Gradient-based autonomous driving speed planning method, device, medium and equipment

Abstract

The application discloses a slope-based automatic driving speed planning method and device, a medium and equipment, and belongs to the technical field of automatic driving. Mainly includes, according to the perception information that automatic driving vehicle travels in the process of real-time sensing, the road slope of the current section is calculated;If the road slope of the current section is greater than the preset slope threshold, then according to the road slope of the current section and the current driving speed of the automatic driving vehicle, the slope speed correction coefficient of the current section is calculated;And the current planning strategy is obtained according to the slope speed correction coefficient, and then the speed of the automatic driving vehicle is planned according to the current planning strategy. The application can reduce the fuel consumption of the vehicle under the premise that the average speed of the vehicle is similar, prolong the service life of the related parts of the vehicle.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and in particular to a slope-based autonomous driving speed planning method, apparatus, medium, and device. Background Technology

[0002] As autonomous driving technology matures, it is attracting increasing market attention, which in turn places new and higher demands on it. Speed ​​planning, a key technology in autonomous driving, also faces continuous improvement and enhancement requirements.

[0003] Existing technologies, such as cruise control or adaptive cruise control, do not consider the gradient when planning speeds on uphill sections. They will keep the vehicle traveling at the set speed, which will result in excessive fuel consumption and affect the lifespan of related vehicle components. Summary of the Invention

[0004] To address the problems existing in the prior art, this application mainly provides a slope-based autonomous driving speed planning method, device, medium, and equipment, which adjusts the speed of autonomous vehicles according to the slope of the road segment being driven, thereby greatly saving fuel consumption.

[0005] To achieve the above objectives, one technical solution adopted in this application is: providing a slope-based autonomous driving speed planning method, which includes:

[0006] Based on the real-time perception information obtained by the autonomous vehicle during its operation, the slope of the current road segment is calculated. If the slope of the current road segment is greater than the preset slope threshold, the slope speed correction coefficient of the current road segment is calculated based on the slope of the current road segment and the current driving speed of the autonomous vehicle. The current planning strategy is obtained based on the slope speed correction coefficient, and then the speed of the autonomous vehicle is planned according to the current planning strategy.

[0007] Another technical solution adopted in this application is: providing a slope-based autonomous driving speed planning and prompting method, which includes:

[0008] Based on the real-time perception information obtained by the autonomous vehicle during its operation, the slope of the current road segment is calculated. If the slope of the current road segment is greater than a preset slope threshold, a slope speed correction coefficient is calculated based on the slope of the current road segment and the current speed of the autonomous vehicle. A current planning strategy is obtained based on the slope speed correction coefficient, and the speed of the autonomous vehicle is planned according to the current planning strategy. The real-time speed change process determined by the speed planning is gradually visualized to provide corresponding prompts to the driver.

[0009] Another technical solution adopted in this application is: providing a slope-based autonomous driving speed planning device, which includes:

[0010] The system includes a slope calculation module, which calculates the slope of the current road segment based on real-time perception information obtained by the autonomous vehicle during its operation; a correction coefficient calculation module, which calculates a slope speed correction coefficient for the current road segment based on the current slope and the current speed of the autonomous vehicle if the slope of the current road segment exceeds a preset slope threshold; and a speed planning module, which obtains a current planning strategy based on the slope speed correction coefficient and then plans the speed of the autonomous vehicle according to the current planning strategy. Another technical solution adopted in this application is to provide a computer-readable storage medium storing computer instructions that are operated to execute the slope-based autonomous driving speed planning method described above.

[0011] Another technical solution adopted in this application is to provide a computer-readable storage medium storing computer instructions that are operated to execute the slope-based autonomous driving speed planning method in the above solution.

[0012] Another technical solution adopted in this application is: providing a computer device, which includes a processor and a memory, the memory storing computer instructions that are operated to execute the slope-based autonomous driving speed planning method in the above solution.

[0013] The beneficial effects achievable by the technical solution of this application are: a slope-based autonomous driving speed planning method, device, medium, and equipment. By considering the slope of the road segment being traveled to plan and adjust the vehicle's speed, this application can reduce vehicle fuel consumption and extend the service life of related vehicle components while maintaining a relatively consistent average vehicle speed. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart illustrating a specific implementation of a slope-based autonomous driving speed planning method according to this application.

[0016] Figure 2 This is a flowchart illustrating a specific implementation of a slope-based autonomous driving speed planning and prompting method according to this application;

[0017] Figure 3 This is a schematic diagram of a specific embodiment of an autonomous driving speed planning device based on slope according to this application;

[0018] Figure 4 This is a schematic diagram of a specific embodiment of a slope-based autonomous driving speed planning prompt device according to this application;

[0019] The accompanying drawings have illustrated specific embodiments of this disclosure, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this disclosure to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0020] The preferred embodiments of this application will now be described in detail with reference to the accompanying drawings, so that the advantages and features of this application can be more easily understood by those skilled in the art, thereby providing a clearer and more definite definition of the scope of protection of this application.

[0021] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0022] Existing autonomous vehicles employ two speed planning modes: cruise control and following mode. These modes are typically activated by the driver using a lever or pressing a button. Once in cruise control or following mode, the vehicle mechanically executes pre-set driving patterns or focuses solely on information such as lane obstacles and potential obstacles when planning speed. Unlike manual driving, it cannot allow for slightly lower speeds uphill and slightly higher speeds downhill on steep inclines.

[0023] This application plans and adjusts the vehicle's speed by taking into account the gradient of the road section it travels, so as to reduce the vehicle's fuel consumption and extend the service life of related vehicle components while keeping the average vehicle speed similar.

[0024] The technical solutions of this application will now be described in detail with reference to specific embodiments and accompanying drawings. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0025] Figure 1 This invention illustrates a specific implementation of a slope-based autonomous driving speed planning method.

[0026] exist Figure 1 The specific implementation of the slope-based autonomous driving speed planning method of this application shown includes the following steps: Step S101, calculating the slope of the current road segment based on the perception information perceived in real time during the autonomous driving process; Step S102, if the slope of the current road segment is greater than a preset slope threshold, calculating the slope speed correction coefficient of the current road segment based on the slope of the current road segment and the current driving speed of the autonomous driving vehicle; and Step S103, obtaining the current planning strategy based on the slope speed correction coefficient, and then performing speed planning for the autonomous driving vehicle based on the current planning strategy.

[0027] This application plans and adjusts the vehicle's speed by taking into account the gradient of the road section it travels on, thereby reducing fuel consumption and extending the service life of vehicle components while keeping the average vehicle speed relatively consistent.

[0028] Process S101 represents the process of calculating the slope of the current road segment based on the perception information perceived in real time during the driving of the autonomous vehicle. This can help determine whether to adjust the driving speed of the autonomous vehicle accordingly based on the road segment slope.

[0029] In an optional embodiment of this application, the autonomous vehicle acquires perception information using its own configured perception devices during driving. Specifically, the aforementioned perception devices may include AVM (Around View Fisheye Camera), USS (Ultrasonic Radar), IMU (Inertial Measurement Unit), or slope sensor.

[0030] In an optional specific example of this application, the slope of the current road segment is obtained directly from the slope sensor configured on the autonomous vehicle.

[0031] In an optional specific example of this application, the longitudinal motion of the autonomous vehicle is detected by the IMU (inertial sensor) configured on the autonomous vehicle, and the slope of the current driving segment is calculated based on the longitudinal motion of the vehicle.

[0032] In an optional embodiment of this application, the perception information obtained by the perception device configured on the autonomous vehicle itself also includes the surrounding environment information of the autonomous vehicle, which may include surrounding obstacle information such as vehicle information, traffic sign information such as speed limit information, or lane line information, etc.

[0033] Process S102 represents the process of calculating the slope speed correction coefficient of the current road segment based on the current road segment slope and the current driving speed of the autonomous vehicle if the current road segment slope is greater than the preset slope threshold. This process can help to plan and adjust the driving speed of the autonomous vehicle based on the slope speed correction coefficient.

[0034] Specifically, in manual driving scenarios, a road segment with a very small gradient is often overlooked. Only when the gradient exceeds a certain threshold does it attract the driver's attention. Similarly, this application, when considering speed planning based on road gradient, only considers road segments with gradients exceeding a preset threshold. Furthermore, when calculating the gradient speed correction coefficient, it considers not only the gradient of the corresponding road segment but also the vehicle's current speed. For example, if the autonomous vehicle's current speed is already relatively low on an uphill section, there is no need to consider the gradient to slow it down. If the vehicle's current speed is already close to the maximum speed limit on a downhill section, there is no need to consider the gradient to accelerate it.

[0035] In an optional specific embodiment of this application, the process of calculating the slope speed correction coefficient of the current road segment based on the road slope and the driving speed of the autonomous vehicle is to calculate the slope speed correction coefficient of the current road segment using a lookup table method based on the road slope and the driving speed of the autonomous vehicle.

[0036] In an optional embodiment of this application, if the current road segment is an uphill section, the slope speed correction coefficient is less than 1, that is, the vehicle speed is appropriately reduced to reduce fuel consumption and extend the service life of vehicle-related components.

[0037] In an optional embodiment of this application, if the current road segment is a downhill segment, the slope speed correction coefficient is greater than 1 and not greater than 1.2. That is, the vehicle's acceleration is fully utilized to reduce fuel consumption and compensate for the decrease in average speed caused by deceleration when going uphill. However, excessive acceleration should be avoided to prevent speeding or other unexpected situations.

[0038] The process 103 represents obtaining the current planning strategy based on the slope speed correction coefficient, and then performing speed planning for the autonomous vehicle based on the current planning strategy. This process ultimately yields a final planning strategy that uses road slope as an influencing factor for speed planning, and performs corresponding speed planning.

[0039] In an optional embodiment of this application, the process of obtaining the current planning strategy based on the ramp speed correction coefficient further includes obtaining the current planning strategy based on perception information in addition to the ramp speed correction coefficient. The perception information includes the drivable space ahead of the autonomous vehicle, the driving speeds of other related vehicles of the autonomous vehicle, the historical driving speed records of the autonomous vehicle in the current road segment, and the maximum speed limit of the current road segment; and at least one of them.

[0040] Specifically, gradient is just one factor to consider in speed planning. In actual speed planning, other factors also need to be considered to ensure that autonomous vehicles can operate safely and efficiently.

[0041] In one optional specific example of this application, the acceleration of the autonomous vehicle is determined based on the drivable space in front of it, which is the free space in front of the vehicle in the lane and largely determines the upper limit of the vehicle's acceleration.

[0042] In one optional specific example of this application, the speed of the first vehicle in front of the autonomous vehicle is used to determine its planned speed. In order to ensure driving safety, it is necessary to maintain a safe distance between the vehicle and the vehicle in front. Therefore, the speed of the first vehicle in front is determined as the planned speed.

[0043] In one optional specific example of this application, the decision to allow the autonomous vehicle to change lanes and accelerate is determined based on the speed of vehicles in adjacent lanes. In a human driving scenario, if the speed of vehicles in adjacent lanes is significantly faster than that in the current lane, a lane change and acceleration are often sought. Therefore, simulating the human decision-making process involves paying attention to the speed of vehicles in adjacent lanes and making a decision on whether to change lanes and accelerate.

[0044] In one optional specific example of this application, the planned speed is initialized based on the historical speed records of the autonomous vehicle. By directly using the speed in the historical speed records as the planned speed, the computational load of the speed planning process can be reduced and the system response can be improved.

[0045] Optionally, the slope-based autonomous driving speed planning method of this application further includes locating and identifying the current road segment during the autonomous driving process, and storing the current road segment and the current speed in a one-to-one correspondence, so as to facilitate the retrieval of this record when passing through the same road segment again, thereby reducing the computational load of calculating and obtaining the speed planning strategy.

[0046] In one optional specific example of this application, the maximum speed limit of the current road segment is determined as the planned speed of the autonomous vehicle. In order to ensure safety and comply with relevant traffic regulations, it is necessary to ensure that the vehicle speed does not exceed the maximum speed limit of the current road segment. Therefore, the maximum speed limit of the current road segment is used as the planned speed of the vehicle.

[0047] Figure 2 This paper illustrates a specific embodiment of a slope-based autonomous driving speed planning prompt method according to this application.

[0048] exist Figure 2 In the specific embodiments shown, the slope-based autonomous driving speed planning prompt method of this application includes,

[0049] In process S201, the slope of the current road segment is calculated based on the perception information obtained in real time during the autonomous vehicle's operation. In process S202, if the slope of the current road segment is greater than a preset slope threshold, a slope speed correction coefficient for the current road segment is calculated based on the slope of the current road segment and the current driving speed of the autonomous vehicle. In process S203, the current planning strategy is obtained based on the slope speed correction coefficient, and then the speed of the autonomous vehicle is planned according to the current planning strategy. In process S204, the real-time speed change process determined by the speed planning is gradually visualized to provide corresponding prompts to the driver.

[0050] Process S204 represents the process of gradually visualizing the real-time speed changes determined by the speed planning, thereby providing corresponding prompts to the driver and enabling passengers to be psychologically prepared for changes in vehicle speed.

[0051] Existing autonomous vehicles can only display the vehicle's current speed and the planned speed. Drivers cannot be prepared in time for changes in the vehicle's driving status. In contrast, in real-world human driving scenarios, drivers adjust the vehicle's driving status by adjusting the accelerator and have good psychological anticipation. This application aims to display the required driving states of the vehicle and provide prompts to the driver, simulating the perception of driving states in human driving. This helps drivers prepare for changes in the vehicle's driving status in a timely manner, thereby improving the driver's experience during autonomous vehicle operation.

[0052] In an optional specific embodiment of this application, the process of gradually visualizing the real-time speed change process determined by the speed planning to provide corresponding prompts to the driver includes distinguishing between the acceleration state, deceleration state, and constant speed state of the autonomous vehicle.

[0053] Optionally, the current speed of the autonomous vehicle can be displayed as a progress bar instead of a number. The progress bar is displayed in red when the autonomous vehicle needs to accelerate, in yellow when it needs to decelerate, and in green when it needs to maintain a constant speed. This way, the driver can know the vehicle's upcoming driving status as long as they see the corresponding color of the progress bar, thus making timely psychological preparations and improving the driver's riding experience.

[0054] Preferably, when the acceleration required for the autonomous vehicle to accelerate or the deceleration required to decelerate exceeds a preset acceleration threshold, an alarm is issued to the driver by flashing the color of the speed progress bar, or an alarm is issued to the driver by voice broadcast.

[0055] In an optional embodiment of this application, the process of gradually visually displaying the real-time speed change process determined by the speed planning, thereby providing corresponding prompts to the driver, includes:

[0056] The system compares the actual driving speed of the autonomous vehicle with the current planned speed obtained according to the current planning strategy. If the first speed difference obtained by subtracting the actual driving speed from the current planned speed is less than a preset first speed difference threshold, a first visual alarm prompt is given to the driver. The system also compares the actual driving speed of the autonomous vehicle with the current lane minimum drivable speed calculated based on the current perception information. If the second speed difference obtained by subtracting the current lane minimum drivable speed from the actual driving speed is less than a preset second speed difference threshold, a second visual alarm prompt is given to the driver.

[0057] If the current speed of an autonomous vehicle is close to the planned speed or the minimum drivable speed, it means that the vehicle will transition from one driving state to another, such as from accelerating to constant speed, from decelerating to constant speed, or from decelerating to accelerating. Providing appropriate prompts at this time can help the driver have a correct psychological expectation of the vehicle's next driving state and improve the riding experience.

[0058] Optionally, if the first speed difference is less than a preset first speed difference threshold, a visual prompt can be given to the driver by flashing a red and green speed progress bar, or a visual prompt can be given to the driver by voice broadcast.

[0059] Optionally, if the second speed difference is less than a preset second speed difference threshold, a visual prompt can be given to the driver by flashing a yellow-green speed progress bar, or a visual prompt can be given to the driver by voice broadcast.

[0060] Figure 3 This application illustrates a specific embodiment of a slope-based autonomous driving speed planning device.

[0061] exist Figure 3 In the specific implementation of the slope-based autonomous driving speed planning device of this application shown, the slope calculation module 301 calculates the slope of the current road segment based on the perception information perceived in real time during the autonomous driving process; the correction coefficient calculation module 302 is used to calculate the slope speed correction coefficient of the current road segment based on the slope of the current road segment and the current driving speed of the autonomous driving vehicle if the slope of the current road segment is greater than a preset slope threshold; and the speed planning module 303 is used to obtain the current planning strategy based on the slope speed correction coefficient, and then perform speed planning for the autonomous driving vehicle based on the current planning strategy.

[0062] The gradient-based autonomous driving speed planning device of this application can plan and adjust the vehicle speed by taking into account the gradient of the road segment traveled, thereby reducing vehicle fuel consumption and extending the service life of vehicle-related components while keeping the average vehicle speed relatively constant.

[0063] Module 301 is used to calculate the slope of the current road segment based on the perception information perceived in real time during the driving of the autonomous vehicle. This can help determine whether to adjust the driving speed of the autonomous vehicle accordingly based on the road segment slope.

[0064] In an optional embodiment of this application, the slope calculation module 301 includes an IMU (inertial measurement unit) or a slope sensor.

[0065] The correction coefficient calculation module 302 is used to calculate the slope speed correction coefficient of the current road segment based on the current road segment slope and the current driving speed of the autonomous vehicle if the current road segment slope is greater than the preset slope threshold. This can help to plan and adjust the driving speed of the autonomous vehicle based on the slope speed correction coefficient.

[0066] Specifically, in manual driving scenarios, a road segment with a very small gradient is often overlooked. Only when the gradient exceeds a certain threshold does it attract the driver's attention. Similarly, this application, when considering speed planning based on road gradient, only considers road segments with gradients exceeding a preset threshold. Furthermore, when calculating the gradient speed correction coefficient, it considers not only the gradient of the corresponding road segment but also the vehicle's current speed. For example, if the autonomous vehicle's current speed is already relatively low on an uphill section, there is no need to consider the gradient to slow it down. If the vehicle's current speed is already close to the maximum speed limit on a downhill section, there is no need to consider the gradient to accelerate it.

[0067] The speed planning module 303 is used to obtain the current planning strategy based on the slope speed correction coefficient, and then to plan the speed of the autonomous vehicle based on the current planning strategy. It can ultimately obtain the final planning strategy that takes the road slope as an influencing factor for speed planning, and perform the corresponding speed planning.

[0068] Figure 4 This paper illustrates a specific embodiment of a slope-based autonomous driving speed planning prompt device according to this application.

[0069] exist Figure 4 In the specific embodiment shown, the slope-based autonomous driving speed planning prompt device of this application includes: a slope calculation module 401, which calculates the slope of the current road segment based on the perception information perceived in real time during the autonomous driving process; a correction coefficient calculation module 402, which calculates the slope speed correction coefficient of the current road segment based on the slope of the current road segment and the current driving speed of the autonomous driving vehicle if the slope of the current road segment is greater than a preset slope threshold; a speed planning module 403, which obtains the current planning strategy based on the slope speed correction coefficient and then performs speed planning for the autonomous driving vehicle based on the current planning strategy; and a display prompt module 404, which gradually visualizes the real-time speed change process determined by the speed planning, thereby providing corresponding prompts to the driver.

[0070] The display prompt module 404 is used to gradually visualize the real-time speed change process determined by the speed plan, thereby providing corresponding prompts to the driver. It can gradually visualize the change process of vehicle speed, making it easier for passengers to be psychologically prepared for changes in vehicle speed.

[0071] Existing autonomous vehicles can only display the vehicle's current speed and the planned speed. Drivers cannot be prepared in time for changes in the vehicle's driving status. This application, by displaying the necessary driving states and providing prompts to the driver, helps the driver prepare for changes in the vehicle's driving status in a timely manner, thereby improving the driver's experience during autonomous vehicle operation.

[0072] The extended slope-based autonomous driving speed planning device provided in this application can be used to execute the extended slope-based autonomous driving speed planning method described in any of the above embodiments. Its implementation principle and technical effect are similar, and will not be repeated here.

[0073] In one specific embodiment of this application, the functional modules of the slope-based autonomous driving speed planning device of this application can be directly in hardware, in software modules executed by a processor, or in a combination of both.

[0074] Software modules may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in this art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from and write information to the storage medium.

[0075] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or any combination thereof. A general-purpose processor can be a microprocessor, but alternatively, it can be any conventional processor, controller, microcontroller, or state machine. The processor can also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors incorporating a DSP core, or any other such configuration. Alternatively, the storage medium can be integrated with the processor. The processor and storage medium can reside in an ASIC. The ASIC can reside in the user terminal. Alternatively, the processor and storage medium can reside as discrete components in the user terminal.

[0076] In another specific embodiment of this application, a computer-readable storage medium stores computer instructions that are operated to perform an extended slope-based autonomous driving speed planning method as described above.

[0077] In another specific embodiment of this application, a computer device includes a processor and a memory, the memory storing computer instructions that are operated to execute the slope-based autonomous driving speed planning method in the above-described scheme.

[0078] In another specific embodiment of this application, an autonomous driving system includes the slope-based autonomous driving speed planning device described above.

[0079] In another specific embodiment of this application, an autonomous vehicle includes the autonomous driving system described above.

[0080] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0081] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0082] The above are merely embodiments of this application and do not limit the scope of this patent application. Any equivalent structural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of this application.

Claims

1. A slope-based speed planning method for autonomous driving, characterized in that, include, The slope of the current road segment is calculated based on the perception information perceived in real time during the driving of the autonomous vehicle. The calculation of the slope of the current road segment based on the perception information perceived in real time during the driving of the autonomous vehicle includes: detecting the longitudinal motion of the vehicle through the IMU inertial sensor configured in the autonomous vehicle to calculate the slope of the current road segment, or directly obtaining the slope of the current road segment through a slope sensor. If the slope of the current road segment is greater than a preset slope threshold, a slope speed correction coefficient for the current road segment is calculated based on the slope of the current road segment and the current speed of the autonomous vehicle. This calculation includes: if the current road segment is uphill and the current speed of the autonomous vehicle is lower than a preset speed threshold, no deceleration adjustment based on the slope speed correction coefficient is performed; if the current road segment is downhill and the current speed of the autonomous vehicle is higher than or equal to a second preset speed threshold, no acceleration adjustment based on the slope speed correction coefficient is performed, where the second preset speed threshold is less than the maximum speed limit of the current road segment. The current planning strategy is obtained based on the ramp speed correction coefficient, and then the speed of the autonomous vehicle is planned according to the current planning strategy.

2. The slope-based autonomous driving speed planning method according to claim 1, characterized in that, The process of calculating the slope speed correction coefficient for the current road segment based on the road slope and the current speed of the autonomous vehicle includes: Based on the current road slope and the current speed of the autonomous vehicle, the slope speed correction coefficient for the current road segment is calculated using a lookup table method.

3. The slope-based autonomous driving speed planning method according to claim 1, characterized in that, If the current road segment is an uphill segment, then the slope speed correction factor is less than 1; If the current road segment is a downhill segment, then the slope speed correction factor is greater than 1 and not greater than 1.

2.

4. The slope-based autonomous driving speed planning method according to claim 1, characterized in that, The process of obtaining the current planning strategy based on the ramp speed correction coefficient further includes, In addition to the slope speed correction factor, the current planning strategy is also derived based on the perceived information. The perceived information includes at least one of the following: the drivable space ahead of the autonomous vehicle, the relevant vehicle speed of the autonomous vehicle, the historical speed record of the autonomous vehicle on the current road segment, and the maximum speed limit of the current road segment.

5. A slope-based speed planning and prompting method for autonomous driving, characterized in that, include: The slope of the current road segment is calculated based on the perception information perceived in real time during the driving of the autonomous vehicle. The calculation of the slope of the current road segment based on the perception information perceived in real time during the driving of the autonomous vehicle includes: detecting the longitudinal motion of the vehicle through the IMU inertial sensor configured in the autonomous vehicle to calculate the slope of the current road segment, or directly obtaining the slope of the current road segment through a slope sensor. If the slope of the current road segment is greater than a preset slope threshold, then a slope speed correction coefficient for the current road segment is calculated based on the slope of the current road segment and the current speed of the autonomous vehicle. This calculation includes: if the current road segment is uphill and the current speed of the autonomous vehicle is lower than a preset speed threshold, then no deceleration adjustment based on the slope speed correction coefficient is performed; if the current road segment is downhill and the current speed of the autonomous vehicle is higher than or equal to a second preset speed threshold, then no acceleration adjustment based on the slope speed correction coefficient is performed, wherein the second preset speed threshold is less than the maximum speed limit of the current road segment. The current planning strategy is obtained based on the aforementioned ramp speed correction coefficient; Speed ​​planning for the autonomous vehicle is performed according to the current planning strategy; and The real-time speed change process determined by the speed planning is gradually visualized to provide corresponding prompts to the driver.

6. The slope-based autonomous driving speed planning and prompting method according to claim 5, characterized in that, The process of gradually visualizing the real-time speed changes determined by the speed planning to provide corresponding prompts to the driver includes: The system distinguishes between accelerating, decelerating, and constant-speed driving states.

7. The slope-based autonomous driving speed planning and prompting method according to claim 5, characterized in that, The process of gradually visualizing the real-time speed changes determined by the speed planning to provide corresponding prompts to the driver includes: The actual driving speed of the autonomous vehicle is compared with the current planned speed obtained according to the current planning strategy. If the first speed difference obtained by subtracting the actual driving speed from the current planned speed is less than a preset first speed difference threshold, a first visual alarm is issued to the driver. The actual driving speed of the autonomous vehicle is compared with the minimum driving speed of the current lane calculated based on the current perception information. If the second speed difference obtained by subtracting the minimum driving speed of the current lane from the actual driving speed is less than a preset second speed difference threshold, a second visual alarm prompt is given to the driver.

8. A slope-based automatic driving speed planning device, characterized in that, include, The slope calculation module calculates the slope of the current road segment based on the perception information perceived in real time during the autonomous vehicle's operation. The calculation of the slope of the current road segment based on the perception information perceived in real time during the autonomous vehicle's operation includes: detecting the longitudinal motion of the vehicle through the IMU inertial sensor configured in the autonomous vehicle to calculate the slope of the current road segment, or directly obtaining the slope of the current road segment through a slope sensor. The correction coefficient calculation module is used to calculate a slope speed correction coefficient for the current road segment based on the current road segment slope and the current driving speed of the autonomous vehicle if the current road segment slope is greater than a preset slope threshold. The calculation of the slope speed correction coefficient based on the current road segment slope and the current driving speed of the autonomous vehicle includes: if the current road segment is uphill and the current driving speed of the autonomous vehicle is lower than a preset speed threshold, then no deceleration adjustment based on the slope speed correction coefficient is performed; if the current road segment is downhill and the current driving speed of the autonomous vehicle is higher than or equal to a second preset speed threshold, then no acceleration adjustment based on the slope speed correction coefficient is performed, wherein the second preset speed threshold is less than the maximum speed limit of the current road segment; and The speed planning module is used to obtain the current planning strategy based on the ramp speed correction coefficient, and then perform speed planning for the autonomous vehicle based on the current planning strategy.

9. A computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are operated to perform the slope-based autonomous driving speed planning method according to any one of claims 1-4.

10. A computer device comprising a processor and a memory storing computer instructions, wherein the processor operates the computer instructions to perform the slope-based autonomous driving speed planning method according to any one of claims 1-4.

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