Vehicle speed limit control method and device, electronic equipment and storage medium

Abstract

The invention discloses a vehicle speed limit control method and device, electronic equipment and a storage medium. Comprising the following steps: acquiring vehicle driving information and lane visual information of a target vehicle; determining a vehicle driving condition based on the vehicle driving information and the lane visual information; determining a target curvature determination mode based on the vehicle driving condition, and determining curvature data of a driving route of the target vehicle based on the target curvature determination mode; and determining a target speed limit value based on the curvature data, determining a target acceleration limit value based on the target speed limit value and the driving speed, and performing speed limit control on the target vehicle based on the target acceleration. According to the scheme, the driving working condition and the curvature calculation mode are dynamically determined by fusing the vehicle driving information and the lane visual information, then the speed and acceleration speed limiting values are deduced to implement control, and the driving stability, driving safety and comfort of the vehicle under the complex road condition are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle control, and in particular to a vehicle speed limit control method and device, an electronic device and a storage medium. BACKGROUND

[0002] In recent years, more and more intelligent driving vehicles are driving on the road, and the driving scene is complex and the road curvature is constantly changing. In order to be able to drive the vehicle stably and safely, it is necessary to decelerate in advance when encountering roads with different curvatures, so as to avoid accidents such as rollover due to too fast speed when decelerating in time.

[0003] The existing curve speed limit method uses a visual sensor or a high-precision map as input. The high-precision map is updated slowly, and if it is not updated in time, the curve judgment will be inaccurate, so that accurate speed limit control cannot be performed. Moreover, the cost of the high-precision map is high, which increases the overall cost of the system. It is also possible to use a visual sensor alone to obtain visual information to determine the curve situation for speed limit control. However, if the sensor fails or the signal is poor, the function will fail or the speed limit control effect will be poor. For complex driving conditions, accurate speed limit control cannot be performed. SUMMARY

[0004] The present application provides a vehicle speed limit control method, device, electronic device and storage medium to solve the problem of poor vehicle speed limit control.

[0005] According to an aspect of the present application, a vehicle speed limit control method is provided, comprising:

[0006] obtaining vehicle driving information and lane visual information of a target vehicle, determining a vehicle driving condition based on the vehicle driving information and the lane visual information;

[0007] determining a target curvature determination method based on the vehicle driving condition, determining curvature data of a driving route of the target vehicle based on the target curvature determination method;

[0008] determining a target speed limit value based on the curvature data, determining a target acceleration limit value based on the target speed limit value and a driving speed, and performing speed limit control on the target vehicle based on the target acceleration.

[0009] Optionally, determining the vehicle driving condition based on the vehicle driving information and the lane visual information comprises: determining a straight / bend running state of the target vehicle based on the vehicle driving information; determining the effectiveness of the lane line in the driving environment of the target vehicle based on the lane visual information; and determining the vehicle driving condition based on the straight / bend running state of the target vehicle and the effectiveness of the lane line.

[0010] Optionally, the vehicle driving working condition is determined based on the straight curve running state of the target vehicle and the validity of the lane line, including: if the validity of the lane line meets the validity condition, determining that the vehicle driving working condition is a first working condition; if the validity of the lane line does not meet the validity condition, determining whether the straight curve running state of the target vehicle meets a preset running state, if the straight curve running state of the target vehicle meets the preset running state, determining that the vehicle driving working condition is a second working condition; if the straight curve running state of the target vehicle does not meet the preset running state, determining that the vehicle driving working condition is a third working condition.

[0011] Optionally, the target curvature determination mode is determined based on the vehicle driving working condition, including: if the vehicle driving working condition is the first working condition, determining that the target curvature determination mode is a visual curvature determination mode; if the vehicle driving working condition is the second working condition, determining that the target curvature determination mode is a yaw rate curvature determination mode; if the vehicle driving working condition is the third working condition, determining that the target curvature determination mode is a steering wheel curvature determination mode.

[0012] Optionally, the target curvature determination mode includes the visual curvature determination mode, the yaw rate curvature determination mode and the steering wheel curvature determination mode; the curvature data of the driving route of the target vehicle is determined based on the target curvature determination mode, including: in the case that the target curvature determination mode is the visual curvature determination mode, acquiring farthest point information of available lane lines, determining a lane line forward distance range based on the farthest point information of the available lane lines, sampling the lane line forward distance range to obtain a plurality of distance data, substituting each distance data into a lane line curvature algorithm to obtain a plurality of candidate curvature data, and determining the minimum curvature data in the plurality of candidate curvature data as the curvature data of the driving route of the target vehicle; in the case that the target curvature determination mode is the yaw rate curvature determination mode, acquiring a driving speed and a yaw rate, and determining the curvature data of the driving route of the target vehicle based on the driving speed and the yaw rate; in the case that the target curvature determination mode is the steering wheel curvature determination mode, acquiring a steering wheel steering angle, a steering ratio and a vehicle wheelbase, and determining the curvature data of the driving route of the target vehicle based on the steering wheel steering angle, the steering ratio and the vehicle wheelbase.

[0013] Optionally, the target speed limit value is determined based on the curvature data, including: determining a first limit speed value based on the curvature data, wherein the first limit speed value is a limit speed threshold value based on vehicle roll stability constraint; determining a second limit speed value based on the curvature data, wherein the second limit speed value is a limit speed threshold value based on tire and road friction force constraint; and determining the target speed limit value based on the first limit speed value and the second limit speed value.

[0014] Optionally, the target acceleration limit value is determined based on the target speed limit value and the driving speed, including: if the driving speed is greater than the target speed limit value, calculating acceleration data based on the target speed limit value and the driving speed, and updating the target acceleration limit value based on the acceleration data; and if the driving speed is greater than the target speed limit value, determining the current acceleration data as the target acceleration limit value.

[0015] According to another aspect of the present application, there is provided a vehicle speed limit control device, comprising:

[0016] a vehicle driving condition determination module configured to acquire vehicle driving information and lane visual information of a target vehicle, and determine a vehicle driving condition of the target vehicle based on the vehicle driving information and the lane visual information;

[0017] a curvature data determination module configured to determine a target curvature determination manner based on the vehicle driving condition, and determine curvature data of a driving route of the target vehicle based on the target curvature determination manner;

[0018] a speed limit control module configured to determine a target speed limit value based on the curvature data, determine a target acceleration limit value based on the target speed limit value and a driving speed, and perform speed limit control on the target vehicle based on the target acceleration.

[0019] According to another aspect of the present application, there is provided an electronic device, comprising:

[0020] at least one processor; and

[0021] a memory in communication with the at least one processor; wherein

[0022] the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the vehicle speed limit control method of any one of the embodiments of the present application.

[0023] According to another aspect of the present application, there is provided a computer readable storage medium storing computer instructions for enabling a processor to perform the vehicle speed limit control method of any one of the embodiments of the present application when executed by the processor.

[0024] The technical scheme of the embodiment of the present application comprises the following steps: obtaining vehicle driving information and lane visual information of a target vehicle; determining a vehicle driving working condition based on the vehicle driving information and the lane visual information; comprehensively and accurately perceiving the vehicle running state and the road environment by fusing multi-source data, avoiding information deviation, accurately distinguishing different driving scenes based on the fused information, providing a reliable basis for formulating a subsequent vehicle control strategy, and enhancing the safety and control stability of vehicle driving; determining a target curvature determination mode based on the vehicle driving working condition, determining curvature data of a driving route of the target vehicle based on the target curvature determination mode, flexibly adapting the most suitable curvature calculation model according to different working conditions, which helps to improve the accuracy of determining the curvature data, and through the dynamic matching strategy, the vehicle can better adapt to the actual road characteristics to plan a driving path, ensuring that the vehicle can also drive along the optimal trajectory under complex road conditions, and enhancing the driving stability and safety, and providing accurate data support for subsequent speed control and steering operation; determining a target speed limit value based on the curvature data, determining a target acceleration limit value based on the target speed limit value and the driving speed, and performing speed limit control on the target vehicle based on the target acceleration, which realizes deriving the target speed limit value based on the curvature data, accurately matches the vehicle speed with the road curvature, and avoids the risk of rollover caused by too fast speed on a curve; and in combination with the current driving speed, the target acceleration limit value is determined, which can effectively prevent sudden acceleration or deceleration, realize smooth transition of the speed, and help to improve the safety and comfort of vehicle driving. The present scheme fuses the vehicle driving and lane visual information, dynamically determines the driving working condition and the curvature calculation mode, and then derives the curvature data, the speed and the acceleration layer by layer, to build a closed-loop speed limit control system, so that the vehicle speed closely matches the road conditions, provides strong support for fine control of intelligent driving, and improves the driving stability, safety and comfort of the vehicle under complex road conditions.

[0025] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0027] Figure 1 is a flowchart of a vehicle speed limit control method provided by the first embodiment of the present application;

[0028] Figure 2is a flow chart of a vehicle speed limiting control method provided by the second embodiment of the present application;

[0029] Figure 3 is a structural schematic diagram of a vehicle speed limiting control device provided by the third embodiment of the present application;

[0030] Figure 4 is a structural schematic diagram of an electronic device for implementing the vehicle speed limiting control method of the present application. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should fall within the scope of protection of the present application.

[0032] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] Embodiment one

[0034] Figure 1 is a flow chart of a vehicle speed limiting control method provided by the first embodiment of the present application. The present embodiment can be applied to the case of vehicle speed limiting control. The method can be executed by a vehicle speed limiting control device, which can be realized in the form of hardware and / or software, and can be configured in an electronic device such as a vehicle controller. As shown in the figure, the method comprises: Figure 1

[0035] S110, obtaining vehicle driving information and lane visual information of a target vehicle, and determining a vehicle driving condition based on the vehicle driving information and the lane visual information.

[0036] ​The vehicle driving information can be understood as information representing the running characteristics and working conditions of the vehicle, and can be embodied by data collected by corresponding sensors. The vehicle driving information includes, but is not limited to, vehicle speed, acceleration and yaw rate, and the sensors include, but are not limited to, a vehicle speed sensor, an acceleration sensor and a yaw rate detection sensor. The lane visual information represents lane line related information obtained by image acquisition and processing technology through visual devices such as cameras, and includes, but is not limited to, lane line confidence. The lane line confidence is used to represent whether the lane line is detected. For example, when the lane line is detected, the lane line confidence can be set to 1, and vice versa, when the lane line is not detected, the lane line confidence can be set to 0. It should be noted that the representation of the lane line confidence can also use other strings, which is set according to actual needs. The vehicle driving condition represents the condition of the vehicle during driving, which is determined by the vehicle driving information and the lane visual information. The vehicle driving condition can be classified according to the vehicle driving information and the lane visual information in advance, and then the mapping relationship between the vehicle driving information, the lane visual information and the vehicle driving condition is set according to the classification result, so as to directly call the vehicle driving condition in subsequent determination.

[0037] Specifically, the vehicle driving information is collected in real time by the information detection device carried by the vehicle. At the same time, the lane visual information is collected by using visual devices such as cameras to determine whether the lane line is detected. The vehicle driving information and the lane visual information can be input into an algorithm model, and the driving condition of the vehicle can be determined by data fusion and analysis combined with the preset working condition judgment rule, such as straight driving, curve driving, intersection driving and the like.

[0038] In the embodiment, the driving condition is determined by the vehicle driving information and the lane visual information and the like, which can accurately identify complex and diverse road scenes, make up for the limitations of a single information source, provide a reliable premise for subsequent intelligent decision and control of the vehicle, and thus improve the safety, stability and comfort of vehicle driving, enhance the adaptability of the vehicle to different road environments, and lay a solid foundation for efficient operation of the intelligent driving system.

[0039] Optionally, the vehicle driving condition is determined based on the vehicle driving information and the lane visual information, including: determining the straight / curve running state of the target vehicle based on the vehicle driving information; determining the effectiveness of the lane line in the driving environment of the target vehicle based on the lane visual information; and determining the vehicle driving condition based on the straight / curve running state of the target vehicle and the effectiveness of the lane line.

[0040] Specifically, vehicle driving information is acquired in real time during vehicle driving, the vehicle driving information includes driving speed and yaw rate, the yaw rate curvature is determined according to the driving speed and the yaw rate, and the straight / curve running state of the target vehicle is determined according to the yaw rate curvature, wherein the calculation formula of the yaw rate curvature is as follows:

[0041]

[0042] wherein c y represents the yaw rate curvature, v represents the driving speed, and Yawrate represents the yaw rate.

[0043] Further, the yaw rate curvature is compared with a preset curvature threshold, and the straight / curve running state of the target vehicle is determined according to the comparison result, for example, if the yaw rate curvature is greater than or equal to the preset curvature threshold, it is determined that the target vehicle is in a curve running state, and if the yaw rate curvature is less than the preset curvature threshold, it is determined that the target vehicle is in a straight running state. Lane visual information is acquired in real time during vehicle driving, the lane visual information includes lane confidence, and the effectiveness of the lane line in the driving environment of the target vehicle is determined according to the lane confidence. If a left lane line is detected, Q l == 1, that is, indicating that the left lane line is effective, and if a right lane line is detected, Q r == 1, that is, indicating that the right lane line is effective. It should be noted that the representation method of the lane line confidence can also use other representation methods, which are specifically set according to actual conditions. Further, the straight / curve running state and the lane line effectiveness are cross-verified to determine the vehicle driving working condition. For example, if the vehicle is in a straight and the lane line is effective, the vehicle driving working condition is determined as a straight effective working condition; if it is in a curve and the lane line is effective, the vehicle driving working condition is determined as a curve effective working condition; and if the lane line is invalid, no matter straight or curve, the vehicle driving working condition is determined as a lane line invalid working condition.

[0044] In the embodiment, through the double verification of the vehicle state and the road environment information, the limitation of single dimension judgment is avoided, the accuracy and robustness of working condition recognition are improved, a more refined working condition basis is provided for the intelligent driving system, the control strategy can be adjusted accordingly under different working conditions, the body stability control is enhanced when the lane line is invalid, and the adaptability and safety of the vehicle in complex environment are improved, thereby providing a more reliable bottom support for automatic driving decision.

[0045] Optionally, the vehicle driving condition is determined based on the straight / curve running state of the target vehicle and the validity of the lane line, including: if the validity of the lane line meets the validity condition, determining that the vehicle driving condition is a first condition; if the validity of the lane line does not meet the validity condition, determining whether the straight / curve running state of the target vehicle meets a preset running state, if the straight / curve running state of the target vehicle meets the preset running state, determining that the vehicle driving condition is a second condition; and if the straight / curve running state of the target vehicle does not meet the preset running state, determining that the vehicle driving condition is a third condition.

[0046] Specifically, if the validity of the detected lane line is valid, it means that the lane line is complete and identifiable, and the vehicle driving condition can be determined as the first condition; if the validity of the detected lane line is invalid, it means that the lane line is not identifiable or there is no lane line on the current driving road, and then the vehicle driving information is analyzed to determine whether the straight / curve running state of the target vehicle meets the preset running state, if the straight / curve running state of the target vehicle is a curve running state, it is determined whether the straight / curve running state of the target vehicle meets the preset running state, and the vehicle driving condition can be determined as the second condition, i.e. the condition that the lane line is invalid but the vehicle is in a clear curve state; if the straight / curve running state of the target vehicle is a straight running state, it is determined that the straight / curve running state of the target vehicle does not meet the preset running state, and the vehicle driving condition can be determined as the third condition, i.e. the condition that the lane line is invalid and the vehicle has no obvious turning feature.

[0047] In this embodiment, by using the hierarchical judgment logic of the straight / curve running state of the target vehicle and the validity of the lane line, the lane line information is preferentially used to quickly locate the typical condition, and when the environmental information is unreliable, the vehicle state is relied on for supplementary judgment, forming a redundant decision mechanism, which improves the stability of the condition recognition in complex environments, for example, the curve condition can still be determined through the steering angle when the lane line is blurred in rainy weather; and the validity of the lane line and the motion state of the vehicle are combined, which can cover multiple scenarios such as valid lane line, invalid lane line but clear motion state, invalid lane line and unclear motion state, so that the intelligent driving system can match different control strategies according to different conditions, enhance the adaptability and decision flexibility of the system to environmental changes, and provide a more accurate condition basis for safe operation of automatic driving.

[0048] S120, determining a target curvature determination mode based on the vehicle driving condition, and determining curvature data of a driving route of the target vehicle based on the target curvature determination mode.

[0049] The target curvature determination manner can be specifically understood as a curvature calculation logic or method dynamically selected according to the vehicle driving condition, which is a core rule connecting the condition recognition and the curvature data generation. Different conditions correspond to different calculation logics to adapt to the environmental requirements. The target curvature determination manner includes but is not limited to a visual curvature determination manner, a yaw rate curvature determination manner and a steering wheel curvature determination manner. The mapping relationship between the vehicle driving condition and the target curvature determination manner can be set in advance, so that the target curvature determination manner can be directly called when it is determined. The curvature data can be specifically understood as a quantitative parameter calculated by the target curvature determination manner, which is used to represent the bending degree of the vehicle driving route. If the curvature value is less than a preset curvature threshold, it represents straight driving. If the curvature value is greater than or equal to the preset curvature threshold, it represents curve driving. The greater the value, the higher the route bending degree, which provides accurate support from strategy to execution for automatic driving decision.

[0050] Specifically, the mapping relationship between the vehicle driving condition and the target curvature determination manner is called, the determined vehicle driving condition is matched with the mapping relationship, the target curvature determination manner matched with the vehicle driving condition is obtained, and then the algorithm corresponding to the target curvature determination manner is called for calculation to determine the curvature data of the driving route of the target vehicle.

[0051] In the embodiment, scene adaptation is realized through dynamic matching of the vehicle driving condition and the curvature determination manner, avoiding the failure risk of a single algorithm in a complex scene. The hierarchical curvature determination logic covers the full scene requirements from accurate calculation to safe bottom, which guarantees the route planning accuracy in normal conditions and prevents vehicle out-of-control through conservative curvature setting in extreme conditions such as no lane line and ambiguous steering, providing appropriate curvature data support for the automatic driving system, improving the safety and rationality of the driving route planning, and ensuring that the vehicle can drive along a reasonable path at a reasonable speed in different conditions, which helps to improve the stability and comfort of vehicle driving.

[0052] On the basis of the above embodiment, the target curvature determination manner is determined based on the vehicle driving condition, including: if the vehicle driving condition is a first condition, the target curvature determination manner is determined as a visual curvature determination manner; if the vehicle driving condition is a second condition, the target curvature determination manner is determined as a yaw rate curvature determination manner; and if the vehicle driving condition is a third condition, the target curvature determination manner is determined as a steering wheel curvature determination manner.

[0053] Specifically, according to the classification result of the vehicle driving condition, logical matching is performed. When the vehicle is in the first condition, the visual curvature determination mode is directly called to calculate accurate curvature data. If the vehicle is in the second condition, the yaw rate curvature determination mode is enabled to calculate accurate curvature data. If the vehicle is in the third condition, the steering wheel curvature determination mode is used. Through the preset mapping rule of the condition and the curvature determination mode, the automatic switching of the calculation logic is realized.

[0054] For example, when Q l == 1 || Q r == 1, that is, when any of the left and right lane lines is valid, the current condition can be set to the first condition, the visual curvature determination mode is enabled, and accurate curvature data is calculated. When Q l == 0 && Q r == 0 and the yaw rate curvature c y > C c , the current condition can be set to the second condition, the yaw rate curvature determination mode is enabled, and accurate curvature data is calculated, wherein C c is a curvature threshold close to a straight line, which can be calibrated and can be taken as C c ≤ 0.001; when Q l == 0 && Q r == 0 and the yaw rate curvature c y ≤ C c , the current condition can be set to the third condition, the steering wheel curvature determination mode is enabled, and accurate curvature data is calculated.

[0055] In this embodiment, by constructing the preset mapping mechanism of the condition and the curvature determination mode, a multi-level and adaptive curvature calculation logic is realized. Through dynamic switching, a redundant calculation system covering all scenarios is constructed, which avoids the limitations of a single algorithm in environmental changes and accurately matches algorithm resources according to the condition characteristics, so that the curvature calculation can consider both accuracy and reliability in different scenarios, providing flexible and robust bottom data support for intelligent driving control.

[0056] In S130, a target speed limit value is determined based on the curvature data, a target acceleration limit value is determined based on the target speed limit value and the driving speed, and the target vehicle is controlled by limiting the speed based on the target acceleration.

[0057] The target speed limit value can be understood as the upper limit of the safe driving speed of the vehicle, and can be calculated according to the road curvature data through a preset speed limit value algorithm. Essentially, the road geometry is converted into a quantitative parameter of speed constraint. The greater the curvature, the lower the target speed limit value. For example, the speed limit of a sharp curve section can be set to 30 km / h, and the speed limit of a straight section can be restored to 60 km / h, to ensure that the vehicle speed matches the road conditions. The target acceleration limit value is the acceleration and deceleration amplitude limit calculated based on the difference between the target speed limit value and the current driving speed, which avoids both overspeed caused by sudden acceleration and the jerk caused by sudden deceleration. For example, when the current vehicle speed is 50 km / h and the target speed limit is 30 km / h, the system will calculate a gentle deceleration as the control instruction. The former sets a safe speed reference for vehicle driving, and the latter realizes smooth transition of speed through acceleration control, to jointly ensure safety and comfort during automatic driving.

[0058] Specifically, a preset speed limit algorithm is called to calculate the target speed limit value based on the curvature data, to ensure that the vehicle speed matches the road curvature. Then, the target speed limit value is compared with the current driving speed, and whether to call the acceleration limit value calculation method is determined according to the comparison result. If the target acceleration limit value is calculated, the value is used to control the amplitude of vehicle acceleration or deceleration to avoid sudden acceleration or deceleration. After the target speed limit value and / or the target acceleration limit value are determined, the target speed limit value and / or the target acceleration limit value can be converted into specific control instructions by an electronic control unit to implement speed limit control on the vehicle and realize smooth adjustment of speed.

[0059] In this embodiment, by constructing a progressive control logic of curvature-speed-acceleration, the safe speed is derived from the road geometry, and reasonable acceleration and deceleration instructions are generated in combination with the real-time vehicle speed, to form a closed-loop control that conforms to the driving logic, such as automatically reducing the speed limit value and gently decelerating in a sharp curve scenario to avoid the risk of rollover. While implementing the dynamic speed limit strategy, safety and comfort are also taken into account. Through accurate calculation of the target acceleration, the vehicle speed can be prevented from exceeding the road safety threshold, and the jerk in the control process can be avoided, to improve the driving experience and enhance the environmental adaptability and decision reliability of the intelligent driving system.

[0060] Optionally, the target speed limit value is determined based on the curvature data, including: determining a first speed limit value based on the curvature data, wherein the first speed limit value is a speed limit threshold based on vehicle roll stability constraint; determining a second speed limit value based on the curvature data, wherein the second speed limit value is a speed limit threshold based on tire and road friction constraint; and determining the target speed limit value based on the first speed limit value and the second speed limit value.

[0061] The first speed limit value is a speed limit threshold based on a vehicle roll stability constraint, and a calculation formula of the first speed limit value is a relational expression based on curvature data and corresponding calibration parameters, and the calculation formula is as follows:

[0062]

[0063] V1 is a speed limit calculated based on roll stability, a unit of which is m / s, g is a gravitational acceleration, and 9.8 m / s 2 is taken, l is a wheel base, C f is curvature data, h is a vehicle gravity center height, and the vehicle gravity center height can be obtained through a sensor or a state estimation method.

[0064] The second speed limit value is a speed limit threshold based on a tire and road surface friction force constraint, and a calculation formula of the second speed limit value is a relational expression based on curvature data and corresponding calibration parameters, and the calculation formula is as follows:

[0065]

[0066] V2 represents a speed limit calculated based on a tire and road surface friction force, u is a road friction coefficient, g is a gravitational acceleration, and 9.8 m / s 2 is taken, and the road friction coefficient can be obtained through a sensor or a state estimation method.

[0067] Specifically, according to the curvature data, a calculation method of the first speed limit value is called to calculate a highest vehicle speed under a premise that roll danger does not occur, that is, a corresponding first speed limit value is obtained, so as to guarantee vehicle roll stability, a calculation method of the second speed limit value is called to calculate a maximum safe vehicle speed under a condition that a tire does not slip, that is, a corresponding second speed limit value is obtained, so as to ensure vehicle grip safety. By comparing the first speed limit value and the second speed limit value, a smaller one of the two is selected as a target speed limit value, so as to set a most stringent and safest speed upper limit for the vehicle. Optionally, a safety coefficient can also be set to adjust the target speed limit value, so as to obtain a target speed limit value that is more in line with a current driving condition, that is, a calculation formula of the target speed limit value is as follows:

[0068] V f = θ * min(V1, V2);

[0069] θ represents the safety coefficient, and a value of the safety coefficient is 0 < θ < 1, and the safety coefficient can be calibrated in advance.

[0070] In the embodiment, the speed limiting mechanism is constructed from multiple safety dimensions to comprehensively guarantee vehicle driving safety, avoid safety hazards caused by insufficient consideration of a single factor through double constraints of roll stability and tire friction, take the minimum value as the target speed limiting value to provide more reliable safety redundancy for the vehicle, and meanwhile, the method combines vehicle characteristics and road conditions to realize accurate calculation of the speed limiting value, so that the vehicle can drive at a reasonable speed under different road conditions, and the safety and reliability of the intelligent driving system are improved.

[0071] Optionally, the target acceleration limiting value is determined based on the target speed limiting value and the driving speed, including: if the driving speed is greater than the target speed limiting value, calculating acceleration data based on the target speed limiting value and the driving speed, and updating the target acceleration limiting value based on the acceleration data; and if the driving speed is greater than the target speed limiting value, determining the current acceleration data as the target acceleration limiting value.

[0072] Specifically, in the vehicle driving process, the driving speed of the vehicle can be obtained in real time, and the driving speed is compared with the target speed limiting value. If the driving speed is greater than the target speed limiting value, an acceleration limiting value calculation method is called to calculate acceleration data based on the target speed limiting value and the driving speed. The data reflects the acceleration and deceleration degree required by the vehicle to reach the target speed limiting value, and then the target acceleration limiting value is updated based on the acceleration data to provide a control basis for subsequent speed adjustment of the vehicle. If the driving speed is less than or equal to the target speed limiting value, the system directly determines the current acceleration data as the target acceleration limiting value to maintain or fine-tune the existing speed state of the vehicle. The calculation formula of the acceleration limiting value is as follows:

[0073]

[0074] wherein, ΔE = 0.5 * m (V f 2 -V c 2 ), V f is the target speed limiting value, V c is the driving speed of the vehicle, k is a calibration parameter, generally between 0.05 and 0.5, j max represents the maximum rate limiting value of acceleration change, generally between -1.5 m / s 3 and -3 m / s 3 , and λ is a calibration parameter, generally between 0.1 and 10. Optionally, the maximum comfortable deceleration can be set according to actual driving requirements, and the greater value between the acceleration limiting value and the maximum comfortable deceleration is selected as the target acceleration limiting value.

[0075] In the embodiment, through dynamic comparison and differential processing, the precise regulation of vehicle speed is realized, when the vehicle speed is too fast, the reasonable acceleration is actively calculated to reduce the speed, ensuring that the vehicle returns to the safe speed range in time, effectively avoiding the risk of overspeeding; on the other hand, when the vehicle speed meets the requirements, the current acceleration data is retained, unnecessary frequent adjustment is reduced, the stability and comfort of driving are ensured, and the operation load of the control system is reduced, so that the vehicle can efficiently and safely operate under different speed conditions, and the adaptability and reliability of the intelligent driving system are enhanced.

[0076] The technical scheme of the embodiment, by acquiring vehicle driving information and lane visual information of the target vehicle, determining the vehicle driving condition based on the vehicle driving information and the lane visual information; determining the target curvature determination mode based on the vehicle driving condition, determining the curvature data of the driving route of the target vehicle based on the target curvature determination mode; determining the target speed limit value based on the curvature data, determining the target acceleration limit value based on the target speed limit value and the driving speed, and controlling the speed of the target vehicle based on the target acceleration. The scheme fuses vehicle driving and lane visual information, dynamically determines the driving condition and the curvature calculation mode, and then deduces the curvature data, speed and acceleration layer by layer to build a closed-loop speed control system, so that the vehicle speed closely adapts to the road conditions, provides strong support for fine control of intelligent driving, and improves the driving stability, safety and comfort of the vehicle in complex road conditions.

[0077] Embodiment two

[0078] Figure 2 It is a flowchart of a vehicle speed control method provided by the second embodiment of the application. The method of the embodiment is a further optimization of the method of the above-mentioned embodiment. Optionally, the target curvature determination mode includes a visual curvature determination mode, a yaw rate curvature determination mode and a steering wheel curvature determination mode; in the case that the target curvature determination mode is the visual curvature determination mode, the farthest point information of the available lane line is acquired, the lane line forward distance range is determined based on the farthest point information of the available lane line, the lane line forward distance range is sampled to obtain a plurality of distance data, each distance data is substituted into the lane line curvature algorithm to obtain a plurality of candidate curvature data, and the minimum curvature data in the plurality of candidate curvature data is determined as the curvature data of the driving route of the target vehicle; in the case that the target curvature determination mode is the yaw rate curvature determination mode, the driving speed and the yaw rate are acquired, and the curvature data of the driving route of the target vehicle is determined based on the driving speed and the yaw rate; in the case that the target curvature determination mode is the steering wheel curvature determination mode, the steering wheel angle, the steering ratio and the vehicle wheelbase are acquired, and the curvature data of the driving route of the target vehicle is determined based on the steering wheel angle, the steering ratio and the vehicle wheelbase. As shown in Figure 2 the method comprises:

[0079] S210, vehicle driving information and lane visual information of the target vehicle are acquired, and a vehicle driving condition is determined based on the vehicle driving information and the lane visual information.

[0080] S220, a target curvature determination mode is determined based on the vehicle driving condition.

[0081] S230, in the case that the target curvature determination mode is a visual curvature determination mode, farthest point information of available lane lines is acquired, a lane line forward distance range is determined based on the farthest point information of the available lane lines, the lane line forward distance range is sampled to obtain a plurality of distance data, each distance data is substituted into a lane line curvature algorithm to obtain a plurality of candidate curvature data, and a minimum curvature data in the plurality of candidate curvature data is determined as curvature data of a driving route of the target vehicle.

[0082] The farthest point specifically refers to related data of a farthest end point of the available lane lines that can be detected in the driving direction of the vehicle after the vehicle visual perception system identifies the front road environment, which covers the position coordinates (including lateral and longitudinal distances, etc.) of the point relative to the vehicle, and is a basis for subsequent determination of the lane line forward distance range and sampling calculation of the candidate curvature. By determining the position state in which the lane line extends to the farthest point, initial key information for accurately deriving the curvature of the driving route is provided.

[0083] Specifically, when the target curvature determination mode is the visual curvature determination mode, the image information collected by the camera is used to identify the available lane lines and acquire the coordinates of the farthest point on the lane line from the vehicle and other information; based on the farthest point information, the relative position relationship between the vehicle and the lane line is determined to determine the distance range of the forward extension of the lane line; then, sampling is performed at certain intervals within the distance range to obtain a plurality of discrete distance data; subsequently, each distance data is substituted into a pre-set lane line curvature algorithm to calculate the candidate curvature data of the corresponding position; finally, the minimum curvature value is selected from all the candidate curvature data and determined as the curvature data of the driving route of the target vehicle, which provides a basis for subsequent speed planning and path control. The lane line curvature algorithm can be determined according to the lane line equation, and the lane line curvature algorithm is as follows:

[0084]

[0085] The left and right lane line curvature equations are c l and c r , respectively. By substituting different lane line distances x, the road curvatures of the left and right lane lines at different forward distances x can be obtained. Then, the output visual curvature c v is calculated in three cases, which are: when Q l == Q r , c = 0.5 * (cl +c r ), and then the lane lines are sampled at a preset distance interval of 0.5, so that x=(0, 0.5,...min(E l ,E r )) respectively, E l ,E r being the farthest point distance of the left lane line and the farthest point distance of the right lane line respectively, x is brought into c respectively to obtain a group of curvatures c, and finally c v =min(c); when Q l =0 and Q r =1, it indicates that the left lane line is invalid and the right lane line is valid, x=(0, 0.5,...E r ) is taken, x is brought into c r respectively to obtain a group of curvatures c r , and finally c v =min(c r ) is taken; when Q l =1 and Q r =0, it indicates that the left lane line is valid and the right lane line is invalid, x=(0, 0.5,...E l ) is taken, x is brought into c l respectively to obtain a group of curvatures c l , and finally c v =min(c l ) is taken.

[0086] In this embodiment, by sampling the forward distance range of the lane line and multiple data calculation, the bending characteristics of the lane line at different positions can be comprehensively captured, the limitations of a single data point are avoided, the curvature calculation is more in line with the actual road form, the minimum curvature data is selected as the result, the safety first principle is followed, even in the case that the lane line exists local fluctuations or interference, the vehicle can also ensure to travel planning with a conservative and safe curvature, and the driving risk caused by the misjudgment of the road curvature is reduced; the dynamic sampling strategy combined with the visual information enhances the adaptability of the system to the complex road environment, provides more accurate and reliable curvature data support for the intelligent driving system, and improves the driving safety and stability.

[0087] In the case that the target curvature determination manner is the yaw rate curvature determination manner, the driving speed and the yaw rate are acquired, and the curvature data of the driving route of the target vehicle is determined based on the driving speed and the yaw rate.

[0088] Specifically, when the target curvature determination manner is the yaw rate curvature determination manner, the driving speed and the yaw rate of the vehicle are collected in real time, wherein the yaw rate reflects the speed of the rotation of the vehicle around the vertical axis, and the curvature data of the driving route is directly calculated by using a yaw rate curvature calculation formula, which is:

[0089]

[0090] wherein c y represents the yaw rate curvature, v represents the driving speed, and Yawrate represents the yaw rate.

[0091] S250, in the case where the target curvature determination manner is the steering wheel curvature determination manner, the steering wheel rotation angle, the steering ratio and the vehicle wheelbase are obtained, and the curvature data of the driving route of the target vehicle is determined based on the steering wheel rotation angle, the steering ratio and the vehicle wheelbase.

[0092] wherein the steering wheel rotation angle refers to the rotation angle of the steering wheel relative to the initial position when the driver rotates the steering wheel, is usually measured by a steering wheel rotation angle sensor, and is a direct parameter reflecting the steering intention of the driver, for example, in the case where the right direction is positive, the steering wheel rotates 90 degrees to the left, which means that the steering wheel rotation angle is -90°. The steering ratio refers to an important design parameter of the vehicle steering system, which refers to the proportional relationship between the steering wheel rotation angle and the front wheel rotation angle, for example, when the steering ratio is 16:1, the steering wheel rotates 16 degrees, and the front wheel rotates 1 degree accordingly, which determines the magnification of the steering wheel operation and the wheel steering, and affects the steering feeling and sensitivity of the driver. The vehicle wheelbase refers to the horizontal distance between the front wheel axis and the rear wheel axis, which is a basic structural parameter of the vehicle and directly affects the steering characteristics and stability of the vehicle, for example, the vehicle with longer wheelbase has larger trajectory radius when steering, and the vehicle with shorter wheelbase is more flexible when steering.

[0093] Specifically, when the target curvature determination manner is the steering wheel curvature determination manner, the steering wheel rotation angle, the steering ratio and the vehicle wheelbase are obtained, and the curvature data of the driving route is calculated by using a calculation formula corresponding to the steering wheel curvature determination manner, which is:

[0094]

[0095] wherein Wangle is the steering wheel rotation angle, i is the steering ratio, and l is the vehicle wheelbase.

[0096] In the embodiment, based on the visual curvature determination mode, the farthest point of the lane line is sampled and the minimum curvature is selected, which can capture the real bending shape of the road through multiple data points, reduce the risk of curvature misjudgment in a conservative strategy, and enhance the adaptability to complex road conditions. The yaw rate curvature determination mode directly calculates based on the dynamic parameters of the vehicle, without the need for environmental perception, and can still respond in real time in the absence of lane lines or sensor obstruction, improving driving stability. The steering wheel curvature determination mode is based on the physical parameters of vehicle steering, is not affected by external environment, accurately reflects the driver's intention, optimizes the steering feel, and through the complementary cooperation of the three modes, it covers the curvature calculation requirements in different scenes, and through dynamic adaptation, it improves the environmental adaptability, calculation real-time performance and decision safety of the intelligent driving system, providing multi-source reliable data support for path planning and speed control.

[0097] In S260, a target speed limit value is determined based on the curvature data, a target acceleration limit value is determined based on the target speed limit value and the driving speed, and the target vehicle is controlled by limiting the speed based on the target acceleration.

[0098] The technical scheme of the embodiment is characterized in that: vehicle driving information and lane visual information of a target vehicle are acquired, a vehicle driving condition is determined based on the vehicle driving information and the lane visual information, a target curvature determination mode is determined based on the vehicle driving condition, in the case that the target curvature determination mode is a visual curvature determination mode, farthest point information of available lane lines is acquired, a lane line forward distance range is determined based on the farthest point information of the available lane lines, the lane line forward distance range is sampled to obtain a plurality of distance data, each distance data is substituted into a lane line curvature algorithm to obtain a plurality of candidate curvature data, and minimum curvature data in the plurality of candidate curvature data is determined as curvature data of a driving route of the target vehicle, in the case that the target curvature determination mode is a yaw rate curvature determination mode, driving speed and yaw rate are acquired, and the curvature data of the driving route of the target vehicle is determined based on the driving speed and the yaw rate, in the case that the target curvature determination mode is a steering wheel curvature determination mode, steering wheel rotation angle, steering ratio and vehicle wheelbase are acquired, and the curvature data of the driving route of the target vehicle is determined based on the steering wheel rotation angle, the steering ratio and the vehicle wheelbase, target speed limit value is determined based on the curvature data, target acceleration limit value is determined based on the target speed limit value and the driving speed, and the target vehicle is controlled based on the target acceleration. The scheme constructs a whole-process intelligent decision system of condition recognition-curvature adaptation-speed limit control, accurately judges the driving condition by fusing the vehicle driving information and the lane visual information, dynamically matches the condition requirement by the curvature determination mode, and through farthest point sampling and minimum curvature screening in the clear lane line scene, the visual curvature method considers road real form capture and safety redundancy; the yaw rate curvature method relies on dynamic parameters to maintain real-time performance when there is no lane line; the steering wheel curvature method guarantees accurate transmission of the driver's intention in a complex environment by physical parameters, the three curvature algorithms complement each other to form full-scene coverage capability, and the logic of deriving the target speed limit value based on the curvature and controlling the acceleration in conjunction realizes closed-loop control from path curvature to speed planning, can automatically generate a safe speed limit according to the curve curvature, can smoothly transition the speed by limiting the acceleration, avoids the risk of sudden braking or overspeed, completes condition adaptive switching and dynamic speed limit control, and significantly improves the environmental adaptability, decision safety and ride comfort of the intelligent driving system in different road conditions.

[0099] Embodiment three

[0100] Figure 3 is a structural schematic diagram of a vehicle speed limit control device provided by the embodiment three of the application.

[0101] As Figure 3 shown, the device comprises:

[0102] The vehicle driving condition determination module 310 is configured to acquire vehicle driving information and lane visual information of a target vehicle, and determine a vehicle driving condition based on the vehicle driving information and the lane visual information.

[0103] The curvature data determination module 320 is configured to determine a target curvature determination mode based on the vehicle driving condition, and determine the curvature data of the driving route of the target vehicle based on the target curvature determination mode.

[0104] The speed limit control module 330 is configured to determine a target speed limit value based on the curvature data, determine a target acceleration limit value based on the target speed limit value and the driving speed, and perform speed limit control on the target vehicle based on the target acceleration.

[0105] In the technical scheme, the vehicle driving information and the lane visual information of the target vehicle are obtained by the information acquisition module, the vehicle driving condition is determined based on the vehicle driving information and the lane visual information by the vehicle driving condition determination module, the target curvature determination mode is determined based on the vehicle driving condition by the curvature data determination module, and the curvature data of the driving route of the target vehicle is determined based on the target curvature determination mode, the target speed limit value is determined based on the curvature data by the speed limit control module, the target acceleration limit value is determined based on the target speed limit value and the driving speed, and the speed limit control is performed on the target vehicle based on the target acceleration. The scheme fuses the vehicle driving information and the lane visual information, dynamically determines the driving condition and the curvature calculation mode, and then deduces the curvature data, the speed and the acceleration layer by layer to construct a closed-loop speed limit control system, so that the vehicle speed is closely adapted to the road condition, and strong support is provided for the fine control of intelligent driving, and the driving stability, the driving safety and the comfort of the vehicle in complex road conditions are improved.

[0106] In the above embodiment, the vehicle driving condition determination module 310 is configured to determine the straight / curve running state of the target vehicle based on the vehicle driving information, determine the effectiveness of the lane line in the driving environment of the target vehicle based on the lane visual information, and determine the vehicle driving condition based on the straight / curve running state of the target vehicle and the effectiveness of the lane line.

[0107] Optionally, the vehicle driving condition determination module 310 is configured to determine the vehicle driving condition as a first condition if the effectiveness of the lane line meets the effectiveness condition, determine the vehicle driving condition as a second condition if the straight / curve running state of the target vehicle meets the preset running state, and determine the vehicle driving condition as a third condition if the straight / curve running state of the target vehicle does not meet the preset running state.

[0108] Optionally, the curvature data determination module 320 is specifically configured to determine that the target curvature determination mode is a visual curvature determination mode if the vehicle driving condition is the first condition, determine that the target curvature determination mode is a yaw rate curvature determination mode if the vehicle driving condition is the second condition, and determine that the target curvature determination mode is a steering wheel curvature determination mode if the vehicle driving condition is the third condition.

[0109] Optionally, the target curvature determination mode includes the visual curvature determination mode, the yaw rate curvature determination mode, and the steering wheel curvature determination mode; the curvature data determination module 320 is specifically configured to, in the case where the target curvature determination mode is the visual curvature determination mode, acquire farthest point information of available lane lines, determine a lane line forward distance range based on the farthest point information of the available lane lines, sample the lane line forward distance range to obtain a plurality of distance data, substitute each distance data into a lane line curvature algorithm to obtain a plurality of candidate curvature data, and determine minimum curvature data in the plurality of candidate curvature data as the curvature data of the driving route of the target vehicle; in the case where the target curvature determination mode is the yaw rate curvature determination mode, acquire a driving speed and a yaw rate, and determine the curvature data of the driving route of the target vehicle based on the driving speed and the yaw rate; and in the case where the target curvature determination mode is the steering wheel curvature determination mode, acquire a steering wheel turning angle, a steering ratio, and a vehicle wheelbase, and determine the curvature data of the driving route of the target vehicle based on the steering wheel turning angle, the steering ratio, and the vehicle wheelbase.

[0110] Optionally, the speed limit control module 330 is specifically configured to determine a first speed limit value based on the curvature data, wherein the first speed limit value is a speed limit threshold value based on a vehicle roll stability constraint; determine a second speed limit value based on the curvature data, wherein the second speed limit value is a speed limit threshold value based on a tire and road friction force constraint; and determine a target speed limit value based on the first speed limit value and the second speed limit value.

[0111] Optionally, the speed limit control module 330 is specifically configured to, if the driving speed is greater than the target speed limit value, calculate acceleration data based on the target speed limit value and the driving speed, and update a target acceleration limit value based on the acceleration data; and if the driving speed is greater than the target speed limit value, determine a current acceleration data as the target acceleration limit value.

[0112] The vehicle speed limit control device provided in the embodiments of the present application can execute the vehicle speed limit control method provided in any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0113] Embodiment Four

[0114] Figure 4This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0115] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0116] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0117] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with 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 special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as vehicle speed limit control methods.

[0118] In some embodiments, the vehicle speed limit control method can be implemented as a computer program tangibly embodied in a computer readable storage medium, e.g., storage unit 18. In some embodiments, part or all of the computer program can be loaded onto and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. One or more steps of the vehicle speed limit control method described above can be performed when the computer program is loaded into RAM 13 and executed by processor 11. Alternatively, in other embodiments, processor 11 can be configured to perform the vehicle speed limit control method by any other suitable means, e.g., by means of firmware.

[0119] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (PLD), a computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0120] Computer programs used to implement the vehicle speed limit control method of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program running on the processor implements the functions / operations specified in the flowcharts and / or the block diagrams. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, and partially on a remote machine or a server.

[0121] Embodiment Five

[0122] Embodiment five of the present application also provides a computer readable storage medium, which stores computer instructions for causing a processor to execute a vehicle speed limit control method, the method comprising:

[0123] obtaining vehicle driving information and lane visual information of a target vehicle, determining a vehicle driving condition based on the vehicle driving information and the lane visual information;

[0124] The target curvature determination mode is determined based on the driving condition of the vehicle, and the curvature data of the driving route of the target vehicle is determined based on the target curvature determination mode.

[0125] The target speed limit value is determined based on the curvature data, the target acceleration limit value is determined based on the target speed limit value and the driving speed, and the target vehicle is controlled based on the target acceleration.

[0126] In the context of the present application, a computer-readable storage medium can 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. A computer-readable storage medium can 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, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of electrical connections, 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.

[0127] 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 the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0128] The systems and techniques described herein can be implemented in a computing system that includes a back end 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 herein, or any combination of such back end, 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 a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0129] The computing system can include clients and servers. A client and server are generally 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. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0130] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be performed in parallel, in series, or in a different order, and the present disclosure is not limited herein as long as the desired results of the technical solutions of the present disclosure can be achieved.

[0131] The above detailed description does not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A vehicle speed limit control method characterized by comprising: The method comprises the following steps: acquiring vehicle driving information and lane visual information of a target vehicle, determining a vehicle driving condition based on the vehicle driving information and the lane visual information; determining a target curvature determination mode based on the vehicle driving condition, determining curvature data of a driving route of the target vehicle based on the target curvature determination mode; determining a target speed limit value based on the curvature data, determining a target acceleration limit value based on the target speed limit value and the driving speed, and performing speed limit control on the target vehicle based on the target acceleration.

2. The method of claim 1, wherein, The step of determining the vehicle driving condition based on the vehicle driving information and the lane visual information comprises the following steps: determining a straight / curve running state of the target vehicle based on the vehicle driving information; determining the effectiveness of lane lines in the driving environment of the target vehicle based on the lane visual information; determining the vehicle driving condition based on the straight / curve running state of the target vehicle and the effectiveness of the lane lines.

3. The method of claim 2, wherein, The step of determining the vehicle driving condition based on the straight / curve running state of the target vehicle and the effectiveness of the lane lines comprises the following steps: if the effectiveness of the lane lines meets an effectiveness condition, determining that the vehicle driving condition is a first condition; if the effectiveness of the lane lines does not meet the effectiveness condition, determining whether the straight / curve running state of the target vehicle meets a preset running state, if the straight / curve running state of the target vehicle meets the preset running state, determining that the vehicle driving condition is a second condition, and if the straight / curve running state of the target vehicle does not meet the preset running state, determining that the vehicle driving condition is a third condition.

4. The method of claim 3, wherein, The step of determining the target curvature determination mode based on the vehicle driving condition comprises the following steps: if the vehicle driving condition is the first condition, determining that the target curvature determination mode is a visual curvature determination mode; if the vehicle driving condition is the second condition, determining that the target curvature determination mode is a yaw rate curvature determination mode; if the vehicle driving condition is the third condition, determining that the target curvature determination mode is a steering wheel curvature determination mode.

5. The method of claim 1, wherein, The target curvature determination mode comprises a visual curvature determination mode, a yaw rate curvature determination mode and a steering wheel curvature determination mode. The step of determining the curvature data of the driving route of the target vehicle based on the target curvature determination mode comprises the following steps: in the case that the target curvature determination mode is the visual curvature determination mode, acquiring farthest point information of available lane lines, determining a lane line forward distance range based on the farthest point information of the available lane lines, sampling the lane line forward distance range to obtain a plurality of distance data, substituting each distance data into a lane line curvature algorithm to obtain a plurality of candidate curvature data, and determining the minimum curvature data in the plurality of candidate curvature data as the curvature data of the driving route of the target vehicle; in the case that the target curvature determination mode is the yaw rate curvature determination mode, acquiring a driving speed and a yaw rate, and determining the curvature data of the driving route of the target vehicle based on the driving speed and the yaw rate. In a case where the target curvature determination manner is a steering wheel curvature determination manner, a steering wheel rotation angle, a steering ratio and a vehicle wheelbase are acquired, and curvature data of a travel route of the target vehicle is determined based on the steering wheel rotation angle, the steering ratio and the vehicle wheelbase.

6. The method of claim 1, wherein, The target speed limit value is determined based on the curvature data, including: a first limit value is determined based on the curvature data, where the first limit value is a limit threshold value based on vehicle roll stability constraint; a second limit value is determined based on the curvature data, where the second limit value is a limit threshold value based on tire and road friction force constraint; the target speed limit value is determined based on the first limit value and the second limit value.

7. The method of claim 1, wherein, The target acceleration limit value is determined based on the target speed limit value and the travel speed, including: if the travel speed is greater than the target speed limit value, acceleration data is calculated based on the target speed limit value and the travel speed, and the target acceleration limit value is updated based on the acceleration data; if the travel speed is greater than the target speed limit value, current acceleration data is determined as the target acceleration limit value.

8. A vehicle speed limiting control device characterized by comprising: including: a vehicle travel condition determination module, configured to acquire vehicle travel information and lane visual information of a target vehicle, and determine a vehicle travel condition based on the vehicle travel information and the lane visual information; a curvature data determination module, configured to determine a target curvature determination manner based on the vehicle travel condition, and determine curvature data of a travel route of the target vehicle based on the target curvature determination manner; a speed limit control module, configured to determine a target speed limit value based on the curvature data, determine a target acceleration limit value based on the target speed limit value and the travel speed, and perform speed limit control on the target vehicle based on the target acceleration.

9. An electronic device, comprising: The electronic device includes: at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the vehicle speed limit control method in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to execute the vehicle speed limit control method in any one of claims 1-7 when executed.

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