CN113191258A - Driving behavior monitoring method, system, device and storage medium - Google Patents
Driving behavior monitoring method, system, device and storage medium Download PDFInfo
- Publication number
- CN113191258A CN113191258A CN202110473758.7A CN202110473758A CN113191258A CN 113191258 A CN113191258 A CN 113191258A CN 202110473758 A CN202110473758 A CN 202110473758A CN 113191258 A CN113191258 A CN 113191258A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- alarm
- current
- projection
- image
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/59—Context or environment of the image inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
- G06V20/597—Recognising the driver's state or behaviour, e.g. attention or drowsiness
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/0202—Child monitoring systems using a transmitter-receiver system carried by the parent and the child
- G08B21/0205—Specific application combined with child monitoring using a transmitter-receiver system
- G08B21/0208—Combination with audio or video communication, e.g. combination with "baby phone" function
Abstract
The invention discloses a driving behavior monitoring method, a system, a device and a storage medium, wherein the driving behavior monitoring method comprises the following steps: the driving behavior monitoring device and the monitoring method thereof acquire a current in-vehicle image P1 in the driving process of the vehicle; inputting the current in-vehicle image P1 into a pre-trained deep learning model for recognition to obtain a first recognition result of whether the driver is dangerous or not; determining whether to project the current in-vehicle image P1 to a road behind the vehicle according to the first recognition result; if the first recognition result is dangerous behavior, generating a projection instruction, and sending the current in-vehicle image P1 and the projection instruction to a projection device arranged at the tail of the vehicle to control the projection device to project the current in-vehicle image P1 to a road behind the vehicle. By the invention, the driving safety can be improved.
Description
Technical Field
The invention relates to the technical field of safe driving, in particular to a driving behavior monitoring method, a driving behavior monitoring system, a driving behavior monitoring device and a storage medium.
Background
With the increasing improvement of the living standard of residents in China, automobiles enter thousands of households, and according to statistics of relevant departments, the increase of the number of the automobiles marks the improvement of the living quality of the residents in China, can bring great convenience to daily trips of people and enlarge the living radius of the people, but the following negative effects of road congestion, traffic safety and the like are also generated, and particularly motor vehicle traffic accidents become one of important factors influencing public safety.
At present, in the process of vehicle driving, particularly, under the condition that only one driver is available, fatigue driving is easy to occur, and after the fatigue driving lasts for a period of time, the driver can even directly sleep, so that the safety of other people is seriously dangerous; in addition, in some cases, behaviors that the driver is harmed, the passengers in the vehicle are hijacked and the like may occur, but the existing vehicle intelligent systems cannot effectively solve the above situations.
Disclosure of Invention
The invention aims to provide a driving behavior monitoring method, a driving behavior monitoring system, a driving behavior monitoring device and a storage medium, so as to solve the technical problems and improve the driving safety.
In order to achieve the above object, a first aspect of the present invention provides a driving behavior monitoring method, including:
acquiring a current in-vehicle image P1 in the driving process of the vehicle;
inputting the current in-vehicle image P1 into a pre-trained deep learning model for recognition to obtain a first recognition result of whether the driver is dangerous or not;
determining whether to project the current in-vehicle image P1 to a road behind the vehicle according to the first recognition result; if the first recognition result is dangerous behavior, generating a projection instruction, and sending the current in-vehicle image P1 and the projection instruction to a projection device arranged at the tail of the vehicle to control the projection device to project the current in-vehicle image P1 to a road behind the vehicle.
Optionally, the method further comprises:
in the projection process, a current in-vehicle image P2 in the vehicle driving process is obtained, and the current in-vehicle image P2 is input into a pre-trained deep learning model to be recognized to obtain a second recognition result of whether the driver is dangerous or not;
determining whether to project the current in-vehicle image P2 to a road behind the vehicle according to the second recognition result; if the second recognition result is dangerous behavior, generating a projection instruction, and sending the current in-vehicle image P2 and the projection instruction to a projection device arranged at the tail of the vehicle to control the projection device to project the current in-vehicle image P2 to a road behind the vehicle; and if the second recognition result is a normal behavior, generating a projection canceling instruction, and sending the projection canceling instruction to projection equipment arranged at the tail of the vehicle to control the projection equipment to stop projecting.
Optionally, the method further comprises:
determining whether to alarm or not according to the first identification result; and if the first recognition result is dangerous behavior, generating an alarm instruction, and simultaneously sending the alarm instruction to an in-vehicle alarm arranged in the vehicle and an out-vehicle alarm arranged outside the vehicle so as to control the in-vehicle alarm and the out-vehicle alarm to alarm.
Optionally, the method further comprises:
in the alarming process, a current in-vehicle image P2 in the vehicle driving process is obtained, and the current in-vehicle image P2 is input into a pre-trained deep learning model to be recognized to obtain a second recognition result of whether the driver is dangerous or not;
determining whether to project the current in-vehicle image P2 to a road behind the vehicle according to the second recognition result; if the second identification result is a dangerous behavior, no operation is performed; and if the second recognition result is normal behavior, generating a cancellation alarm instruction, and sending the cancellation alarm instruction to the in-vehicle alarm and the out-vehicle alarm to control the in-vehicle alarm and the out-vehicle alarm to stop alarming.
A second aspect of the present invention provides a driving behavior monitoring system, the system comprising:
the image acquisition unit is used for acquiring a current in-vehicle image P1 in the driving process of the vehicle;
the image identification unit is used for inputting the current in-vehicle image P1 into a pre-trained deep learning model for identification to obtain a first identification result of whether the driver is dangerous or not; and
a control unit for determining whether to project the current in-vehicle image P1 to a road behind a vehicle according to the first recognition result; if the first recognition result is dangerous behavior, generating a projection instruction, and sending the current in-vehicle image P1 and the projection instruction to a projection device arranged at the tail of the vehicle to control the projection device to project the current in-vehicle image P1 to a road behind the vehicle.
Optionally, the image recognition unit is further configured to, during the projection, obtain a current in-vehicle image P2 during vehicle driving;
the image identification unit is further used for inputting the current in-vehicle image P2 into a pre-trained deep learning model for identification to obtain a second identification result of whether the driver is dangerous or not;
the control unit is further used for determining whether to project the current in-vehicle image P2 to a road behind the vehicle according to the second identification result; if the second recognition result is dangerous behavior, generating a projection instruction, and sending the current in-vehicle image P2 and the projection instruction to a projection device arranged at the tail of the vehicle to control the projection device to project the current in-vehicle image P2 to a road behind the vehicle; and if the second recognition result is a normal behavior, generating a projection canceling instruction, and sending the projection canceling instruction to projection equipment arranged at the tail of the vehicle to control the projection equipment to stop projecting.
Optionally, the control unit is further configured to determine whether to perform an alarm according to the first identification result; and if the first recognition result is dangerous behavior, generating an alarm instruction, and simultaneously sending the alarm instruction to an in-vehicle alarm arranged in the vehicle and an out-vehicle alarm arranged outside the vehicle so as to control the in-vehicle alarm and the out-vehicle alarm to alarm.
Optionally, the image recognition unit is further configured to, during the warning, obtain a current in-vehicle image P2 during the driving of the vehicle;
the image identification unit is further used for inputting the current in-vehicle image P2 into a pre-trained deep learning model for identification to obtain a second identification result of whether the driver is dangerous or not;
the control unit is further used for determining whether to project the current in-vehicle image P2 to a road behind the vehicle according to the second identification result; if the second identification result is a dangerous behavior, no operation is performed; and if the second recognition result is normal behavior, generating a cancellation alarm instruction, and sending the cancellation alarm instruction to the in-vehicle alarm and the out-vehicle alarm to control the in-vehicle alarm and the out-vehicle alarm to stop alarming.
The third aspect of the invention provides a driving behavior monitoring device, which comprises a camera device, a projection device, an in-vehicle alarm, an out-vehicle alarm and the driving behavior monitoring system of the second aspect;
the camera device is used for acquiring a current in-vehicle image in the vehicle driving process and sending the current in-vehicle image to the driving behavior monitoring system;
the projection equipment is used for receiving a current in-vehicle image and a projection instruction sent by the driving behavior monitoring system and projecting the current in-vehicle image to a road behind a vehicle according to the projection instruction;
the alarm inside the vehicle and the alarm outside the vehicle are used for receiving the alarm instruction sent by the driving behavior monitoring system and giving an alarm according to the alarm instruction.
A fourth aspect of the present invention proposes a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the driving behavior monitoring method of the first aspect.
The implementation of the driving behavior monitoring method, the driving behavior monitoring system, the driving behavior monitoring device and the storage medium has at least the following beneficial effects: the method comprises the steps of acquiring an in-vehicle picture in real time, identifying whether the picture condition belongs to dangerous behaviors or not, and projecting the picture to a road behind the vehicle through projection equipment when the dangerous behaviors exist in the vehicle, such as fatigue driving of a driver and behaviors of damaging the driver by other people in the vehicle so as to prompt other vehicle owners, reduce traffic accidents and improve driving safety.
Additional features and advantages of the invention will be set forth in the description which follows.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flow chart of a driving behavior monitoring method according to an embodiment of the present invention.
Fig. 2 is a partial flow diagram of a driving behavior monitoring method according to an embodiment of the present invention.
Fig. 3 is a partial flow diagram of a driving behavior monitoring method according to an embodiment of the present invention.
Fig. 4 is a schematic structural diagram of a driving behavior monitoring system according to an embodiment of the present invention.
Fig. 5 is a schematic structural diagram of a driving behavior monitoring device according to an embodiment of the present invention.
Detailed Description
Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In addition, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present invention. It will be understood by those skilled in the art that the present invention may be practiced without some of these specific details. In some instances, well known means have not been described in detail so as not to obscure the present invention.
Referring to fig. 1, an embodiment of the present invention provides a driving behavior monitoring method, including the following steps:
s10, acquiring a current in-vehicle image P1 in the driving process of the vehicle;
step S21, inputting the current in-vehicle image P1 into a pre-trained deep learning model for recognition to obtain a first recognition result of whether the driver is dangerous or not;
specifically, in the present embodiment, it is preferable, but not limited to, to identify whether the driver is tired by using a perclos fatigue algorithm, wherein there are many deep learning models, and the deep learning models most commonly used by developers at present include CNN, RNN, RNTN, GAN, and the like; in the embodiment, a deep learning model which can be used for identifying whether a driver is dangerous or not can be obtained by training based on any model; the judgment and recognition of fatigue driving is not the gist of the method of the present embodiment, and the gist of the present embodiment is how to perform processing to improve driving safety when a fatigue driving situation is recognized;
step S31, determining whether to project the current in-vehicle image P1 to the road behind the vehicle according to the first recognition result; if the first recognition result is dangerous behavior, generating a projection instruction, and sending the current in-vehicle image P1 and the projection instruction to a projection device arranged at the tail of a vehicle to control the projection device to project the current in-vehicle image P1 to a road behind the vehicle;
specifically, the in-vehicle picture is acquired in real time, whether the picture condition belongs to dangerous behaviors or not is recognized, the dangerous behaviors include but are not limited to fatigue driving of a driver and behaviors harmful to the driver of other people in the vehicle, and when the dangerous behaviors exist in the vehicle, the picture can be projected onto a road behind the vehicle through the projection device to prompt other vehicle owners behind, rear-end collision is avoided, car accidents are reduced, and driving safety is improved.
In one embodiment, referring to fig. 2, the method further comprises the steps of:
s41, in the projection process, acquiring a current in-vehicle image P2 in the vehicle driving process, inputting the current in-vehicle image P2 into a pre-trained deep learning model for recognition to obtain a second recognition result of whether the driver is dangerous or not;
step S51, determining whether to project the current in-vehicle image P2 to the road behind the vehicle according to the second recognition result; if the second recognition result is dangerous behavior, generating a projection instruction, and sending the current in-vehicle image P2 and the projection instruction to a projection device arranged at the tail of the vehicle to control the projection device to project the current in-vehicle image P2 to a road behind the vehicle; if the second recognition result is a normal behavior, generating a projection canceling instruction, and sending the projection canceling instruction to projection equipment arranged at the tail of the vehicle to control the projection equipment to stop projecting;
specifically, during the projection, it is necessary to monitor the latest in-vehicle conditions in real time, and for example, when the driver recovers from a fatigue driving state to a normal driving state, the projection is cancelled.
In a specific example, referring to fig. 3, the method further comprises the steps of:
step S32, determining whether to alarm according to the first identification result; if the first recognition result is dangerous behavior, generating an alarm instruction, and simultaneously sending the alarm instruction to an in-vehicle alarm arranged in the vehicle and an out-vehicle alarm arranged outside the vehicle so as to control the in-vehicle alarm and the out-vehicle alarm to alarm;
particularly, this embodiment is reported to the police through setting up in the inside alarm in the car of vehicle and the outside alarm outside the car of vehicle in step, and on the one hand, the driver can be reminded to the alarm in the car, and on the other hand, other vehicles on the road can be reminded to the alarm outside the car, avoid traffic accident's emergence effectively.
S42, in the process of alarming, acquiring a current in-vehicle image P2 in the vehicle driving process, inputting the current in-vehicle image P2 into a pre-trained deep learning model for identifying to obtain a second identification result of whether the driver is in dangerous behavior;
step S52, determining whether to project the current in-vehicle image P2 to the road behind the vehicle according to the second recognition result; if the second identification result is a dangerous behavior, no operation is performed; and if the second recognition result is normal behavior, generating a cancellation alarm instruction, and sending the cancellation alarm instruction to the in-vehicle alarm and the out-vehicle alarm to control the in-vehicle alarm and the out-vehicle alarm to stop alarming.
Specifically, during the warning process, the latest in-vehicle conditions need to be monitored in real time, and for example, when the driver recovers from a fatigue driving state to a normal driving state, the warning is cancelled.
Referring to fig. 4, another embodiment of the present invention provides a driving behavior monitoring system, including:
the image acquisition unit 11 is used for acquiring a current in-vehicle image P1 in the driving process of the vehicle;
the image recognition unit 12 is used for inputting the current in-vehicle image P1 into a pre-trained deep learning model for recognition to obtain a first recognition result of whether the driver is dangerous or not; and
a control unit 13 for determining whether to project the current in-vehicle image P1 to a road behind the vehicle according to the first recognition result; if the first recognition result is dangerous behavior, generating a projection instruction, and sending the current in-vehicle image P1 and the projection instruction to a projection device arranged at the tail of the vehicle to control the projection device to project the current in-vehicle image P1 to a road behind the vehicle.
In a specific example, the image recognition unit 12 is further configured to obtain a current in-vehicle image P2 during vehicle driving during projection and warning;
the image recognition unit 12 is further configured to input the current in-vehicle image P2 into a pre-trained deep learning model for recognition to obtain a second recognition result of whether the driver is a dangerous behavior;
the control unit 13 is further configured to determine whether to project the current in-vehicle image P2 to a road behind the vehicle according to the second recognition result; if the second recognition result is dangerous behavior, generating a projection instruction, and sending the current in-vehicle image P2 and the projection instruction to a projection device arranged at the tail of the vehicle to control the projection device to project the current in-vehicle image P2 to a road behind the vehicle; and if the second recognition result is a normal behavior, generating a projection canceling instruction, and sending the projection canceling instruction to projection equipment arranged at the tail of the vehicle to control the projection equipment to stop projecting.
In a specific example, the control unit 13 is further configured to determine whether to perform an alarm according to the first identification result; and if the first recognition result is dangerous behavior, generating an alarm instruction, and simultaneously sending the alarm instruction to an in-vehicle alarm arranged in the vehicle and an out-vehicle alarm arranged outside the vehicle so as to control the in-vehicle alarm and the out-vehicle alarm to alarm.
In a specific example, the image recognition unit 12 is further configured to obtain a current in-vehicle image P2 during vehicle driving during projection and warning;
the image recognition unit 12 is further configured to input the current in-vehicle image P2 into a pre-trained deep learning model for recognition to obtain a second recognition result of whether the driver is a dangerous behavior;
the control unit 13 is further configured to determine whether to project the current in-vehicle image P2 to a road behind the vehicle according to the second recognition result; if the second identification result is a dangerous behavior, no operation is performed; and if the second recognition result is normal behavior, generating a cancellation alarm instruction, and sending the cancellation alarm instruction to the in-vehicle alarm and the out-vehicle alarm to control the in-vehicle alarm and the out-vehicle alarm to stop alarming.
The above-described system embodiments are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
It should be noted that the system described in the foregoing embodiment corresponds to the method described in the foregoing embodiment, and therefore, a part of the system described in the foregoing embodiment that is not described in detail can be obtained by referring to the content of the method described in the foregoing embodiment, that is, the specific step content described in the method of the foregoing embodiment can be understood as the function that can be realized by the system of the present embodiment, and is not described herein again.
In addition, the driving behavior monitoring system according to the above embodiment may be stored in a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as an independent product.
Referring to fig. 5, another embodiment of the present invention provides a driving behavior monitoring apparatus, which includes a camera device 2, a projection device 3, an in-vehicle alarm 4, an out-vehicle alarm 5, and the driving behavior monitoring system 1 according to the above embodiment;
the camera device 2 is used for acquiring a current in-vehicle image in the vehicle driving process and sending the current in-vehicle image to the driving behavior monitoring system;
specifically, the camera device 2 may include a plurality of hidden cameras, which are respectively installed on the roof above the front windshield of the vehicle, and are respectively directed to the driver, the passenger in front of the vehicle, and the passenger behind the vehicle, and the videos shot by the cameras are transmitted to the driving behavior monitoring system 1 for dangerous behavior identification;
the projection device 3 is used for receiving a current in-vehicle image and a projection instruction sent by the driving behavior monitoring system and projecting the current in-vehicle image to a road behind a vehicle according to the projection instruction;
specifically, the projection device 3 is a high-brightness projector usable in daytime, and the projector is mounted at the rear of the vehicle;
the in-vehicle alarm 4 and the out-vehicle alarm 5 are used for receiving an alarm instruction sent by the driving behavior monitoring system and giving an alarm according to the alarm instruction;
specifically, the in-vehicle alarm 4 and the out-vehicle alarm 5 may be audible and visual alarms.
Another embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the driving behavior monitoring method according to the above-mentioned embodiment.
Specifically, the computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1. A driving behavior monitoring method, characterized in that the method comprises:
acquiring a current in-vehicle image P1 in the driving process of the vehicle;
inputting the current in-vehicle image P1 into a pre-trained deep learning model for recognition to obtain a first recognition result of whether the driver is dangerous or not;
determining whether to project the current in-vehicle image P1 to a road behind the vehicle according to the first recognition result; if the first recognition result is dangerous behavior, generating a projection instruction, and sending the current in-vehicle image P1 and the projection instruction to a projection device arranged at the tail of the vehicle to control the projection device to project the current in-vehicle image P1 to a road behind the vehicle.
2. The driving behavior monitoring method according to claim 1, characterized in that the method further comprises:
in the projection process, a current in-vehicle image P2 in the vehicle driving process is obtained, and the current in-vehicle image P2 is input into a pre-trained deep learning model to be recognized to obtain a second recognition result of whether the driver is dangerous or not;
determining whether to project the current in-vehicle image P2 to a road behind the vehicle according to the second recognition result; if the second recognition result is dangerous behavior, generating a projection instruction, and sending the current in-vehicle image P2 and the projection instruction to a projection device arranged at the tail of the vehicle to control the projection device to project the current in-vehicle image P2 to a road behind the vehicle; and if the second recognition result is a normal behavior, generating a projection canceling instruction, and sending the projection canceling instruction to projection equipment arranged at the tail of the vehicle to control the projection equipment to stop projecting.
3. The driving behavior monitoring method according to claim 1, characterized in that the method further comprises:
determining whether to alarm or not according to the first identification result; and if the first recognition result is dangerous behavior, generating an alarm instruction, and simultaneously sending the alarm instruction to an in-vehicle alarm arranged in the vehicle and an out-vehicle alarm arranged outside the vehicle so as to control the in-vehicle alarm and the out-vehicle alarm to alarm.
4. The driving behavior monitoring method according to claim 3, characterized in that the method further comprises:
in the alarming process, a current in-vehicle image P2 in the vehicle driving process is obtained, and the current in-vehicle image P2 is input into a pre-trained deep learning model to be recognized to obtain a second recognition result of whether the driver is dangerous or not;
determining whether to project the current in-vehicle image P2 to a road behind the vehicle according to the second recognition result; if the second identification result is a dangerous behavior, no operation is performed; and if the second recognition result is normal behavior, generating a cancellation alarm instruction, and sending the cancellation alarm instruction to the in-vehicle alarm and the out-vehicle alarm to control the in-vehicle alarm and the out-vehicle alarm to stop alarming.
5. A driving behavior monitoring system, the system comprising:
the image acquisition unit is used for acquiring a current in-vehicle image P1 in the driving process of the vehicle;
the image identification unit is used for inputting the current in-vehicle image P1 into a pre-trained deep learning model for identification to obtain a first identification result of whether the driver is dangerous or not; and
a control unit for determining whether to project the current in-vehicle image P1 to a road behind a vehicle according to the first recognition result; if the first recognition result is dangerous behavior, generating a projection instruction, and sending the current in-vehicle image P1 and the projection instruction to a projection device arranged at the tail of the vehicle to control the projection device to project the current in-vehicle image P1 to a road behind the vehicle.
6. The driving behavior monitoring system according to claim 5, wherein the image recognition unit is further configured to, during the projection, obtain a current in-vehicle image P2 during the driving of the vehicle;
the image identification unit is further used for inputting the current in-vehicle image P2 into a pre-trained deep learning model for identification to obtain a second identification result of whether the driver is dangerous or not;
the control unit is further used for determining whether to project the current in-vehicle image P2 to a road behind the vehicle according to the second identification result; if the second recognition result is dangerous behavior, generating a projection instruction, and sending the current in-vehicle image P2 and the projection instruction to a projection device arranged at the tail of the vehicle to control the projection device to project the current in-vehicle image P2 to a road behind the vehicle; and if the second recognition result is a normal behavior, generating a projection canceling instruction, and sending the projection canceling instruction to projection equipment arranged at the tail of the vehicle to control the projection equipment to stop projecting.
7. The driving behavior monitoring system of claim 5, wherein the control unit is further configured to determine whether to alert based on the first recognition result; and if the first recognition result is dangerous behavior, generating an alarm instruction, and simultaneously sending the alarm instruction to an in-vehicle alarm arranged in the vehicle and an out-vehicle alarm arranged outside the vehicle so as to control the in-vehicle alarm and the out-vehicle alarm to alarm.
8. The driving behavior monitoring system according to claim 7, wherein the image recognition unit is further configured to obtain a current in-vehicle image P2 during vehicle driving during warning;
the image identification unit is further used for inputting the current in-vehicle image P2 into a pre-trained deep learning model for identification to obtain a second identification result of whether the driver is dangerous or not;
the control unit is further used for determining whether to project the current in-vehicle image P2 to a road behind the vehicle according to the second identification result; if the second identification result is a dangerous behavior, no operation is performed; and if the second recognition result is normal behavior, generating a cancellation alarm instruction, and sending the cancellation alarm instruction to the in-vehicle alarm and the out-vehicle alarm to control the in-vehicle alarm and the out-vehicle alarm to stop alarming.
9. A driving behavior monitoring apparatus comprising a camera device, a projection device, an in-vehicle alarm, an out-vehicle alarm, and the driving behavior monitoring system according to any one of claims 5 to 8;
the camera device is used for acquiring a current in-vehicle image in the vehicle driving process and sending the current in-vehicle image to the driving behavior monitoring system;
the projection equipment is used for receiving a current in-vehicle image and a projection instruction sent by the driving behavior monitoring system and projecting the current in-vehicle image to a road behind a vehicle according to the projection instruction;
the alarm inside the vehicle and the alarm outside the vehicle are used for receiving the alarm instruction sent by the driving behavior monitoring system and giving an alarm according to the alarm instruction.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the driving behavior monitoring method according to any one of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110473758.7A CN113191258A (en) | 2021-04-29 | 2021-04-29 | Driving behavior monitoring method, system, device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110473758.7A CN113191258A (en) | 2021-04-29 | 2021-04-29 | Driving behavior monitoring method, system, device and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113191258A true CN113191258A (en) | 2021-07-30 |
Family
ID=76980639
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110473758.7A Pending CN113191258A (en) | 2021-04-29 | 2021-04-29 | Driving behavior monitoring method, system, device and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113191258A (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109229017A (en) * | 2018-09-29 | 2019-01-18 | 史记 | A kind of early warning system of pre- vehicle rear-end collision prevention |
| CN109733277A (en) * | 2019-01-22 | 2019-05-10 | 京东方科技集团股份有限公司 | Warning system and alarming method for power |
| CN109808582A (en) * | 2018-04-24 | 2019-05-28 | 长城汽车股份有限公司 | Projecting method, device based on headlamp |
| CN110991353A (en) * | 2019-12-06 | 2020-04-10 | 中国科学院自动化研究所 | Early warning method for recognizing driving behaviors of driver and dangerous driving behaviors |
-
2021
- 2021-04-29 CN CN202110473758.7A patent/CN113191258A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109808582A (en) * | 2018-04-24 | 2019-05-28 | 长城汽车股份有限公司 | Projecting method, device based on headlamp |
| CN109229017A (en) * | 2018-09-29 | 2019-01-18 | 史记 | A kind of early warning system of pre- vehicle rear-end collision prevention |
| CN109733277A (en) * | 2019-01-22 | 2019-05-10 | 京东方科技集团股份有限公司 | Warning system and alarming method for power |
| CN110991353A (en) * | 2019-12-06 | 2020-04-10 | 中国科学院自动化研究所 | Early warning method for recognizing driving behaviors of driver and dangerous driving behaviors |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10913450B2 (en) | Method and device for detecting a rule infringement | |
| CN103871200A (en) | Safety warning system and method used for automobile driving | |
| CN111731284A (en) | Driving assistance method and device, vehicle-mounted terminal equipment and storage medium | |
| DE102016209552A1 (en) | A method of warning a pedestrian in an environment of a vehicle when parked and a method of controlling a vehicle | |
| CN109740433A (en) | A kind of vehicle preventing collision method and car-mounted terminal | |
| KR20160144286A (en) | Method and apparatus for limiting vehicle overspeed | |
| CN110641467A (en) | Vehicle distance control method and device of adaptive cruise system | |
| CN109131321B (en) | Lane changing auxiliary method and device based on image processing and risk coefficient calculation | |
| CN104318641A (en) | Automobile data recorder and vehicle alarming method | |
| US11951997B2 (en) | Artificial intelligence-enabled alarm for detecting passengers locked in vehicle | |
| CN113901979A (en) | Driving trend prediction method and system | |
| CN110103954B (en) | Electric control-based automobile rear-end collision prevention early warning device and method | |
| CN115743031A (en) | System and method for deterrence of intruders | |
| US11263837B2 (en) | Automatic real-time detection of vehicular incidents | |
| CN111251991B (en) | Driving safety auxiliary system and method | |
| CN111508232A (en) | Road safety snapshot all-in-one | |
| CN113191258A (en) | Driving behavior monitoring method, system, device and storage medium | |
| CN209290341U (en) | A kind of anti-collision detection device applied in automatic Pilot | |
| CN115064004A (en) | Vehicle collision active early warning system and method | |
| CN113428169B (en) | Vehicle monitoring method and device and electronic equipment | |
| CN115760970A (en) | System and method for capturing images of the surroundings of a vehicle for insurance claim processing | |
| CN113370991A (en) | Driving assistance method, device, equipment, storage medium and computer program product | |
| CN112950873A (en) | Alarm method and device based on workshop distance detection and electronic equipment | |
| CN114834351A (en) | Vehicle anti-collision control method, device and system and vehicle | |
| CN115946690A (en) | Safety control method, system and device for starting of travelling crane, storage medium and vehicle machine |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |