved the proportion of key feature to the result influence, further improved the detection precision to driver's driving state, and possess better use value.
Preferably, the terminal module is a smart phone, the central control module has a unique serial number, the remote cloud terminal has a storage list, the unique serial number in the storage list corresponds to a mobile phone registration number, when the in-vehicle environment monitoring module monitors that the current driver is stranger, the central control module sends a confirmation instruction to the remote cloud terminal through the communication module, the remote cloud terminal pushes a message to the mobile phone registration number corresponding to the central control module after receiving the confirmation instruction, and when the terminal module corresponding to the mobile phone registration number receives the push message, the vehicle can be normally driven as if the terminal module is started; and if the vehicle is refused to start, the vehicle refuses to start and sends out an alarm prompt. In the structure, because the action of driving the vehicle by strangers occurs, in order to conveniently identify people of the type, once a stranger is located at a driving position, the remote cloud end can immediately push a message to the corresponding terminal module, if the terminal module agrees to start the vehicle, the vehicle can be normally sent under the condition of no key, and if the terminal module refuses to start the vehicle, the vehicle is considered to be illegally intruded, and an alarm prompt is sent to ensure the safety of the vehicle.
Compared with the prior art, the invention has the advantages that: surrounding environment monitoring module, an environmental aspect for monitoring the vehicle is around, in-vehicle environmental monitoring module, an environmental aspect for monitoring the vehicle inside, vehicle state monitoring module, an operation conditions for monitoring the vehicle body, driver state monitoring module, a fatigue state for monitoring current driver, thereby carry out diversified comprehensive monitoring, with obtain more outside and inside data, so that central control module better carries out the judgement analysis, remind driver's normal operation, safe road safety risk, guarantee personnel and vehicle safety, reduce the emergence of traffic accident.
Drawings
FIG. 1 is a functional block diagram of the present invention;
FIG. 2 is a schematic block diagram of an ambient monitoring module according to the present invention;
FIG. 3 is a schematic block diagram of a vehicle condition monitoring module of the present invention;
fig. 4 is a schematic flow chart of the in-vehicle environment monitoring module according to the present invention during operation.
Detailed Description
The present invention will be described in further detail with reference to the following drawings and examples, but the present invention is not limited thereto.
The first embodiment is as follows: as shown in the figure, the auxiliary driving system based on artificial intelligence comprises
A central control module 1;
the surrounding environment monitoring module 2 is used for monitoring the surrounding environment condition of the vehicle and feeding back the surrounding environment condition to the central control module 1;
the vehicle internal environment monitoring module 3 is used for monitoring the environment condition inside the vehicle and feeding back the environment condition to the central control module 1;
the vehicle state monitoring module 4 is used for monitoring the running condition of the vehicle body and feeding back the running condition to the central control module 1;
the driver state monitoring module 5 is used for monitoring the fatigue state of the current driver and feeding the fatigue state back to the central control module 1;
the driving auxiliary module 6 is used for realizing the functions of vehicle speed adjustment, lane adjustment, overtaking, braking and parking under the control of the central control module 1 so as to realize the driving assistance of a driver to the vehicle in the driving process;
the warning module 7 realizes alarm reminding under the control of the central control module 1;
the communication module 8 is used for the communication between the central control module 1 and the remote cloud 10;
and the terminal module 9 is electrically connected with the remote cloud 10 and used for realizing interactive communication with the remote cloud 10.
Preferably, the surrounding environment monitoring module 2 includes an atmosphere monitoring unit 21, a millimeter wave radar module 22, a camera module 23 and a fill-in light 24, the atmosphere monitoring unit 21 is disposed on the vehicle and used for monitoring the current atmospheric environment condition and feeding back to the central control module 1, the millimeter wave radar module 22 is disposed on the top of the vehicle and used for monitoring the distance between the obstacle and the vehicle in real time, the millimeter wave radar module 22 is electrically connected to the central control module 1, the camera module 23 is used for detecting the road condition around the vehicle and feeding back to the central control module 1, and the fill-in light 24 is disposed on the vehicle and electrically connected to the central control module 1 to improve the brightness of the target area. In the structure, the atmosphere monitoring unit 21 is used for monitoring the atmosphere environment of the current vehicle environment, wherein the atmosphere environment comprises air temperature and humidity, illumination intensity, rainfall information, PM2.5 and the like, so that equipment in the vehicle is better controlled through the central control module 1; thereby millimeter wave radar module 22 and camera module 23 cooperate the use can discern the pedestrian of vehicle week side road conditions, the image information of target vehicle and barrier etc. in real time, and cooperate driving auxiliary module 6, make when driving, adjust according to the site conditions pertinence, and can also play the real-time road conditions information and the alarm information of receiving, remind the driver in real time, send the warning to detecting nearer target distance, further improve the security of driving.
Example two: as shown in the figure, the difference from the first embodiment is that when the atmosphere monitoring unit 21 monitors that the current vehicle is in a dark environment, a first instruction is sent to the central control module 1, the central control module 1 generates a light supplement instruction after receiving the first instruction and sends the light supplement instruction to the light supplement lamp 24, and the light supplement lamp 24 performs light supplement processing on a target area after receiving the light supplement instruction. In this structure, under the not enough condition of light, central control module 1 sends the light filling instruction and starts light filling lamp 24 and improve the luminance to the target area and carry out the light filling to keep other regional original luminance, can consequently at night, under the not good condition of lighting conditions such as sleet haze, the not enough of image blurring is gathered in effectual improvement, through the light filling of target area, can improve imaging quality and detection distance, reduce weather environment to driver's influence, reduce the emergence of accident.
Preferably, the atmosphere monitoring unit 21 includes a rainfall sensor, an illumination sensor, a PM2.5 sensor, a wind speed sensor and a temperature and humidity sensor, and the rainfall sensor, the illumination sensor, the PM2.5 sensor, the wind speed sensor and the temperature and humidity sensor are respectively electrically connected to the central control module 1. In the structure, the rainfall sensor is used for detecting the rainfall, and once the rainfall exceeds a threshold value, the windscreen wiper is started; the illumination sensor is used for detecting the illumination intensity of the current environment, and once the illumination intensity exceeds a threshold value, the light supplement lamp 24 and the searchlight are started; the PM2.5 sensor is used for detecting a PM2.5 value of the current environment, and once the PM2.5 value exceeds a threshold value, the air purification equipment is started to ensure the health of people in the vehicle; the wind speed sensor is used for detecting the wind volume of the current environment, and once the wind volume exceeds a threshold value, the wind speed sensor can intervene in the driving auxiliary module 6 to enhance the driving safety; the temperature and humidity sensor is used for detecting the temperature and humidity of the current environment so as to be beneficial to the adjustment of the air temperature in the vehicle.
Preferably, the in-vehicle environment monitoring module 3 comprises a high-definition camera, which is disposed in the vehicle and electrically connected to the central control module 1, so that when an emergency occurs in the vehicle, the central control module 1 controls the warning module 7 to issue an alarm prompt, which comprises the following specific steps,
s1: pushing an instruction whether to open the high-definition camera to a user, if the user selects to open the high-definition camera, entering S2, and if the user does not agree, enabling the high-definition camera to be in a silent state;
s2: the high-definition camera starts to work, shoots the conditions in the vehicle in real time, and sends the collected video information to the image processing module for storage;
s3: the image processing module is used for storing video information sent by the high-definition camera and forwarding the video information to the deep learning judgment module for behavior analysis;
s4: the deep learning judgment module is pre-trained with an abnormal behavior judgment model, performs behavior judgment on the received video information according to the abnormal behavior judgment model, and feeds back a judgment result to the central control module 1;
s5: if the judgment result is abnormal, the central control module 1 controls the warning module 7 to send out warning prompt, reports the warning prompt to a remote cloud end through the communication module 8, and simultaneously transmits video information to a vehicle-mounted storage medium for backup; and if the judgment result is normal, directly inputting the video information into the vehicle-mounted storage medium for backup.
In the structure, when camera shooting is carried out in the vehicle, user consent can be obtained to protect the privacy of people in the vehicle, the identification method is accurate in motion identification, human motion detection is carried out by utilizing the deep learning judgment module, the identification method has strong generalization capability, and the motion model is analyzed and modeled through offline data, so that the specific rule and the motion range of motion are captured very accurately, the motion of the people can be matched accurately, and once abnormal behaviors such as robbery, falling into water, traffic accidents and driver unconsciousness occur, the alarm can be given rapidly, and meanwhile, the driving auxiliary module 6 intervenes in the work.
Example three: as shown in the figure, different from the second embodiment, the vehicle state monitoring module 4 includes a vehicle speed monitoring unit 41, a tire pressure monitoring unit 42, a water temperature monitoring unit 43, an oil amount monitoring unit 44 and a circuit monitoring unit 45, and the vehicle speed monitoring unit 41, the tire pressure monitoring unit 42, the water temperature monitoring unit 43, the oil amount monitoring unit 44 and the circuit monitoring unit 45 are respectively electrically connected to the central control module 1.
The driver state monitoring module 5 is preferably implemented by a face acquisition unit provided in the vehicle, and includes the following steps,
the method comprises the following steps: the method comprises the steps that a face image of a driver is collected through a face collecting unit at regular time;
step two: normalizing and graying the acquired face image of the driver to acquire a face area;
step three: positioning a pupil mass center and a pupil area of the human eye in the human face area, acquiring human eye state parameters and tracking the human eye;
step four: putting the human eye state parameters into a pre-trained fatigue characteristic state judgment model, judging whether the driver is in a fatigue state, and if so, executing a fifth step; otherwise, returning to execute the first step;
step five: the central control module 1 controls the warning module 7 to give an alarm prompt and reports to the remote cloud through the communication module 8.
In this structure, need not with driver direct contact to carry out the interference of effectively getting rid of environmental noise through driver face image, and possess high real-time, combine people's eye pupil barycenter and pupil region, improved the proportion of key feature to the result influence, further improved the detection precision to driver's driving state, and possess better use value.
Preferably, the terminal module 9 is a smart phone, the central control module 1 has a unique serial number, the remote cloud has a storage list, the unique serial number in the storage list corresponds to a mobile phone registration number, when the in-vehicle environment monitoring module 3 monitors that the current driver is stranger, the central control module 1 sends a confirmation instruction to the remote cloud through the communication module 8, the remote cloud pushes a message to the mobile phone registration number corresponding to the central control module 1 after receiving the confirmation instruction, and when the terminal module 9 corresponding to the mobile phone registration number receives the push message, the vehicle can be normally driven as if the vehicle is started in a random manner; and if the vehicle is refused to start, the vehicle refuses to start and sends out an alarm prompt. In the structure, as the action of driving the vehicle by a stranger occurs, in order to conveniently identify people of the type, once the stranger is located at a driving position, the remote cloud end can immediately push a message to the corresponding terminal module 9, if the terminal module 9 agrees to start the vehicle, the vehicle can be normally sent under the condition of no key, and if the terminal module 9 refuses to start the vehicle, the vehicle is considered to be illegally rushed in, and an alarm prompt is sent out to ensure the safety of the vehicle.
It should be noted that the above mentioned embodiments are only preferred embodiments of the present invention, and are not intended to limit the scope of the present invention, and the present invention may be modified in materials and structures, or replaced with technical equivalents. Therefore, structural equivalents made by using the description and drawings of the present invention or by directly or indirectly applying to other related arts are also encompassed within the scope of the present invention.

Claims (8)

1. The utility model provides a driver assistance system based on artificial intelligence which characterized in that: comprises that
A central control module;
the surrounding environment monitoring module is used for monitoring the surrounding environment condition of the vehicle and feeding back the surrounding environment condition to the central control module;
the vehicle internal environment monitoring module is used for monitoring the environment condition inside the vehicle and feeding back the environment condition to the central control module;
the vehicle state monitoring module is used for monitoring the running condition of the vehicle body and feeding back the running condition to the central control module;
the driver state monitoring module is used for monitoring the fatigue state of the current driver and feeding back the fatigue state to the central control module;
the driving auxiliary module is used for realizing the functions of vehicle speed adjustment, lane adjustment, overtaking, braking and parking under the control of the central control module so as to realize the driving assistance of a driver to the vehicle in the driving process;
the warning module is used for realizing alarm reminding under the control of the central control module;
the communication module is used for the communication between the central control module and the remote cloud;
and the terminal module is electrically connected with the remote cloud end and used for realizing the interactive communication between the terminal module and the remote cloud end.
2. The artificial intelligence based driver assistance system according to claim 1, wherein: the surrounding environment monitoring module comprises an atmosphere monitoring unit, a millimeter wave radar module, a camera module and a light supplementing lamp, wherein the atmosphere monitoring unit is arranged on the vehicle and used for monitoring the current atmospheric environment condition and feeding back to the central control module, the millimeter wave radar module is arranged at the top of the vehicle and used for monitoring the distance between an obstacle and the vehicle in real time, the millimeter wave radar module is electrically connected with the central control module, the camera module is used for detecting the road condition around the vehicle and feeding back to the central control module, and the light supplementing lamp is arranged on the vehicle and electrically connected with the central control module to improve the brightness of a target area.
3. The artificial intelligence based driving assistance system according to claim 2, wherein: when the atmosphere monitoring unit monitors that the current vehicle is in a dark environment, a first instruction is sent to the central control module, the central control module generates a light supplement instruction after receiving the first instruction and sends the light supplement instruction to the light supplement lamp, and the light supplement lamp performs light supplement processing on a target area after receiving the light supplement instruction.
4. The artificial intelligence based driver assistance system according to claim 2, wherein: the atmosphere monitoring unit comprises a rainfall sensor, an illumination sensor, a PM2.5 sensor, a wind speed sensor and a temperature and humidity sensor, wherein the rainfall sensor, the illumination sensor, the PM2.5 sensor, the wind speed sensor and the temperature and humidity sensor are respectively and electrically connected with the central control module.
5. The artificial intelligence based driver assistance system according to claim 1, wherein: the in-vehicle environment monitoring module comprises a high-definition camera which is arranged in a vehicle and is electrically connected with the central control module so that the central control module controls the warning module to give out alarm prompt when an emergency occurs in the vehicle, and the method comprises the following specific steps,
s1: pushing an instruction whether to open the high-definition camera to a user, if the user selects to open the high-definition camera, entering S2, and if the user does not agree, enabling the high-definition camera to be in a silent state;
s2: the high-definition camera starts to work, shoots the conditions in the vehicle in real time, and sends the collected video information to the image processing module for storage;
s3: the image processing module is used for storing video information sent by the high-definition camera and forwarding the video information to the deep learning judgment module for behavior analysis;
s4: the deep learning judgment module is pre-trained with an abnormal behavior judgment model, performs behavior judgment on the received video information according to the abnormal behavior judgment model, and feeds back a judgment result to the central control module;
s5: if the judgment result is abnormal, the central control module controls the warning module to send out an alarm prompt, reports the alarm prompt to a remote cloud end through the communication module, and simultaneously transmits video information to a vehicle-mounted storage medium for backup; and if the judgment result is normal, directly inputting the video information into the vehicle-mounted storage medium for backup.
6. The artificial intelligence based driver assistance system according to claim 1, wherein: the vehicle state monitoring module comprises a vehicle speed monitoring unit, a tire pressure monitoring unit, a water temperature monitoring unit, an oil amount monitoring unit and a circuit monitoring unit, wherein the vehicle speed monitoring unit, the tire pressure monitoring unit, the water temperature monitoring unit, the oil amount monitoring unit and the circuit monitoring unit are respectively electrically connected with the central control module.
7. The artificial intelligence based driver assistance system according to claim 1, wherein: the driver state monitoring module is realized by a face acquisition unit arranged in a vehicle and comprises the following specific steps,
the method comprises the following steps: the method comprises the steps that a face image of a driver is collected through a face collecting unit at regular time;
step two: normalizing and graying the acquired face image of the driver to acquire a face area;
step three: positioning a pupil mass center and a pupil area of a human eye in the human face area, acquiring human eye state parameters and tracking the human eye;
step four: putting the human eye state parameters into a pre-trained fatigue characteristic state judgment model, judging whether the driver is in a fatigue state, and if so, executing a fifth step; otherwise, returning to execute the first step;
step five: the central control module controls the warning module to send out warning reminding, and reports to the remote cloud through the communication module.
8. The artificial intelligence based driver assistance system according to claim 1, wherein: the terminal module is a smart phone, the central control module is provided with a unique serial number, the remote cloud end is provided with a storage list, the unique serial number in the storage list corresponds to a mobile phone registration number, when the in-vehicle environment monitoring module monitors that the current driver is stranger, the central control module sends a confirmation instruction to the remote cloud end through the communication module, the remote cloud end pushes a message to the mobile phone registration number corresponding to the central control module after receiving the confirmation instruction, and when the terminal module corresponding to the mobile phone registration number receives the push message, the vehicle can be normally driven as if the terminal module is started; and if the vehicle is refused to start, the vehicle refuses to start and sends out an alarm prompt.
CN202210848230.8A 2022-07-19 2022-07-19 Driving assistance system based on artificial intelligence Pending CN114954307A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116684142A (en) * 2023-06-06 2023-09-01 深圳华石供应链科技有限公司 Auxiliary driving system and method based on Internet of things
CN117622177A (en) * 2024-01-23 2024-03-01 青岛创新奇智科技集团股份有限公司 Vehicle data processing method and device based on industrial large model

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116684142A (en) * 2023-06-06 2023-09-01 深圳华石供应链科技有限公司 Auxiliary driving system and method based on Internet of things
CN116684142B (en) * 2023-06-06 2024-03-08 深圳华石供应链科技有限公司 Auxiliary driving system and method based on Internet of things
CN117622177A (en) * 2024-01-23 2024-03-01 青岛创新奇智科技集团股份有限公司 Vehicle data processing method and device based on industrial large model

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