Emergency Lane Keeping Method, Device, Equipment and Storage Medium

Abstract

The present application discloses an emergency lane keeping method, device, equipment and storage medium, relating to the technical field of vehicle control. The disclosed emergency lane keeping method includes: determining whether the vehicle has a deviation trend based on the left and right lane line information to obtain a vehicle deviation determination result; determining whether the human-machine risk perception is consistent based on the system risk perception area and the driver risk attention area to obtain a human-machine risk perception consistency determination result; when the vehicle deviation determination result indicates that the vehicle has a deviation trend and the human-machine risk perception consistency determination result indicates that the human-machine risk perception is inconsistent, activating the emergency lane keeping system, taking into account the driver's risk recognition ability, thereby accurately identifying the triggering time of the emergency lane keeping system.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle control, and in particular, to an emergency lane keeping method, device, equipment and storage medium. Background Art

[0002] The emergency lane keeping system can perform emergency control on the vehicle when the vehicle deviates from the lane line or the road edge, so as to avoid collisions between the vehicle itself and nearby vehicles or the road edge. The system trigger is generally relatively urgent, often causing large fluctuations in vehicle control, which affects driving safety and the riding experience. Therefore, how to accurately identify the trigger timing has become the key to emergency lane keeping.

[0003] In the existing solutions, the emergency lane keeping method only judges whether to trigger the emergency lane keeping system based on the deviation trend of the vehicle, without considering the driver's aggressive driving situation and risk recognition ability, which affects the driving experience.

[0004] The above content is only used to assist in understanding the technical solution of the present application, and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of the present 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 trigger timing of the emergency lane keeping system.

[0006] To achieve the above purpose, the present application proposes an emergency lane keeping method, and the emergency lane keeping method includes:

[0007] Judging whether the vehicle has a deviation trend based on the left and right lane line information to obtain a vehicle deviation judgment result;

[0008] Judging whether the human-machine risk perception is consistent based on the system risk perception area and the driver risk attention area to obtain a human-machine risk perception consistency judgment result;

[0009] 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, activate the emergency lane keeping system.

[0010] In an embodiment, before the step of judging the vehicle deviation trend based on the left and right lane line information, the method further includes:

[0011] Collecting vehicle state information and driver operation information;

[0012] Judging whether to perform an emergency lane trigger determination based on the vehicle state information and the driver operation information to obtain a pre-judgment result;

[0013] When the pre - judgment result is to perform the emergency lane trigger determination, perform the operation of judging the vehicle deviation trend based on the left and right lane line information.

[0014] In one embodiment, the step of judging whether the vehicle has a deviation trend based on the left and right lane line information to obtain a vehicle deviation judgment result includes:

[0015] Judge whether the vehicle has a deviation side based on the left and right lane line information to obtain a deviation side judgment result;

[0016] When the deviation side judgment result is that the vehicle has a deviation side, obtain the lane line information of the deviation side;

[0017] Judge whether the vehicle has a deviation trend based on the lane line information of the deviation side to obtain the vehicle deviation judgment result.

[0018] In one embodiment, the step of judging whether the vehicle has a deviation trend based on the lane line information of the deviation side to obtain the vehicle deviation judgment result includes:

[0019] Calculate the deviation distance based on the lane line information of the deviation side;

[0020] When the deviation distance is less than a preset distance and the duration of the state where 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.

[0021] 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, it further includes:

[0022] Divide a preset number of driving risk areas based on the visible range of the cockpit;

[0023] Collect driving risk source information, and determine the system risk perception area from the preset number of driving risk areas according to the driving risk source information;

[0024] Collect driver attention information,

[0025] Determine the driver risk attention area from the preset number of driving risk areas according to the driver attention information.

[0026] In one embodiment, the step of determining the system risk perception area from the preset number of driving risk areas according to the driving risk source information includes:

[0027] Obtain the static risk source information and moving risk source information of the preset number of driving risk areas based on the driving risk source information;

[0028] Calculate the driving risk potential field strength and the driving risk kinetic field strength based on the stationary risk source information and the moving risk source information, and obtain the calculation result of the driving risk field strength;

[0029] Determine the system risk perception area from the preset driving risk areas according to the calculation result of the driving risk field strength.

[0030] In one embodiment, the step of judging whether the human-machine risk perception is consistent based on the system perception risk area and the driver's attention risk area to obtain the human-machine risk perception consistency judgment result includes:

[0031] Determine the human-machine risk perception matching degree based on the system risk perception area and the driver risk attention area;

[0032] Evaluate the human-machine risk perception matching index within a preset sampling period based on the human-machine risk perception matching degree;

[0033] 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.

[0034] In addition, to achieve the above object, the present application also proposes an emergency lane keeping device, which includes:

[0035] A judgment module, configured to judge the vehicle deviation trend based on the left and right lane line information to obtain a vehicle deviation judgment result;

[0036] An evaluation module, configured to evaluate the consistency between the system perception risk area and the driver perception risk area based on the driving risk area matching rule to obtain a human-machine risk consistency evaluation result;

[0037] An activation module, configured 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 evaluation result is that the human-machine perception risk evaluation is inconsistent.

[0038] In addition, to achieve the above object, 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 as described above.

[0039] In addition, to achieve the above object, 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, and when the computer program is executed by a processor, it implements the steps of the emergency lane keeping method as described above.

[0040] In addition, to achieve the above object, the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps of the emergency lane keeping method described above.

[0041] One or more technical solutions proposed in the present application have at least the following technical effects:

[0042] By judging whether the vehicle has a deviation trend based on the left and right lane line information to obtain a vehicle deviation judgment result, and judging whether the human-machine risk perception is consistent based on the system risk perception area and the driver risk attention area to obtain a human-machine risk perception consistency judgment result. When the vehicle deviation judgment result indicates that the vehicle has a deviation trend and the human-machine risk perception consistency judgment result indicates that the human-machine risk perception is inconsistent, the emergency lane keeping system is activated, taking into account the driver's risk recognition ability, thereby accurately identifying the triggering timing of the emergency lane keeping system to avoid mis-triggering the emergency lane keeping system and causing large vehicle control fluctuations, which may affect driving safety and the normal riding experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The drawings herein are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0044] To more clearly illustrate the technical solutions in the embodiments of the present application or in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0045] Figure 1 It is a schematic flowchart provided for Embodiment 1 of the emergency lane keeping method of the present application;

[0046] Figure 2 It is a schematic flowchart provided for Embodiment 2 of the emergency lane keeping method of the present application;

[0047] Figure 3 It is a schematic diagram of the division of driving risk areas provided for Embodiment 2 of the emergency lane keeping method of the present application;

[0048] Figure 4 It is a schematic diagram of matching rules provided for Embodiment 2 of the emergency lane keeping method of the present application;

[0049] Figure 5 It is a schematic diagram of the module structure of the emergency lane keeping device in the embodiment of the present application;

[0050] Figure 6This is a schematic diagram of the device structure of the hardware operating environment involved in the emergency lane keeping method in the embodiments of the present application.

[0051] The implementation, functional features, and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. Specific embodiments

[0052] 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.

[0053] To better understand the technical solutions of the present application, the following will be described in detail with reference to the accompanying drawings of the specification and specific embodiments.

[0054] 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, a vehicle control device, etc. that can implement the above functions. Hereinafter, taking the vehicle control device as an example, this embodiment and the following embodiments will be described.

[0055] Based on this, the embodiments of the present application provide an emergency lane keeping method, referring to Figure 1 , Figure 1 This is a flowchart of the first embodiment of the emergency lane keeping method of the present application.

[0056] In this embodiment, the emergency lane keeping method includes steps S10 to S40:

[0057] Step S10, judging whether the vehicle has a deviation trend based on the left and right lane line information, and obtaining a vehicle deviation judgment result;

[0058] It should be noted that an in-vehicle camera or other sensors (such as lidar, radar) can be used to capture the road image or data in front of the vehicle. By processing the collected road image or data, relevant parameters of the lane line information can be obtained, and the linear equations of the left and right lane line information can be determined. The linear equations of the left and right lane line information can be cubic polynomials, and the coefficients of the cubic polynomials are 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 according to the left and right lane line information, and then it can be judged whether the vehicle has a deviation trend. If the analysis result shows that the vehicle continuously moves towards one side lane line, it indicates that the vehicle may have a deviation trend.

[0059] Exemplarily, the linear equations of the left and right lane line information are as follows:

[0060] f L = c 0,L + c 1,L s + c2,L s 2 +c 3,L s 3

[0061] f R = c 0,R +c 1,R s + c 2,R s 2 +c 3,R s 3

[0062] Among them, L and R are respectively used to represent left and right; f is used to represent the change in the length of the perpendicular line segment when the points on the lane line are projected onto the longitudinal center line direction of the vehicle itself; 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.

[0063] In this embodiment, step S10 includes steps A11 to A13:

[0064] Step A11, based on the left and right lane line information, determine whether the vehicle has a deviation side, and obtain a deviation side judgment result;

[0065] It should be understood that when judging whether the vehicle has a deviation trend, 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 according to the left and right lane line information. Based on the lateral distance and lateral speed of the lane line, it can be determined whether the vehicle has a deviation side. When the vehicle has a deviation side, it is necessary to judge whether the deviation side of the vehicle is the left side or the right side.

[0066] Exemplarily, the acquisition formulas for the lateral distance and lateral speed are as follows:

[0067] d L = |c 0,L |

[0068] d R = |c 0,R |

[0069]

[0070] Among them, d L and d R respectively represent the lateral distances of the vehicle relative to the left and right lane lines, V lat represents the lateral speed, and V represents the vehicle speed (km / h).

[0071] It should be noted that if d L < d R, and V lat,L If the direction is to the left, the deviation side judgment result is that the vehicle has a deviation side, and the deviation side of the vehicle is the left side; if d R <d L , and V lat,R If the direction is to the right, the deviation side judgment result is that the vehicle has a deviation side, and the deviation side of the vehicle is the right side; if it is other situations, the deviation side judgment result is that the vehicle has no deviation side.

[0072] Step A12, when the deviation side judgment result is that the vehicle has a deviation side, obtain the lane line information of the deviation side;

[0073] It should be understood that after obtaining the deviation side judgment result, the lane line information of the vehicle's deviation side can be obtained based on the deviation side judgment result. If the deviation side of the vehicle is the left side, obtain the left lane line information; if the deviation side of the vehicle is the right side, obtain the right lane line information. The lane line information of the vehicle's deviation side can be obtained based on the linear equation of the deviation side lane line information.

[0074] Exemplarily, the linear equation of the deviation side lane line information is as follows:

[0075] f = c0 + c1s + c2s 2 + c3s 3

[0076] Step A13, judge whether the vehicle has a deviation trend based on the lane line information of the deviation side, and obtain the vehicle deviation judgment result.

[0077] Furthermore, step A13 includes steps B1 to B2:

[0078] Step B1, calculate the deviation distance based on the lane line information of the deviation side;

[0079] It should be understood that when it is detected that the vehicle has a deviation side during driving, early warning can be carried out through voice broadcast or visual display, etc., to remind the driver to pay attention to the collision risk, and based on the lane line information of the deviation side, predict the longitudinal distance of the vehicle movement along with the lane line change within the first warning time, so as to calculate the deviation distance, and then judge whether the vehicle has a deviation trend.

[0080] Exemplarily, the calculation formula of the deviation distance is as follows:

[0081]

[0082] d = |c0 + c1s1 + c2s1 2 + c3s1 3 |

[0083]

[0084] Among them, the orientation angle of the vehicle relative to the lane line; T1 is used for the first warning time (s); s1 is used to represent the longitudinal distance (m) of the predicted vehicle movement changing with the lane line within the first warning time; W is used to represent the vehicle width (m); d is used to represent the lateral compensation distance (m) of the lane line; D0 is used to represent the deviation distance (m).

[0085] Step B2, when the deviation distance is less than the preset distance, and the duration of the state that the deviation distance is less than the preset distance reaches the preset time, the vehicle deviation judgment result is that the vehicle has a deviation trend.

[0086] It should be noted that the preset distance is the deviation determination distance D1 set according to the vehicle speed. The faster the vehicle speed, the larger the preset distance set. The preset time can be 0.1 s. That is, if D0 < D1 and this state lasts for 0.1 s, the vehicle deviation judgment result is that the vehicle has a deviation trend; if it is other situations, the vehicle deviation judgment result is that the vehicle does not have a deviation trend.

[0087] Step S20, based on the system risk perception area and the driver risk attention area, judge whether the human-machine risk perception is consistent, and obtain the human-machine risk perception consistency judgment result;

[0088] It should be noted that during the vehicle driving process, the intelligent driving system will collect the risk source information around the vehicle, obtain the risk source type in the driving environment and the distance between the vehicle itself and the risk source based on the collected risk source information, analyze the driving risk distribution in each area, and take the area with the largest recognized driving risk as the system risk perception area. At the same time, the eye tracker will continuously capture the eye movement data of the driver, including the line of sight direction, fixation duration, eye movement speed, etc., analyze the change of the driver's attention to the risk areas around the vehicle through the eye movement data, and locate the driving risk area where the driver's attention is located as the driver risk attention area.

[0089] 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 the preset matching rules, or the risk matching degree between the system risk perception area and the driver risk attention area can be calculated by using a mathematical model or algorithm, so as to judge whether the human-machine risk perception is consistent. This embodiment does not make specific limitations on this.

[0090] Step S30, 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, activate the emergency lane keeping system.

[0091] It should be understood that the triggering timing of the emergency lane keeping system needs to satisfy two conditions simultaneously: the vehicle has a deviation trend and the human-machine risk perception is inconsistent. When the vehicle has no deviation trend, it indicates that the vehicle is currently driving stably within the lane and there is no need to perform emergency control on the vehicle; when the vehicle has a deviation trend but the human-machine risk perception is consistent, it means that the driver has a full understanding of the collision risk in the current driving environment and can perform corresponding operations according to the actual driving risk.

[0092] In another feasible implementation manner, before step S10, the emergency lane keeping method further includes steps S01 to S03:

[0093] Step S01, collect vehicle state information and driver operation information;

[0094] It should be noted that the vehicle state information includes vehicle speed information, steering wheel angle information, and steering wheel rotation speed information, and the driver operation information includes driver hand torque information and the driver's operation information on the deviation side.

[0095] Step S02, based on the vehicle state information and the driver operation information, determine whether to perform an emergency lane trigger determination to obtain a pre-judgment result;

[0096] It should be understood that after obtaining the vehicle state information and the driver operation information, it is possible to determine whether the vehicle state information or the driver operation information meets the preset conditions, and when the preset conditions are met, an emergency lane trigger determination is performed. Specifically, if the vehicle speed is lower than the preset vehicle speed V P , or the driver 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 rotation speed is greater than the preset rotation speed α, or the driver has turned on the turn signal on the deviation side, the pre-judgment result is not to perform an emergency lane trigger determination; in other cases, the pre-judgment result is to perform an emergency lane trigger determination.

[0097] Step S03, when the pre-judgment result is to perform an emergency lane trigger determination, execute the operation of judging the vehicle deviation trend based on the left and right lane line information.

[0098] It should be understood that when the vehicle state information or the driver 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 deviation of the vehicle from the lane may not cause serious safety consequences; when the driver's hand torque, steering wheel angle, or rotation speed is greater than a specific value, it means that the driver is actively controlling the vehicle and may deliberately deviate from the lane due to avoiding obstacles, preparing to stop, or other reasons; when the driver has turned on the turn signal on the deviation side, it means that the driver may intend to change lanes or turn, and the intervention of the emergency lane keeping system at this time will interfere with the driver's planned driving route.

[0099] In this embodiment, by judging whether the vehicle has a deviation trend based on the left and right lane line information, a 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 risk attention area, a human-machine risk perception consistency judgment result is obtained. When the vehicle deviation judgment result indicates that the vehicle has a deviation trend and the human-machine risk perception consistency judgment result indicates that the human-machine risk perception is inconsistent, the emergency lane keeping system is activated, taking into account the driver's risk recognition ability, thereby accurately identifying the triggering timing of the emergency lane keeping system to avoid mis-triggering the emergency lane keeping system and causing large vehicle control fluctuations, which may affect driving safety and the normal riding experience.

[0100] Based on the first embodiment of the present application, a second embodiment of the present application is proposed. In the second embodiment of the present application, for the same or similar content as in the above-mentioned first embodiment, reference can be made to the above introduction and will not be elaborated hereinafter. On this basis, please refer to Figure 2 , before step S20, the emergency lane method further includes steps S11 to S13:

[0101] Step S11, dividing a preset number of driving risk areas based on the visible range of the cockpit;

[0102] 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 number of driving risk areas can be divided according to the visible range of the vehicle cockpit first.

[0103] Exemplarily, as Figure 3 shown, 8 driving risk areas can be divided. Among them, 2, 3, 4, 5, 6, and 7 are the respective partitions in the direction of the front windshield; 1 and 8 are the risk partitions on the left and right sides and in the direction of the rearview mirrors (rear).

[0104] Step S12, collecting driving risk source information, and determining the system risk perception area from the preset number of driving risk areas according to the driving risk source information;

[0105] It should be noted that the visual information of the road can be captured by the vehicle's front, side, or rear cameras to identify the types of driving risk sources, such as roadblocks, traffic lights, pedestrians, and other vehicles; the quality, speed, and distance of each driving risk source are collected through radar sensors. Corresponding risk coefficients are assigned to different types of driving risk sources, and the driving risk field intensity of each driving risk area is calculated based on the risk coefficients and sensor data. The driving risk area with the maximum driving risk field intensity is used as the system risk perception area.

[0106] In this embodiment, step S12 includes steps A21 to A23:

[0107] Step A21: Obtain the static risk source information and moving risk source information of the preset driving risk areas based on the driving risk source information;

[0108] It should be understood that driving risk sources include static risk sources and moving risk sources. Among them, static risk sources include static 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 host vehicle.

[0109] Step A22: Calculate the driving risk potential energy field strength and the driving risk kinetic energy field strength based on the static risk source information and the moving risk source information, and obtain the driving risk field strength calculation result;

[0110] It should be understood that according to the type information and location information corresponding to the driving risk sources, the driving risk potential energy field strength and the driving risk kinetic energy field strength of each driving risk area can be calculated respectively, and the sum of the driving risk field strengths in the area is used as the driving risk field strength calculation result. Among them, the static risk source information is used to calculate the risk potential energy field strength, and the moving risk source information is used to calculate the risk potential kinetic energy field strength.

[0111] Exemplarily, the calculation formula for the risk potential energy field strength is as follows:

[0112]

[0113] M = mv

[0114] where E R is used to represent the size of the potential energy field; k R is used to represent the potential energy field calculation coefficient; M is used to represent the equivalent mass size of the host vehicle; T is used to represent the risk coefficient corresponding to the risk source type; D is used to represent the distance (m) between the vehicle and the risk source; m is used to represent the vehicle mass (kg); v is used to represent the vehicle speed of the host vehicle (m / s).

[0115] In a feasible implementation manner, the corresponding formula between different types of driving risk sources and risk coefficients can be as follows:

[0116]

[0117] The calculation formula for the risk kinetic energy field strength is as follows:

[0118]

[0119] where E V is used to represent the size of the kinetic energy field; k V is used to represent the kinetic energy field calculation coefficient; M i is used to represent the equivalent mass of the moving object i.

[0120] The sum of the driving risk field strengths in different driving risk areas, that is, the calculation formula of the driving risk field strength calculation result is as follows:

[0121] E i = E R,i + E V,i

[0122] Among them, E i is used to represent the sum of the driving risk field strengths in driving risk area i.

[0123] Step A23, determine the system risk perception area from the preset driving risk areas according to the driving risk field strength calculation result.

[0124] It should be understood that after calculating the sum of the driving risk field strengths in each driving risk area to obtain the driving risk field strength calculation result, 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 the driving risk field strengths will be used as the system risk perception area.

[0125] Exemplarily, if the preset areas are 8, for the set of driving risk field strength sums 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 will be used as the system risk perception area.

[0126] Step S13, collect driver attention information, and determine the driver risk attention area from the preset driving risk areas according to the driver attention information.

[0127] It should be understood that the driver attention information can be eye movement data, and the eye movement data can be obtained by a eye tracker to capture the driver's line of sight direction, fixation duration, eye movement speed, etc. in real time. Through the driver attention information, the driving risk area that the driver is most concerned about at present can be determined and used as the driver risk attention area.

[0128] In this embodiment, step S20 includes steps S21 to S23:

[0129] Step S21, determine the human-machine risk perception matching degree based on the system risk perception area and the driver risk attention area;

[0130] In a feasible implementation manner, 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 Figure 4 shown Figure 4It is a schematic diagram of the matching rules for driving risk areas. In the figure, the yellow circles are used to mark the driver's risk attention areas. The number c in the blue box indicates the risk matching degrees corresponding to when the driver's risk attention area is the area marked by the yellow circle and the driving system's risk perception areas are 1, 2, 3, 4, 5, 6, 7, and 8 respectively. The areas not marked by the blue box indicate a low matching degree, and by default, c is 0.

[0131] Exemplarily, according to Figure 4 the shown matching rules, at a certain moment, if the driver's risk attention area is 1 and the system's risk perception area is area 1, the risk matching degree is 1; if the driver's risk attention area is 2 and the system's 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's 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's risk perception area is area 2, the risk matching degree is 0.

[0132] Step S22, evaluate the human-machine risk perception matching index within the preset sampling period based on the human-machine risk perception matching degree;

[0133] It should be understood that if there are other target vehicles or road edges near the host vehicle, it is necessary to comprehensively evaluate the human-machine risk perception matching degrees at the current moment and in the past period of time, that is, within the preset sampling period, to obtain the human-machine risk perception matching index.

[0134] Exemplarily, within n sampling periods, calculate the number of times the driving risk area k is marked as the driver's risk

[0135] where A k is used to represent the number of times the area k is marked as the driver's risk attention area within n sampling moments; a i,k is used to represent whether the area k is marked as the driver's risk attention area at the i-th sampling moment. When it is marked as the driver's risk attention area, a i,k = 1, and when it is not marked as the driver's risk attention area, a i,k = 0.

[0136] Within n sampling periods, the formula for calculating the number of times the driving risk area k is marked as the system's risk perception area is:

[0137]

[0138] where R k is used to represent the number of times the area k is marked as the system's risk perception area within n sampling moments; r i,kUsed to indicate whether area k is marked as a system risk perception area at the i-th sampling moment. When it is marked as a system risk perception area, r i,k = 1, and when it is not marked as a system risk perception area, r i,k = 0.

[0139] It should be noted that if A k = 0 (k = 1, 2,..., 8), it means that the driver's attention is not in the driving risk area within the preset sampling period. The driving right is returned to the driver, and a danger warning is issued through voice broadcast or visual display, etc.; if A k > 0, A1 = R1, A8 = R8, it means that the driver notices the system risk perception area at least once within the preset sampling period, and the human-machine risk perception matching index starts to be calculated.

[0140] Exemplarily, within n sampling periods, the calculation formula of the human-machine risk perception matching index is:

[0141]

[0142] Among them, M is used to represent the human-machine risk perception matching index; c i is used to represent the human-machine risk perception matching degree obtained at the i-th sampling moment, and can be determined based on Figure 4 the driving risk area matching rules shown.

[0143] 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.

[0144] 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 degrees within n sampling periods. The larger M is, the higher the human-machine risk perception consistency within the preset sampling period. It can be judged whether the human-machine risk perception is consistent based on the set preset index threshold C. When the human-machine risk perception matching index M is greater than the preset index threshold C, the human-machine risk perception consistency judgment result 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 human-machine risk perception consistency judgment result is that the human-machine risk perception is inconsistent.

[0145] In this embodiment, by dividing a preset number of driving risk areas based on the visible range of the cockpit, collecting driving risk source information, determining the system risk perception area from the preset number of driving risk areas according to the driving risk source information, collecting driver attention information, determining the driver risk attention area from the preset number of driving risk areas according to the driver attention information, determining the human-machine risk perception matching degree based on the system risk perception area and the driver risk attention area, evaluating the human-machine risk perception matching index within a preset sampling period based on the human-machine risk perception matching degree, comprehensively considering the intelligent driving system evaluation and the driver's attention behavior, and measuring the consistency between the driver and the system in risk perception according to the quantified indicators, it helps the system to make more reasonable responses, gives full play to the advantages of human-machine co-driving, and thus realizes more intelligent and accurate risk management, which helps to improve driving safety and optimize the driving experience.

[0146] It should be noted that the above examples are only for understanding the present application and do not constitute a limitation on the emergency lane keeping method of the present application. Based on this technical concept, more forms of simple transformation are within the protection scope of the present application.

[0147] The present application also provides an emergency lane keeping device. Please refer to Figure 5 , and the emergency lane keeping device includes:

[0148] A judgment module 10, configured to judge the vehicle deviation trend based on the left and right lane line information to obtain a vehicle deviation judgment result;

[0149] An evaluation module 20, configured to evaluate the consistency between the system perception risk area and the driver perception risk area based on the driving risk area matching rule to obtain a human-machine risk consistency evaluation result;

[0150] An activation module 30, configured to activate the emergency lane keeping system when the vehicle deviation judgment result indicates that the vehicle has a deviation trend and the human-machine risk consistency evaluation result indicates that the human-machine perception risk evaluation is inconsistent.

[0151] The emergency lane keeping device provided by the present application adopts the emergency lane keeping method in the above embodiment, 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 emergency lane keeping device provided by the present application are the same as those of the emergency lane keeping method provided by the above embodiment, and the other technical features in the emergency lane keeping device are the same as those disclosed in the method of the above embodiment, and will not be elaborated here.

[0152] The present application provides an emergency lane keeping device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the emergency lane keeping method in Embodiment 1 above.

[0153] Reference is made below to Figure 6 , which shows a schematic structural diagram of an emergency lane keeping device suitable for implementing the embodiments of the present application. The emergency lane device in the embodiments 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), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The emergency lane device shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present application.

[0154] As Figure 6 shown, the emergency lane device may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can execute various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM: Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of the emergency lane device are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems may 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: Liquid Crystal Display), 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 may allow the emergency lane device to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows an emergency lane device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be alternatively implemented or had.

[0155] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the methods shown in the flowcharts. 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 a processing device 1001, the above-mentioned functions defined in the methods of the embodiments disclosed in the present application are executed.

[0156] The emergency lane device provided by the present application adopts the emergency lane method in the above embodiments, 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 emergency lane device provided by the present application are the same as those of the emergency lane method provided by the above embodiments, and other technical features in the emergency lane device are the same as those disclosed in the method of the previous embodiment, and will not be elaborated here.

[0157] It should be understood that each part disclosed in the present 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 a suitable manner in any one or more embodiments or examples.

[0158] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0159] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the emergency lane method in the above embodiments.

[0160] The computer-readable storage medium provided by this application can be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this embodiment, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or component. The program code contained on the computer-readable storage medium can 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.

[0161] The above computer-readable storage medium can be included in the emergency lane device; it can also exist separately without being assembled into the emergency lane device.

[0162] The above computer-readable storage medium carries one or more programs. When the above one or more programs are executed by the emergency lane device, the emergency lane device is caused to: judge whether the vehicle has a deviation trend based on the left and right lane line information to obtain a vehicle deviation judgment result; judge whether the human-machine risk perception is consistent based on the system risk perception area and the driver risk attention area to obtain a human-machine risk perception consistency judgment result; when the vehicle deviation judgment result indicates that the vehicle has a deviation trend and the human-machine risk perception consistency judgment result indicates that the human-machine risk perception is inconsistent, activate the emergency lane keeping system.

[0163] Computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent 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 can be connected to the user's computer through any kind of network, including a local area network (LAN: Local Area Network) or a wide area network (WAN: Wide Area Network), or it can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0164] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and this module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order from that marked in the accompanying drawings. For example, two consecutively represented blocks can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0165] The modules involved in the embodiments described in this application can be implemented in software or in hardware. Among them, the name of the module does not constitute a limitation to the unit itself in some cases.

[0166] The readable storage medium provided in this application is a computer-readable storage medium. The computer-readable storage medium stores computer-readable program instructions (i.e., computer programs) for performing the above-mentioned emergency lane method, 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 computer-readable storage medium provided in this application are the same as those of the emergency lane method provided in the above embodiments, and will not be elaborated here.

[0167] The present application also provides a computer program product, including a computer program which, when executed by a processor, implements the steps of the emergency lane method as described above.

[0168] The computer program product provided by the present application 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 computer program product provided by the present application are the same as those of the emergency lane method provided by the above embodiments, and will not be elaborated herein.

[0169] The above are only partial embodiments of the present application, and thus do not limit the patent scope of the present application. Any equivalent structural transformation made under the technical concept of the present application by using the content of the specification and drawings of the present application, or direct / indirect application in other related technical fields, is included in the patent protection scope of the present application.

Claims

1. An emergency lane keeping method, characterized in that: The emergency lane keeping method comprises: Collecting vehicle status information and driver operation information, wherein 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 turn signal operation information on the deviation side; When the vehicle speed information, the steering wheel angle information, the steering wheel speed information, the driver's hand torque information, and the driver's operation information on the deviation side meet preset conditions, determining that the pre-judgment result is to perform an emergency lane triggering judgment; When the pre-judgment result is to perform an emergency lane triggering judgment, judging whether the vehicle has a deviation trend based on the left and right lane line information, and obtaining 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, activating the emergency lane keeping system; The step of determining whether the vehicle has a deviation trend based on the left and right lane line information and obtaining a 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 lane line information of the deviated side, it is determined whether the vehicle has a tendency to deviate, and a vehicle deviation determination result is obtained; The step of determining whether the vehicle has a tendency to deviate based on the deviating lane line information to obtain 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; The step of calculating the deviation distance based on the deviating lane line information includes: predicting the longitudinal distance of the vehicle moving as the lane line changes within a preset warning time based on the deviating lane line information, and calculating the deviation distance based on the longitudinal distance.

2. 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, 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; The driver attention information is collected, and a driver risk attention area is determined from the preset driving risk areas according to the driver attention information.

3. The emergency lane keeping method according to claim 2, 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 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.

4. 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, and obtaining a result of judging the consistency of the human-machine risk perception, 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.

5. An emergency lane keeping device, characterized in that: The device comprises: A judgment module, used for collecting vehicle status information and driver operation information, wherein 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 turn signal operation information on the deviation side; when the vehicle speed information, the steering wheel angle information, the steering wheel speed information, the driver hand torque information and the driver operation information on the deviation side do not meet preset conditions, determining that a pre-judgment result is to perform an emergency lane triggering judgment; when the pre-judgment result is to perform an emergency lane triggering judgment, judging a vehicle deviation trend based on left and right lane line information to obtain a vehicle deviation judgment result; The judgment module is further used to judge whether the vehicle has a deviating side based on the left and right lane line information, and obtain a deviating side judgment result; when the deviating side judgment result is that the vehicle has a deviating side, obtain the deviating side lane line information; judge whether the vehicle has a deviating trend based on the deviating side lane line information, and obtain the vehicle deviation judgment result; calculate the deviation distance based on the deviating side lane line information; when the deviation distance is less than a preset distance, and the state of the deviation distance being less than the preset distance lasts for a preset time, the vehicle deviation judgment result is that the vehicle has a deviating trend; predict the longitudinal distance of the vehicle moving with the lane line change within a preset warning time based on the deviating side lane line information, and calculate the deviation distance based on the longitudinal distance; 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.

6. 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 according to any one of claims 1 to 4.

7. A storage medium, characterized in that: The storage medium 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 according to any one of claims 1 to 4 are implemented.

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