CN118618355A - Emergency lane keeping method, device, equipment and storage medium - Google Patents

Abstract

The present application discloses an emergency lane keeping method, device, equipment and storage medium, which relates to the field of vehicle control technology. The emergency lane keeping method is disclosed, including: judging whether the vehicle has a deviation trend based on left and right lane line information, obtaining a vehicle deviation judgment result, judging whether the human-machine risk perception is consistent based on the system risk perception area and the driver's risk attention area, and obtaining a human-machine risk perception consistency judgment result. When the vehicle deviation judgment result is that the vehicle has a deviation trend, and the human-machine risk perception consistency judgment result is that the human-machine risk perception is inconsistent, the emergency lane keeping system is activated, the driver's risk identification ability is taken into account, and the triggering timing of the emergency lane keeping system is accurately identified.

Description

Emergency lane keeping method, device, equipment and storage medium

Technical Field

The present application relates to the field of vehicle control technology, and in particular to an emergency lane keeping method, device, equipment and storage medium.

Background Art

The emergency lane keeping system can perform emergency control of the vehicle when the vehicle deviates from the lane line or the curb to avoid collision between the vehicle and nearby vehicles or the curb. The system is usually triggered in an emergency, which often causes large fluctuations in vehicle control, affecting driving safety and riding experience. Therefore, how to accurately identify the triggering time has become the key to emergency lane keeping.

The emergency lane keeping method in the existing solution only determines whether to trigger the emergency lane keeping system based on the vehicle's deviation trend, without considering the driver's intense driving conditions and risk identification ability, which affects the driving experience.

The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art.

Summary of the invention

The main purpose of this application is to provide an emergency lane keeping method, device, equipment and storage medium, aiming to solve the technical problem of how to accurately identify the triggering timing of the emergency lane keeping system.

To achieve the above objectives, the present application proposes an emergency lane keeping method, which includes:

Based on the left and right lane line information, determine whether the vehicle has a tendency to deviate, and obtain a vehicle deviation judgment result;

Based on the system risk perception area and the driver's risk attention area, it is judged whether the human-machine risk perception is consistent, and the human-machine risk perception consistency judgment result is obtained;

When the vehicle deviation judgment result is that the vehicle has a tendency to deviate, and the human-machine risk perception consistency judgment result is that the human-machine risk perception is inconsistent, the emergency lane keeping system is activated.

In one embodiment, before the step of determining the vehicle deviation trend based on the left and right lane line information, the method further includes:

Collect vehicle status information and driver operation information;

Determine whether to perform an emergency lane triggering determination based on the vehicle state information and the driver operation information, and obtain a pre-determination result;

When the pre-judgment result is to perform an emergency lane triggering judgment, the operation of judging the vehicle deviation trend based on the left and right lane line information is performed.

In one embodiment, the step of determining whether the vehicle has a tendency to deviate based on the left and right lane line information and obtaining a vehicle deviation determination result includes:

Based on the left and right lane line information, determine whether the vehicle has a deviation side, and obtain a deviation side determination result;

When the deviating side judgment result is that the vehicle has deviated from the side, obtaining the lane line information of the deviating side;

Based on the deviating lane line information, it is determined whether the vehicle has a tendency to deviate, and the vehicle deviation determination result is obtained.

In one embodiment, the step of determining whether the vehicle has a tendency to deviate based on the deviating lane line information and obtaining the vehicle deviation determination result comprises:

Calculating a deviation distance based on the lane line information on the deviated side;

When the deviation distance is less than a preset distance, and the duration of the state in which the deviation distance is less than the preset distance reaches a preset time, the vehicle deviation judgment result is that the vehicle has a deviation trend.

In one embodiment, before the step of judging whether the human-machine risk perception is consistent based on the system risk perception area and the driver risk attention area, the method further includes:

Divide and preset driving risk areas based on the visible range of the cockpit;

Collecting driving risk source information, and determining a system risk perception area from the preset driving risk areas according to the driving risk source information;

Collect driver attention information,

A driver risk attention area is determined from the preset driving risk areas according to the driver attention information.

In one embodiment, the step of determining the system risk perception area from the preset driving risk areas according to the driving risk source information includes:

Based on the driving risk source information, acquiring the stationary risk source information and the moving risk source information of the preset driving risk area;

Calculate the driving risk potential energy field strength and the driving risk kinetic energy field strength based on the stationary risk source information and the moving risk source information to obtain a driving risk field strength calculation result;

A system risk perception area is determined from the preset driving risk areas according to the driving risk field strength calculation result.

In one embodiment, the step of determining whether the human-machine risk perception is consistent based on the system-perceived risk area and the driver-noticed risk area to obtain a result of the human-machine risk perception consistency determination includes:

Determining a human-machine risk perception matching degree based on the system risk perception area and the driver risk attention area;

Evaluate the human-machine risk perception matching index within a preset sampling period based on the human-machine risk perception matching degree;

When the human-machine risk perception matching index does not exceed a preset index threshold, the human-machine risk perception consistency judgment result is that the human-machine risk perception is inconsistent.

In addition, to achieve the above-mentioned purpose, the present application also proposes an emergency lane keeping device, the emergency lane keeping device comprising:

A judgment module, used to judge the vehicle deviation trend based on the left and right lane line information and obtain the vehicle deviation judgment result;

An evaluation module is used to evaluate the consistency between the risk area perceived by the system and the risk area perceived by the driver based on the driving risk area matching rule, and obtain a human-machine risk consistency evaluation result;

The activation module is used to activate the emergency lane keeping system when the vehicle deviation judgment result is that the vehicle has a deviation trend and the human-machine risk consistency assessment result is inconsistent with the human-machine perception risk assessment.

In addition, to achieve the above-mentioned purpose, the present application also proposes an emergency lane keeping device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the emergency lane keeping method described above.

In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the emergency lane keeping method described above are implemented.

In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the emergency lane keeping method described above are implemented.

One or more technical solutions proposed in this application have at least the following technical effects:

By judging whether the vehicle has a tendency to deviate based on the left and right lane line information, the vehicle deviation judgment result is obtained. By judging whether the human-machine risk perception is consistent based on the system risk perception area and the driver's risk attention area, the human-machine risk perception consistency judgment result is obtained. When the vehicle deviation judgment result is that the vehicle has a tendency to deviate, and the human-machine risk perception consistency judgment result is that the human-machine risk perception is inconsistent, the emergency lane keeping system is activated, taking into account the driver's risk identification ability, so as to accurately identify the triggering timing of the emergency lane keeping system, so as to avoid mis-triggering the emergency lane keeping system and causing large vehicle control fluctuations, affecting driving safety and normal riding experience.

BRIEF DESCRIPTION OF THE DRAWINGS

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

In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

FIG1 is a schematic diagram of a flow chart of an emergency lane keeping method according to an embodiment of the present invention;

FIG2 is a schematic diagram of a flow chart of a second embodiment of an emergency lane keeping method of the present application;

FIG3 is a schematic diagram of driving risk area division provided in Embodiment 2 of the emergency lane keeping method of the present application;

FIG4 is a schematic diagram of matching rules provided in Embodiment 2 of the emergency lane keeping method of the present application;

FIG5 is a schematic diagram of the module structure of an emergency lane keeping device according to an embodiment of the present application;

FIG6 is a schematic diagram of the device structure of the hardware operating environment involved in the emergency lane keeping method in an embodiment of the present application.

The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings.

DETAILED DESCRIPTION

It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a vehicle control device, etc. The following takes the vehicle control device as an example to illustrate this embodiment and the following embodiments.

Based on this, an embodiment of the present application provides an emergency lane keeping method. Referring to FIG. 1 , FIG. 1 is a flow chart of a first embodiment of the emergency lane keeping method of the present application.

In this embodiment, the emergency lane keeping method includes steps S10 to S40:

Step S10, judging whether the vehicle has a tendency to deviate based on the left and right lane line information, and obtaining a vehicle deviation judgment result;

It should be noted that the road image or data in front of the vehicle can be captured by using an on-board camera or other sensors (such as lidar, radar). By processing the collected road image or data, the lane line information related parameters can be obtained to determine the linear equations of the left and right lane line information. The linear equations of the left and right lane line information can be a cubic polynomial, and each coefficient of the cubic polynomial is the lane line information at the current moment. After obtaining the left and right lane line information based on the linear equations of the left and right lane line information, the position change of the vehicle relative to the lane line can be analyzed based on the left and right lane line information, and then it can be determined whether the vehicle has a tendency to deviate. If the analysis result shows that the vehicle continues to move toward one lane line, it indicates that the vehicle may have a tendency to deviate.

Exemplarily, the linear equations of the left and right lane information are as follows:

f _L ＝c _0,L +c _1,L s+c _2,L s ² +c _3,L s ³

f _R ＝c _0,R +c _1,R s+c _2,R s ² +c _3,R s ³

Among them, L and R are used to represent left and right respectively; f is used to represent the change in the length of the vertical segment projected from a point on the lane line to the longitudinal centerline of the vehicle; s is used to represent the longitudinal distance between the vehicle's current position and each projection point; _c0 is used to represent the lateral distance between the vehicle and the lane line at the current moment, _c1 is used to represent the orientation angle of the vehicle relative to the lane line at the current moment, _c2 is used to represent the curvature of the road at the current moment, and _c3 is used to represent the curvature change rate of the road at the current moment.

In this embodiment, step S10 includes steps A11 to A13:

Step A11, judging whether the vehicle has a deviation side based on the left and right lane line information, and obtaining a deviation side judgment result;

It should be understood that when judging whether the vehicle has a tendency to deviate, it is necessary to first obtain the lateral distance and lateral speed of the vehicle relative to the left lane line and the right lane line respectively based on the left and right lane line information. Based on the lateral distance and lateral speed of the lane lines, it can be determined whether the vehicle has a deviating side. When the vehicle has a deviating side, it is necessary to determine whether the vehicle is deviating to the left or right side.

For example, the formulas for obtaining the lateral distance and lateral speed are as follows:

d _L =|c _0,L |

d _R = |c _0,R |

Wherein, _dL and _dR represent the lateral distance of the vehicle relative to the left and right lane lines respectively, _Vlat represents the lateral velocity, and V represents the vehicle speed (km/h).

It should be noted that, if d _L <d _R , and V _lat,L is in the left direction, the deviation side judgment result is that the vehicle has a deviation side, and the vehicle deviation side is the left side; if d _R <d _L , and V _lat,R is in the right direction, the deviation side judgment result is that the vehicle has a deviation side, and the vehicle deviation side is the right side; if it is other situations, the deviation side judgment result is that the vehicle has no deviation side.

Step A12, when the deviating side judgment result is that the vehicle has deviated from the side, obtaining the deviating side lane line information;

It should be understood that after obtaining the deviation side judgment result, the lane line information of the vehicle deviation side can be obtained based on the deviation side judgment result. If the vehicle deviation side is the left side, the left lane line information is obtained; if the vehicle deviation side is the right side, the right lane line information is obtained. The lane line information of the vehicle deviation side can be obtained based on the linear equation of the deviation side lane line information.

Exemplarily, the linear equation of the deviated side lane line information is as follows:

f＝c ₀ +c ₁ s+c ₂ s ² +c ₃ s ³

Step A13, judging whether the vehicle has a tendency to deviate based on the deviating lane line information, and obtaining the vehicle deviation judgment result.

Furthermore, step A13 includes steps B1 to B2:

Step B1, calculating the deviation distance based on the lane line information of the deviated side;

It should be understood that when it is detected that the vehicle has deviated from the side during driving, an early warning can be issued through voice broadcast or visual display to remind the driver of the risk of collision, and based on the lane line information on the deviated side, the longitudinal distance of the vehicle's movement as the lane line changes during the first warning time can be predicted to calculate the deviation distance, and then determine whether the vehicle has a deviation trend.

For example, the calculation formula of the deviation distance is as follows:

d＝|c ₀ +c ₁ s ₁ +c ₂ s ₁ ² +c ₃ s ₁ ³ |

in, Used for the vehicle's heading angle relative to the lane line; T ₁ is used for the first warning time (s); s ₁ is used to indicate the longitudinal distance (m) predicted for the vehicle's movement as the lane line changes during the first warning time; W is used to indicate the vehicle width (m); d is used to indicate the lateral compensation distance of the lane line (m); D ₀ is used to indicate the deviation distance (m).

Step B2: when the deviation distance is less than a preset distance, and the duration of the state in which the deviation distance is less than the preset distance reaches a preset time, the vehicle deviation judgment result is that the vehicle has a deviation trend.

It should be noted that the preset distance is the deviation determination distance D ₁ set according to the vehicle speed. The faster the vehicle speed, the larger the preset distance is set. The preset time may be 0.1s. That is, if D ₀ <D ₁ and this state lasts for 0.1s, the vehicle deviation determination result is that the vehicle has a deviation trend; if it is other cases, the vehicle deviation determination result is that the vehicle does not have a deviation trend.

Step S20, judging whether the human-machine risk perception is consistent based on the system risk perception area and the driver risk attention area, and obtaining a human-machine risk perception consistency judgment result;

It should be noted that during the driving process, the intelligent driving system will collect information about risk sources around the vehicle, obtain the risk source type in the driving environment and the distance between the vehicle and the risk source based on the collected risk source information, analyze the distribution of driving risks in each area, and identify the area with the greatest driving risk as the system risk perception area. At the same time, the eye tracker will capture the driver's eye movement data in real time, including line of sight direction, gaze duration, eye movement speed, etc., analyze the changes in the driver's attention to the risk area around the vehicle through eye movement data, and locate the driving risk area where the driver's attention is located as the driver's risk attention area.

It should be understood that the risk matching degree between the system risk perception area and the driver risk attention area can be determined based on preset matching rules, or the risk matching degree between the system risk perception area and the driver risk attention area can be calculated using a mathematical model or algorithm to determine whether the human-machine risk perception is consistent. This embodiment does not impose any specific restrictions on this.

Step S30, when the vehicle deviation judgment result is that the vehicle has a tendency to deviate, and the human-machine risk perception consistency judgment result is that the human-machine risk perception is inconsistent, the emergency lane keeping system is activated.

It should be understood that the triggering time of the emergency lane keeping system needs to meet two conditions: the vehicle has a tendency to deviate and the risk perception of human and machine is inconsistent. When the vehicle has no tendency to deviate, it means that the vehicle is currently driving stably in the lane and there is no need for emergency control of the vehicle; when the vehicle has a tendency to deviate but the risk perception of human and machine is consistent, it means that the driver has a full understanding of the collision risk of the current driving environment and can perform corresponding operations according to the actual driving risk.

In another feasible implementation, before step S10, the emergency lane keeping method further includes steps S01 to S03:

Step S01, collecting vehicle status information and driver operation information;

It should be noted that the vehicle status information includes vehicle speed information, steering wheel angle information and steering wheel speed information, and the driver operation information includes driver hand torque information and driver operation information on the deviation side.

Step S02, judging whether to perform an emergency lane triggering judgment based on the vehicle state information and the driver operation information, and obtaining a pre-judgment result;

It should be understood that after obtaining the vehicle state information and the driver operation information, it can be determined whether the vehicle state information or the driver operation information meets the preset conditions, and the emergency lane triggering determination is performed when the preset conditions are met. Specifically, if the vehicle speed is lower than the preset vehicle speed V _P , or the driver's hand torque is greater than the preset torque F, or the steering wheel angle is greater than the preset angle θ, or the steering wheel speed is greater than the preset speed α, or the driver has turned on the turn signal on the deviation side, the pre-judgment result is not to perform the emergency lane triggering determination; if it is other cases, the pre-judgment result is to perform the emergency lane triggering determination.

Step S03, when the pre-judgment result is to perform an emergency lane triggering judgment, the operation of judging the vehicle deviation trend based on the left and right lane line information is executed.

It should be understood that when the vehicle status information or the driver's operation information does not meet the preset conditions, it means that there is no need to activate the emergency lane keeping system. For example, when driving at a low speed, the vehicle's deviation from the lane may not cause serious safety consequences; when the driver's hand torque, steering wheel angle or speed is greater than a certain value, it means that the driver is actively controlling the vehicle and may intentionally deviate from the lane to avoid obstacles, prepare to stop or for other reasons; when the driver has turned on the turn signal on the deviating side, it means that the driver may intend to change lanes or turn, and the intervention of the emergency lane keeping system will interfere with the driver's planned driving route.

This embodiment determines whether the vehicle has a tendency to deviate based on the left and right lane line information to obtain a vehicle deviation judgment result, determines whether the human-machine risk perception is consistent based on the system risk perception area and the driver's risk attention area, and obtains a human-machine risk perception consistency judgment result. When the vehicle deviation judgment result is that the vehicle has a tendency to deviate, and the human-machine risk perception consistency judgment result is that the human-machine risk perception is inconsistent, the emergency lane keeping system is activated, taking into account the driver's risk identification ability, so as to accurately identify the triggering timing of the emergency lane keeping system, so as to avoid erroneous triggering of the emergency lane keeping system causing large vehicle control fluctuations, affecting driving safety and normal riding experience.

Based on the first embodiment of the present application, the second embodiment of the present application is proposed. In the second embodiment of the present application, the same or similar contents as those of the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated later. On this basis, please refer to Figure 2. Before step S20, the emergency lane method also includes steps S11 to S13:

Step S11, dividing a preset driving risk area based on the visible range of the cockpit;

It should be understood that before judging whether the human-machine risk perception is consistent based on the system risk perception area and the driver risk attention area, a preset driving risk area can be divided according to the visible range of the vehicle cockpit.

For example, as shown in FIG3 , 8 driving risk areas can be divided, among which 2, 3, 4, 5, 6, and 7 are the risk areas in the direction of the front windshield, and 1 and 8 are the risk areas on the left and right sides and in the direction of the rearview mirror (rear).

Step S12, collecting driving risk source information, and determining a system risk perception area from the preset driving risk areas according to the driving risk source information;

It should be noted that the forward, side or rear-facing cameras of the vehicle can be used to capture visual information of the road and identify the types of driving risk sources, such as roadblocks, traffic lights, pedestrians and other vehicles; radar sensors can be used to collect the mass, speed and distance of each driving risk source. Corresponding risk coefficients are assigned to different types of driving risk sources, and the driving risk field strength of each driving risk area is calculated based on the risk coefficient and sensor data, and the driving risk area with the largest driving risk field strength is used as the system risk perception area.

In this embodiment, step S12 includes steps A21 to A23:

Step A21, acquiring the stationary risk source information and the moving risk source information of the preset driving risk areas based on the driving risk source information;

It should be understood that driving risk sources include stationary risk sources and moving risk sources. Among them, stationary risk sources include stationary objects such as roadblocks, stationary vehicles and traffic lights around the vehicle; moving risk sources include moving vehicles, pedestrians and other moving objects that may collide with the vehicle.

Step A22, calculating the driving risk potential energy field strength and the driving risk kinetic energy field strength based on the stationary risk source information and the moving risk source information, and obtaining a driving risk field strength calculation result;

It should be understood that, according to the type information corresponding to the driving risk source and its location information, the driving risk potential field strength and driving risk kinetic field strength of each driving risk area can be calculated respectively, and the sum of the driving risk field strength in the area is used as the driving risk field strength calculation result. Among them, the stationary risk source information is used to calculate the risk potential field strength, and the moving risk source information is used to calculate the risk potential kinetic field strength.

For example, the calculation formula of the risk potential field strength is as follows:

M＝mv

Among them, _ER is used to indicate the size of the potential energy field; _kR is used to indicate the potential energy field calculation coefficient; M is used to indicate the equivalent mass of the vehicle; T is used to indicate the risk coefficient corresponding to the risk source type; D is used to indicate the distance between the vehicle and the risk source (m); m is used to indicate the vehicle mass (kg); and v is used to indicate the vehicle speed (m/s).

In a feasible implementation, the corresponding formulas of different types of driving risk sources and risk coefficients may be as follows:

The calculation formula of risk kinetic energy field strength is as follows:

Among them, _EV is used to indicate the size of the kinetic energy field; _kV is used to indicate the kinetic energy field calculation coefficient; and _Mi is used to indicate the equivalent mass of the moving object i.

The calculation formula of the sum of the driving risk field strength in different driving risk areas, that is, the calculation result of the driving risk field strength, is as follows:

E _i ＝ _ER,i + _EV,i

Among them, Ei _is used to represent the total driving risk field strength in driving risk area i.

Step A23: determining a system risk perception area from the preset driving risk areas according to the driving risk field strength calculation result.

It should be understood that after calculating the sum of the driving risk field strengths in each driving risk area and obtaining the driving risk field strength calculation results, the sum of the driving risk field strengths in each area will be sorted from small to large, and the area with the largest sum of driving risk field strength will be used as the system risk perception area.

Exemplarily, if there are 8 preset areas, for the total set of driving risk field strengths E = { _E1 , _E2 , _E3 , _E4 , _E5 , _E6 , _E7 , _E8 }, if the sorting result is _E3 > _E2 > _E8 > _E1 > _E5 > _E6 > _E7 > _E4 , then driving risk area 3 is used as the system risk perception area.

Step S13: collecting driver attention information, and determining a driver risk attention area from the preset driving risk areas according to the driver attention information.

It should be understood that the driver's attention information may be eye movement data, which may be obtained by capturing the driver's line of sight, gaze duration, and eye movement speed in real time through an eye tracker. The driver's attention information may be used to determine the driving risk area that the driver is most concerned about at the moment and use it as the driver's risk attention area.

In this embodiment, step S20 includes steps S21 to S23:

Step S21, determining a human-machine risk perception matching degree based on the system risk perception area and the driver risk attention area;

In a feasible implementation, the risk matching degree between the system risk perception area and the driver risk attention area can be determined based on a preset matching rule, as shown in Figure 4, which is a schematic diagram of the driving risk area matching rule. In the figure, the yellow circle is used to mark the driver risk attention area. The number c in the blue box represents the risk matching degree corresponding to when the driver risk attention area is the area marked by the yellow circle and the driving system risk perception areas are 1, 2, 3, 4, 5, 6, 7 and 8 respectively. The area not marked by the blue box indicates a low degree of matching, and the default c is 0.

Exemplarily, according to the matching rules shown in Figure 4, at a certain moment, if the driver's risk attention area is 1 and the system risk perception area is area 1, the risk matching degree is 1; if the driver's risk attention area is 2 and the system risk perception area is area 5, the risk matching degree is 0.8; if the driver's risk attention area is 3 and the system risk perception area is area 4, the risk matching degree is 0.5; if the driver's risk attention area is 4 and the system risk perception area is area 2, the risk matching degree is 0.

Step S22, evaluating a human-machine risk perception matching index within a preset sampling period based on the human-machine risk perception matching degree;

It should be understood that if there are other target vehicles or curbs near the vehicle, it is necessary to conduct a comprehensive evaluation of the human-machine risk perception matching degree at the current moment and within a period of time in the past, that is, within the preset sampling period, to obtain the human-machine risk perception matching index.

For example, within n sampling periods, the driving risk area k is calculated to be marked as driver risk

Among them, _Ak is used to indicate the number of times area k is marked as a driver risk attention area within n sampling moments; _ai,k is used to indicate whether area k is marked as a driver risk attention area at the i-th sampling moment, and when it is marked as a driver risk attention area, _ai,k = 1; when it is not marked as a driver risk attention area _{, ai,k} = 0.

The formula for calculating the number of times the driving risk area k is marked as a system risk perception area within n sampling cycles is:

Among them, R _k is used to indicate the number of times that area k is marked as a system risk perception area within n sampling moments; _ri,k is used to indicate whether area k is marked as a system risk perception area at the i-th sampling moment, _ri,k = 1 when it is marked as a system risk perception area, and ri _,k = 0 when it is not marked as a system risk perception area.

It should be noted that if _Ak = 0 (k = 1, 2, ..., 8), it means that the driver's attention is not in the driving risk area during the preset sampling period, the driving right is returned to the driver, and a danger warning is issued through voice broadcast or visual display; if _Ak > 0, A ₁ =R ₁ , A ₈ =R ₈ , which means that the driver noticed the system risk perception area at least once in the preset sampling period, and the human-machine risk perception matching index was calculated.

For example, within n sampling periods, the calculation formula of the human-machine risk perception matching index is:

Among them, M is used to represent the human-machine risk perception matching index; ci _is used to represent the human-machine risk perception matching degree obtained at the i-th sampling moment, which can be determined based on the driving risk area matching rule shown in Figure 4.

Step S23, when the human-machine risk perception matching index does not exceed the preset index threshold, the human-machine risk perception consistency judgment result is that the human-machine risk perception is inconsistent.

It should be understood that the human-machine risk perception matching index M is obtained by calculating the average value of the human-machine risk perception matching degree within n sampling periods. The larger M is, the higher the consistency of human-machine risk perception within the preset sampling period. Whether the human-machine risk perception is consistent can be judged based on the preset index threshold C. When the human-machine risk perception matching index M is greater than the preset index threshold C, the result of the human-machine risk perception consistency judgment is that the human-machine risk perception is consistent; when the human-machine risk perception matching index M does not exceed the preset index threshold C, the result of the human-machine risk perception consistency judgment is that the human-machine risk perception is inconsistent.

This embodiment divides the preset driving risk areas based on the visible range of the cockpit, collects driving risk source information, determines the system risk perception area from the preset driving risk areas according to the driving risk source information, collects driver attention information, determines the driver risk attention area from the preset driving risk areas according to the driver attention information, determines the human-machine risk perception matching degree based on the system risk perception area and the driver risk attention area, evaluates the human-machine risk perception matching index within a preset sampling period based on the human-machine risk perception matching degree, comprehensively considers the intelligent driving system evaluation and the driver's attention behavior, and measures the consistency of the driver and the system in risk perception according to quantitative indicators, which helps the system to make a more reasonable response and give full play to the advantages of human-machine co-driving, thereby achieving more intelligent and accurate risk management, and helping to improve driving safety and optimize the driving experience.

It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the emergency lane keeping method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

The present application also provides an emergency lane keeping device, referring to FIG. 5 , the emergency lane keeping device comprises:

A judgment module 10, for judging the vehicle deviation trend based on the left and right lane line information, and obtaining a vehicle deviation judgment result;

An evaluation module 20 is used to evaluate the consistency between the risk area perceived by the system and the risk area perceived by the driver based on the driving risk area matching rule, and obtain a human-machine risk consistency evaluation result;

The activation module 30 is used to activate the emergency lane keeping system when the vehicle deviation judgment result is that the vehicle has a deviation trend and the human-machine risk consistency assessment result is inconsistent with the human-machine perception risk assessment.

The emergency lane keeping device provided by the present application adopts the emergency lane keeping method in the above embodiment, which can solve the technical problem of accurately identifying the triggering time of the emergency lane keeping system. Compared with the prior art, the beneficial effects of the emergency lane keeping device provided by the present application are the same as the beneficial effects of the emergency lane keeping method provided by the above embodiment, and the other technical features of the emergency lane keeping device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

The present application provides an emergency lane keeping device, which includes: at least one processor; and a memory that is communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the emergency lane keeping method in the above-mentioned embodiment one.

Reference is made to FIG6 , which shows a schematic diagram of the structure of an emergency lane keeping device suitable for implementing an embodiment of the present application. The emergency lane device in the embodiment of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. The emergency lane device shown in FIG6 is only an example and should not impose any limitations on the functions and scope of use of the embodiments of the present application.

As shown in FIG6 , the emergency lane device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM) 1004. Various programs and data required for the operation of the emergency lane device are also stored in the RAM 1004. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other via a bus 1005. An input/output (I/O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I/O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the emergency lane device to communicate with other devices wirelessly or by wire to exchange data. Although the emergency lane device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

The emergency lane device provided by the present application adopts the emergency lane method in the above embodiment, which can solve the technical problem of accurately identifying the triggering time of the emergency lane keeping system. Compared with the prior art, the beneficial effects of the emergency lane device provided by the present application are the same as the beneficial effects of the emergency lane method provided by the above embodiment, and the other technical features in the emergency lane device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer program) stored thereon, and the computer-readable program instructions are used to execute the emergency lane method in the above-mentioned embodiment.

The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

The computer-readable storage medium may be included in the emergency lane device; or may exist independently without being assembled into the emergency lane device.

The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the emergency lane device, the emergency lane device: determines whether the vehicle has a deviation trend based on the left and right lane line information, and obtains a vehicle deviation judgment result; determines whether the human-machine risk perception is consistent based on the system risk perception area and the driver risk attention area, and obtains a human-machine risk perception consistency judgment result; when the vehicle deviation judgment result is that the vehicle has a deviation trend, and the human-machine risk perception consistency judgment result is that the human-machine risk perception is inconsistent, the emergency lane keeping system is activated.

Computer program code for performing the operations of the present application may be written in one or more programming languages or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and/or flow chart, and the combination of the boxes in the block diagram and/or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

The modules involved in the embodiments of the present application may be implemented by software or hardware, wherein the name of the module does not, in some cases, constitute a limitation on the unit itself.

The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned emergency lane method, and can solve the technical problem of accurately identifying the triggering timing of the emergency lane keeping system. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the emergency lane method provided in the above-mentioned embodiment, and will not be repeated here.

The present application also provides a computer program product, including a computer program, which implements the steps of the emergency lane method as described above when executed by a processor.

The computer program product provided by the present application can solve the technical problem of accurately identifying the triggering timing of the emergency lane keeping system. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the emergency lane method provided by the above embodiment, which will not be repeated here.

The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct/indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims (10)

1. An emergency lane keeping method, characterized in that the emergency lane keeping method comprises: Based on the left and right lane line information, determine whether the vehicle has a tendency to deviate, and obtain a vehicle deviation judgment result; Based on the system risk perception area and the driver's risk attention area, it is judged whether the human-machine risk perception is consistent, and the human-machine risk perception consistency judgment result is obtained; When the vehicle deviation judgment result is that the vehicle has a tendency to deviate, and the human-machine risk perception consistency judgment result is that the human-machine risk perception is inconsistent, the emergency lane keeping system is activated. 2. The emergency lane keeping method according to claim 1, characterized in that before the step of determining the vehicle deviation trend based on the left and right lane line information, it also includes: Collect vehicle status information and driver operation information; Determine whether to perform an emergency lane triggering determination based on the vehicle state information and the driver operation information, and obtain a pre-determination result; When the pre-judgment result is to perform an emergency lane triggering judgment, the operation of judging the vehicle deviation trend based on the left and right lane line information is performed. 3. The emergency lane keeping method according to claim 1, wherein the step of determining whether the vehicle has a deviation trend based on the left and right lane line information and obtaining the vehicle deviation determination result comprises: Based on the left and right lane line information, determine whether the vehicle has a deviation side, and obtain a deviation side determination result; When the deviating side judgment result is that the vehicle has deviated from the side, obtaining the lane line information of the deviating side; Based on the deviating lane line information, it is determined whether the vehicle has a tendency to deviate, and the vehicle deviation determination result is obtained. 4. The emergency lane keeping method according to claim 2, wherein the step of determining whether the vehicle has a tendency to deviate based on the lane line information on the deviating side and obtaining the vehicle deviation determination result comprises: Calculating a deviation distance based on the lane line information on the deviated side; When the deviation distance is less than a preset distance, and the state in which the deviation distance is less than the preset distance lasts for a preset time, the vehicle deviation judgment result is that the vehicle has a deviation trend. 5. The emergency lane keeping method according to claim 1, characterized in that before the step of judging whether the human-machine risk perception is consistent based on the system risk perception area and the driver risk attention area, it also includes: Divide and preset driving risk areas based on the visible range of the cockpit; Collecting driving risk source information, and determining a system risk perception area from the preset driving risk areas according to the driving risk source information; Collect driver attention information, A driver risk attention area is determined from the preset driving risk areas according to the driver attention information. 6. The emergency lane keeping method according to claim 5, characterized in that the step of determining the system risk perception area from the preset driving risk areas according to the driving risk source information comprises: Based on the driving risk source information, acquiring the stationary risk source information and the moving risk source information of the preset driving risk area; Calculate the driving risk potential energy field strength and the driving risk kinetic energy field strength based on the stationary risk source information and the moving risk source information to obtain a driving risk field strength calculation result; A system risk perception area is determined from the preset driving risk areas according to the driving risk field strength calculation result. 7. The emergency lane keeping method according to claim 1, characterized in that the step of judging whether the human-machine risk perception is consistent based on the system-perceived risk area and the driver-noticed risk area to obtain the human-machine risk perception consistency judgment result comprises: Determining a human-machine risk perception matching degree based on the system risk perception area and the driver risk attention area; Evaluate the human-machine risk perception matching index within a preset sampling period based on the human-machine risk perception matching degree; When the human-machine risk perception matching index does not exceed a preset index threshold, the human-machine risk perception consistency judgment result is that the human-machine risk perception is inconsistent. 8. An emergency lane keeping device, characterized in that the device comprises: A judgment module, used to judge the vehicle deviation trend based on the left and right lane line information and obtain the vehicle deviation judgment result; An evaluation module is used to evaluate the consistency between the risk area perceived by the system and the risk area perceived by the driver based on the driving risk area matching rule, and obtain a human-machine risk consistency evaluation result; The activation module is used to activate the emergency lane keeping system when the vehicle deviation judgment result is that the vehicle has a deviation trend and the human-machine risk consistency assessment result is inconsistent with the human-machine perception risk assessment. 9. An emergency lane keeping device, characterized in that the device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the emergency lane keeping method as described in any one of claims 1 to 7. 10. A storage medium, characterized in that the storage medium is a computer-readable storage medium, a computer program is stored on the storage medium, and when the computer program is executed by a processor, the steps of the emergency lane keeping method according to any one of claims 1 to 7 are implemented.