CN112348718A - Intelligent auxiliary driving guidance method, device and computer storage medium - Google Patents
Intelligent auxiliary driving guidance method, device and computer storage medium Download PDFInfo
- Publication number
- CN112348718A CN112348718A CN202011159002.7A CN202011159002A CN112348718A CN 112348718 A CN112348718 A CN 112348718A CN 202011159002 A CN202011159002 A CN 202011159002A CN 112348718 A CN112348718 A CN 112348718A
- Authority
- CN
- China
- Prior art keywords
- driver
- intelligent
- vehicle
- guidance
- content
- 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
- 238000000034 method Methods 0.000 title claims abstract description 69
- 230000003068 static effect Effects 0.000 claims abstract description 27
- 230000006870 function Effects 0.000 claims description 90
- 230000001960 triggered effect Effects 0.000 claims description 5
- 230000003213 activating effect Effects 0.000 claims 1
- 238000010586 diagram Methods 0.000 description 16
- 238000001514 detection method Methods 0.000 description 14
- 230000008569 process Effects 0.000 description 10
- 238000012545 processing Methods 0.000 description 9
- 238000004590 computer program Methods 0.000 description 7
- 238000007781 pre-processing Methods 0.000 description 6
- 238000012512 characterization method Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 5
- 238000012986 modification Methods 0.000 description 5
- 230000004048 modification Effects 0.000 description 5
- 230000001360 synchronised effect Effects 0.000 description 5
- 238000000605 extraction Methods 0.000 description 4
- 230000003044 adaptive effect Effects 0.000 description 3
- 238000013473 artificial intelligence Methods 0.000 description 3
- 230000001815 facial effect Effects 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 210000000056 organ Anatomy 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000004297 night vision Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
- G06Q50/2057—Career enhancement or continuing education service
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/168—Feature extraction; Face representation
- G06V40/171—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/172—Classification, e.g. identification
- G06V40/173—Classification, e.g. identification face re-identification, e.g. recognising unknown faces across different face tracks
Abstract
The invention discloses an intelligent auxiliary driving guidance method, which comprises the following steps: when the driver is judged to be a new driver through face recognition, providing guidance content of an intelligent auxiliary driving function for the driver; judging whether the vehicle is in a static state or not; when the vehicle is in a stationary state, the tutorial content continues to be provided to the driver for the driver to learn the tutorial content. The invention also discloses a device and a computer readable storage medium, which solve the problem that the intelligent auxiliary driving operation instruction guidance content can not be intelligently pushed to the driver in the prior art.
Description
Technical Field
The invention relates to the technical field of intelligent driving, in particular to an intelligent auxiliary driving guidance method, an intelligent auxiliary driving guidance device and a computer storage medium.
Background
At present, the intelligent driving assistance (ADAS) operation instruction contents provided for a driver by a vehicle central control are played only after a user sends a request, and are not distinguished according to driver information, and the played contents are the same for all users sending the requests. The defects of the technology are as follows:
for the vast majority of users, it is not known which ADAS functions the car has, nor is it unknown how well the user is operating which functions, and therefore the user will not actively make a request to the car central control.
Even if the user actively sends a playing request, the guidance content played by the central control to all the users is the same, and the learned users hear the repeated guidance content and are bored, so that the user viscosity is reduced. Therefore, the prior art has the problem that the intelligent driving assistance operation instruction content cannot be intelligently pushed to the driver.
Disclosure of Invention
The invention mainly aims to provide an intelligent assistant driving instruction method, an intelligent assistant driving instruction device and a computer storage medium, and aims to solve the problem that the instruction content of an intelligent assistant driving operation instruction cannot be intelligently pushed to a driver in the prior art.
In order to achieve the above object, the present invention provides an intelligent driving assistance guidance method, including the steps of:
when the driver is judged to be a new driver through face recognition, providing guidance content of an intelligent auxiliary driving function for the driver;
judging whether the vehicle is in a static state or not;
continuing to provide the tutorial content to the driver for the driver to learn the tutorial content while the vehicle is stationary.
In one embodiment, the guidance content is presented in the form of at least one of the following documents:
audio files, video files, graphics files, web link files, and text files.
In one embodiment, the method further comprises:
and starting a face recognition system to judge whether the driver is a brand-new driver.
In an embodiment, after the step of starting the face recognition system to determine whether the driver is a brand new driver, the method further includes:
and when the face recognition system judges that the driver is a non-brand-new driver, providing no guidance content of the intelligent auxiliary driving function.
In an embodiment, after the step of not providing the guidance content of the intelligent assistant driving function when the face recognition system determines that the driver is a non-brand-new driver, the method further includes:
acquiring a target retrieval instruction corresponding to a target intelligent auxiliary driving function triggered by the driver at a target function starting component;
and determining corresponding instruction contents in the instruction contents according to the target retrieval instruction, and providing the corresponding instruction contents for the driver to learn.
In one embodiment, the determining whether the vehicle is in a stationary state includes:
acquiring the running speed of the vehicle through a vehicle sensor;
and judging whether the vehicle is in a static state or not according to the running speed.
In one embodiment, the method further comprises:
stopping providing the tutorial content to the driver when the vehicle is in a non-stationary state.
In one embodiment, after the step of stopping providing the guidance content to the driver when the vehicle is in the non-stationary state, the method further includes:
acquiring an operation instruction of the driver in real time;
when an operation instruction that the driver actively requests to provide the guidance content is acquired, the guidance content is provided to the driver again so that the driver can learn the guidance content.
To achieve the above object, the present invention further provides an apparatus comprising a memory, a processor, and an intelligent assistant driving guide program stored in the memory and executable on the processor, wherein the intelligent assistant driving guide program, when executed by the processor, implements the steps of the intelligent assistant driving guide method as described above.
To achieve the above object, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores an intelligent assistant driving guide program, and the intelligent assistant driving guide program, when executed by a processor, implements the steps of the intelligent assistant driving guide method as described above.
According to the method, the device and the computer storage medium for determining the reason of the black screen of the display, when the driver is judged to be a new driver through face recognition, the vehicle central control automatically provides guidance content of an intelligent auxiliary driving function for the driver, and the guidance content can be presented in the form of an audio file, a video file, a graphic file, a network link file and a text file; and then, synchronously judging whether the vehicle is in a static state or not by the vehicle central control, and continuously providing guidance contents to the driver when the judged result shows that the vehicle is in the static state so that the driver can learn the guidance contents of the intelligent auxiliary driving function. The method has the advantages that the driver is judged to be a new driver through face recognition, the guidance content of the intelligent auxiliary driving function is actively pushed to the driver, the guidance content of the intelligent auxiliary driving function is continuously provided for the driver when the vehicle is in a static state, and the driver is guaranteed to be capable of finishing learning the guidance content, so that the problem that the guidance content of the intelligent auxiliary driving operation instruction cannot be intelligently pushed to the driver in the prior art is solved.
Drawings
FIG. 1 is a schematic diagram of an apparatus according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart diagram of a first embodiment of the intelligent driver assistance guidance method of the present invention;
FIG. 3 is a schematic flow chart diagram of a second embodiment of the intelligent driver assistance guidance method of the present invention;
FIG. 4 is a schematic flow chart diagram illustrating a third exemplary intelligent driver assistance guidance method in accordance with the present invention;
FIG. 5 is a schematic flow chart diagram illustrating a fourth embodiment of the intelligent driver assistance guidance method of the present invention;
FIG. 6 is a schematic flow chart diagram illustrating a fifth embodiment of the intelligent driver assistance guidance method of the present invention;
FIG. 7 is a schematic flow chart diagram illustrating a sixth embodiment of the intelligent driver assistance guidance method of the present invention;
fig. 8 is a flowchart illustrating a seventh embodiment of the intelligent assistant driving guidance method according to the present invention.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The main solution of the embodiment of the invention is as follows: when the driver is judged to be a new driver through face recognition, the vehicle central control automatically provides guidance content with an intelligent auxiliary driving function for the driver, and the guidance content can be presented in the form of an audio file, a video file, a graphic file, a network link file and a text file; and then, synchronously judging whether the vehicle is in a static state or not by the vehicle central control, and continuously providing guidance contents to the driver when the judged result shows that the vehicle is in the static state so that the driver can learn the guidance contents of the intelligent auxiliary driving function. The method has the advantages that the driver is judged to be a new driver through face recognition, the guidance content of the intelligent auxiliary driving function is actively pushed to the driver, the guidance content of the intelligent auxiliary driving function is continuously provided for the driver when the vehicle is in a static state, and the driver is guaranteed to be capable of finishing learning the guidance content, so that the problem that the guidance content of the intelligent auxiliary driving operation instruction cannot be intelligently pushed to the driver in the prior art is solved.
As an implementation manner, fig. 1 may be shown, where fig. 1 is a schematic structural diagram of an apparatus according to an embodiment of the present invention.
It will be appreciated that memory 1200 in embodiments of the invention may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile Memory may be a Read Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of illustration and not limitation, many forms of RAM are available, such as Static random access memory (Static RAM, SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic random access memory (Synchronous DRAM, SDRAM), Double Data Rate Synchronous Dynamic random access memory (ddr Data Rate SDRAM, ddr SDRAM), Enhanced Synchronous SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory 1200 of the systems and methods described in connection with the embodiments of the invention is intended to comprise, without being limited to, these and any other suitable types of memory.
For a software implementation, the techniques described in this disclosure may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described in this disclosure. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.
Based on the above structure, an embodiment of the present invention is proposed.
Referring to fig. 2, fig. 2 is a first embodiment of an intelligent driving assistance guidance method according to the present invention, which includes the steps of:
and step S110, when the driver is judged to be a new driver through face recognition, providing guidance content of the intelligent auxiliary driving function for the driver.
In this embodiment, an intelligent Driving Assistance System (Advanced Driving Assistance System) is implemented by using various sensors (millimeter wave radar, laser radar, monocular/binocular camera, and satellite navigation) installed in a vehicle to sense the surrounding environment at any time during the Driving process of the vehicle, collect data, perform identification, detection, and tracking of static and dynamic objects, and perform systematic operation and analysis by combining with navigation map data, so as to let a driver perceive possible dangers in advance, and effectively increase the comfort and safety of Driving the vehicle. The intelligent driving assistance guidance refers to guiding a user to learn an intelligent driving assistance function.
Face recognition is a biometric technology for identity recognition based on facial feature information of a person. A series of related technologies, also commonly called face recognition and face recognition, are used to collect images or video streams containing faces by using a camera or a video camera, automatically detect and track the faces in the images, and then perform face recognition on the detected faces. The key to the success of the face recognition system is whether the face recognition system has a core algorithm with a sharp end or not, and the recognition result has practical recognition rate and recognition speed; the human face recognition system integrates various professional technologies such as artificial intelligence, machine recognition, machine learning, model theory, expert system and video image processing, and meanwhile, the theory and implementation of intermediate value processing need to be combined, so that the human face recognition system is the latest application of biological feature recognition, the core technology of the human face recognition system is implemented, and the conversion from weak artificial intelligence to strong artificial intelligence is shown.
The face recognition system mainly comprises four components, which are respectively: the method comprises the steps of face image acquisition and detection, face image preprocessing, face image feature extraction, matching and identification.
Acquiring a face image: different face images can be collected through the camera lens, and for example, static images, dynamic images, different positions, different expressions and the like can be well collected. When the user is in the shooting range of the acquisition equipment, the acquisition equipment can automatically search and shoot the face image of the user. Face detection: in practice, face detection is mainly used for preprocessing of face recognition, namely, the position and size of a face are accurately calibrated in an image. The face image contains abundant pattern features, such as histogram features, color features, template features, structural features, Haar features, and the like. The face detection is to extract the useful information and to use the features to realize the face detection. The mainstream face detection method adopts an Adaboost learning algorithm based on the characteristics, wherein the Adaboost algorithm is a method for classification, and combines weak classification methods to form a new strong classification method. In the process of face detection, an Adaboost algorithm is used for picking out some rectangular features (weak classifiers) which can represent the face most, the weak classifiers are constructed into a strong classifier according to a weighted voting mode, and then a plurality of strong classifiers obtained by training are connected in series to form a cascade-structured stacked classifier, so that the detection speed of the classifier is effectively improved.
Preprocessing a face image: the image preprocessing for the human face is a process of processing the image based on the human face detection result and finally serving for feature extraction. The original image acquired by the system is limited by various conditions and random interference, so that the original image cannot be directly used, and the original image needs to be subjected to image preprocessing such as gray scale correction, noise filtering and the like in the early stage of image processing. For the face image, the preprocessing process mainly includes light compensation, gray level transformation, histogram equalization, normalization, geometric correction, filtering, sharpening, and the like of the face image.
Extracting the features of the face image: features that can be used by a face recognition system are generally classified into visual features, pixel statistical features, face image transform coefficient features, face image algebraic features, and the like. The face feature extraction is performed on some features of the face. Face feature extraction, also known as face characterization, is a process of feature modeling for a face. The methods for extracting human face features are classified into two main categories: one is a knowledge-based characterization method; the other is a characterization method based on algebraic features or statistical learning. The knowledge-based characterization method mainly obtains feature data which is helpful for face classification according to shape description of face organs and distance characteristics between the face organs, and feature components of the feature data generally comprise Euclidean distance, curvature, angle and the like between feature points. The human face is composed of parts such as eyes, nose, mouth, and chin, and geometric description of the parts and their structural relationship can be used as important features for recognizing the human face, and these features are called geometric features. The knowledge-based face characterization mainly comprises a geometric feature-based method and a template matching method.
Matching and identifying the face image: and searching and matching the extracted feature data of the face image with a feature template stored in a database, and outputting a result obtained by matching when the similarity exceeds a threshold value by setting the threshold value. The face recognition is to compare the face features to be recognized with the obtained face feature template, and judge the identity information of the face according to the similarity degree. This process is divided into two categories: one is confirmation, which is a process of performing one-to-one image comparison, and the other is recognition, which is a process of performing one-to-many image matching comparison.
In the present embodiment, the algorithms to which face recognition is mainly applied include, but are not limited to: a face-based recognition algorithm (Feature-based recognition algorithms); an identification algorithm (application-based recognition algorithms) based on the whole face image; template-based recognition algorithms (Template-based recognition algorithms); an algorithm for Recognition using neural networks (Recognition algorithms using neural networks).
In the present embodiment, when the driver is determined to be a new driver through face recognition, that is, the driver is not operating to control the vehicle, the vehicle includes, but is not limited to, a car, an SUV vehicle, a van, a truck, a JEEP vehicle, and the like. The automobile central control is a place for controlling comfortable entertainment devices such as automobile air conditioners, audio equipment and the like. The automobile central control system comprises a central control door lock system, and a driver can control the opening and closing of the whole automobile door and the glass lifting system through the automobile central control system. The central control door lock system mainly has three functions of central control, speed control and independent control. The driver can control all door lock switches, and simultaneously, the speed of driving reaches a timing, and the door is automatic to be locked, has independent switch to other doors, can the independent control own door. The automobile central control also comprises a central console and various vehicle controllers such as an audio control panel and the like. And automatically providing the operation instruction guidance contents of the intelligent auxiliary driving function to the driver by the vehicle central control.
It should be noted that the instruction content is presented in at least one of the following documents: audio files, video files, graphics files, web link files, and text files. The audio file may be provided to the driver for learning by the vehicle audio. The network link file refers to a file including network links, when the file is presented in the form of a network link file, the network links in the network link file can be displayed on a screen, and a driver can click the displayed network links to acquire guidance content. The tutorial content may preferably be presented in the form of an animation (UI design) of the intelligent driving assist function.
Step S120, it is determined whether the vehicle is in a stationary state.
In the embodiment, the vehicle central control automatically provides the driver with the synchronous judgment of the operation guidance content of the intelligent auxiliary driving function to judge whether the vehicle is in a static state.
And step S130, when the vehicle is in a static state, continuously providing the instruction content to the driver so as to enable the driver to learn the instruction content.
In the embodiment, when the vehicle center control judgment result shows that the vehicle is in a static state, the guidance content of the intelligent driving assistance function is continuously provided for the driver, so that the driver can learn the guidance content.
In the technical scheme provided by the embodiment, when the driver is judged to be a new driver through face recognition, the vehicle central control automatically provides guidance content of the intelligent auxiliary driving function for the driver, and the guidance content can be presented in the forms of audio files, video files, graphic files, network link files and text files; and then, synchronously judging whether the vehicle is in a static state or not by the vehicle central control, and continuously providing guidance contents to the driver when the judged result shows that the vehicle is in the static state so that the driver can learn the guidance contents of the intelligent auxiliary driving function. The method has the advantages that the driver is judged to be a new driver through face recognition, the guidance content of the intelligent auxiliary driving function is actively pushed to the driver, the guidance content of the intelligent auxiliary driving function is continuously provided for the driver when the vehicle is in a static state, and the driver is guaranteed to be capable of finishing learning the guidance content, so that the problem that the guidance content of the intelligent auxiliary driving operation instruction cannot be intelligently pushed to the driver in the prior art is solved.
Referring to fig. 3, fig. 3 is a second embodiment of the intelligent assistant driving guidance method of the present invention, which includes:
compared with the first embodiment, the second embodiment includes step S210, and other steps are the same as those of the first embodiment and are not repeated.
Step S210, the face recognition system is started to determine whether the driver is a brand new driver.
In this embodiment, after the vehicle is powered on, the face recognition system is automatically started to obtain the facial information of the driver, and whether the driver is a brand-new driver is determined.
And step S220, when the driver is judged to be a new driver through face recognition, providing guidance content of the intelligent auxiliary driving function for the driver.
In step S230, it is determined whether the vehicle is in a stationary state.
And step S240, when the vehicle is in a static state, continuously providing the instruction content to the driver so that the driver can learn the instruction content.
In the technical scheme provided by this embodiment, after the vehicle is powered on, the face recognition system is automatically started to obtain the facial information of the driver, and whether the driver is a brand-new driver is determined, which is a precondition for actively providing guidance content of an intelligent auxiliary driving function for the driver.
Referring to fig. 4, fig. 4 is a third embodiment of the intelligent assistant driving guidance method of the present invention, which includes:
step S310, the face recognition system is started to judge whether the driver is a brand new driver.
In step S320, when the face recognition system determines that the driver is a non-brand-new driver, guidance content of the intelligent assistant driving function is not provided.
In the present embodiment, when the face recognition system determines that the driver is a non-brand-new driver, it indicates that the driver has learned the guidance content of the intelligent assistant driving function before that, and therefore, the guidance content of the intelligent assistant driving function is not actively provided to the driver.
In the technical solution provided in this embodiment, when the face recognition system determines that the driver is a non-brand-new driver, it indicates that the driver has learned the guidance content of the intelligent assistant driving function before that, and therefore, the guidance content of the intelligent assistant driving function is not actively provided to the driver. Therefore, the problems that the learnt users can be bored and the viscosity of the users is reduced when hearing repeated instruction contents are solved.
Referring to fig. 5, fig. 5 illustrates a fourth embodiment of the intelligent driving assistance guidance method according to the present invention, which includes:
and step S410, starting the face recognition system to judge whether the driver is a brand-new driver.
Step S420, when the face recognition system determines that the driver is a non-brand-new driver, no guidance content of the intelligent assistant driving function is provided.
Compared with the third embodiment, the fourth embodiment includes step S430 and step S440, and other steps are the same as those of the third embodiment and are not repeated.
And step S430, acquiring a target retrieval instruction corresponding to the target intelligent auxiliary driving function triggered by the driver at the target function starting component.
In this embodiment, the target starting part may be a key that can be pressed up and down in the vehicle; the key which can be pulled upwards can be pressed downwards; a deflector rod which can be pulled left and right; a rotatable knob or a left-right slidable button. The target intelligent driving assistance functions include, but are not limited to: an Adaptive cruise control (acc) function, a lane departure warning function, a lane keeping function, a collision avoidance or pre-collision function, a night vision function, an Adaptive light control (Adaptive light control) function, a pedestrian protection function, an automatic parking function, a Traffic sign recognition (Traffic sign recognition) function, a Blind spot detection (blindshot detection) function, a Driver fatigue detection (Driver fatigue detection) function, a downhill control function, an electric vehicle warning function, and the like. For example, if there is a button on the upper right side of the steering wheel of the vehicle corresponding to the intelligent driving assistance function as the lane keeping function, when the driver presses the button, a lane keeping function search command corresponding to the lane keeping function is triggered, and the vehicle central control obtains the command.
Step S440, determining corresponding guidance content in the guidance content according to the target retrieval instruction, and providing the corresponding guidance content to the driver for the driver to learn.
In this embodiment, when the lane keeping function search instruction is obtained according to the vehicle central control, guidance content corresponding to the lane keeping function is determined from the intelligent driving assistance function guidance content according to the lane keeping function search instruction, and the guidance content corresponding to the lane keeping function is provided to the driver for learning.
In the technical scheme provided by this embodiment, a target retrieval instruction corresponding to a target intelligent auxiliary driving function triggered by a target function starting component by a driver is acquired; preferably, the lane keeping function retrieval instruction is used, the guidance content corresponding to the lane keeping function is determined in the intelligent driving assisting function guidance content according to the lane keeping function retrieval instruction, and the guidance content corresponding to the lane keeping function is provided for the driver to learn. The target intelligent auxiliary driving function which the user wants to learn or know is correspondingly provided for the user, so that the experience of the user is improved.
Referring to fig. 6, fig. 6 is a fifth embodiment of the intelligent driving assistance guidance method of the present invention, including:
and step S510, when the driver is judged to be a new driver through face recognition, providing guidance content of the intelligent auxiliary driving function for the driver.
Compared with the first embodiment, the fifth embodiment includes step S520 and step S530, and other steps are the same as those of the first embodiment and are not repeated.
And step S520, acquiring the running speed of the vehicle through a vehicle sensor.
In the embodiment, the vehicle central control unit acquires the running speed of the vehicle through an engine speed sensor of the vehicle.
And step S530, judging whether the vehicle is in a static state or not according to the running speed.
In this embodiment, the vehicle central control determines whether the vehicle is in a stationary state according to the acquired running speed, that is, the vehicle central control determines whether the acquired running speed is zero as a determination condition.
And step S540, when the vehicle is in a static state, continuously providing the instruction content to the driver so as to enable the driver to learn the instruction content.
In the technical scheme provided by the embodiment, the vehicle central control acquires the running speed of the vehicle through an engine speed sensor of the vehicle; and the vehicle central control judges whether the vehicle is in a static state according to the acquired running speed, namely the vehicle central control takes the judgment condition of whether the acquired running speed is zero or not.
Referring to fig. 7, fig. 7 is a sixth embodiment of the intelligent driving assistance guidance method according to the present invention, which includes:
and step S610, when the driver is judged to be a new driver through face recognition, providing guidance content of the intelligent auxiliary driving function for the driver.
And step S620, acquiring the running speed of the vehicle through a vehicle sensor.
And step S630, judging whether the vehicle is in a static state or not according to the running speed.
Compared with the fifth embodiment, the sixth embodiment includes step S640, and other steps are the same as those in the fifth embodiment and are not repeated.
And step S640, stopping providing the guidance content to the driver when the vehicle is in a non-stationary state.
In this embodiment, when the running speed obtained by the vehicle central control is not zero, that is, when the vehicle is in a non-stationary state, the guidance content of the intelligent auxiliary driving function is stopped being provided to the driver, so that it is ensured that the driver is not interfered by the guidance content in the running process of the vehicle.
Referring to fig. 8, fig. 8 is a seventh embodiment of the intelligent driving assistance guidance method according to the present invention, including:
and step S710, when the driver is judged to be a new driver through face recognition, providing guidance content of the intelligent auxiliary driving function for the driver.
And S720, acquiring the running speed of the vehicle through a vehicle sensor.
And step S730, judging whether the vehicle is in a static state or not according to the running speed.
And step S740, when the vehicle is in a non-stationary state, stopping providing the guidance content to the driver.
Compared with the sixth embodiment, the seventh embodiment includes step S750 and step S760, and other steps are the same as those of the sixth embodiment and are not repeated.
And step S750, acquiring the operation instruction of the driver in real time.
In the embodiment, the vehicle central control acquires the operation instruction of the driver in real time, wherein the operation instruction refers to the operation instruction that the driver actively requests to provide the guidance content of the intelligent driving assistance function. The user can send the instruction through a specific key button; the instruction can also be sent by clicking a corresponding button on a vehicle display screen; and are not intended to be unduly limited herein.
Step S760, when an operation instruction that the driver actively requests to provide the guidance content is acquired, re-providing the guidance content to the driver, so that the driver learns the guidance content.
In the embodiment, when the vehicle central control obtains an operation instruction that the driver actively requests to provide the guidance content of the intelligent driving assistance function, the guidance content of the intelligent driving assistance function is provided to the driver again for the driver to learn. Since the vehicle is not in a stationary state at this time, it is preferable to provide the user with the tutorial contents of the intelligent driving assistance function in the form of an audio file for learning in order to ensure driving safety.
In the technical scheme provided by the embodiment, a vehicle central control acquires an operation instruction of a driver in real time, wherein the operation instruction refers to an operation instruction for the driver to actively request to provide guidance content of an intelligent auxiliary driving function; when the vehicle central control obtains an operation instruction that the driver actively requests to provide the guidance content of the intelligent auxiliary driving function, the guidance content of the intelligent auxiliary driving function is provided for the driver again for learning. Since the vehicle is not in a stationary state at this time, it is preferable to provide the user with the tutorial contents of the intelligent driving assistance function in the form of an audio file for learning in order to ensure driving safety. Therefore, the driving safety is ensured, and meanwhile, guidance content can be provided for the user to learn.
The invention also provides a device comprising a memory, a processor and a program for determining the cause of the display black screen, which is stored in the memory and can run on the processor, wherein the program for determining the cause of the display black screen realizes the steps of the method for determining the cause of the display black screen.
The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a program for determining a cause of a display black screen, and the program for determining a cause of a display black screen, when executed by a processor, implements the steps of the method for determining a cause of a display black screen as described above.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. An intelligent driving assistance guidance method is characterized by comprising the following steps:
when the driver is judged to be a new driver through face recognition, providing guidance content of an intelligent auxiliary driving function for the driver;
judging whether the vehicle is in a static state or not;
continuing to provide the tutorial content to the driver for the driver to learn the tutorial content while the vehicle is stationary.
2. The intelligent assistant driving guidance method according to claim 1, wherein the guidance content is presented in the form of at least one of the following documents:
audio files, video files, graphics files, web link files, and text files.
3. The intelligent assisted driving guidance method of claim 2, further comprising:
and starting a face recognition system to judge whether the driver is a brand-new driver.
4. The intelligent assistant driving guidance method as claimed in claim 3, wherein after the step of activating the face recognition system to determine whether the driver is a brand-new driver, the method further comprises:
and when the face recognition system judges that the driver is a non-brand-new driver, providing no guidance content of the intelligent auxiliary driving function.
5. The intelligent assistant driving guide method according to claim 4, wherein after the step of not providing the guidance content of the intelligent assistant driving function when the face recognition system determines that the driver is a non-brand-new driver, further comprising:
acquiring a target retrieval instruction corresponding to a target intelligent auxiliary driving function triggered by the driver at a target function starting component;
and determining corresponding instruction contents in the instruction contents according to the target retrieval instruction, and providing the corresponding instruction contents for the driver to learn.
6. The intelligent assisted driving guidance method of claim 2, wherein the determining whether the vehicle is in a stationary state comprises:
acquiring the running speed of the vehicle through a vehicle sensor;
and judging whether the vehicle is in a static state or not according to the running speed.
7. The intelligent assisted driving guidance method of claim 6, further comprising:
stopping providing the tutorial content to the driver when the vehicle is in a non-stationary state.
8. The intelligent assisted-driving coaching method of claim 7, wherein, after the step of ceasing to provide the coaching content to the driver when the vehicle is in the non-stationary state, further comprising:
acquiring an operation instruction of the driver in real time;
when an operation instruction that the driver actively requests to provide the guidance content is acquired, the guidance content is provided to the driver again so that the driver can learn the guidance content.
9. An apparatus comprising a memory, a processor, and an intelligent driver assistance guidance program stored in the memory and executable on the processor, the intelligent driver assistance guidance program when executed by the processor implementing the steps of the intelligent driver assistance guidance method according to any one of claims 1-8.
10. A computer-readable storage medium storing an intelligent driving assistance guidance program which, when executed by a processor, implements the steps of the intelligent driving assistance guidance method according to any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011159002.7A CN112348718A (en) | 2020-10-26 | 2020-10-26 | Intelligent auxiliary driving guidance method, device and computer storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011159002.7A CN112348718A (en) | 2020-10-26 | 2020-10-26 | Intelligent auxiliary driving guidance method, device and computer storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112348718A true CN112348718A (en) | 2021-02-09 |
Family
ID=74359011
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011159002.7A Pending CN112348718A (en) | 2020-10-26 | 2020-10-26 | Intelligent auxiliary driving guidance method, device and computer storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112348718A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113928247A (en) * | 2021-09-01 | 2022-01-14 | 北京汽车研究总院有限公司 | Learning method and device for vehicle driving assistance |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0911859A (en) * | 1995-06-30 | 1997-01-14 | Nissan Motor Co Ltd | Vehicle condition detecting device of on-vehicle receiver |
| WO2005108171A1 (en) * | 2004-05-06 | 2005-11-17 | Matsushita Electric Industrial Co., Ltd. | Parking assisting apparatus |
| US20070010938A1 (en) * | 2005-07-08 | 2007-01-11 | Aisin Aw Co., Ltd. | Navigation systems, methods, and programs |
| JP2007141223A (en) * | 2005-10-17 | 2007-06-07 | Omron Corp | Information processing apparatus and method, recording medium, and program |
| DE102007032720A1 (en) * | 2007-07-13 | 2009-01-15 | Daimler Ag | Driver assisting method for use during shunting or parking of vehicle e.g. vehicle with trailer, involves implementing simulation for determining optimal trajectory or motion sequence of vehicle and displaying trajectory or sequence |
| JP2009083771A (en) * | 2007-10-02 | 2009-04-23 | Mitsubishi Fuso Truck & Bus Corp | Control device for vehicle |
| US20140170602A1 (en) * | 2012-12-13 | 2014-06-19 | Alliance Wireless Technologies, Inc. | Vehicle activity information system |
| US20150262484A1 (en) * | 2012-09-17 | 2015-09-17 | Volvo Truck Corporation | Method and system for providing a tutorial message to a driver of a vehicle |
| US20160009295A1 (en) * | 2014-07-10 | 2016-01-14 | Hyundai Mobis Co., Ltd. | On-vehicle situation detection apparatus and method |
| WO2017193248A1 (en) * | 2016-05-08 | 2017-11-16 | 深圳市欸阿技术有限公司 | Vehicle travel control method and device |
| CN109774722A (en) * | 2017-11-15 | 2019-05-21 | 欧姆龙株式会社 | Information processing unit, methods and procedures, driver's monitoring system and preservation media |
| US20190168767A1 (en) * | 2017-12-06 | 2019-06-06 | Toyota Motor Engineering & Manufacturing North America, Inc. | Systems and methods for selective driver coaching based on driver efficiency |
| CN111368640A (en) * | 2020-02-10 | 2020-07-03 | 上海应用技术大学 | Illegal driving auxiliary identification method based on identity authentication |
| CN111572555A (en) * | 2020-04-28 | 2020-08-25 | 东风汽车集团有限公司 | Self-learning auxiliary driving method |
| US20200331475A1 (en) * | 2017-08-30 | 2020-10-22 | Great Wall Motor Company Limited | Emergency brake control method and device, ecu and vehicle |
-
2020
- 2020-10-26 CN CN202011159002.7A patent/CN112348718A/en active Pending
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0911859A (en) * | 1995-06-30 | 1997-01-14 | Nissan Motor Co Ltd | Vehicle condition detecting device of on-vehicle receiver |
| WO2005108171A1 (en) * | 2004-05-06 | 2005-11-17 | Matsushita Electric Industrial Co., Ltd. | Parking assisting apparatus |
| US20070010938A1 (en) * | 2005-07-08 | 2007-01-11 | Aisin Aw Co., Ltd. | Navigation systems, methods, and programs |
| JP2007141223A (en) * | 2005-10-17 | 2007-06-07 | Omron Corp | Information processing apparatus and method, recording medium, and program |
| DE102007032720A1 (en) * | 2007-07-13 | 2009-01-15 | Daimler Ag | Driver assisting method for use during shunting or parking of vehicle e.g. vehicle with trailer, involves implementing simulation for determining optimal trajectory or motion sequence of vehicle and displaying trajectory or sequence |
| JP2009083771A (en) * | 2007-10-02 | 2009-04-23 | Mitsubishi Fuso Truck & Bus Corp | Control device for vehicle |
| US20150262484A1 (en) * | 2012-09-17 | 2015-09-17 | Volvo Truck Corporation | Method and system for providing a tutorial message to a driver of a vehicle |
| US20140170602A1 (en) * | 2012-12-13 | 2014-06-19 | Alliance Wireless Technologies, Inc. | Vehicle activity information system |
| US20160009295A1 (en) * | 2014-07-10 | 2016-01-14 | Hyundai Mobis Co., Ltd. | On-vehicle situation detection apparatus and method |
| WO2017193248A1 (en) * | 2016-05-08 | 2017-11-16 | 深圳市欸阿技术有限公司 | Vehicle travel control method and device |
| US20200331475A1 (en) * | 2017-08-30 | 2020-10-22 | Great Wall Motor Company Limited | Emergency brake control method and device, ecu and vehicle |
| CN109774722A (en) * | 2017-11-15 | 2019-05-21 | 欧姆龙株式会社 | Information processing unit, methods and procedures, driver's monitoring system and preservation media |
| US20190168767A1 (en) * | 2017-12-06 | 2019-06-06 | Toyota Motor Engineering & Manufacturing North America, Inc. | Systems and methods for selective driver coaching based on driver efficiency |
| CN111368640A (en) * | 2020-02-10 | 2020-07-03 | 上海应用技术大学 | Illegal driving auxiliary identification method based on identity authentication |
| CN111572555A (en) * | 2020-04-28 | 2020-08-25 | 东风汽车集团有限公司 | Self-learning auxiliary driving method |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113928247A (en) * | 2021-09-01 | 2022-01-14 | 北京汽车研究总院有限公司 | Learning method and device for vehicle driving assistance |
| CN113928247B (en) * | 2021-09-01 | 2023-08-18 | 北京汽车研究总院有限公司 | Learning method and device for vehicle auxiliary driving |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Martin et al. | Drive&act: A multi-modal dataset for fine-grained driver behavior recognition in autonomous vehicles | |
| CN108995654B (en) | Driver state identification method and system | |
| US10176405B1 (en) | Vehicle re-identification techniques using neural networks for image analysis, viewpoint-aware pattern recognition, and generation of multi- view vehicle representations | |
| US10338600B2 (en) | Autonomous driving control apparatus, vehicle having the same, and method for controlling the same | |
| US10157441B2 (en) | Hierarchical system for detecting object with parallel architecture and hierarchical method thereof | |
| CN111723596B (en) | Gaze area detection and neural network training method, device and equipment | |
| CN105654753A (en) | Intelligent vehicle-mounted safe driving assistance method and system | |
| US8520893B2 (en) | Method and system for detecting object | |
| CN110826370B (en) | Method and device for identifying identity of person in vehicle, vehicle and storage medium | |
| CN109145719B (en) | Driver fatigue state identification method and system | |
| KR101845769B1 (en) | Car rear detection system using convolution neural network, and method thereof | |
| KR101165106B1 (en) | Driver certification device and method that do fetters in face realization | |
| DE102018213787A1 (en) | Vehicle terminal and face authentication method | |
| CN109664889B (en) | Vehicle control method, device and system and storage medium | |
| CN114049677B (en) | Vehicle ADAS control method and system based on driver emotion index | |
| CN107284449A (en) | A kind of traffic safety method for early warning and system, automobile, readable storage medium storing program for executing | |
| CN112348718A (en) | Intelligent auxiliary driving guidance method, device and computer storage medium | |
| US20200298847A1 (en) | Systems for determining object importance in on-road driving scenarios and methods thereof | |
| Panicker et al. | Open-eye detection using iris–sclera pattern analysis for driver drowsiness detection | |
| CN108363968A (en) | A kind of tired driver driving monitoring system and method based on key point extraction | |
| WO2021024905A1 (en) | Image processing device, monitoring device, control system, image processing method, computer program, and recording medium | |
| CN117644870B (en) | Driving anxiety detection and vehicle control method and system based on context awareness | |
| WO2020261820A1 (en) | Image processing device, monitoring device, control system, image processing method, and program | |
| Zhang et al. | A Novel Method for the Driver Lane-Changing Intention Recognition | |
| JP2021009503A (en) | Personal data acquisition system, personal data acquisition method, face sensing parameter adjustment method for image processing device and computer program |
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 |