CN117104258A

CN117104258A - Lane departure early warning method, device, equipment and medium

Info

Publication number: CN117104258A
Application number: CN202311101055.7A
Authority: CN
Inventors: 付仁涛; 蒋子明; 刘柯旺
Original assignee: Faw Nanjing Technology Development Co ltd; FAW Group Corp
Current assignee: Faw Nanjing Technology Development Co ltd; FAW Group Corp
Priority date: 2023-08-29
Filing date: 2023-08-29
Publication date: 2023-11-24

Abstract

The embodiment of the invention discloses a lane departure warning method, a lane departure warning device, lane departure warning equipment and a lane departure warning medium. Wherein the method comprises the following steps: determining a current flying lead distance of the target vehicle at the current vehicle position, and determining a predicted flying lead distance of the target vehicle at the predicted vehicle position; the crossing distance is used for representing the distance between a reference front wheel of the target vehicle and a reference lane line, wherein the reference lane line is a lane line positioned on the same side of the reference front wheel in the lane line where the target vehicle is positioned; determining the lane departure risk degree of the target vehicle according to a preset fuzzy rule based on the current overpass distance and the predicted overpass distance; and determining the lane departure warning state of the target vehicle according to the lane departure risk degree. According to the technical scheme, lane departure warning is carried out on the vehicle based on the fuzzy control mode, different working conditions are not required to be distinguished, the complexity of a warning algorithm can be reduced while the warning accuracy is ensured, and therefore the warning efficiency is improved.

Description

Lane departure early warning method, device, equipment and medium

Technical Field

The present invention relates to the field of vehicle control technologies, and in particular, to a lane departure warning method, apparatus, device, and medium.

Background

With the continuous development of intelligent driving vehicles, more and more vehicles are equipped with lane keeping assist systems. When the vehicle is detected to deviate from the original lane, the system can send out an alarm prompt and actively control the vehicle to return to the lane, so that the occurrence of traffic accidents is reduced. Therefore, a reasonable lane departure early warning algorithm has important significance for a lane keeping auxiliary system.

In the related scheme, a cross-line time algorithm is adopted to perform lane departure early warning. The algorithm calculates the time when the wheels touch the outer boundary of the lane line under the current running state of the vehicle, and determines whether to send an alarm or deviation correcting signal based on the calculated time.

However, the above solution requires distinguishing between different vehicle operating conditions and taking corresponding measures for the different conditions. Specifically, the straight line road section, the curve road section, the straight line running track and the curve running track need to be considered, the straight line road section, the curve road section, the straight line running track and the curve running track are respectively combined with four algorithm models, and each model algorithm is different, so that the algorithm is complex and tedious, and the early warning efficiency is low.

Disclosure of Invention

The invention provides a lane departure early warning method, a lane departure early warning device, lane departure early warning equipment and a lane departure early warning medium, which are based on a fuzzy control mode, do not need to distinguish different working conditions, and can reduce the complexity of an early warning algorithm while ensuring the early warning accuracy, thereby improving the early warning efficiency.

According to an aspect of the present invention, there is provided a lane departure warning method, the method including:

determining a current flying lead distance of a target vehicle at a current vehicle position, and determining a predicted flying lead distance of the target vehicle at a predicted vehicle position; the overline distance is used for representing the distance between a reference front wheel of the target vehicle and a reference lane line, the reference lane line is a lane line which is positioned on the same side of the reference front wheel in the lane lines where the target vehicle is positioned, and the predicted vehicle position is a predicted vehicle position when the target vehicle runs according to the current running state;

determining the lane departure risk degree of the target vehicle according to a preset fuzzy rule based on the current overline distance and the predicted overline distance; the preset fuzzy rule is determined based on the current line crossing distance and the change trend of the predicted line crossing distance;

and determining the lane departure warning state of the target vehicle according to the lane departure risk degree.

According to another aspect of the present invention, there is provided a lane departure warning apparatus including:

the system comprises a line crossing distance determining module, a line crossing distance determining module and a line crossing distance determining module, wherein the line crossing distance determining module is used for determining the current line crossing distance of a target vehicle at the current vehicle position and determining the predicted line crossing distance of the target vehicle at the predicted vehicle position; the overline distance is used for representing the distance between a reference front wheel of the target vehicle and a reference lane line, the reference lane line is a lane line which is positioned on the same side of the reference front wheel in the lane lines where the target vehicle is positioned, and the predicted vehicle position is a predicted vehicle position when the target vehicle runs according to the current running state;

The deviation risk degree determining module is used for determining the lane deviation risk degree of the target vehicle according to a preset fuzzy rule based on the current overline distance and the predicted overline distance; the preset fuzzy rule is determined based on the current line crossing distance and the change trend of the predicted line crossing distance;

and the early warning state determining module is used for determining the lane departure early warning state of the target vehicle according to the lane departure risk degree.

According to another aspect of the present invention, there is provided an electronic apparatus including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the lane departure warning method according to any one of the embodiments of the present invention.

According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the lane departure warning method according to any one of the embodiments of the present invention when executed.

According to the technical scheme, the current overline distance of the target vehicle at the current vehicle position is determined, and the predicted overline distance of the target vehicle at the predicted vehicle position is determined; the overline distance is used for representing the distance between a reference front wheel of the target vehicle and a reference lane line, the reference lane line is a lane line positioned on the same side of the reference front wheel in the lane line where the target vehicle is positioned, and the predicted vehicle position is a predicted vehicle position when the target vehicle runs according to the current running state; determining the lane departure risk degree of the target vehicle according to a preset fuzzy rule based on the current overpass distance and the predicted overpass distance; the preset fuzzy rule is determined based on the current overline distance and the change trend of the predicted overline distance; and determining the lane departure warning state of the target vehicle according to the lane departure risk degree. According to the technical scheme, lane departure warning is carried out on the vehicle based on the fuzzy control mode, different working conditions are not required to be distinguished, the complexity of a warning algorithm can be reduced while the warning accuracy is ensured, and therefore the warning efficiency is improved.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a flowchart of a lane departure warning method according to a first embodiment of the present invention;

FIG. 2 is a schematic illustration of a vehicle flying lead distance provided in accordance with a first embodiment of the present invention;

fig. 3 is a flowchart of a lane departure warning method according to a second embodiment of the present invention;

fig. 4 is a schematic structural diagram of a lane departure warning device according to a third embodiment of the present invention;

fig. 5 is a schematic structural diagram of an electronic device for implementing a lane departure warning method according to an embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.

It should be noted that the terms "first," "second," "target," and the like in the description and claims of the present invention and in the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example 1

Fig. 1 is a flowchart of a lane departure warning method according to an embodiment of the present invention, where the method may be applied to a situation where a vehicle in a lane departure state is quickly and accurately warned, and the method may be performed by a lane departure warning device, where the lane departure warning device may be implemented in hardware and/or software, and where the lane departure warning device may be configured in an electronic device having data processing capability. As shown in fig. 1, the method includes:

S110, determining a current overpass distance of the target vehicle at the current vehicle position, and determining a predicted overpass distance of the target vehicle at the predicted vehicle position.

The crossing distance is used for representing the distance between a reference front wheel of the target vehicle and a reference lane line, wherein the reference lane line is a lane line positioned on the same side of the reference front wheel in the lane line where the target vehicle is positioned. For example, when it is determined whether the target vehicle has a tendency to deviate to the left, the front left wheel may be selected as the reference front wheel, and the reference lane line is the left lane in the lane line where the target vehicle is located; when it is determined whether the target vehicle has a tendency to deviate rightward, the right front wheel may be selected as the reference front wheel, and the reference lane line is the right lane in the lane line where the target vehicle is located. It should be noted that, the overline distance may be a vertical distance or a distance in a preset direction, so long as the distance between the reference front wheel and the reference lane line of the target vehicle can be represented, and the distance may be specifically set according to actual requirements.

The predicted vehicle position is a vehicle position predicted when the target vehicle runs according to the current running state. Specifically, the predicted vehicle position may be a vehicle position reached after the target vehicle travels for a preset period of time according to the current running state. The preset duration may be determined according to a current running speed of the target vehicle. If the current running speed is smaller, the preset time length can be set to be a larger value, so that the waste of detection resources is avoided; if the current running speed is high, the preset time length can be set to be a small value, so that inaccurate detection is avoided.

In this embodiment, a current flying lead distance of the target vehicle at the current vehicle position is first determined, and a predicted flying lead distance of the target vehicle at the predicted vehicle position is determined. Optionally, determining the current flying lead distance of the target vehicle at the current vehicle position includes: acquiring current reference lane line information and current vehicle position information of a target vehicle; determining current wheel position information of a reference front wheel in the target vehicle according to the current vehicle position information; and determining the current overline distance of the target vehicle at the current vehicle position according to the current wheel position information and the current reference lane line information.

The current vehicle position information may refer to position information of the target vehicle at the current moment. The current reference lane line information may refer to reference lane line information corresponding to current vehicle position information. For example, the current reference lane line information may include a lane line length, a lane line type (dot or line, etc.), a lane line shape (straight line or curve), etc. within the detection range. The current wheel position information may be used to characterize position information of a reference front wheel corresponding to the current vehicle position.

In this embodiment, when determining the current overpass distance of the target vehicle at the current vehicle position, the current reference lane line information and the current vehicle position information of the target vehicle are first acquired. Wherein the current reference lane line information may be determined by a visual sensor (e.g., a camera) on the target vehicle. Specifically, a plurality of pieces of lane line information can be obtained through the vision sensor, wherein the left lane line and the right lane line closest to the target vehicle are used as lane lines where the target vehicle is located, and therefore the current reference lane line information of the target vehicle is determined. Wherein the current vehicle location information may be determined by a location sensor on the target vehicle. Specifically, a reference point (such as a front bumper center of the target vehicle) may be selected on the target vehicle to represent the target vehicle, and the current vehicle position information of the target vehicle may be determined by locating the reference point.

Further, the lane line equation can be usedThe current reference lane line information is characterized. Specifically, a vehicle coordinate system is first established with the center of the front bumper of the vehicle as the origin, the vehicle coordinate system having the head direction of the target vehicle as the vertical axis and the direction parallel to the ground and perpendicular to the head direction as the horizontal axis. In the vehicle coordinate system, it is assumed that the expression of the lane line equation is y=c ₀ +C ₁ x+C ₂ x ² +C ₃ x ³ Wherein C ₀ 、C ₁ 、C ₂ 、C ₃ Is the coefficients. The current reference lane line information detected by the vision sensor can determine the numerical value of each coefficient, so that a function expression corresponding to the lane line equation can be obtained, and the function expression reflects the current reference lane line information.

Fig. 2 is a schematic diagram of a vehicle overpass distance according to an embodiment of the present invention. The xO1y is a vehicle coordinate system, and DLC and PDLC respectively represent the current overline distance and the predicted overline distance. As shown in fig. 2, taking an example of detecting whether the vehicle deviates to the left, the overline distance here refers to the distance between the left front wheel of the vehicle and the left lane line where the vehicle is located in the y-axis direction, that is, dlc=am, pdlc=bn.

After the current reference lane line information and the current vehicle position information of the target vehicle are acquired, the current wheel position information of the reference front wheels in the target vehicle may be determined according to the current vehicle position information. It is to be understood that the vehicle is a rigid body, and the relative positional relationship of the respective components on the vehicle is fixed, so that in the case of locating the current vehicle position information of the target vehicle by the reference point position, the current wheel position information can be determined from the relative positional relationship of the reference point and the reference front wheels. For example, assuming that the reference point is the front bumper center of the target vehicle, the width of the target vehicle is w, and the reference front wheel is the left front wheel, the current wheel position information may be determined as a position that is w/2 away to the left of the front bumper center of the vehicle.

After determining the current wheel position information of the reference front wheel, a current overpass distance of the target vehicle at the current vehicle position may be determined based on the current wheel position information and the current reference lane line information. ExampleOn the basis of representing the current reference lane line information through a lane line equation, if the line crossing distance is a vertical distance, determining the current line crossing distance of the target vehicle at the current vehicle position according to a distance formula from a point to a straight line (or curve); if the crossing distance is the distance in the transverse axis direction of the vehicle coordinate system, C in the lane line equation can be directly calculated ₀ A current overpass distance of the target vehicle at the current vehicle location is determined.

In this embodiment, optionally, determining the predicted overline distance of the target vehicle at the predicted vehicle position includes: acquiring current reference lane line information, current vehicle position information and current running state information of a target vehicle; determining predicted vehicle position information of the target vehicle according to the current running state information and the current vehicle position information; determining predicted wheel position information for reference front wheels in the target vehicle based on the predicted vehicle position information; and determining the predicted overline distance of the target vehicle at the predicted vehicle position according to the predicted wheel position information and the current reference lane line information.

The current running state information can be used for representing the running state of the target vehicle at the current moment. For example, the current operation state information may include a current vehicle speed, a current acceleration, a current yaw rate, and the like. The predicted vehicle location information may be used to characterize the predicted vehicle location. The predicted wheel-position information may be used to characterize position information of a reference front wheel corresponding to the predicted vehicle position.

In this embodiment, when determining the predicted overpass distance of the target vehicle at the predicted vehicle position, the current reference lane line information, the current vehicle position information, and the current running state information of the target vehicle are first acquired. Wherein the current operating state information may be determined by a motion sensor (e.g., a speed sensor, an acceleration sensor, etc.) on the target vehicle.

The predicted vehicle location information of the target vehicle may then be determined based on the current operating state information and the current vehicle location information. Optionally, the target vehicle reaches the predicted vehicle position after a preset time period from the current vehicle position, and the current running state information includes a current vehicle speed and a current yaw rate. Accordingly, determining predicted vehicle location information of the target vehicle based on the current operating state information and the current vehicle location information, including: determining a predicted moving distance of the target vehicle according to the current vehicle speed and a preset duration; determining a predicted turning angle of the target vehicle according to the current yaw rate and the preset duration; the predicted turning angle refers to a rotation angle of the target vehicle corresponding to the current vehicle position to the predicted vehicle position; and determining predicted vehicle position information of the target vehicle according to the current vehicle position information, the predicted moving distance and the predicted turning angle.

In this embodiment, when determining the predicted vehicle position information of the target vehicle, the predicted movement distance of the target vehicle is first determined according to the current vehicle speed and the preset duration. For example, the lateral speed and the longitudinal speed of the target vehicle are first determined according to the current vehicle speed, then the lateral movement distance of the target vehicle is determined according to the product of the lateral speed and the preset time period, and the longitudinal movement distance of the target vehicle is determined according to the product of the longitudinal speed and the preset time period. After determining the predicted movement distance of the target vehicle, the predicted turning angle of the target vehicle may be determined according to the product of the current yaw rate and the preset duration, and further the predicted vehicle position information of the target vehicle may be determined according to the current vehicle position information, the predicted movement distance, and the predicted turning angle. For example, the predicted vehicle location information may be determined by a location prediction formula, which is expressed as follows:

wherein (x ', y') represents predicted vehicle position information, (x, y) represents current vehicle position information, Δy represents a lateral movement distance, Δx represents a longitudinal movement distance,indicating the predicted turning angle.

After the predicted vehicle position information of the target vehicle is determined, the predicted wheel position information of the reference front wheels in the target vehicle can be further determined according to the predicted vehicle position information, and then the predicted overline distance of the target vehicle at the predicted vehicle position can be determined according to the predicted wheel position information and the current reference lane line information. The determination methods of the predicted wheel position information and the predicted line crossing distance may refer to the determination process of the current wheel position information and the current line crossing distance, which is not described herein.

S120, determining the lane departure risk degree of the target vehicle according to a preset fuzzy rule based on the current crossing distance and the predicted crossing distance.

The preset fuzzy rule is determined based on the current overline distance and the change trend of the predicted overline distance. Exemplary, the preset fuzzy rules are shown in table 1:

TABLE 1 preset fuzzy rules

The DLC and the PDLC respectively represent the current overline distance and the predicted overline distance, the GHS represents the lane departure danger degree, the DLC and the PDLC are input parameters of fuzzy control, and the GHS is an output parameter of the fuzzy control. Specifically, a first fuzzy set is preset for the current crossing distance, a second fuzzy set is preset for the predicted crossing distance, a third fuzzy set is set for the lane departure risk degree, and a preset fuzzy rule is set according to the three preset fuzzy sets. Wherein the first fuzzy set and the second fuzzy set are { far, near }, and the corresponding fuzzy language is { NL, ML, MS, NS }; the third fuzzy set is { secondary security, primary danger, secondary danger }, and the corresponding fuzzy language is { MS, LS, LH, MH }. It should be noted that the preset fuzzy rules in table 1 are only shown as examples, and the present embodiment is not limited to this, and may be flexibly set according to actual application scenarios.

For example, the preset fuzzy rule may be described using the IF-THEN rule. For example, if DLC is NL and PDLC is NL then GHS is MS indicates that when the current overpass distance is long and the predicted overpass distance is also long, the corresponding lane departure risk level is two-level safety, and the target vehicle does not have a tendency to deviate to the left, so the safety level on the left side of the vehicle is high.

In this embodiment, after determining the current overpass distance and the predicted overpass distance, the lane departure risk degree of the target vehicle may be determined according to a preset fuzzy rule based on the current overpass distance and the predicted overpass distance. Optionally, determining the lane departure risk degree of the target vehicle according to the preset fuzzy rule based on the current overpass distance and the predicted overpass distance includes: blurring processing is carried out on the current overline distance based on the first domain to obtain first parameter information; the first domain is a domain associated with the current line crossing distance; blurring processing is carried out on the predicted overline distance based on a second domain to obtain second parameter information; the second domain is associated with the predicted line crossing distance; performing fuzzy reasoning on the first parameter information and the second parameter information according to a preset fuzzy rule to obtain third parameter information; wherein the third parameter information is used for describing the lane departure risk degree of the target vehicle; performing deblurring processing on the third parameter information based on the third domain to obtain fourth parameter information; the third domain is a domain related to the lane departure risk degree; and determining the lane departure risk degree of the target vehicle according to the fourth parameter information.

In this embodiment, when determining the lane departure risk degree of the target vehicle according to the preset fuzzy rule, the current interline distance is first subjected to fuzzy processing based on the first domain to obtain the first parameter information (corresponding to DLC in table 1). Illustratively, the first domain is [0,1.5]. And blurring the predicted interline distance based on the second domain to obtain second parameter information (corresponding to the PDLC in table 1). Illustratively, the second domain is also [0,1.5]. And then, the first parameter information and the second parameter information are used as input parameters of fuzzy control, fuzzy reasoning is carried out on the first parameter information and the second parameter information according to a preset fuzzy rule, and third parameter information (corresponding to GHS in table 1) is obtained. By way of example, the Manadani maximum minimum method may be used for fuzzy reasoning. And then performing deblurring processing on the third parameter information based on the third theory domain to obtain fourth parameter information (namely, correspondingly converting the third parameter information into numbers in the third theory domain). Illustratively, the third domain is [0,1], which may be defuzzified using the centroid method. And determining the lane departure risk degree of the target vehicle according to the fourth parameter information. For example, the fourth parameter information may be directly used as the lane departure risk level of the target vehicle. In the fuzzy control, the input and output variables are triangle membership functions.

S130, determining the lane departure warning state of the target vehicle according to the lane departure risk degree.

The lane departure warning state may include an open state or a closed state, and the open state may further include a first open state (corresponding to a first-level hazard) and a second open state (corresponding to a second-level hazard), which may be specifically set according to actual requirements.

In this embodiment, after determining the lane departure risk level, the lane departure warning state of the target vehicle may be determined according to the lane departure risk level. Optionally, determining the lane departure warning state of the target vehicle according to the lane departure risk degree includes: determining whether the lane departure risk degree is greater than a preset early warning threshold value, wherein the preset early warning threshold value refers to a reference value of the lane departure risk degree for triggering lane departure early warning; if yes, determining that the lane departure warning state of the target vehicle is an on state; otherwise, determining that the lane departure warning state of the target vehicle is the closed state.

In this embodiment, after the lane departure risk degree is obtained, the lane departure risk degree may be compared with a preset early warning threshold, and the lane departure early warning state of the target vehicle may be determined according to the comparison result. Specifically, if the lane departure risk is greater than a preset early warning threshold, the lane departure is indicated to be serious, and the lane departure early warning state of the target vehicle is required to be determined to be an on state; otherwise, if the lane departure risk is smaller than or equal to the preset early warning threshold, the lane departure risk is not serious, the target vehicle is not caused to deviate to other lanes, at this time, the lane departure early warning state of the target vehicle can be directly set to be in a closed state without intervention, and the process of determining the overline distance (including the current overline distance and the predicted overline distance), the lane departure risk and the lane departure early warning state is repeated until the next detection moment.

In this embodiment, optionally, determining the lane departure warning state of the target vehicle according to the lane departure risk level includes: if the lane departure risk degree is greater than the first early warning threshold value, determining that the lane departure early warning state of the target vehicle is an on-first early warning state; if the lane departure risk degree is greater than the second early warning threshold value and less than or equal to the first early warning threshold value, determining that the lane departure early warning state of the target vehicle is an on-second early warning state; the second early warning threshold value is smaller than the first early warning threshold value.

In this embodiment, when determining the lane departure warning state of the target vehicle, different warning thresholds may be preset, so that different levels of warning states are adopted for different levels of lane departure risk degrees. Specifically, whether the lane departure risk degree is larger than a first early warning threshold value is judged first, if yes, the lane departure risk degree of the target vehicle is judged to be very serious, the lane departure early warning state of the target vehicle is determined to be an on-first early warning state (the grade is higher), and if not, whether the lane departure risk degree is between the first early warning threshold value and a second early warning threshold value is judged continuously. The second early warning threshold value is smaller than the first early warning threshold value. If the lane departure risk degree is greater than the second early warning threshold value and less than or equal to the first early warning threshold value, indicating that the target vehicle departure is generally serious, determining the lane departure early warning state of the target vehicle as an on-second early warning state; otherwise, the lane departure warning state of the target vehicle may be determined to be the off state when the target vehicle is indicated that the departure of the target vehicle is not serious.

Through such setting, this scheme can adopt the early warning state of different grades to the lane departure dangerous degree of different grades for lane departure early warning is more meticulous and intelligent.

Example two

Fig. 3 is a flowchart of a lane departure warning method according to a second embodiment of the present invention, which is optimized based on the foregoing embodiments.

As shown in fig. 3, the method of this embodiment specifically includes the following steps:

s210, current reference lane line information and current vehicle position information of the target vehicle are acquired.

The reference lane line is the lane line positioned on the same side of the reference front wheel in the lane line where the target vehicle is positioned.

S220, determining current wheel position information of the reference front wheels in the target vehicle according to the current vehicle position information.

S230, determining the current overline distance of the target vehicle at the current vehicle position according to the current wheel position information and the current reference lane line information.

Wherein the interline distance is used to characterize the distance between the reference front wheel of the target vehicle and the reference lane line.

S240, acquiring current running state information of the target vehicle, and determining predicted vehicle position information of the target vehicle according to the current running state information and the current vehicle position information.

The predicted vehicle position is a vehicle position predicted when the target vehicle runs according to the current running state.

S250, determining predicted wheel position information of the reference front wheels in the target vehicle according to the predicted vehicle position information.

S260, determining the predicted overline distance of the target vehicle at the predicted vehicle position according to the predicted wheel position information and the current reference lane line information.

S270, determining the lane departure risk degree of the target vehicle according to a preset fuzzy rule based on the current crossing distance and the predicted crossing distance.

The preset fuzzy rule is determined based on the current overline distance and the change trend of the predicted overline distance.

S280, determining the lane departure warning state of the target vehicle according to the lane departure risk degree.

In the embodiment, the current line crossing distance and the predicted line crossing distance are used as input parameters, and the vehicle is subjected to lane departure early warning based on a fuzzy control mode, so that different working conditions are not required to be distinguished, the early warning accuracy is ensured, the complexity of an early warning algorithm is reduced, and the early warning efficiency is improved.

Example III

Fig. 4 is a schematic structural diagram of a lane departure warning device according to a third embodiment of the present invention, where the lane departure warning device may execute the lane departure warning method according to any embodiment of the present invention, and the lane departure warning device has functional modules and beneficial effects corresponding to the execution method. As shown in fig. 4, the apparatus includes:

a flying lead distance determination module 310 for determining a current flying lead distance of a target vehicle at a current vehicle location and determining a predicted flying lead distance of the target vehicle at a predicted vehicle location; the overline distance is used for representing the distance between a reference front wheel of the target vehicle and a reference lane line, the reference lane line is a lane line which is positioned on the same side of the reference front wheel in the lane lines where the target vehicle is positioned, and the predicted vehicle position is a predicted vehicle position when the target vehicle runs according to the current running state;

The deviation risk degree determining module 320 is configured to determine a lane deviation risk degree of the target vehicle according to a preset fuzzy rule based on the current overpass distance and the predicted overpass distance; the preset fuzzy rule is determined based on the current line crossing distance and the change trend of the predicted line crossing distance;

the early warning state determining module 330 is configured to determine a lane departure early warning state of the target vehicle according to the lane departure risk level.

Optionally, the overline distance determining module 310 is configured to:

acquiring current reference lane line information and current vehicle position information of a target vehicle;

determining current wheel position information of a reference front wheel in the target vehicle according to the current vehicle position information;

and determining the current overpass distance of the target vehicle at the current vehicle position according to the current wheel position information and the current reference lane line information.

Optionally, the cross-line distance determining module 310 includes:

an information acquisition unit for acquiring current reference lane line information, current vehicle position information, and current running state information of a target vehicle;

a predicted vehicle position information determining unit configured to determine predicted vehicle position information of the target vehicle based on the current running state information and the current vehicle position information;

A predicted wheel-position-information determining unit configured to determine predicted wheel-position information of a reference front wheel in the target vehicle, based on the predicted vehicle-position information;

and the predicted line crossing distance determining unit is used for determining the predicted line crossing distance of the target vehicle at the predicted vehicle position according to the predicted wheel position information and the current reference lane line information.

Optionally, the target vehicle reaches the predicted vehicle position after a preset time period from the current vehicle position, and the current running state information includes a current vehicle speed and a current yaw rate;

accordingly, the predicted vehicle position information determining unit is configured to:

determining a predicted moving distance of the target vehicle according to the current vehicle speed and the preset duration;

determining a predicted turning angle of the target vehicle according to the current yaw rate and the preset duration; wherein the predicted turning angle refers to a rotation angle of the target vehicle corresponding to a predicted vehicle position from a current vehicle position;

and determining the predicted vehicle position information of the target vehicle according to the current vehicle position information, the predicted moving distance and the predicted turning angle.

Optionally, the deviation risk degree determining module 320 is configured to:

blurring processing is carried out on the current overline distance based on a first domain to obtain first parameter information; the first domain is a domain associated with the current overline distance;

blurring the predicted overline distance based on a second domain to obtain second parameter information; the second domain is a domain associated with the predicted overline distance;

performing fuzzy reasoning on the first parameter information and the second parameter information according to a preset fuzzy rule to obtain third parameter information; wherein the third parameter information is used for describing a lane departure risk degree of the target vehicle;

performing defuzzification processing on the third parameter information based on a third domain to obtain fourth parameter information; wherein the third domain is a domain associated with the lane departure risk level;

and determining the lane departure risk degree of the target vehicle according to the fourth parameter information.

Optionally, the early warning state determining module 330 is configured to:

determining whether the lane departure risk degree is greater than a preset early warning threshold value, wherein the preset early warning threshold value is a reference value of the lane departure risk degree for triggering lane departure early warning;

If yes, determining that the lane departure warning state of the target vehicle is an on state;

otherwise, determining that the lane departure warning state of the target vehicle is a closed state.

Optionally, the early warning state determining module 330 is further configured to:

if the lane departure risk degree is greater than a first early warning threshold value, determining that the lane departure early warning state of the target vehicle is an on-first early warning state;

if the lane departure risk degree is greater than a second early warning threshold value and smaller than or equal to the first early warning threshold value, determining that the lane departure early warning state of the target vehicle is a secondary early warning state; wherein the second early warning threshold is less than the first early warning threshold.

The lane departure warning device provided by the embodiment of the invention can execute the lane departure warning method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

Example IV

Fig. 5 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the respective methods and processes described above, such as a lane departure warning method.

In some embodiments, the lane departure warning method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the lane departure warning method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the lane departure warning method by any other suitable means (e.g. by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems-on-chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.

The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims

1. A lane departure warning method, the method comprising:

2. The method of claim 1, wherein determining a current flying lead distance of the target vehicle at the current vehicle location comprises:

3. The method of claim 1, wherein determining a predicted interline distance of the target vehicle at a predicted vehicle location comprises:

acquiring current reference lane line information, current vehicle position information and current running state information of a target vehicle;

Determining predicted vehicle position information of the target vehicle according to the current running state information and the current vehicle position information;

determining predicted wheel position information of a reference front wheel in the target vehicle based on the predicted vehicle position information;

and determining the predicted overline distance of the target vehicle at the predicted vehicle position according to the predicted wheel position information and the current reference lane line information.

4. The method of claim 3, wherein the target vehicle reaches the predicted vehicle position after a preset period of time from the current vehicle position, the current operating state information including a current vehicle speed and a current yaw rate;

accordingly, determining predicted vehicle location information of the target vehicle based on the current operating state information and the current vehicle location information includes:

5. The method of claim 1, wherein determining the lane departure risk level of the target vehicle according to a preset fuzzy rule based on the current overpass distance and the predicted overpass distance comprises:

6. The method according to any one of claims 1-5, wherein determining a lane departure warning status of the target vehicle based on the lane departure risk level comprises:

7. The method according to any one of claims 1-5, wherein determining a lane departure warning status of the target vehicle based on the lane departure risk level comprises:

8. A lane departure warning device, the device comprising:

9. An electronic device, the electronic device comprising:

At least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the lane departure warning method of any one of claims 1-7.

10. A computer readable storage medium storing computer instructions for causing a processor to perform the lane departure warning method of any one of claims 1-7.