CN112141119A

CN112141119A - Intelligent driving control method and device, vehicle, electronic equipment and storage medium

Info

Publication number: CN112141119A
Application number: CN202011010951.9A
Authority: CN
Inventors: 伍俊; 范亦卿; 陶莹; 许亮; 祁凯悦
Original assignee: Shanghai Sensetime Lingang Intelligent Technology Co Ltd
Current assignee: Shanghai Lingang Jueying Intelligent Technology Co ltd
Priority date: 2020-09-23
Filing date: 2020-09-23
Publication date: 2020-12-29
Anticipated expiration: 2040-09-23
Also published as: CN112141119B; WO2022062659A1; JP2023542992A

Abstract

The present disclosure relates to an intelligent driving control method and apparatus, a vehicle, an electronic device, and a storage medium, the method including: acquiring running state information of a vehicle; acquiring image information of a driving area of the vehicle; determining the driving state of the driver of the vehicle according to the driving state information of the vehicle and the image information of the driving area; and responding to the detected situation that the driver of the vehicle is in a preset dangerous driving state, and performing intelligent driving control.

Description

Intelligent driving control method and device, vehicle, electronic equipment and storage medium

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to an intelligent driving control method and apparatus, a vehicle, an electronic device, and a storage medium.

Background

In the driving process of the vehicle, a driver is in a dangerous driving state, so that hidden dangers are caused to driving safety, and traffic accidents are easily caused. Therefore, how to accurately detect the dangerous driving state of the driver and take measures in time has important significance for safe driving.

Disclosure of Invention

The present disclosure provides an intelligent driving control technical scheme.

According to an aspect of the present disclosure, there is provided an intelligent driving control method including:

acquiring running state information of a vehicle;

acquiring image information of a driving area of the vehicle;

determining the driving state of the driver of the vehicle according to the driving state information of the vehicle and the image information of the driving area;

and responding to the detected situation that the driver of the vehicle is in a preset dangerous driving state, and performing intelligent driving control.

In one possible implementation, the determining the driving state of the driver of the vehicle according to the driving state information of the vehicle and the image information of the driving area includes:

detecting the behavior of the driver of the vehicle according to the image information of the driving area;

and determining the driving state of the driver according to the driving state information of the vehicle and the behavior detection result of the driver.

In one possible implementation, the detecting the behavior of the driver of the vehicle includes:

determining a danger level corresponding to the behavior detection result of the driver;

the determining the driving state of the driver according to the driving state information of the vehicle and the behavior detection result of the driver includes:

in response to determining that the vehicle is in an abnormal driving state according to the driving state information of the vehicle, upgrading a risk level of the behavior detection result of the driver to a first risk level;

in response to determining that the first risk level reaches a preset warning level, determining that the driving state of the driver is in a preset dangerous driving state.

In one possible implementation, the determining the driving state of the driver according to the driving state information of the vehicle and the behavior detection result of the driver includes:

and determining that the driver is detected to be in a preset dangerous driving state in response to determining that the driving state of the vehicle and the behavior state of the driver are abnormal states according to the driving state information of the vehicle and the behavior detection result of the driver.

in response to detecting that the vehicle is in an abnormal driving state according to the driving state information of the vehicle, performing behavior detection on the driver according to the image information of the driving area;

determining that the driver is in a preset dangerous driving state in response to determining that the behavior detection result of the driver is driver distraction driving or fatigue driving.

In one possible implementation, the abnormal driving state of the vehicle includes at least one of:

the line pressing times of the vehicle in a first set time reach a set line pressing time threshold; the left-right swing amplitude of the vehicle reaches a set amplitude threshold value; the vehicle does not pass according to the indication of the traffic sign or the traffic signal lamp; the vehicle speed of the vehicle exceeds a set speed threshold.

In one possible implementation, the performing intelligent driving control includes generating warning information based on at least one of:

driving state information of the vehicle;

detecting a behavior detection result of a driver of the vehicle based on the image information of the driving area;

a driving state of the driver.

In one possible implementation, the performing intelligent driving control includes:

and controlling the alarm equipment to continuously output alarm information until the vehicle running state and the driving state of the driver are both recovered to a normal state.

and in response to the detection that the duration of the driver in the preset dangerous driving state exceeds the preset duration, controlling the auxiliary driving/automatic driving function to be started, and/or controlling the vehicle to decelerate and stop.

In one possible implementation, the method further includes:

acquiring environmental information around the vehicle;

the determining the driving state of the driver of the vehicle according to the driving state information of the vehicle and the image information of the driving area includes:

and determining the driving state of the driver of the vehicle according to the environmental information around the vehicle, the driving state information of the vehicle and the image information of the driving area.

In one possible implementation, the environment information includes road condition information;

the acquiring environmental information around the vehicle includes: acquiring road condition information of a road on which the vehicle runs;

the determining a driving state of a driver of the vehicle according to the environmental information around the vehicle, the driving state information of the vehicle, and the image information of the driving area includes:

and determining that the driver is detected to be in a preset dangerous driving state in response to the condition that the driving state information of the vehicle and the road condition information do not meet a preset matching relation and indicating that the behavior state of the driver of the vehicle is an abnormal state based on the behavior detection result of the image information of the driving area to the driver of the vehicle.

In one possible implementation manner, the acquiring the driving state information of the vehicle includes:

acquiring driving sensing data, wherein the sensing data comprises at least one of the following items: the system comprises image information acquired by an advanced driving auxiliary system of the vehicle, image information acquired by a vehicle data recorder of the vehicle and speed information sensed by a speed sensor of the vehicle;

and determining the running state information of the vehicle according to the sensing data.

and inputting the acquired driving state information of the vehicle and the image information of the driving area into a trained neural network, and determining the driving state of the driver of the vehicle through the neural network.

According to an aspect of the present disclosure, there is provided an intelligent driving control apparatus including:

the first acquisition module is used for acquiring the running state information of the vehicle;

the second acquisition module is used for acquiring the image information of the driving area of the vehicle;

the driving state determining module is used for determining the driving state of a driver of the vehicle according to the driving state information of the vehicle and the image information of the driving area;

and the control module is used for responding to the situation that the driver of the vehicle is in a preset dangerous driving state and carrying out intelligent driving control.

In one possible implementation, the driving state determination module includes a first driving state determination submodule and a second driving state determination submodule, wherein:

the first driving state determining submodule is used for carrying out behavior detection on a driver of the vehicle according to the image information of the driving area;

and the second driving state determining submodule is used for determining the driving state of the driver according to the running state information of the vehicle and the behavior detection result of the driver.

In a possible implementation manner, the first driving state determining submodule is configured to determine a risk level corresponding to the behavior detection result of the driver;

the second driving state determination submodule configured to:

In a possible implementation manner, the second driving state determining submodule is configured to determine that the driver is detected to be in a preset dangerous driving state in response to determining that the driving state of the vehicle and the behavior state of the driver are both abnormal states according to the driving state information of the vehicle and the behavior detection result of the driver.

In one possible implementation, the driving state determination module includes a third driving state determination submodule and a fourth driving state determination submodule, wherein:

the third driving state determining submodule is used for responding to the fact that the vehicle is detected to be in an abnormal driving state according to the driving state information of the vehicle, and conducting behavior detection on the driver according to the image information of the driving area;

and the fourth driving state determination submodule is used for responding to the fact that the behavior detection result of the driver is determined to be driver distraction driving or fatigue driving, and determining that the driver is in a preset dangerous driving state.

In one possible implementation, the control module is configured to generate the warning information based on at least one of:

driving state information of the vehicle;

a driving state of the driver.

In a possible implementation manner, the control module is configured to control the warning device to continuously output the warning information until the vehicle driving state and the driving state of the driver both return to a normal state.

In one possible implementation manner, the control module is configured to control the start of the driving assistance/automatic driving function and/or control the deceleration and stop of the vehicle in response to detecting that the duration of the driver in the preset dangerous driving state exceeds a preset duration.

In one possible implementation, the apparatus further includes:

a third acquisition module for acquiring environmental information around the vehicle;

the driving state determining module is used for determining the driving state of the driver of the vehicle according to the environmental information around the vehicle, the running state information of the vehicle and the image information of the driving area.

the third acquisition module is used for acquiring road condition information of a road on which the vehicle runs;

the driving state determining module is used for responding to the condition that the driving state information of the vehicle and the road condition information do not meet the preset matching relationship, indicating that the behavior state of the driver of the vehicle is an abnormal state based on the behavior detection result of the image information of the driving area to the driver of the vehicle, and determining that the driver is detected to be in the preset dangerous driving state.

In one possible implementation manner, the first obtaining module includes a sensing data obtaining sub-module and a driving state information determining sub-module, wherein:

the sensing data acquisition submodule is used for acquiring driving sensing data, and the sensing data comprises at least one of the following items: the system comprises image information acquired by an advanced driving auxiliary system of the vehicle, image information acquired by a vehicle data recorder of the vehicle and speed information sensed by a speed sensor of the vehicle;

and the driving state information determining submodule is used for determining the driving state information of the vehicle according to the sensing data.

In a possible implementation manner, the driving state determining module is configured to input the acquired driving state information of the vehicle and the image information of the driving area into a trained neural network, and determine the driving state of the driver of the vehicle through the neural network.

A vehicle, characterized by comprising:

the first sensor is used for collecting image information of a driving area of the vehicle;

the controller is used for acquiring the running state information of the vehicle, determining the driving state of the driver of the vehicle according to the running state information of the vehicle and the image information of the driving area, and performing intelligent driving control in response to the fact that the driver of the vehicle is detected to be in a preset dangerous driving state.

In one possible implementation, the vehicle further includes: the second sensor is used for collecting driving sensing data;

the controller is used for acquiring the driving state information of the vehicle according to the driving sensing data.

According to an aspect of the present disclosure, there is provided an electronic device including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the above-described method.

According to an aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the above-described method.

In the embodiment of the disclosure, by acquiring the driving state information of the vehicle and the image information of the driving area of the vehicle, the driving state of the driver of the vehicle is determined according to the driving state information of the vehicle and the image information of the driving area, and the intelligent driving control is performed in response to the detection that the driver of the vehicle is in the preset dangerous driving state. Therefore, the driving state of the driver of the vehicle can be determined by combining the driving state of the vehicle and the image information of the driving area, and the accuracy of the determined driving state of the driver is improved. In addition, the intelligent driving control is carried out in response to the situation that the driver of the vehicle is detected to be in the preset dangerous driving state, and the driving safety of the vehicle is further improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure.

Fig. 1 shows a flowchart of an intelligent driving control method provided by an embodiment of the present disclosure.

Fig. 2 shows a block diagram of an intelligent driving control device provided in an embodiment of the present disclosure.

Fig. 3 illustrates a block diagram of an electronic device 800 provided by an embodiment of the disclosure.

Fig. 4 shows a block diagram of an electronic device 1900 provided by an embodiment of the disclosure.

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 the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.

The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.

Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.

The present disclosure provides an intelligent driving control method, and an execution subject of the method may be an intelligent driving control apparatus mounted on a vehicle. In one possible implementation, the method may be performed by a terminal device or a server or other processing device. The terminal device may be a vehicle-mounted device, a User Equipment (UE), a mobile device, a User terminal, a cellular phone, a cordless phone, a Personal Digital Assistant (PDA), a handheld device, a computing device, or a wearable device. The vehicle-mounted device may be a vehicle or a domain controller in a vehicle cabin, and may also be a device host used for executing an intelligent Driving control method in an ADAS (Advanced Driving Assistance System), an OMS (Occupant Monitoring System), or a DMS (Driver Monitoring System). In some possible implementations, the intelligent driving control method may be implemented by a processor calling computer readable instructions stored in a memory.

Fig. 1 illustrates a flowchart of an intelligent driving control method according to an embodiment of the present disclosure, which includes, as illustrated in fig. 1:

in step S11, the running state information of the vehicle is acquired;

the vehicle may be at least one of private cars, shared cars, net appointment cars, taxis, trucks, and the like, and the specific type of vehicle is not limited by the present disclosure.

The driving state information is used for representing the driving state of the vehicle, and in a possible implementation mode, the driving state can be divided into a normal driving state and an abnormal driving state, and the normal driving state can be that the vehicle drives according to rules such as preset speed, direction, route and traffic regulations. The abnormal driving state corresponds to a normal driving state, and may be a state in which the vehicle is not driven according to a predetermined rule such as a speed, a direction, a route, and a traffic regulation.

The running state information may include information representing a normal running state or an abnormal running state of the vehicle, such as at least one of speed, direction, acceleration, information of a lane where the vehicle is located, line pressing information, vehicle body sway information, lane change information, speed change information, brake information, route information, overspeed condition, consistency information of the running state with a traffic sign, and the like.

In one possible implementation, the abnormal driving state of the vehicle includes at least one of: the line pressing times of the vehicle in a first set time reach a set line pressing time threshold; the left-right swing amplitude of the vehicle reaches a set amplitude threshold value; the vehicle does not pass according to the indication of the traffic sign or the traffic signal lamp; the vehicle speed of the vehicle exceeds a set speed threshold.

The vehicle is continuously pressed within a short time when the pressing times of the vehicle within the first set time reach a set pressing time threshold, and the vehicle can be determined to be in an abnormal driving state under the condition. The first set time period may be set according to an actual situation, for example, 1 minute or 15 seconds, and the set threshold of the number of pressing lines may also be set according to an actual situation, for example, 2 times, which is not specifically limited in this disclosure.

The left-right swinging amplitude of the vehicle reaches a set amplitude threshold value, which indicates that the left-right swinging amplitude of the vehicle body is large, and the condition can determine that the vehicle is in an abnormal driving state. The set amplitude threshold value may be set according to actual conditions such as the type of the vehicle, and the disclosure does not specifically limit this.

In addition, for the fact that the vehicle does not pass according to the indication of the traffic sign or the traffic light, and the vehicle speed of the vehicle exceeds the set speed threshold, the vehicle is also indicated to be in an abnormal driving state, and details are not repeated here.

The running state information of the vehicle may be determined from information such as a running speed, a running direction, a position, and a surrounding environment of the vehicle, which may be acquired using a sensor for sensing environment information outside the vehicle cabin and/or a sensor for sensing a running state of the vehicle.

In an optional implementation manner, the acquiring the driving state information of the vehicle includes: acquiring driving sensing data, wherein the sensing data comprises at least one of the following items: the method comprises the following steps of acquiring image information by an Advanced Driving Assistance System (ADAS) of a vehicle, acquiring image information by a vehicle event data recorder of the vehicle and sensing speed information by a speed sensor of the vehicle; and determining the running state information of the vehicle according to the sensing data.

For example, images, temperature, pressure and other information for monitoring the state of the automobile outside the cabin are acquired by an image acquisition device, a millimeter wave/laser radar, a sonar sensor and the like on the automobile body, and the sensors may be disposed on a front bumper, a side view mirror, the inside of a driving lever or a windshield of the automobile to acquire sensing data outside the cabin. The environment around the vehicle, including objects such as pedestrians and vehicles around the vehicle, and information such as lane lines, traffic lights, and traffic markers can be detected from these sensing data. In addition, information such as the speed, acceleration, position, and attitude of the vehicle may be determined by a speed/acceleration sensor, an attitude sensor, a positioning device, and the like.

In step S12, image information of the driving area of the vehicle is acquired;

the driving area here may be the area in which the driver is located in the cabin or any area containing the area in which the driver is located, which is often the area of the main driver's seat.

Then, the image information of the driving area may be image information of an area where a driver is located inside the vehicle cabin, and the image information may be acquired by a vehicle-mounted image acquisition device disposed inside or outside the vehicle cabin of the vehicle, and the vehicle-mounted image acquisition device may be a vehicle-mounted camera or an image acquisition device provided with a camera. The camera can be a camera for collecting image information inside the vehicle, and can also be a camera for collecting image information outside the vehicle.

For example, the camera may include a camera in the DMS and/or a camera in the OMS, etc., which may be used to capture image information of the interior of the vehicle; the camera may also include a camera in the ADAS, which may be used to collect image information outside the vehicle. Of course, the vehicle-mounted image capturing device may also be a camera in other systems, or may also be a separately configured camera, and the embodiment of the present disclosure does not limit the specific vehicle-mounted image capturing device.

The carrier of the image information can be a two-dimensional image or video, for example, the image information can be a visible light image/video or an infrared light image/video; the method may also be a three-dimensional image formed by a point cloud scanned by a radar, and the like, which may be determined according to an actual application scenario, and this disclosure does not limit this.

The image information collected by the vehicle-mounted image collecting device can be acquired through the communication connection established between the vehicle-mounted image collecting device and the vehicle-mounted image collecting device. In one example, the vehicle-mounted image capturing device may transmit the captured image information to the vehicle-mounted controller or the remote server through the bus or the wireless communication channel in real time, and the vehicle-mounted controller or the remote server may receive the real-time image information through the bus or the wireless communication channel.

In step S13, determining a driving state of a driver of the vehicle based on the driving state information of the vehicle and the image information of the driving area;

the driving state of the driver may be a state of the driver while driving the vehicle, and the driving state may be classified into a normal driving state and a dangerous driving state. The dangerous driving state may be a state in which a driver has a preset irregular driving behavior, for example, a state in which a call is made, a cell phone is seen, a hand is separated from a steering wheel, fatigue driving, and the like.

In the embodiment of the present disclosure, it is considered that in the case where there is irregular driving behavior of the driver, there is a tendency that a change in the running state of the vehicle is accompanied, for example, when the driver is in a dangerous driving state such as distraction, fatigue driving, or the like, there is a tendency that an abnormality such as an abnormal running state such as unstable vehicle speed, continuous pressing of a line, or the like occurs in the running state of the vehicle. Therefore, in the embodiment of the disclosure, the determination of the driving state of the driver can improve the reliability of the determined driving state of the driver according to the image information of the driving area and the driving state information of the vehicle.

As described above, the image information of the driving area is the image information of the area where the driver is located inside the vehicle cabin, and then the image information of the driving area is analyzed and processed by the image processing technology, so that the characteristics of the driver, such as the body characteristics, the facial characteristics, and the like, can be detected, and the behavior of the driver can be analyzed based on the detected characteristics of the driver, so as to determine whether the driver has the behaviors of making a call, drinking water, separating hands from the steering wheel, closing eyes, and the like.

In the case where the running state of the vehicle has been abnormal, the probability that the driver is in the preset dangerous driving state is high. Thus, the driving state of the driver can be determined by combining the driving state information of the vehicle with the image information of the driving area. For example, by analyzing the image information of the driving area, it is determined that the confidence level of the driver in the fatigue driving state is 0.5, and at this time, the driving state of the vehicle with continuous pressing lines occurs, so that the confidence level of the driver in the fatigue driving state can be raised to 0.8; for another example, the image information of the driving area is analyzed to determine that the driver is in a light fatigue driving state, and at the time, the vehicle has a driving state of continuously pressing a line, so that the light fatigue driving state can be upgraded to a medium fatigue driving state or a heavy fatigue driving state.

In step S14, in response to detection that the driver of the vehicle is in a preset dangerous driving state, smart driving control is performed.

The preset dangerous driving state is a preset driving state with driving safety hidden danger, and can comprise one or more predefined driver states, such as a moderate fatigue driving state, a severe distraction driving state and the like. The preset dangerous driving state may also be a state defined by a preset driver behavior in combination with a preset vehicle state, for example, a state when the driver makes a call and the vehicle presses a line twice or more in succession is defined as a preset dangerous driving state. The intelligent driving control can be performed according to a preset dangerous driving state, wherein the intelligent driving control can be driving intervention by a vehicle-mounted control center or a remote control end according to the road condition of the vehicle, and the specific intelligent driving control mode can be measures such as sending out alarm information, controlling the starting of an auxiliary driving/automatic driving function, controlling the deceleration of the vehicle and the like. The following detailed description will be specifically described in conjunction with possible implementations of the present disclosure, and will not be repeated herein.

In some optional implementation manners, the danger level can be determined according to the dangerous driving state of the driver and the driving state of the vehicle, and then the corresponding intelligent driving control manner can be selected according to the danger level. For example, when a driver is in a dangerous driving state such as making a call, watching a mobile phone, and separating a hand from a steering wheel, and the continuous line pressing time of the vehicle does not exceed a preset time (for example, 5 seconds), intelligent driving control is performed by sending out alarm information; when a driver is in dangerous driving states such as calling, watching a mobile phone, separating hands from a steering wheel and the like and the vehicle is in a state of overspeed of 50%, the danger is higher than that of the state that the continuous line pressing time of the vehicle does not exceed the preset time (for example, 5 seconds), intelligent driving control can be performed by adopting a mode of controlling the vehicle to decelerate, and therefore flexible safe driving control is achieved.

In some possible implementation manners, the image information of the driving area may be image information in the cabin, and the driving state information of the vehicle may be information determined according to sensing data outside the cabin, that is, in the present disclosure, the driving state of the driver may be determined by combining the information in the cabin and the information outside the cabin, so that the accuracy of the determined driving state of the driver is improved, and the driving state of the driver is determined by combining the information in the cabin and the information outside the cabin, so as to perform intelligent driving control on the vehicle, thereby improving the driving safety of the vehicle.

In one possible implementation, determining a driving state of a driver of the vehicle based on the driving state information of the vehicle and the image information of the driving area includes: detecting the behavior of the driver of the vehicle according to the image information of the driving area; and determining the driving state of the driver according to the driving state information of the vehicle and the behavior detection result of the driver.

In the implementation mode, the behavior of the driver of the vehicle is detected according to the image information of the driving area to obtain the behavior detection result, and then the driving state of the driver is determined by combining the driving state information of the vehicle and the behavior detection result, so that the accuracy of obtaining the driving state of the driver is improved.

The image information of the driving area can be analyzed through an image processing technology so as to detect the behavior of the driver, and a behavior detection result is obtained. In the embodiment of the present disclosure, the preset dangerous driving state may be that the driver has a preset irregular driving behavior, and then, the behavior detection is performed on the driver of the vehicle according to the image information of the driving area, which may be the detection of the irregular driving behavior of the driver. For example, in a case where it is detected that the driver holds a phone in his hand and the phone is near his ear, it may be determined that the behavior detection result is that the driver has a behavior of opening a car and making a phone call; the behavior detection result can be determined as the behavior that the hands of the driver are separated from the steering wheel under the condition that the duration that the hands of the driver are not on the steering wheel is detected to reach the first duration; the behavior detection result can be determined to be the behavior of the driver with slight fatigue driving under the condition that the duration of the eye closing of the driver reaches the second duration; the behavior detection result may be determined to be that the driver has behavior of moderate fatigue driving in a case where it is detected that the period in which the driver closes his eyes reaches the third period.

In some optional implementations, in a case where the detection result is that the unsafe driving behavior of the driver is not detected, if the driving state information of the vehicle indicates that the driving state of the vehicle is also a normal state, it may be determined that the driving state of the driver is a normal driving state.

In some optional implementations, in a case where the detection result is that unsafe driving behavior of the driver is detected, if the driving state information of the vehicle indicates that the driving state of the vehicle is a normal state, the driving state of the driver may be determined according to the detected irregular driving behavior. For example, in a case where the behavior detection result is within a section corresponding to the eye-closing frequency or the number of continuous eye-closures of the driver for light fatigue driving, if the traveling state of the vehicle is a normal state, the driving state of the driver is still determined to be light fatigue driving.

In some optional implementation manners, in a case that the detection result is that unsafe driving behavior of the driver is detected, if the driving state information of the vehicle indicates that the driving state of the vehicle is an abnormal state, the driving state of the driver may be obtained by further modifying the behavior detection result of the driver according to the driving state information of the vehicle, so as to improve reliability of the obtained behavior detection result. For example, since the abnormal driving state of the vehicle is often caused by the irregular driving behavior of the driver, the confidence that the driver behavior belongs to the unsafe driving behavior in the behavior detection result may be improved to improve the reliability of the obtained dangerous driving state; in addition, since the vehicle has already occurred in the abnormal running state, the risk level of the detection result can be raised so as to take a higher level of response measures.

In one possible implementation, the detecting the behavior of the driver of the vehicle includes: determining a danger level corresponding to the behavior detection result of the driver; the determining the driving state of the driver according to the driving state information of the vehicle and the behavior detection result of the driver includes: in response to determining that the vehicle is in an abnormal driving state according to the driving state information of the vehicle, upgrading a risk level of the behavior detection result of the driver to a first risk level; in response to determining that the first risk level reaches a preset warning level, determining that the driving state of the driver is in a preset dangerous driving state.

The behavior detection result of the driver may correspond to a risk level, where the risk level represents a risk degree of a dangerous driving state of the driver, and for example, the behavior detection result may be determined as a behavior of the driver with mild fatigue driving when it is detected that a duration of eye closure of the driver reaches a second duration; the behavior detection result may be determined to be that the driver has behavior of moderate fatigue driving in a case where it is detected that the period in which the driver closes his eyes reaches the third period. Wherein the third duration is longer than the second duration, obviously, the longer the driver closes the eyes, the higher the danger degree is indicated, and the higher the probability of the vehicle accident is.

Since the vehicle has already occurred in the abnormal driving state, the risk level of the detection result may be upgraded to take a higher level of response measures, and for convenience of description, the upgraded risk level will be referred to as a first risk level. For the first danger level, it may be determined whether the first danger level reaches a preset alarm level, and if so, an alarm may be performed or other intelligent control operations may be performed.

For example, for a behavior of light fatigue driving, the warning may not be performed, whereas for a behavior of heavy fatigue driving, the warning may be performed because it belongs to a preset dangerous driving state.

In the implementation manner, by determining the danger level corresponding to the behavior detection result of the driver, the danger level of the behavior detection result of the driver is upgraded to a first danger level when the vehicle is in the abnormal driving state, and the driving state of the driver is determined to be in the preset dangerous driving state when the first danger level reaches the preset warning level. Therefore, under the condition that the vehicle has an abnormal running state, the danger level of the detection result is improved, so that response measures can be timely and accurately taken, and the running safety of the vehicle is improved.

In one possible implementation, the determining the driving state of the driver according to the driving state information of the vehicle and the behavior detection result of the driver includes: and determining that the driver is detected to be in a preset dangerous driving state in response to determining that the driving state of the vehicle and the behavior state of the driver are abnormal states according to the driving state information of the vehicle and the behavior detection result of the driver.

In this implementation, considering that a traffic accident may be caused when the driving state of the vehicle is an abnormal state, if the behavior state of the driver is also an abnormal state at this time, it indicates that the driver is not driving safely, and there is a great potential safety hazard. Therefore, under the condition that the driving state of the vehicle and the behavior state of the driver are both abnormal states, the driver can be determined to be in the preset dangerous driving state, so that the intelligent driving control is performed in response to the fact that the driver of the vehicle is detected to be in the preset dangerous driving state, and the driving safety of the vehicle is improved.

In one possible implementation, the determining the driving state of the driver of the vehicle according to the driving state information of the vehicle and the image information of the driving area includes: in response to detecting that the vehicle is in an abnormal driving state according to the driving state information of the vehicle, performing behavior detection on the driver according to the image information of the driving area; determining that the driver is in a preset dangerous driving state in response to determining that the behavior detection result of the driver is driver distraction driving or fatigue driving.

The distracted driving of the driver can be the behavior that the driver is distracted by the fact that hands are separated from a steering wheel, the driver is driving to make a call, the driver is driving to drink water, the driver is driving to play a mobile phone and the like, and the fatigue driving can be the behavior that the driver is closed eyes and yawns for a long time.

In this implementation, considering that a traffic accident may be caused when the driving state of the vehicle is an abnormal state, if the behavior detection result of the driver is driver distraction or fatigue driving, it may be determined that the vehicle is in an unsafe state due to the driver distraction or fatigue driving, and therefore, when the driving state of the vehicle is an abnormal driving state and the behavior detection result of the driver is driver distraction or fatigue driving, it may be determined that the driver is in a preset dangerous driving state, so as to improve safety by timely intervention of intelligent driving control.

In one possible implementation, the performing intelligent driving control includes generating warning information based on at least one of: driving state information of the vehicle; detecting a behavior detection result of a driver of the vehicle based on the image information of the driving area; a driving state of the driver.

In this implementation, to achieve a better warning effect, the driver may be informed of at least one of the driving state information of the vehicle, the behavior detection result of the driver, and the driving state of the driver, so that the driver is made aware of the severity of the current dangerous driving state. For example: if the driving state information of the vehicle is "the vehicle has pressed a line three times continuously", and the detection result of the behavior of the driver of the vehicle based on the image information of the driving area is "the driver is making a call", the generated warning information may be: "you have pressed the line three times in succession, do not drive the car and make a call". The specific form of sending the warning information may be a form of voice or video, and the disclosure does not limit this.

In the implementation mode, the warning information is generated based on at least one of the driving state information of the vehicle, the behavior detection result of the driver and the driving state of the driver, so that the driver can know the severity of the unsafe state and the current dangerous driving state of the vehicle, and a good warning effect is achieved.

In one possible implementation, the performing intelligent driving control includes: and controlling the alarm equipment to continuously output alarm information until the vehicle running state and the driving state of the driver are both recovered to a normal state. The warning effect can be further improved by continuously outputting the warning information to continuously warn the driver, so that the continuous monitoring and warning of the state of the driver are realized, and the driving safety is further improved. The warning is stopped after the driving state of the vehicle and the driving state of the driver are both recovered to be normal, so that the interference of unnecessary warning to the driver can be reduced.

In one possible implementation, the performing intelligent driving control includes: and in response to the detection that the duration of the driver in the preset dangerous driving state exceeds the preset duration, controlling the auxiliary driving/automatic driving function to be started, and/or controlling the vehicle to decelerate and stop.

The duration that the driver is in the preset dangerous driving state exceeds the preset duration, which indicates that the driver cannot normally operate the vehicle for a long time, and this can seriously threaten the driving safety of the vehicle and bring great potential safety hazard.

The auxiliary driving/automatic driving function can control the accelerator, the brake and the direction to make the vehicle adapt to the changed traffic conditions according to the driving sensing data collected by the sensor of the vehicle, and realize the driving functions such as self-adaptive constant-speed cruising, keeping the vehicle running in a lane, controlling the following distance between the vehicle and the front vehicle and the like.

In the implementation mode, under the condition that the duration of the preset dangerous driving state of the driver exceeds the preset duration, the auxiliary driving/automatic driving function is controlled to be started, and/or the vehicle is controlled to be decelerated and stopped, so that the driving state of the vehicle can be controlled under the condition that the driver cannot normally control the vehicle for a long time, and the driving safety of the vehicle is improved.

In one possible implementation, the method further includes: acquiring environmental information around the vehicle;

the determining the driving state of the driver of the vehicle according to the driving state information of the vehicle and the image information of the driving area includes: and determining the driving state of the driver of the vehicle according to the environmental information around the vehicle, the driving state information of the vehicle and the image information of the driving area.

In an actual driving scenario, the driving state of the vehicle may be affected by the environment around the vehicle, for example, the speed of the vehicle may be caused by road congestion, and the yaw driving may be caused by the vehicle driving on a curved road, so that the driving state of the driver of the vehicle may be determined according to the environment information around the vehicle, the driving state information of the vehicle, and the image information of the driving area, in order to further improve the accuracy of the determined driving state of the driver of the vehicle.

The environmental information around the vehicle may include road condition information of a road on which the vehicle travels, the road condition information may represent a condition of the road on which the vehicle travels, and may include at least one of information indicating a degree of road congestion, information indicating a shape of the road, information indicating a class of the road, information indicating a speed limit condition of the road, and the like. According to the degree of congestion of the road, the road condition can be divided into a plurality of levels, for example, a plurality of levels such as very congested, crowded and unobstructed, the shape of the road can be divided into shapes such as curved lines and straight lines, and the type of the road can be divided into types such as urban roads, rural roads and expressways.

In one possible implementation, the acquiring environmental information around the vehicle includes: acquiring road condition information of a road on which the vehicle runs; the determining a driving state of a driver of the vehicle according to the environmental information around the vehicle, the driving state information of the vehicle, and the image information of the driving area includes: and determining that the driver is detected to be in a preset dangerous driving state in response to the condition that the driving state information of the vehicle and the road condition information do not meet a preset matching relation and indicating that the behavior state of the driver of the vehicle is an abnormal state based on the behavior detection result of the image information of the driving area to the driver of the vehicle.

The road condition information of the road may be obtained by a third party platform according to the current geographic position of the vehicle, for example, by a navigation service platform.

Whether the running state information of the vehicle and the road condition information meet the preset matching relationship or not can be determined according to the surrounding environment information of the vehicle and the running state information of the vehicle, and the vehicle is in the abnormal running state if the running state information of the vehicle and the road condition information do not meet the preset matching relationship.

The preset matching relationship refers to the matching relationship between the road condition information and the driving state information of the vehicle which safely drives under the road condition represented by the road condition information. For example, at least one of the following may be included: the matching relation between the smooth road condition and the uniform speed running of the vehicle; the matching relation between the straight road condition and the left-right swinging amplitude of the vehicle does not exceed a set amplitude threshold value; the matching relation between the road speed limit information and the driving in the speed limit range.

Correspondingly, the running state information of the vehicle and the road condition information do not satisfy the preset matching relationship, and for example, the method may include: the left-right swing amplitude of the vehicle on a straight road section reaches a set amplitude threshold value, the speed of the vehicle on the road section with smooth road is high or low, the vehicle is overspeed, and the like, which indicate that the vehicle is in an abnormal driving state.

Therefore, when the vehicle is in the abnormal driving state and the behavior state of the driver is the abnormal state, the driver can be determined and detected to be in the preset dangerous driving state, the accuracy of the determined driving state of the driver is improved, and the driving safety of the vehicle is further improved.

In an optional implementation manner, the determining the driving state of the driver of the vehicle according to the driving state information of the vehicle and the image information of the driving area includes: and inputting the acquired driving state information of the vehicle and the image information of the driving area into a trained neural network, and determining the driving state of the driver of the vehicle through the neural network. In this implementation, the driving state of the vehicle driver is determined by the neural network, and the accuracy and speed of determining the driving state can be improved. And the neural network can be trained and deployed in advance, and can quickly realize the detection of images or video streams with larger data volume, so that the neural network can be applied to the intelligent driving control of a real-time driving scene.

An application scenario of the embodiment of the present disclosure is explained below. In the application scene, the image information of the driving area can be acquired through the DMS camera, the processor can acquire the image information of the driving area from the DMS camera, and the behavior detection is carried out on the driver of the vehicle based on the image information of the driving area to obtain a behavior detection result; in addition, the ADAS camera is used for collecting image information outside the vehicle cabin, and the processor can acquire the image information outside the vehicle cabin from the ADAS camera to determine the driving state information of the vehicle. If the determined running state information of the vehicle is 'three continuous vehicle line pressing', and the behavior detection result of the driver of the vehicle is 'the driver is making a call', generating voice alarm information: 'you have pressed the line three times continuously, do not take the car to make a call', and carry out voice broadcast.

It is understood that the above-mentioned method embodiments of the present disclosure can be combined with each other to form a combined embodiment without departing from the logic of the principle, which is limited by the space, and the detailed description of the present disclosure is omitted. Those skilled in the art will appreciate that in the above methods of the specific embodiments, the specific order of execution of the steps should be determined by their function and possibly their inherent logic.

In addition, the present disclosure also provides an intelligent driving control device, an electronic device, a computer-readable storage medium, and a program, which can be used to implement any one of the intelligent driving control methods provided by the present disclosure, and the corresponding technical solutions and descriptions and corresponding descriptions in the methods section are not repeated.

Fig. 2 shows a block diagram of an intelligent driving control apparatus according to an embodiment of the present disclosure, which includes, as shown in fig. 2:

a first obtaining module 21, configured to obtain driving state information of a vehicle;

a second obtaining module 22, configured to obtain image information of a driving area of the vehicle;

a driving state determining module 23, configured to determine a driving state of a driver of the vehicle according to the driving state information of the vehicle and the image information of the driving area;

and the control module 24 is used for responding to the detection that the driver of the vehicle is in a preset dangerous driving state and carrying out intelligent driving control.

In one possible implementation, the driving state determination module 23 includes a first driving state determination submodule and a second driving state determination submodule, wherein:

the second driving state determination submodule configured to:

In one possible implementation, the driving state determination module 23 includes a third driving state determination submodule and a fourth driving state determination submodule, wherein:

In one possible implementation, the control module 24 is configured to generate the alarm information based on at least one of:

driving state information of the vehicle;

a driving state of the driver.

In a possible implementation manner, the control module 24 is configured to control the warning device to continuously output the warning information until the vehicle driving state and the driving state of the driver are both recovered to the normal state.

In one possible implementation, the control module 24 is configured to control the start of the driving assistance/automatic driving function and/or the deceleration and stop of the vehicle in response to detecting that the duration of the driver in the preset dangerous driving state exceeds a preset duration.

In one possible implementation, the apparatus further includes:

the driving state determining module 23 is configured to determine a driving state of a driver of the vehicle according to the environmental information around the vehicle, the driving state information of the vehicle, and the image information of the driving area.

the driving state determining module 23 is configured to determine that the driver is detected to be in a preset dangerous driving state in response to that the driving state information of the vehicle and the road condition information do not satisfy a preset matching relationship, and that a behavior detection result of the driver of the vehicle based on the image information of the driving area indicates that the behavior state of the driver is an abnormal state.

In one possible implementation, the first obtaining module 21 includes a sensing data obtaining sub-module and a driving state information determining sub-module, wherein:

In a possible implementation manner, the driving state determining module 23 is configured to input the acquired driving state information of the vehicle and the image information of the driving area into a trained neural network, and determine the driving state of the driver of the vehicle through the neural network.

In some embodiments, functions of or modules included in the apparatus provided in the embodiments of the present disclosure may be used to execute the method described in the above method embodiments, and specific implementation thereof may refer to the description of the above method embodiments, and for brevity, will not be described again here.

An embodiment of the present disclosure also provides a vehicle, including:

The vehicle of the embodiment determines the driving state of the driver of the vehicle by combining the driving state of the vehicle and the image information of the driving area, so that the accuracy of determining the driving state of the driver is improved.

In some possible implementations, the vehicle further includes a second sensor for collecting driving sensing data; the controller is used for acquiring the driving state information of the vehicle according to the driving sensing data. Thereby achieving accurate detection of the driving state through the sensor for sensing different data of the vehicle.

Embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the above-mentioned method. The computer readable storage medium may be a non-volatile computer readable storage medium.

An embodiment of the present disclosure further provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the above-described method.

The disclosed embodiments also provide a computer program product comprising computer readable code, when the computer readable code runs on a device, a processor in the device executes instructions for implementing the intelligent driving control method provided in any one of the above embodiments.

The disclosed embodiments also provide another computer program product for storing computer readable instructions, which when executed, cause a computer to perform the operations of the intelligent driving control method provided in any of the above embodiments.

The electronic device may be provided as a terminal, server, or other form of device.

Fig. 3 illustrates a block diagram of an electronic device 800 in accordance with an embodiment of the disclosure. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, or the like terminal.

Referring to fig. 3, electronic device 800 may include one or more of the following components: processing component 802, memory 804, power component 806, multimedia component 808, audio component 810, input/output (I/O) interface 812, sensor component 814, and communication component 816.

The processing component 802 generally controls overall operation of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.

The memory 804 is configured to store various types of data to support operations at the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.

The power supply component 806 provides power to the various components of the electronic device 800. The power components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 800.

The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the electronic device 800 is in an operation mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.

The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the electronic device 800. For example, the sensor assembly 814 may detect an open/closed state of the electronic device 800, the relative positioning of components, such as a display and keypad of the electronic device 800, the sensor assembly 814 may also detect a change in the position of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, orientation or acceleration/deceleration of the electronic device 800, and a change in the temperature of the electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a Complementary Metal Oxide Semiconductor (CMOS) or Charge Coupled Device (CCD) image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 may access a wireless network based on a communication standard, such as a wireless network (WiFi), a second generation mobile communication technology (2G) or a third generation mobile communication technology (3G), or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

In an exemplary embodiment, the electronic device 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.

In an exemplary embodiment, a non-transitory computer-readable storage medium, such as the memory 804, is also provided that includes computer program instructions executable by the processor 820 of the electronic device 800 to perform the above-described methods.

Fig. 4 shows a block diagram of an electronic device 1900 according to an embodiment of the disclosure. For example, the electronic device 1900 may be provided as a server. Referring to fig. 4, electronic device 1900 includes a processing component 1922 further including one or more processors and memory resources, represented by memory 1932, for storing instructions, e.g., applications, executable by processing component 1922. The application programs stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the above-described method.

The electronic device 1900 may also include a power component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input/output (I/O) interface 1958. The electronic device 1900 may operate based on an operating system, such as the Microsoft Server operating system (Windows Server), stored in the memory 1932^TM) From apple IncOperating system based on graphical user interface (Mac OS X)^TM) Multi-user, multi-process computer operating system (Unix)^TM) Free and open native code Unix-like operating System (Linux)^TM) Open native code Unix-like operating System (FreeBSD)^TM) Or the like.

In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 1932, is also provided that includes computer program instructions executable by the processing component 1922 of the electronic device 1900 to perform the above-described methods.

The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.

The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.

The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.

The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).

Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, 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/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The computer program product may be embodied in hardware, software or a combination thereof. In an alternative embodiment, the computer program product is embodied in a computer storage medium, and in another alternative embodiment, the computer program product is embodied in a Software product, such as a Software Development Kit (SDK), or the like.

Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. 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

1. An intelligent driving control method, comprising:

acquiring running state information of a vehicle;

acquiring image information of a driving area of the vehicle;

2. The method of claim 1, wherein determining the driving status of the driver of the vehicle based on the driving status information of the vehicle and the image information of the driving area comprises:

3. The method of claim 2, wherein the detecting a behavior of the driver of the vehicle comprises:

4. The method according to claim 2, wherein the determining the driving state of the driver based on the driving state information of the vehicle and the behavior detection result of the driver includes:

5. The method according to any one of claims 2 to 4, wherein the determining the driving state of the driver of the vehicle based on the driving state information of the vehicle and the image information of the driving area comprises:

6. The method according to any one of claims 1-5, characterized in that the abnormal driving state of the vehicle comprises at least one of the following:

7. The method according to any one of claims 1-6, wherein said performing intelligent driving control comprises generating warning information based on at least one of:

driving state information of the vehicle;

a driving state of the driver.

8. The method according to any one of claims 1-7, wherein said performing intelligent driving control comprises:

9. The method of any of claims 1-8, wherein said performing intelligent driving control comprises:

10. The method according to any one of claims 1-9, further comprising:

acquiring environmental information around the vehicle;

11. The method of claim 10, wherein the environmental information comprises traffic information;

12. The method according to any one of claims 1 to 11, wherein the acquiring of the running state information of the vehicle includes:

13. The method according to any one of claims 1 to 12, wherein the determining the driving state of the driver of the vehicle based on the driving state information of the vehicle and the image information of the driving area comprises:

14. An intelligent driving control device, comprising:

15. A vehicle, characterized by comprising:

16. The vehicle of claim 15, further comprising:

the second sensor is used for collecting driving sensing data;

17. An electronic device, comprising:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to invoke the memory-stored instructions to perform the method of any one of claims 1 to 13.

18. A computer readable storage medium having computer program instructions stored thereon, which when executed by a processor implement the method of any one of claims 1 to 13.