CN116279572B - Vehicle safety situation assessment and steady driving mode switching method and system - Google Patents

Vehicle safety situation assessment and steady driving mode switching method and system Download PDF

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Publication number
CN116279572B
CN116279572B CN202310081221.5A CN202310081221A CN116279572B CN 116279572 B CN116279572 B CN 116279572B CN 202310081221 A CN202310081221 A CN 202310081221A CN 116279572 B CN116279572 B CN 116279572B
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vehicle
driving
driver
information
instruction
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CN116279572A (en
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彭勇
向国梁
易深根
邓涵文
姚松
王兴华
汪馗
于天剑
伍贤辉
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Central South University
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/005—Handover processes
    • B60W60/0053—Handover processes from vehicle to occupant
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/105—Speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/107—Longitudinal acceleration
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/0098—Details of control systems ensuring comfort, safety or stability not otherwise provided for
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • B60W60/0015—Planning or execution of driving tasks specially adapted for safety
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/005—Handover processes
    • B60W60/0051—Handover processes from occupants to vehicle
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
    • B60W2040/0872—Driver physiology
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
    • B60W2040/089—Driver voice
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/08—Interaction between the driver and the control system
    • B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
    • B60W2050/143—Alarm means
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • B60W2520/105—Longitudinal acceleration
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2540/00—Input parameters relating to occupants
    • B60W2540/21—Voice
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2540/00—Input parameters relating to occupants
    • B60W2540/221—Physiology, e.g. weight, heartbeat, health or special needs
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2540/00—Input parameters relating to occupants
    • B60W2540/223—Posture, e.g. hand, foot, or seat position, turned or inclined
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/80—Spatial relation or speed relative to objects

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  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Human Computer Interaction (AREA)
  • Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Traffic Control Systems (AREA)

Abstract

The invention discloses a vehicle safety situation assessment and steady driving mode switching method and system. In a system in which a vehicle bus network, a vehicle driving control right handing over device, a driver state behavior sensing device and a vehicle information and vehicle circumference environment information sensing device cooperate with each other, acquiring multi-mode parameters in the driving process of a driver through the driver state behavior sensing device, and calculating the internal field security situation of the vehicle; collecting vehicle running state parameters and running environment state parameters through a vehicle information and surrounding environment information sensing device, and calculating a vehicle outfield security situation; the vehicle driving control right transfer device judges the driving risk level according to the safety situation of the internal and external fields of the vehicle and outputs an instruction; and according to the instruction of the adjacent moment, carrying out driving right handover judgment, and sending a judgment result to the vehicle bus network. The invention improves the safety of automatic driving and the stability of the transfer of the driving right of automatic driving.

Description

Vehicle safety situation assessment and steady driving mode switching method and system
Technical Field
The invention relates to the field of vehicle safety and automatic driving, in particular to a vehicle safety situation assessment and steady driving mode switching method and system.
Background
At present, the automation level of a vehicle is in an automatic driving stage under certain conditions: most driving operations are performed by the vehicle, and the human driver needs to concentrate on dealing with emergency dangerous situations, which is regarded as a technique between transition to fully automatic driving level. In order to increase the safety and comfort of a driver in the driving process at present, the driving risk of the driver needs to be evaluated, and meanwhile, the taking over of the vehicle by the driver is completed when the driving risk is high.
However, in the existing research on the driving safety situation of the vehicle, only the influence of the running environment around the vehicle on the driving risk is often considered, such as lane lines, road boundaries, the movement situation of surrounding vehicles and the like, and when the driver is in a bad state or in order to reduce the operation load of the driver in the dangerous situation of the external environment of the vehicle, the overall state indexes of the human-machine-ring are not integrated, namely, the self-perception of the vehicle and the state perception of the driver are not integrated into the judgment of the safety situation; meanwhile, the prior art is not perfect enough for realizing the research of accurate driving takeover identification conditions, lacks a vehicle control right transfer scheme designed on the basis of accurately judging the safety situation, and is deficient in the aspect of steady transfer of control right.
In summary, in order to improve the safety of drivers in the man-machine co-driving stage, there is a need for a driving safety situation assessment method considering overall perception information of a man-machine-loop, and a steady driving right switching mechanism is developed to improve the reliability of the current intelligent vehicle, so as to solve the problems of insufficient consideration and insufficient stable taking over process in the prior safety situation judgment in the vehicle with higher automation level, and lay a foundation for the development of higher-level automatic driving.
Disclosure of Invention
The invention provides a vehicle security situation assessment and steady driving mode switching method and system, which are used for solving the technical problems of insufficient security situation awareness and insufficient taking over process in the existing vehicle with higher automation level.
The invention provides a vehicle security situation assessment and steady driving mode switching method, which is used in a system of mutual cooperation of a vehicle bus network, a vehicle driving control right transfer device, a driver state behavior sensing device and a vehicle information and vehicle surrounding environment information sensing device, and comprises the following steps:
s1, calculating a vehicle internal field safety situation and a vehicle external field safety situation: the multi-mode parameters of the driver in the driving process are collected through the driver state behavior sensing device, and then the internal scene security situation of the vehicle is calculated and sent to the vehicle driving control right handing-over device; the vehicle running state parameters and the running environment state parameters are acquired through the vehicle information and vehicle surrounding environment information sensing device, and then the external field security situation of the vehicle is calculated and sent to the vehicle driving control right transfer device.
S2, judging the driving risk level: the vehicle driving control right transfer device judges the driving risk level according to the vehicle internal field safety situation and the vehicle external field safety situation and outputs an instruction.
S3, driving right handover judgment: the vehicle driving control right transfer device performs driving right transfer judgment according to the instruction of the adjacent time, and sends the judgment result to the vehicle bus network.
Preferably, in S1:
multimodal parameters include driver's facial image, head pose, torso pose, speech intonation, respiration rate, skin electricity, heart rate.
When the internal field safety situation of the vehicle is calculated according to the multi-mode parameters, the driving intention and the driving characteristic observation information are fused, the operation behavior of the driver is identified and predicted based on the noise reduction self-coding depth network, the states of fatigue, emotion and the like of the driver are perceived through the multi-task convolutional neural network, the internal field safety situation of the vehicle is comprehensively calculated, and the internal field safety situation of the vehicle is sent to the vehicle driving control right transfer device after the time stamp is added; vehicle in-field security situation E in The calculation of (2) can be formulated as:
E in ＝η 1 D emo +η 2 D fati +η 3 D beha
wherein D is emo Is a driver emotion risk factor, D fati Is a fatigue risk factor of the driver, D beha Is a risk factor for driver behavior, eta 1 、η 2 、η 3 And the weight coefficients of the risk factors are respectively.
Preferably, in S1:
vehicle operating state parameters include speed, acceleration, and cornering; the running environment state parameters include vehicle surrounding environment information including an outside image and radar point cloud images including surrounding vehicles and obstacle information.
When calculating the external field safety situation of the vehicle according to the running state parameters of the vehicle and the running environment state parameters, carrying out modal fusion on an external image of the vehicle and a radar point cloud image, inputting a YOLOv7 deep neural network, synthesizing the running state parameters of the vehicle, obtaining lane information, vehicle speed and acceleration information of the vehicle, extracting driving risk factors from the position, speed and acceleration information of the adjacent vehicle relative to the vehicle of the vehicle, synthesizing the external field safety situation of the vehicle, adding a time stamp, and then sending the obtained time stamp to a vehicle driving control right transfer device; vehicle outfield security situation E ex The calculation of (2) can be formulated as:
wherein r is ij Representing the distance, k, between the neighboring vehicle i and the host vehicle j 1 、k 2 G is a preset parameter greater than 0, M i For equivalent mass of the adjacent vehicle i, R is road condition influence factor of the vehicle, v i For speeds adjacent to vehicle i, θ i Is adjacent to the i speed direction and r of the vehicle ij And specifies that the clockwise direction is positive and n is the number of neighboring vehicles.
Preferably, the equivalent mass M of the adjacent vehicle i The method can be expressed as:
M i ＝m i (1+α 1 v i +α 2 a i )
wherein m is i Is the mass of the adjacent vehicle i, alpha 1 、α 2 A preset parameter greater than 0, v i Is the speed of the adjacent vehicle i, a i Is the acceleration of the neighboring vehicle i.
The road condition influence factor R can be formulated as:
R＝β 1 F cur +β 2 F slo +β 3 F vis
wherein beta is 1 、β 2 、β 3 For a preset parameter greater than 0, F cur Is the curvature of the road, F slo For road grade, F vis Is road visibility.
Preferably, in S2:
the rule of the output instruction is as follows:
outputting the instruction A when the internal field risk level is judged to be normal and the external field risk level is judged to be normal 1 。
Outputting the instruction A when the internal field risk level is judged to be normal and the external field risk level is judged to be dangerous 2 。
Outputting the instruction B when the risk level of the internal field is judged to be dangerous and the risk level of the external field is judged to be normal 1 。
Outputting the instruction B when the risk level of the internal field is judged to be dangerous and the risk level of the external field is judged to be dangerous 2 。
Preferably, in S3:
the vehicle driving control right transfer device includes the following cases when the vehicle driving control right transfer device performs driving right transfer determination according to the instruction of the adjacent time:
case one: the instruction keeps A at adjacent times 1 From B 1 Change to A 1 And keeping the automatic driving system to master the driving right.
And a second case: the instruction is represented by A at adjacent time 1 Change to A 2 From B 1 Change to A 2 From A 2 Change to B 1 When the driver is presented, the driver is presented and will be presented within 4sControl is transferred to the driver by the autopilot system.
Case three: the instruction keeps B at the adjacent time 1 From A 1 Change to B 1 And prompting the driver and keeping the automatic driving system to master the driving right.
Case four: the instruction is represented by A at adjacent time 1 Change to B 2 From A 2 Change to B 2 From B 1 Change to B 2 And prompting the driver and controlling the vehicle to decelerate to a low speed state.
Case five: the instruction is represented by A at adjacent time 2 Change to A 1 And prompting the driver, and transferring control right from the driver to the automatic driving system within 4s after prompting.
Case six: the instruction keeps A at adjacent times 2 In this case, the driver is kept to grasp the driving right.
Case seven: the instruction is represented by B at adjacent time 2 Change to A 1 And prompting the driver, and converting the low-speed state into the automatic driving system to master the driving right within 4s after prompting.
Case eight: the instruction is represented by B at adjacent time 2 Change to A 2 In this case, the driver is presented, and the driver is changed from the low-speed state to grasp the driving right within 4s after the presentation.
Case nine: the instruction is represented by B at adjacent time 2 Change to B 1 And prompting the driver, and converting the low-speed state into the automatic driving system to master the driving right within 4s after prompting.
Case ten: the instruction keeps B at the adjacent time 2 When the vehicle is in a low-speed state, the driver is prompted.
The invention also provides a system for evaluating the safety situation of the vehicle and switching the steady driving mode, which comprises the following steps: a vehicle bus network, a vehicle driving control right transfer device connected with the vehicle bus network, a driver state behavior sensing device and a vehicle information and surrounding environment information sensing device connected with the vehicle driving control right transfer device.
The driver state behavior sensing device is used for collecting the multi-mode parameters of the driver, further calculating the internal scene security situation of the vehicle and sending the internal scene security situation to the vehicle driving control right handing-over device.
The vehicle information and surrounding environment information sensing device is used for collecting vehicle running state parameters and running environment state parameters, further calculating the external field security situation of the vehicle and sending the external field security situation to the vehicle driving control right handing-over device.
The vehicle driving control right transfer device is used for receiving the security situation information of the internal field and the security situation information of the external field of the vehicle, judging the risk level according to the security situation information, outputting an instruction, and sending steady transfer operation information of the driving control right to the vehicle bus network according to the instruction of the adjacent moment.
Preferably, the driver state behavior sensing device is further used for extracting parameter characteristics of the multi-mode parameters; the vehicle information and vehicle surrounding environment information sensing device is also used for extracting driving risk factors; the vehicle driving control right handing-over device is also used for sending out early warning prompt information to the driver through the loudspeaker and the atmosphere lamp of the vehicle.
Preferably, the driver state behavior sensing means includes:
the system comprises more than one high-definition network camera and an infrared camera, wherein the more than one high-definition network camera is used for acquiring facial expressions, head postures and trunk postures of a driver, the more than one high-definition network camera is installed above a front windshield of a vehicle, and the infrared camera is installed at the upper end of the inner side of a column A of the vehicle.
The physiological information monitoring bracelet is used for collecting the respiratory rate, the skin electricity and the heart rate of the driver, and is worn on the wrist of the non-handedness hand of the driver.
An audio collector for collecting speech utterances of a driver.
The information processing module is used for preprocessing the multi-modal parameters based on the noise reduction self-coding depth network, extracting the characteristics, obtaining the fatigue state, the emotion state and the driving operation action of a driver through the multi-task convolutional neural network, and further calculating the internal field security situation of the vehicle.
Preferably, the vehicle information and surrounding environment information sensing device includes:
the GPS locator, the speed sensor, the acceleration sensor and the steering wheel angle sensor are arranged in the vehicle and used for collecting the running state parameters of the vehicle.
The high-definition network camera and the vehicle-mounted laser radar are arranged outside the vehicle and used for detecting road environment information and adjacent vehicle information outside the vehicle.
And the information processing module is used for processing the acquired external image information and external Lei Dadian cloud information through a YOLOv7 deep neural network, identifying and extracting driving risk factors, and calculating the external field security situation of the vehicle by integrating the vehicle running state parameters.
Preferably, the vehicle driving control right transfer device includes: the system comprises a security situation judging module and a control right handing-over module.
The security situation judging module is used for receiving the security situation information of the internal field and the security situation information of the external field of the vehicle, judging the risk level and outputting an instruction to the control right handing over module.
The control right transfer module is used for receiving the instruction sent by the safety situation judging module and sending adjustment information to a steering mechanism, an accelerating mechanism and a braking mechanism of the vehicle through the vehicle bus network according to the instruction.
The invention has the following beneficial effects:
according to the vehicle safety situation assessment and steady-state driving mode switching method, the corresponding vehicle internal field safety situation and vehicle external field safety situation are calculated through collecting the multi-mode parameters, the vehicle running state parameters and the running environment state parameters of a driver, and the vehicle internal field safety situation and the vehicle external field safety situation are combined to judge the safety situation when the vehicle is automatically driven, so that the overall state indexes of a 'man-machine-ring' are integrated for comprehensive consideration, and the safety and the reliability of the vehicle are obviously improved when the vehicle is automatically driven; after the safety situation is judged, a corresponding instruction is output according to the overall safety situation state, driving right handover judgment is carried out according to the instruction at the adjacent moment, and finally the driving right handover situation of the vehicle is determined, so that the driving right handover process of the vehicle is quite reliable and stable; the vehicle security situation assessment and steady-state driving mode switching system provides a hardware basis for the implementation of the method, so that the vehicle security situation assessment and steady-state driving mode switching system has the same beneficial effects as the method.
In addition to the objects, features and advantages described above, the present invention has other objects, features and advantages. The invention will be described in further detail with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of a vehicle security situation assessment and steady state driving mode switching method in a preferred embodiment 1 of the present invention;
FIG. 2 is a diagram of a vehicle in-field security situation calculation framework in a preferred embodiment 1 of the present invention;
fig. 3 is a diagram of a vehicle outfield security situation calculation frame in a preferred embodiment 1 of the present invention;
fig. 4 is a block diagram of a vehicle security situation evaluation and steady-state driving mode switching system in a preferred embodiment 2 of the present invention.
Detailed Description
Embodiments of the invention are described in detail below with reference to the attached drawings, but the invention can be implemented in a number of different ways, which are defined and covered by the claims.
Example 1:
referring to fig. 1, in a preferred embodiment of the present invention, there is provided a vehicle security situation assessment and steady-state driving mode switching method for a vehicle driving control right handing over device connected to a vehicle bus network, in a system in which a driver state behavior sensing device connected to the vehicle driving control right handing over device and a vehicle information and surrounding environment information sensing device cooperate with each other, comprising the steps of:
s1, calculating a vehicle internal field safety situation and a vehicle external field safety situation: the multi-mode parameters of the driver in the driving process are collected through the driver state behavior sensing device, and then the internal scene security situation of the vehicle is calculated and sent to the vehicle driving control right handing-over device; the vehicle running state parameters and the running environment state parameters are acquired through the vehicle information and vehicle surrounding environment information sensing device, and then the external field security situation of the vehicle is calculated and sent to the vehicle driving control right transfer device.
In S1: multimodal parameters include driver's facial image, head pose, torso pose, speech intonation, respiration rate, skin electricity, heart rate.
Referring to fig. 2, when calculating the safety situation of the vehicle internal field according to the multi-mode parameters, the driving intention and the driving characteristic observation information are fused, the operation behavior of the driver is identified and predicted based on the noise reduction self-coding depth network, the fatigue, emotion and other states of the driver are perceived through the multi-task convolutional neural network, the safety situation of the vehicle internal field is comprehensively calculated, and the safety situation of the vehicle internal field is sent to the vehicle driving control right transfer device after the time stamp is added; vehicle in-field security situation E in The calculation of (2) can be formulated as:
E in ＝η 1 D emo +η 2 D fati +η 3 D beha
wherein D is emo Is a driver emotion risk factor, D fati Is a fatigue risk factor of the driver, D beha Is a risk factor for driver behavior, eta 1 、η 2 、η 3 And the weight coefficients of the risk factors are respectively.
The psychological characterization, the physiological feedback and the external expression of the coupled driver are collected, relevant indexes including facial images, head postures, trunk postures, voice intonation, breathing rate, skin electricity, heart rate and the like are analyzed, driving intention and driving action characteristic observation information are fused, the operation behaviors of the driver are identified and predicted through a noise reduction self-coding depth network, states of the driver including fatigue, emotion and the like are perceived through a multitask convolutional neural network based on the heart physiological indexes and the external expression indexes, and psychological load and operation load of the driver are calculated by combining the states of the driver and the behaviors of the driver to serve as the internal field safety situation of the vehicle.
In S1: vehicle operating state parameters include speed, acceleration, and cornering; the running environment state parameters include vehicle surrounding environment information including an outside image and radar point cloud images including surrounding vehicles and obstacle information.
Referring to fig. 3, when calculating a vehicle outfield safety situation according to a vehicle running state parameter and a running environment state parameter, performing modal fusion on an outside image and a radar point cloud image, inputting a YOLOv7 deep neural network, synthesizing the vehicle running state parameter, obtaining lane information, vehicle speed and acceleration information of a vehicle, extracting driving risk factors from the position, speed and acceleration information of an adjacent vehicle relative to the vehicle of the vehicle, synthesizing the calculated vehicle outfield safety situation, adding a time stamp, and transmitting to a vehicle driving control right transfer device; vehicle outfield security situation E ex The calculation of (2) can be formulated as:
wherein r is ij Representing the distance, k, between the neighboring vehicle i and the host vehicle j 1 、k 2 G is a preset parameter greater than 0, M i For equivalent mass of the adjacent vehicle i, R is road condition influence factor of the vehicle, v i For speeds adjacent to vehicle i, θ i Is adjacent to the i speed direction and r of the vehicle ij And specifies that the clockwise direction is positive and n is the number of neighboring vehicles.
Equivalent mass M of adjacent vehicle i The method can be expressed as:
M i ＝m i (1+α 1 v i +α 2 a i )
wherein m is i Is the mass of the adjacent vehicle i, alpha 1 、α 2 A preset parameter greater than 0, v i Is the speed of the adjacent vehicle i, a i Is the acceleration of the neighboring vehicle i.
The road condition influence factor R can be formulated as:
R＝β 1 F cur +β 2 F slo +β 3 F vis
wherein beta is 1 、β 2 、β 3 For a preset parameter greater than 0, F cur Is the curvature of the road, F slo For road grade, F vis Is road visibility.
The speed, acceleration and deceleration, rotation angle and other information of the vehicle are obtained through the sensors of the vehicle and the detection sensors around the vehicle body, and the images and the point cloud information of the vehicle running environment such as lane lines, adjacent vehicle positions and the like are obtained. And determining a driving risk factor set influencing driving safety through driving risk factor extraction, merging and outputting parameter states of each risk factor, identifying and tracking surrounding vehicles and road environment information based on YOLOv7, and calculating a vehicle outfield security situation as an off-board risk.
In the preferred embodiment of the invention, the psychological characterization, physiological feedback and external expression of the coupled driver are collected, the state of the driver is analyzed, and the internal field security situation of the vehicle is calculated, so that the internal field security situation of the vehicle comprehensively considers various factors of the driver, and the more reliable perception of the behavior state of the driver is realized; the driving risk factors are extracted, identified and tracked through the vehicle driving information and the vehicle periphery road environment information, the external field safety situation of the vehicle is calculated, the behavior state parameters of the driver, the vehicle driving parameters and the running environment parameters are fused, and the safety situation assessment result fused with the whole perception information of the human-machine-ring is comprehensively obtained, so that the method can judge the driving risk very accurately.
S2, judging the driving risk level: the vehicle driving control right transfer device judges the driving risk level according to the vehicle internal field safety situation and the vehicle external field safety situation and outputs an instruction.
In S2: the rule of the output instruction is as follows:
outputting the instruction A when the internal field risk level is judged to be normal and the external field risk level is judged to be normal 1 。
Outputting the instruction A when the internal field risk level is judged to be normal and the external field risk level is judged to be dangerous 2 。
When the risk level of the internal field is judged to be dangerous, the external fieldOutputting instruction B when the field risk level is judged to be normal 1 。
Outputting the instruction B when the risk level of the internal field is judged to be dangerous and the risk level of the external field is judged to be dangerous 2 。
In the preferred embodiment of the invention, the driving risk level judgment is carried out by combining the state behavior parameters of the driver, the state parameters of the vehicle and the state parameters of the driving environment, integrating the internal safety situation of the vehicle and the external safety situation of the vehicle, and outputting the instruction, so that the risk level judgment has higher accuracy, the reliability of automatic driving is further ensured, and the identification technology of the driving takeover condition of the existing intelligent vehicle is broken through.
S3, driving right handover judgment: the vehicle driving control right transfer device performs driving right transfer judgment according to the instruction of the adjacent time, and sends the judgment result to the vehicle bus network.
In S3:
the vehicle driving control right transfer device includes the following cases when the vehicle driving control right transfer device performs driving right transfer determination according to the instruction of the adjacent time:
case one: the instruction keeps A at adjacent times 1 From B 1 Change to A 1 And keeping the automatic driving system to master the driving right.
And a second case: the instruction is represented by A at adjacent time 1 Change to A 2 From B 1 Change to A 2 From A 2 Change to B 1 And prompting the driver, and transferring control from the automatic driving system to the driver within 4s after prompting.
Case three: the instruction keeps B at the adjacent time 1 From A 1 Change to B 1 And prompting the driver and keeping the automatic driving system to master the driving right.
Case four: the instruction is represented by A at adjacent time 1 Change to B 2 From A 2 Change to B 2 From B 1 Change to B 2 And prompting the driver and controlling the vehicle to decelerate to a low speed state.
Case five: the instruction is represented by A at adjacent time 2 Change toA 1 And prompting the driver, and transferring control right from the driver to the automatic driving system within 4s after prompting.
Case six: the instruction keeps A at adjacent times 2 In this case, the driver is kept to grasp the driving right.
Case seven: the instruction is represented by B at adjacent time 2 Change to A 1 And prompting the driver, and converting the low-speed state into the automatic driving system to master the driving right within 4s after prompting.
Case eight: the instruction is represented by B at adjacent time 2 Change to A 2 In this case, the driver is presented, and the driver is changed from the low-speed state to grasp the driving right within 4s after the presentation.
Case nine: the instruction is represented by B at adjacent time 2 Change to B 1 And prompting the driver, and converting the low-speed state into the automatic driving system to master the driving right within 4s after prompting.
Case ten: the instruction keeps B at the adjacent time 2 When the vehicle is in a low-speed state, the driver is prompted.
The vehicle control steady state handover method can be summarized as table 1:
table 1: steady state transfer method of vehicle control right
In the preferred embodiment of the invention, the man-machine game and collision prevention mechanism of the driver-automatic driving system are considered, the stable and smooth driving right transfer mechanism is realized according to the instruction of the adjacent moment output by the driving safety situation of the vehicle, the prompt information is sent to the driver and the taking-over time is reserved when the driving control right is switched, and the warning information is sent to the driver when the risk level of the internal field of the vehicle is higher, namely the driver is in poor driving state, so that the method can furthest ensure the driving safety of the driver and enhance the safety of the driver in the driving process and the smooth stability of the driving taking-over process.
Example 2:
referring to fig. 4, in a preferred embodiment of the present invention, there is provided a system for evaluating a vehicle security situation and switching a steady driving mode, including: a vehicle bus network, a vehicle driving control right transfer device connected with the vehicle bus network, a driver state behavior sensing device and a vehicle information and surrounding environment information sensing device connected with the vehicle driving control right transfer device.
The driver state behavior sensing device is used for collecting the multi-mode parameters of the driver, further calculating the internal scene security situation of the vehicle and sending the internal scene security situation to the vehicle driving control right handing-over device.
The vehicle information and surrounding environment information sensing device is used for collecting vehicle running state parameters and running environment state parameters, further calculating the external field security situation of the vehicle and sending the external field security situation to the vehicle driving control right handing-over device.
The vehicle driving control right transfer device is used for receiving the security situation information of the internal field and the security situation information of the external field of the vehicle, judging the risk level according to the security situation information, outputting an instruction, and sending steady transfer operation information of the driving control right to the vehicle bus network according to the instruction of the adjacent moment.
The driver state behavior sensing device is also used for extracting parameter characteristics of the multi-mode parameters; the vehicle information and vehicle surrounding environment information sensing device is also used for extracting driving risk factors; the vehicle driving control right handing-over device is also used for sending out early warning prompt information to the driver through the loudspeaker and the atmosphere lamp of the vehicle.
In the preferred embodiment of the invention, the internal field safety situation and the external field safety situation of the vehicle are effectively calculated through the cooperative operation among the devices, and finally the internal field safety situation and the external field safety situation of the vehicle are combined to judge the risk level and output the instruction, so that the vehicle realizes the steady-state transfer of the driving control right, and when the internal field risk level of the vehicle is higher, namely the driver is in bad driving state, the driver can send warning information, thereby enhancing the safety of the driver in the driving process and the smooth stability of the driving take-over process.
The driver state behavior sensing device includes:
the system comprises more than one high-definition network camera and an infrared camera, wherein the more than one high-definition network camera is used for acquiring facial expressions, head postures and trunk postures of a driver, the more than one high-definition network camera is installed above a front windshield of a vehicle, and the infrared camera is installed at the upper end of the inner side of a column A of the vehicle.
The physiological information monitoring bracelet is used for collecting the respiratory rate, the skin electricity and the heart rate of the driver, and is worn on the wrist of the non-handedness hand of the driver.
An audio collector for collecting speech utterances of a driver.
The information processing module is used for preprocessing the multi-modal parameters based on the noise reduction self-coding depth network, extracting the characteristics, obtaining the fatigue state, the emotion state and the driving operation action of a driver through the multi-task convolutional neural network, and further calculating the internal field security situation of the vehicle.
In the preferred embodiment of the invention, each module contained in the driver state behavior sensing device realizes the acquisition of multiple data of the driver about safe driving, and finally calculates the internal safety situation of the vehicle, so that the calculation of the internal safety situation of the vehicle has reliable data support.
The vehicle information and surrounding environment information sensing device includes:
the GPS locator, the speed sensor, the acceleration sensor and the steering wheel angle sensor are arranged in the vehicle and used for collecting the running state parameters of the vehicle.
The high-definition network camera and the vehicle-mounted laser radar are arranged outside the vehicle and used for detecting road environment information and adjacent vehicle information outside the vehicle.
And the information processing module is used for processing the acquired external image information and external Lei Dadian cloud information through a YOLOv7 deep neural network, identifying and extracting driving risk factors, and calculating the external field security situation of the vehicle by integrating the vehicle running state parameters.
In the preferred embodiment of the invention, each module contained in the vehicle information and surrounding environment information sensing device realizes the acquisition of road environment information and adjacent vehicle information outside the vehicle in the running process of the vehicle, and finally calculates the external safety situation of the vehicle, so that the calculation of the external safety situation of the vehicle has reliable data support.
The vehicle driving control right transfer device includes: the system comprises a security situation judging module and a control right handing-over module.
The security situation judging module is used for receiving security situation information of the internal field and the external field of the vehicle, judging the risk level and outputting an instruction to the control right handing over module.
The control right transfer module is used for receiving the instruction sent by the safety situation judging module and sending adjustment information to a steering mechanism, an accelerating mechanism and a braking mechanism of the vehicle through the vehicle bus network according to the instruction.
In the preferred embodiment of the invention, the security situation judging module in the vehicle driving control right handing over device judges the risk level and outputs the related instruction according to the security situation information of the internal field and the security situation information of the external field of the vehicle, so that the judgment of the risk level is reliable; the control right transfer module provides state adjustment information for the vehicle according to the related instructions, so that the safety of a driver in the driving process and the smooth stability of the driving takeover process are ensured; when the driving control right is switched, prompt information is sent to the driver, take over time is reserved, and driving safety of the driver is ensured to the greatest extent from the viewpoint of vehicle driving.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (9)

1. The method for evaluating the safety situation of the vehicle and switching the steady driving mode is used in a system of a vehicle bus network, a vehicle driving control right transfer device, a driver state behavior sensing device and a vehicle information and vehicle surrounding environment information sensing device which cooperate with each other, and is characterized by comprising the following steps:
s1, calculating a vehicle internal field safety situation and a vehicle external field safety situation: the multi-mode parameters of the driver in the driving process are collected through the driver state behavior sensing device, and then the internal scene security situation of the vehicle is calculated and sent to the vehicle driving control right handing-over device; collecting vehicle running state parameters and running environment state parameters through a vehicle information and surrounding environment information sensing device, further calculating a vehicle outfield security situation and sending the situation to a vehicle driving control right transfer device;
s2, judging the driving risk level: the vehicle driving control right transfer device judges the driving risk level according to the vehicle internal field safety situation and the vehicle external field safety situation and outputs an instruction;
the rule of the output instruction is as follows:
outputting the instruction A when the internal field risk level is judged to be normal and the external field risk level is judged to be normal 1 ；
Outputting the instruction A when the internal field risk level is judged to be normal and the external field risk level is judged to be dangerous 2 ；
Outputting the instruction B when the risk level of the internal field is judged to be dangerous and the risk level of the external field is judged to be normal 1 ；
Outputting the instruction B when the risk level of the internal field is judged to be dangerous and the risk level of the external field is judged to be dangerous 2 ；
S3, driving right handover judgment: the vehicle driving control right handing over device is used for carrying out driving right handing over judgment according to the instruction of the adjacent moment and sending the judgment result to the vehicle bus network;
the vehicle driving control right transfer device includes the following cases when the vehicle driving control right transfer device performs driving right transfer determination according to the instruction of the adjacent time:
case one: the instruction keeps A at adjacent times 1 From B 1 Change to A 1 In this case, the automatic driving system is kept to grasp the driving right；
And a second case: the instruction is represented by A at adjacent time 1 Change to A 2 From B 1 Change to A 2 From A 2 Change to B 1 When the control method is used, the driver is prompted, and the control right is transferred to the driver from the automatic driving system within 4s after the prompting;
case three: the instruction keeps B at the adjacent time 1 From A 1 Change to B 1 When the driver is prompted, the automatic driving system is kept to master the driving right;
case four: the instruction is represented by A at adjacent time 1 Change to B 2 From A 2 Change to B 2 From B 1 Change to B 2 When the vehicle is in a low-speed state, the driver is prompted, and the vehicle is controlled to be decelerated to the low-speed state;
case five: the instruction is represented by A at adjacent time 2 Change to A 1 When the control method is used, the driver is prompted, and the control right is transferred from the driver to the automatic driving system within 4s after the prompting;
case six: the instruction keeps A at adjacent times 2 When the driver is kept to master the driving right;
case seven: the instruction is represented by B at adjacent time 2 Change to A 1 When the driving system is in a low-speed state, the driver is prompted, and the driving system is turned from the low-speed state to the automatic driving system to master the driving right within 4s after the prompting;
case eight: the instruction is represented by B at adjacent time 2 Change to A 2 When the driving right is mastered by the driver, the driver is prompted, and the low-speed state is changed into the driving right within 4s after the prompting;
case nine: the instruction is represented by B at adjacent time 2 Change to B 1 When the driving system is in a low-speed state, the driver is prompted, and the driving system is turned from the low-speed state to the automatic driving system to master the driving right within 4s after the prompting;
case ten: the instruction keeps B at the adjacent time 2 When the vehicle is in a low-speed state, the driver is prompted.
2. The vehicle security situation assessment and steady-state driving mode switching method according to claim 1, wherein in S1:
the multimodal parameters include facial images, head gestures, torso gestures, voice intonation, respiration rate, skin electricity, heart rate of the driver;
when the safety situation of the vehicle internal field is calculated according to the multi-mode parameters, the driving intention and the driving characteristic observation information are fused, the operation behavior of the driver is identified and predicted based on the noise reduction self-coding depth network, the states of fatigue, emotion and the like of the driver are perceived through the multi-task convolutional neural network, the safety situation of the vehicle internal field is comprehensively calculated, and the vehicle internal field is sent to the vehicle driving control right transfer device after the time stamp is added; the vehicle in-field security situationThe calculation of (2) can be formulated as:
wherein,is a driver emotional risk factor, < >>Is a driver fatigue risk factor, < >>Is a risk factor for driver behavior, < >>、/>、/>And the weight coefficients of the risk factors are respectively.
3. The vehicle security situation assessment and steady-state driving mode switching method according to claim 1, wherein in S1:
the vehicle running state parameters comprise speed, acceleration and rotation angle; the running environment state parameters comprise vehicle surrounding environment information comprising an external image of the vehicle and radar point cloud pictures comprising surrounding vehicles and obstacle information;
when calculating the external field safety situation of the vehicle according to the vehicle running state parameters and the running environment state parameters, carrying out modal fusion on an external image and a radar point cloud image, inputting a YOLOv7 deep neural network, synthesizing the vehicle running state parameters, obtaining lane information, vehicle speed and acceleration information of the vehicle, extracting driving risk factors from the position, speed and acceleration information of the adjacent vehicle relative to the vehicle of the vehicle, synthesizing the external field safety situation of the vehicle, adding a time stamp, and then sending the obtained result to a vehicle driving control right transfer device; the vehicle outfield safety situationThe calculation of (2) can be formulated as:
wherein,indicating adjacent vehicle +.>Is>Distance between->、/>、/>Are all preset parameters which are larger than 0,for adjacent vehicles +.>Equivalent mass of->For the road condition influence factor of the own vehicle, < +.>For adjacent vehicles +.>Speed of->Is adjacent vehicle +.>Speed direction and->Is set to be positive and is in the clockwise direction>Is the number of neighboring vehicles.
4. The vehicle security situation assessment and steady-state driving mode switching method according to claim 3, wherein,
equivalent mass of adjacent vehicleThe method can be expressed as:
wherein,is adjacent vehicle +.>Quality of (1)>、/>Is a preset parameter greater than 0, +.>Is adjacent vehicle +.>Speed of->Is adjacent vehicle +.>Acceleration of (2);
road condition influencing factorThe method can be expressed as:
wherein,、/>、/>is a preset parameter greater than 0, +.>For the curvature of the road->For road grade>Is road visibility.
5. A vehicle security posture assessment and steady state driving mode switching system, characterized in that it is applied to the vehicle security posture assessment and steady state driving mode switching method according to any one of claims 1 to 4, said system comprising: a vehicle bus network, a vehicle driving control right transfer device connected with the vehicle bus network, a driver state behavior sensing device and a vehicle information and surrounding environment information sensing device connected with the vehicle driving control right transfer device;
the driver state behavior sensing device is used for collecting the multi-mode parameters of the driver, further calculating the internal field security situation of the vehicle and sending the internal field security situation to the vehicle driving control right handing-over device;
the vehicle information and surrounding environment information sensing device is used for collecting vehicle running state parameters and running environment state parameters, further calculating the external field security situation of the vehicle and sending the external field security situation to the vehicle driving control right handing-over device;
the vehicle driving control right transfer device is used for receiving the security situation information of the internal field and the security situation information of the external field of the vehicle, judging the risk level according to the security situation information, outputting an instruction, and sending steady transfer operation information of the driving control right to the vehicle bus network according to the instruction of the adjacent moment.
6. The vehicle security posture assessment and steady-state driving mode switching system of claim 5; the driver state behavior sensing device is characterized by also being used for extracting parameter characteristics of multi-mode parameters; the vehicle information and vehicle surrounding environment information sensing device is also used for extracting driving risk factors; the vehicle driving control right transfer device is also used for sending out early warning prompt information to a driver through a loudspeaker and an atmosphere lamp of the vehicle.
7. The vehicle security posture assessment and steady-state driving mode switching system of claim 6; the driver state behavior sensing device is characterized by comprising:
the system comprises more than one high-definition network camera and an infrared camera, wherein the more than one high-definition network camera is used for acquiring facial expressions, head postures and trunk postures of a driver, the more than one high-definition network camera is arranged above a front windshield of a vehicle, and the infrared camera is arranged at the upper end of the inner side of a column A of the vehicle;
the physiological information monitoring bracelet is used for collecting the respiration rate, the skin electricity and the heart rate of the driver, and is worn on the wrist of the non-handedness hand of the driver;
an audio collector for collecting speech utterances of the driver;
the information processing module is used for preprocessing the multi-modal parameters based on the noise reduction self-coding depth network, extracting the characteristics, obtaining the fatigue state, the emotion state and the driving operation action of a driver through the multi-task convolutional neural network, and further calculating the internal field security situation of the vehicle.
8. The vehicle security posture assessment and steady-state driving mode switching system of claim 6; the vehicle information and surrounding environment information sensing device is characterized by comprising:
the GPS locator, the speed sensor, the acceleration sensor and the steering wheel angle sensor are arranged in the vehicle and used for collecting the running state parameters of the vehicle;
the high-definition network camera and the vehicle-mounted laser radar are arranged outside the vehicle and used for detecting road environment information and adjacent vehicle information outside the vehicle;
and the information processing module is used for processing the acquired external image information and external Lei Dadian cloud information through a YOLOv7 deep neural network, identifying and extracting driving risk factors, and calculating the external field security situation of the vehicle by integrating the vehicle running state parameters.
9. The vehicle security posture assessment and steady-state driving mode switching system of claim 6; the vehicle driving control right transfer device is characterized by comprising: the security situation judging module and the control right handing-over module;
the security situation judging module is used for receiving the security situation information of the internal field and the security situation information of the external field of the vehicle, judging the risk level and outputting an instruction to the control right handing-over module;
the control right transfer module is used for receiving the instruction sent by the safety situation judging module and sending adjustment information to a steering mechanism, an accelerating mechanism and a braking mechanism of the vehicle through the vehicle bus network according to the instruction.
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