Vehicle control method, control system and vehicle

Abstract

The invention provides a vehicle control method and system and a vehicle. The control method comprises the steps that in the vehicle driving process of a driver, state information of the driver is detected through a first set of sensors; detecting road condition information outside the vehicle through a second group of sensors; multi-sensor data fusion analysis is conducted on the basis of the state information of the driver and the road condition information outside the vehicle to determine whether protection control over the vehicle is activated or not, and the multi-sensor data fusion analysis conducted on the basis of the state information of the driver and the road condition information outside the vehicle comprises the steps that whether a second set of sensors detect lane lines or not is judged; the condition of the driver is determined based on the state information of the driver, and the vehicle driving condition is determined based on the outside road condition information. According to the invention, the accuracy of detecting the unforeseen circumstances of the driver in the vehicle driving process of the driver can be improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a vehicle control method, a control system and a vehicle. Background Art

[0002] If a bus driver suffers from a serious accident such as a heart attack, stroke, or epilepsy, he or she may suddenly lose consciousness without any warning, and his or her hands may not be able to hold the steering wheel or the pedals, causing the vehicle to be out of control and possibly deviate from the normal driving lane and crash into obstacles such as pedestrians, buildings, or other vehicles on the roadside, thus causing a serious traffic accident. Therefore, how to effectively identify the driver's unexpected situation is an urgent problem to be solved. Summary of the invention

[0003] The purpose of the embodiments of the present application is to provide a vehicle control method, a control system and a vehicle, which can improve the accuracy of detecting unexpected situations of the driver during the driver's driving of the vehicle.

[0004] One aspect of an embodiment of the present application provides a vehicle control method. The control method includes: in the process of the driver driving the vehicle, detecting the driver's state information through a first group of sensors; detecting the road condition information outside the vehicle through a second group of sensors; performing multi-sensor data fusion analysis based on the driver's state information and the road condition information outside the vehicle to determine whether to activate the protection control of the vehicle, wherein the multi-sensor data fusion analysis based on the driver's state information and the road condition information outside the vehicle includes: whether the second group of sensors detects the lane line, the driver's condition determined based on the driver's state information, and the vehicle driving condition determined based on the road condition information outside the vehicle.

[0005] Furthermore, the first group of sensors includes a DMS camera and an in-vehicle millimeter-wave radar, and detecting the driver's status information through the first group of sensors includes: detecting the driver's facial information through the DMS camera; detecting the driver's vital signs through the in-vehicle millimeter-wave radar,

[0006] The second group of sensors includes an ADAS camera and an external millimeter-wave radar. The detecting of external road condition information by the second group of sensors includes: detecting external road condition information by combining the ADAS camera and the external millimeter-wave radar.

[0007] Furthermore, the multi-sensor data fusion analysis is performed based on the driver's status information and the external road condition information to determine whether to activate protection control of the vehicle, including: when the second group of sensors detects lane lines, when the first group of sensors detects that the driver is abnormal, but the second group of sensors does not detect any abnormality in the driving of the vehicle, then the protection control of the vehicle is not activated; when the second group of sensors does not detect lane lines, when the first group of sensors detects that the driver is abnormal, but the second group of sensors does not detect any abnormality in the driving of the vehicle, then the first group of sensors activates protection control of the vehicle.

[0008] Further, the first group of sensors activates protection control of the vehicle including: applying a first braking deceleration to the vehicle.

[0009] Furthermore, the multi-sensor data fusion analysis based on the driver's status information and the external road condition information to determine whether to activate protection control of the vehicle also includes: when the first group of sensors does not detect any abnormality in the driver, but the second group of sensors detects any abnormality in the vehicle's driving, the second group of sensors activates protection control of the vehicle.

[0010] Furthermore, the protection control of the vehicle activated by the second group of sensors includes: when the second group of sensors detects an obstacle but has not yet reached a collision probability threshold, an early warning prompt is given to the vehicle; when the second group of sensors detects an obstacle and reaches the collision probability threshold, a second braking deceleration is applied to the vehicle.

[0011] Furthermore, the multi-sensor data fusion analysis based on the driver's status information and the external road condition information to determine whether to activate protection control of the vehicle also includes: when the first group of sensors detects that the driver is abnormal, and the second group of sensors detects that the vehicle is abnormal, then the first group of sensors and the second group of sensors both activate protection control of the vehicle.

[0012] Furthermore, the protection control of the vehicle activated by both the first group of sensors and the second group of sensors includes: superimposing a first braking deceleration taken on the vehicle when the first group of sensors activates protection and a second braking deceleration taken on the vehicle when the second group of sensors activates protection to perform braking control on the vehicle.

[0013] Furthermore, the control method also includes: using data collected by the forward-looking camera in the ADAS camera to perform machine learning on high-risk sections on the vehicle's route to identify the high-risk sections; when it is detected that the vehicle is traveling on the high-risk section, actively limiting the speed of the vehicle.

[0014] Furthermore, the control method also includes: pre-restricting the area of ​​the vehicle through the GPS electronic fence; when it is detected that the vehicle travels to the area where the GPS electronic fence is located, performing corresponding graded protection on the vehicle.

[0015] Another aspect of the embodiments of the present application provides a vehicle control system, which includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the vehicle control method described above.

[0016] Another aspect of the embodiments of the present application provides a vehicle, wherein the vehicle includes the vehicle control system as described above.

[0017] The vehicle control method, control system and vehicle of one or more embodiments of the present application combine the driver's status information detected by the first group of sensors and the external road condition information detected by the second group of sensors, and perform fusion analysis on these sensor data, and then determine whether to activate protection for the vehicle based on the results of the fusion analysis. In this way, it is possible to accurately judge whether the driver is actually in an unexpected situation, reduce the false alarm rate of the sensor, improve the accuracy of detecting the driver's unexpected situation during driving the vehicle, reduce the probability of accidents, and thus improve driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of a vehicle control method according to an embodiment of the present application.

[0019] Figure 2 A step diagram of an embodiment of the present application for performing multi-sensor data fusion analysis based on driver status information and external road condition information to determine whether to activate protection control of the vehicle.

[0020] Figure 3 A schematic block diagram of a vehicle control system according to an embodiment of the present application. DETAILED DESCRIPTION

[0021] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are merely examples of devices consistent with some aspects of the present application as detailed in the appended claims.

[0022] The vehicle control method, control system and vehicle of each embodiment of the present application are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the features of the following embodiments and implementations can be combined with each other.

[0023] Figure 1 A flow chart of a vehicle control method according to an embodiment of the present application is disclosed. Figure 1 As shown, a vehicle control method according to an embodiment of the present application may include steps S1 to S3.

[0024] In step S1 , while the driver is driving the vehicle, the driver's status information may be detected by a first group of sensors.

[0025] In some embodiments, the first group of sensors may include, for example, a DMS (Driver Monitoring System) camera and an in-vehicle millimeter-wave radar. Therefore, detecting the driver's status information through the first group of sensors may include: detecting the driver's facial information through the DMS camera; and detecting the driver's vital signs through the in-vehicle millimeter-wave radar.

[0026] The DMS camera can identify abnormal driving conditions such as the driver closing his eyes or the driver leaving the camera, and confirm the driver's abnormal state.

[0027] The millimeter-wave radar inside the car can identify the driver's breathing, heartbeat, etc. to confirm the driver's abnormal state.

[0028] In step S2, the road condition information outside the vehicle may be detected by a second group of sensors.

[0029] In some embodiments, the second group of sensors may include, for example, an ADAS (Advanced Driving Assistance System) camera and an external millimeter-wave radar. Therefore, detecting the external road condition information through the second group of sensors may include: combining the ADAS camera and the external millimeter-wave radar to detect the external road condition information.

[0030] ADAS cameras mainly provide visual information, such as lane lines, vehicles ahead, pedestrians and other obstacles, as well as traffic signs and signal lights, while external millimeter-wave radars provide more accurate physical information such as distance, speed, angle and target classification. Combining ADAS cameras and external millimeter-wave radars can more accurately identify external road conditions, provide drivers with more comprehensive and accurate driving assistance information, and improve driving safety and comfort.

[0031] In step S3, a multi-sensor data fusion analysis is performed based on the driver's state information and the road condition information outside the vehicle to determine whether to activate the protection control of the vehicle. The multi-sensor data fusion analysis based on the driver's state information and the road condition information outside the vehicle includes: whether the second group of sensors detects the lane line, the driver's condition determined based on the driver's state information, and the vehicle driving condition determined based on the road condition information outside the vehicle.

[0032] Figure 2 The present invention discloses a step diagram of performing multi-sensor data fusion analysis based on the driver's status information and the road condition information outside the vehicle to determine whether to activate the protection control of the vehicle. Figure 2 As shown, in some embodiments, step S3 may further include steps S301 to S311.

[0033] In step S301, it is determined whether the second group of sensors detects a lane line. If the result of the determination is "yes", the process proceeds to step S302. Otherwise, the process proceeds to step S303.

[0034] In step S302, when the second group of sensors detects lane lines, it continues to determine whether there are any abnormalities in the driver and the vehicle's driving, and proceeds to steps S304 to 307 according to whether the driver and the vehicle are abnormal. When the driver is abnormal and the vehicle is driving normally, the process proceeds to step S304; when the driver is normal and the vehicle is driving abnormal, the process proceeds to step S305; when the driver is abnormal and the vehicle is driving abnormal, the process proceeds to step S306; when the driver is normal and the vehicle is driving normally, the process proceeds to step S307.

[0035] In step S304, when the second group of sensors detects a lane line, when the first group of sensors detects that the driver is abnormal, but the second group of sensors does not detect that the vehicle is driving abnormal, the protection control of the vehicle is not activated.

[0036] When lane lines are detected, when the first group of sensors detects that there is an abnormality in the driver, but the second group of sensors does not detect any abnormality in the vehicle's driving, it means that the first group of sensors may have misdetected something. Therefore, in this case, the request from the first group of sensors to activate vehicle protection is not responded to, that is, the protection control of the vehicle is not activated.

[0037] For example, the DMS camera captures the driver's facial information. When it identifies that the driver has closed his eyes for more than a predetermined period of time or that the driver's facial information cannot be captured for a predetermined period of time, it can be considered that the driver is in an abnormal condition; and / or, the millimeter-wave radar in the vehicle detects the driver's physical condition. When it identifies that the driver has experienced unexpected conditions such as sudden respiratory or cardiac arrest, it can be considered that the driver is in an abnormal condition.

[0038] For example, when the ADAS camera and the external millimeter-wave radar identify that the vehicle has not deviated from the lane and there are no obstacles ahead, it can be considered that the vehicle is driving normally in the lane.

[0039] In step S305, when the first group of sensors does not detect any abnormality in the driver, but the second group of sensors detects any abnormality in the driving of the vehicle, the second group of sensors activates protection control of the vehicle.

[0040] When the ADAS camera and the millimeter-wave radar outside the vehicle detect that the vehicle has drifted from the lane without turning on the turn signal, the ADAS system will prompt the lane deviation. At this time, it can be considered that there is an abnormality in the vehicle's driving, and the protection control of the vehicle is activated, and the collision mitigation system in the ADAS system enters the standby state.

[0041] When the ADAS camera and the millimeter-wave radar outside the vehicle detect that the vehicle has deviated from the lane and turned, the ADAS system does not prompt the lane deviation. At this time, it can be considered that there is no abnormality in the vehicle's driving for the time being. The collision mitigation system in the ADAS system enters or exits the standby state based on whether there is a collision risk.

[0042] In step S306, when the first group of sensors detects that the driver is abnormal and the second group of sensors detects that the vehicle is driving abnormal, both the first group of sensors and the second group of sensors activate protection control of the vehicle.

[0043] In step S307, when the first group of sensors does not detect any abnormality in the driver and the second group of sensors does not detect any abnormality in the vehicle driving, no processing is performed and the vehicle control authority is still left to the driver.

[0044] In step S303, if the second group of sensors does not detect any lane line, it continues to determine whether the driver and the vehicle are abnormal, and proceeds to step S308 to step S311 according to whether the driver and the vehicle are abnormal. When the driver is abnormal and the vehicle is running normally, the process proceeds to step S308; when the driver is normal and the vehicle is abnormal, the process proceeds to step S309; ​​when the driver is abnormal and the vehicle is also abnormal, the process proceeds to step S310; when the driver is normal and the vehicle is also running normally, the process proceeds to step S311.

[0045] In step S308, when the second group of sensors does not detect any lane lines, when the first group of sensors detects that the driver is abnormal, but the second group of sensors does not detect that the vehicle is driving abnormal, the first group of sensors activates protection control of the vehicle.

[0046] No lane markings are detected, including roads with no lane markings or roads where the lane markings cannot be identified.

[0047] When no lane line is detected, when the first group of sensors detects that the driver is abnormal, although the second group of sensors does not detect that the vehicle is driving abnormally, at this time, the first group of sensors still responds to activate the protection request for the vehicle.

[0048] In step S309, when the first group of sensors does not detect any abnormality in the driver, but the second group of sensors detects any abnormality in the driving of the vehicle, the second group of sensors activates protection control of the vehicle.

[0049] In step S310, when the first group of sensors detects that the driver is abnormal and the second group of sensors detects that the vehicle is abnormal, both the first group of sensors and the second group of sensors activate protection control of the vehicle.

[0050] For example, when no lane line can be detected and the driver detects an abnormality, and the vehicle deviates without turning, it is considered that there is an abnormality in the vehicle's driving, and the protection control of the vehicle is activated, and the first braking deceleration and the second braking deceleration are superimposed to stop the vehicle for protection.

[0051] In step S311, when the first group of sensors does not detect any abnormality in the driver and the second group of sensors does not detect any abnormality in the vehicle driving, no processing is performed and the vehicle control authority is still left to the driver.

[0052] In some embodiments, the first set of sensors activating protective control of the vehicle includes: applying a first braking deceleration to the vehicle.

[0053] In some embodiments, the second group of sensors activates protective control of the vehicle, including: when the second group of sensors detects an obstacle but has not yet reached a collision probability threshold, a warning prompt is given to the vehicle; and when the second group of sensors detects an obstacle and reaches a collision probability threshold, a second braking deceleration is applied to the vehicle.

[0054] In some embodiments, when both the first group of sensors and the second group of sensors activate protection control of the vehicle, the first braking deceleration taken on the vehicle when the first group of sensors alone activates protection and the second braking deceleration taken on the vehicle when the second group of sensors alone activates protection can be superimposed, and the vehicle can be braked using the superimposed braking deceleration.

[0055] After the instruction to activate vehicle protection is issued, the EBS (Electronic Brake System) receives the corresponding deceleration request and decelerates the vehicle by setting the target braking deceleration until the vehicle stops.

[0056] In some embodiments, the vehicle control method of the present application may further include step S41.

[0057] In step S41, after the instruction to activate vehicle protection is issued, the gear position is controlled to return from the driving gear to the neutral gear, and the output of the driving torque is prohibited.

[0058] In some embodiments, the vehicle control method of the present application may further include step S42.

[0059] In step S42, after the vehicle decelerates to a speed less than 5 km / h (kilometers per hour), the vehicle enters the lower high-voltage process. At the same time, the corresponding fault code can be prompted on the vehicle's instrument main interface to facilitate the vehicle to query the source of the fault.

[0060] In some embodiments, the vehicle control method of the present application may further include one or more steps from step S43 to step S46.

[0061] In step S43, after the command to activate vehicle protection is issued, the hazard lights and brake lights can be turned on. At the same time, when the vehicle speed is less than 5 km / h, the passenger door is automatically opened and the driver's fence door lock is unlocked to facilitate the rapid evacuation of passengers and rescue of the driver.

[0062] In step S44, after the instruction to activate vehicle protection is issued, an active prompt may be given on the main interface of the vehicle instrument, for example, a message such as "Vehicle protection is being activated, please pay attention, driver" may be displayed on the main interface of the instrument.

[0063] In step S45, after the instruction to activate vehicle protection is issued, an SOS rescue signal can be prompted through the road sign information outside the vehicle, so that people outside the vehicle can detect abnormalities and provide rescue in time.

[0064] In step S46, after the instruction to activate vehicle protection is issued, an SOS rescue signal can be sent to the dispatch background through the cloud system, so that the dispatch background personnel can detect the abnormality and respond to rescue measures in time.

[0065] When the driving route of the vehicle is relatively fixed, for example, for a bus, since the bus routes are usually relatively fixed, in some embodiments, the vehicle control method of the present application may further include step S51 and step S52.

[0066] In step S51, the data collected by the forward-looking camera in the ADAS camera can be used to perform machine learning on high-risk sections on the vehicle's route. By learning about specific road surfaces and route scenes, high-risk sections such as lakes, gullies, obstacles, etc. on specific roads can be identified to complete the hazard identification of high-risk sections.

[0067] In step S52, when it is detected that the vehicle is traveling on a high-risk road section, the speed of the vehicle can be actively limited, thereby reducing driving risks and improving passenger safety.

[0068] In some embodiments, the vehicle control method of the present application may further include step S61 and step S62.

[0069] In step S61, the vehicle can be restricted in advance through the GPS (Global Positioning System) electronic fence, and the electronic fence restrictions can be graded for high-risk sections. For example, a GPS electronic fence first-level alarm area, a GPS electronic fence second-level alarm area, a GPS electronic fence third-level alarm area, etc. can be set.

[0070] In step S62, when it is detected that the vehicle has traveled to the area where the GPS electronic fence is located, the vehicle is subjected to corresponding graded protection.

[0071] For example, when the vehicle drives into the first-level alarm area of ​​the GPS electronic fence, the whole vehicle is controlled to only alarm without any active intervention; when the vehicle drives into the second-level alarm area of ​​the GPS electronic fence, the whole vehicle is controlled to limit the speed; when the vehicle drives into the third-level alarm area of ​​the GPS electronic fence, the whole vehicle is controlled to brake and stop.

[0072] Considering that the status information of the driver detected by the first group of sensors may not truly reflect the actual situation due to factors such as the sensor being affected by lighting conditions, obstructions from objects, installation locations, and the driver's driving habits and actions, there may be a false recognition rate of the sensor, which may result in the inability to accurately control the identified dangerous working conditions. Therefore, the vehicle control method of the present application combines the status information of the driver detected by the first group of sensors and the road condition information outside the vehicle detected by the second group of sensors, and performs a fusion analysis on these sensor data, and determines whether to activate the protection of the vehicle based on the results of the fusion analysis, thereby accurately judging whether the driver is in a real unexpected situation, reducing the false alarm rate of the sensor, and improving the accuracy of detecting unexpected situations of the driver during driving the vehicle, thereby reducing the probability of accidents and improving driving safety.

[0073] The present application also provides a vehicle control system. Figure 3 A schematic block diagram of a vehicle control system 100 according to an embodiment of the present application is disclosed. Figure 3 As shown, a vehicle control system 100 of an embodiment of the present application includes a processor 101, an internal bus 102, a network interface 103, a memory 104, and a non-volatile memory 105, and may also include hardware required for other services. The processor 101 can read the corresponding computer program from the non-volatile memory 105 into the memory 104 and then run it to implement the steps of the vehicle control method as described above. Of course, in addition to the software implementation, the present application does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic components.

[0074] The vehicle control system 100 of the present application can have beneficial technical effects similar to those of the vehicle control method described above, so they will not be described in detail here.

[0075] The present application also provides a vehicle, which includes the vehicle control system 100 as described above.

[0076] The vehicle control method, control system and vehicle provided in the embodiments of the present application are introduced in detail above. Specific examples are used herein to illustrate the vehicle control method, control system and vehicle in the embodiments of the present application. The description of the above embodiments is only used to help understand the core idea of ​​the present application and is not intended to limit the present application. It should be pointed out that for ordinary technicians in this technical field, without departing from the spirit and principles of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications should also fall within the scope of protection of the claims attached to the present application.

Claims

1. A vehicle control method, characterized in that: include: When the driver is driving the vehicle, the driver's state information is detected by the first group of sensors; Detecting road condition information outside the vehicle through a second set of sensors; A multi-sensor data fusion analysis is performed based on the driver's status information and the road condition information outside the vehicle to determine whether to activate protection control of the vehicle, wherein the multi-sensor data fusion analysis based on the driver's status information and the road condition information outside the vehicle includes: whether the second group of sensors detects lane lines, the driver's condition determined based on the driver's status information, and the vehicle's driving condition determined based on the road condition information outside the vehicle.

2. The control method according to claim 1, characterized in that: The first group of sensors includes a DMS camera and an in-vehicle millimeter-wave radar, and the detecting of the driver's status information by the first group of sensors includes: Detecting the driver's facial information through the DMS camera; The driver's vital signs are detected by the in-vehicle millimeter-wave radar. The second group of sensors includes an ADAS camera and an external millimeter-wave radar, and detecting external road condition information through the second group of sensors includes: The ADAS camera and the external millimeter-wave radar are combined to detect external road condition information.

3. The control method according to claim 1 or 2, characterized in that: The performing multi-sensor data fusion analysis based on the driver's state information and the vehicle's external road condition information to determine whether to activate the protection control of the vehicle includes: In the case where the second group of sensors detects a lane line, when the first group of sensors detects that the driver is abnormal, but the second group of sensors does not detect that the vehicle is driving abnormally, the protection control of the vehicle is not activated; In the case where the second group of sensors does not detect any lane lines, when the first group of sensors detects that the driver is abnormal, but the second group of sensors does not detect that the vehicle is driving abnormal, the first group of sensors activates protection control of the vehicle.

4. The control method according to claim 3, characterized in that: The first group of sensors activates the protection control of the vehicle including: A first braking deceleration is applied to the vehicle.

5. The control method according to claim 1 or 2, characterized in that: The performing multi-sensor data fusion analysis based on the driver's state information and the vehicle's external road condition information to determine whether to activate the protection control of the vehicle also includes: When the first group of sensors does not detect any abnormality in the driver, but the second group of sensors detects any abnormality in the driving of the vehicle, the second group of sensors activates protection control of the vehicle.

6. The control method according to claim 5, characterized in that: The second group of sensors activates the protection control of the vehicle including: When the second group of sensors detects an obstacle but has not yet reached a collision probability threshold, an early warning prompt is given to the vehicle; When the second group of sensors detects an obstacle and reaches the collision probability threshold, a second braking deceleration is applied to the vehicle.

7. The control method according to claim 1 or 2, characterized in that: The performing multi-sensor data fusion analysis based on the driver's state information and the vehicle's external road condition information to determine whether to activate the protection control of the vehicle also includes: When the first group of sensors detects that the driver is abnormal, and the second group of sensors detects that the vehicle is abnormal in driving, both the first group of sensors and the second group of sensors activate protection control of the vehicle.

8. The control method according to claim 7, characterized in that: The first group of sensors and the second group of sensors both activate the protection control of the vehicle including: The first braking deceleration taken on the vehicle when the first group of sensors activates protection and the second braking deceleration taken on the vehicle when the second group of sensors activates protection are superimposed to control the braking of the vehicle.

9. The control method according to claim 2, characterized in that: Also includes: Using data collected by a forward-looking camera in an ADAS camera, machine learning is performed on high-risk sections on the vehicle's driving route to identify the high-risk sections; When it is detected that a vehicle is traveling on the high-risk road section, the speed of the vehicle is actively limited.

10. The control method according to claim 1, characterized in that: Also includes: Pre-restrict the area of ​​the vehicle through GPS electronic fence; When it is detected that a vehicle is traveling into the area where the GPS electronic fence is located, the vehicle is subjected to corresponding graded protection.

11. A vehicle control system, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the vehicle control method according to any one of claims 1 to 10.

12. A vehicle, characterized in that: Comprising the vehicle control system as claimed in claim 11.

Images