Vehicle control method and device, electronic equipment and storage medium

Abstract

The invention relates to a vehicle control method and device, electronic equipment and a storage medium. The vehicle control method comprises the steps that feature description information of a road where a vehicle is located and first load information of the vehicle are obtained; determining a driving parameter according to the feature description information and the first load information; according to the driving parameters, starting control is conducted on the vehicle, second load information of the vehicle is determined according to actual starting information, running control is conducted on the vehicle according to the second load information, and the actual starting information comprises starting time consumption, the vehicle speed during starting and the driving torque during starting. The stability and safety of the vehicle in the starting process and the running process are improved.

Description

Technical Field

[0001] This disclosure relates to the field of autonomous driving technology, and in particular to a vehicle control method, apparatus, electronic device, and storage medium. Background Technology

[0002] With the increasing popularity of autonomous vehicles, more and more autonomous vehicles are being deployed and applied.

[0003] Currently, the start-up control strategy for autonomous vehicles typically adopts a fixed control parameter mode, which cannot adapt well to the random and ever-changing vehicle state (such as the road environment where the vehicle is located, vehicle load, etc.), resulting in problems such as unstable start-up or excessive energy consumption.

[0004] In view of the above, this application is hereby submitted. Summary of the Invention

[0005] To solve the above-mentioned technical problems, or at least partially solve them, embodiments of this disclosure provide a vehicle control method, device, electronic device, and storage medium. By combining the characteristic description information of the road where the vehicle is located and the first load information of the vehicle, the driving parameters at the time of vehicle start-up are determined. That is, dynamic driving parameters are used instead of fixed driving parameters to control the vehicle at the start-up, which better adapts to the random and ever-changing vehicle state, thereby improving the stability of the vehicle during the start-up phase.

[0006] In a first aspect, embodiments of this disclosure provide a vehicle control method, the vehicle control method comprising:

[0007] Obtain feature description information of the road where the vehicle is located and the vehicle's initial load information;

[0008] The driving parameters are determined based on the feature description information and the first load information;

[0009] The vehicle is controlled to start based on the driving parameters, and the second load information of the vehicle is determined based on the actual start information. The vehicle is then controlled to drive based on the second load information. The actual start information includes start time, vehicle speed at start, and drive torque at start.

[0010] Secondly, this disclosure also provides a vehicle control device, which includes: an acquisition module for acquiring feature description information of the road where the vehicle is located and first load information of the vehicle.

[0011] The first determining module is used to determine the driving parameters based on the feature description information and the first load information;

[0012] The first control module is used to perform start control on the vehicle based on the driving parameters;

[0013] And a second determining module, used to determine the second load information of the vehicle based on the actual starting information;

[0014] The second control module is used to control the vehicle's driving based on the second load information, wherein the actual start-up information includes start-up time, vehicle speed at start-up, and drive torque at start-up.

[0015] Thirdly, embodiments of this disclosure also provide an electronic device, the electronic device comprising: one or more processors; a storage device for storing one or more programs; and when the one or more programs are executed by the one or more processors, causing the one or more processors to implement the vehicle control method as described above.

[0016] Fourthly, embodiments of this disclosure also provide a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the vehicle control method as described above.

[0017] This disclosure provides a vehicle control method that determines the driving parameters for vehicle start-up by combining the characteristic description information of the road where the vehicle is located and the vehicle's first load information. This achieves the purpose of using dynamic driving parameters to control the vehicle's start-up, better adapting to the random and changing vehicle state, thereby improving the vehicle's stability during the start-up phase and avoiding unnecessary energy consumption, thus achieving rational utilization of resources. Furthermore, after the vehicle starts, the second load information of the vehicle is determined based on the actual start-up information, and the vehicle's driving control is performed based on the second load information, ensuring the stability and safety of the vehicle during driving. Attached Figure Description

[0018] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0019] Figure 1 This is a flowchart of a vehicle control method according to an embodiment of the present disclosure;

[0020] Figure 2 This is a schematic diagram of the structure of a vehicle control device according to an embodiment of the present disclosure;

[0021] Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of this disclosure. Detailed Implementation

[0022] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0023] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0024] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0025] Figure 1 This is a flowchart illustrating a vehicle control method according to an embodiment of this disclosure. The vehicle control method can be executed by a vehicle control device, which can be implemented in software and / or hardware. The vehicle control device can be configured in an electronic device, such as an on-board controller. The vehicle control method provided in this disclosure can be applied to autonomous vehicles with multi-scenario operational needs, such as commercial vehicles and special-purpose vehicles.

[0026] like Figure 1 As shown, the vehicle control method may specifically include the following steps:

[0027] S110. Obtain the feature description information of the road where the vehicle is located and the first load information of the vehicle.

[0028] The characteristic description information of the road where the vehicle is located includes, but is not limited to, one or more of the following: road attributes (e.g., dry, wet, icy, snowy), road type (e.g., cement road, asphalt road, dirt road, grassland, sand road), and road slope.

[0029] The initial load information of a vehicle can be obtained by detecting the load on the vehicle via onboard load sensors, or it can be entered by staff after the vehicle has been loaded with goods (such as a large commercial vehicle).

[0030] The characteristic description information of the road where the vehicle is located can be obtained through the vehicle's environmental perception layer. The vehicle's environmental perception layer typically includes various types of sensors, such as LiDAR, cameras, and millimeter-wave radar. By collecting information about the vehicle's surrounding environment and analyzing the collected information through algorithms, such as image recognition, the characteristic description information of the road where the vehicle is located can be determined.

[0031] Furthermore, the road slope and road type (cement road, asphalt road, dirt road, grass road, or sand road) in the feature description information can also be determined from the high-precision map based on the vehicle's location. Specifically, the key technologies of the vehicle's environmental perception layer include multi-sensor fusion technology and 3D environment modeling technology. The environmental perception layer collects surrounding environmental information in real time and combines it with high-precision map data to construct a 3D environment model, thereby determining the road slope and road type.

[0032] Road attributes in the feature description information can be determined by combining weather conditions released by a meteorological platform. Specifically, the vehicle's environmental perception layer can accurately identify various environmental conditions, including weather conditions (sunny, rainy, snowy) and road attributes (dry, wet). Different road attributes and types have different adhesion; for example, dry asphalt roads have greater adhesion than wet asphalt roads, and asphalt roads have greater adhesion than sandy roads. Road adhesion is a core factor determining the maximum available driving torque when the vehicle starts, directly constraining the upper and lower limits of the driving torque required for vehicle start-up.

[0033] The lower limit of the driving torque required for vehicle start-up is the minimum torque needed to overcome the vehicle's static resistance, ensuring the vehicle can start from a standstill. The magnitude of the static resistance is related to at least the road type, vehicle load, and road gradient. A steeper road gradient results in greater static resistance; a heavier vehicle load also results in greater static resistance; the static resistance on dirt roads is significantly greater than that on asphalt roads. The upper limit of the driving torque required for vehicle start-up is determined by road adhesion. If the driving torque exceeds this limit, it will cause wheel slippage, not only failing to increase driving force but also wasting power and causing tire wear.

[0034] For high-traction surfaces, the upper limit of the driving torque required for vehicle start-up is relatively large. Therefore, a high-torque start-up strategy can be adopted, matched with a high gear ratio, to quickly overcome static resistance and improve starting acceleration performance. For low-traction surfaces, the upper limit of the driving torque required for vehicle start-up is relatively small. Therefore, it is necessary to limit the output torque. A small-torque gradual start-up or anti-slip control strategy can be adopted to avoid the torque exceeding the upper limit and causing wheel slippage.

[0035] In summary, the characteristic description information of the road where the vehicle is located and the initial load information of the vehicle have a significant impact on the driving torque required for vehicle start-up. By determining the driving parameters with reference to the characteristic description information of the road where the vehicle is located and the initial load information of the vehicle, the driving parameters can be better adapted to the road conditions and the vehicle's load, thereby improving the smoothness of the vehicle's start-up under specific operating conditions and avoiding power waste.

[0036] S120. Determine the driving parameters based on the feature description information and the first load information.

[0037] In some implementations, the feature description information and the first load information are used as query fields to search for matching driving parameters from a first mapping relationship. The first mapping relationship is obtained through pre-calibration, as shown in Table 1 as an example of the first mapping relationship.

[0038] Table 1

[0039]

[0040]

[0041] In other embodiments, the feature description information and the first load information are input into a trained intelligent model, and the driving parameters are determined by the intelligent model.

[0042] Understandably, before the feature description information and the first load information are input into the trained intelligent model, they need to be normalized and preprocessed. The purpose is to eliminate the influence of dimensional differences on the model, which can improve the model's prediction accuracy.

[0043] Among them, intelligent models can be selected from those with nonlinear fitting capabilities and high real-time inference efficiency, such as convolutional neural networks, recurrent neural networks, or fully connected neural networks.

[0044] By employing dynamic driving parameters for vehicle start-up control, the system effectively adapts to the random and ever-changing vehicle conditions, thereby improving the vehicle's stability during the start-up phase.

[0045] S130. Perform start control on the vehicle according to the driving parameters.

[0046] Specifically, upon receiving an acceleration command (also known as a start-up command, referring to the vehicle starting from a standstill) from the upper-level planning module, the drive torque is controlled according to the drive parameters. Specifically, the drive parameters are converted into coded instructions that the drive controller (e.g., a motor controller) can understand, and these coded instructions are sent to the drive controller. The drive controller decodes the coded instructions to obtain the specific drive parameters, and then controls the actuator (e.g., a motor) to output the desired drive torque at different times, thus starting the vehicle.

[0047] S140. Determine the second load information of the vehicle based on the actual starting information, and perform driving control on the vehicle based on the second load information. The actual starting information includes starting time, starting speed and starting drive torque.

[0048] Considering that the first load information may not be accurate enough or may not be updated in a timely manner (e.g., the vehicle may have unloaded cargo midway), in order to further improve the stability and safety of the vehicle during driving, this embodiment proposes a method for determining the vehicle load in real time, and then controlling the vehicle's driving based on the actual load, thereby achieving the purpose of improving vehicle stability, safety and energy utilization.

[0049] After the vehicle starts moving, the second load information of the vehicle is determined based on the actual starting information. Specifically, in response to the first detection of vehicle speed information, the vehicle speed information is determined as the vehicle speed at the time of starting, and the current driving torque is determined as the driving torque at the time of starting, and the starting time is determined based on the current moment; here, "first time" refers to the first time the vehicle speed information is detected from a stationary state.

[0050] The second load information of the vehicle is determined based on the feature description information of the road, the vehicle speed at the start, the driving torque at the start, and the start time.

[0051] The actual starting information includes starting time, starting speed, and starting drive torque. A longer starting time indicates a heavier vehicle load or a mismatch between the given initial drive torque and the vehicle load. A longer starting time can also reflect the vehicle's load situation from different perspectives, as a smaller initial drive torque and a slower rate of torque increase can lead to a longer starting time.

[0052] A lower initial speed indicates a higher vehicle load, or a mismatch between the driving torque and the vehicle load, which also reflects the vehicle's load condition. Similarly, a higher initial driving torque directly reflects a higher vehicle load.

[0053] In summary, the start-up time, the vehicle speed at start-up, and the driving torque at start-up are all related to the actual load of the vehicle.

[0054] This is understandable, because starting a vehicle requires overcoming not only the resistance caused by the vehicle's load, but also the resistance caused by the road surface itself. Therefore, the actual starting information is also related to the characteristic description information of the road where the vehicle is located.

[0055] This embodiment fully utilizes the aforementioned objective laws and the superior nonlinear fitting capability of the intelligent model. It leverages the intelligent model to mine the feature description information of the road, the initial vehicle speed, the initial driving torque, and the mapping relationship between the starting time and the actual vehicle load. This achieves a more accurate estimation of the actual vehicle load.

[0056] In general, the feature description information of the road, the vehicle speed at the start, the driving torque at the start, and the start time are input into a pre-trained multidimensional coupled load analysis model to obtain the second load information.

[0057] In some implementations, the vehicle load status diagnostic layer is used to diagnose the actual load of the vehicle in real time. Specifically, vehicle speed information is collected at 10ms intervals. When a non-zero vehicle speed is detected for the first time, the start-up time and current driving torque are determined. The start-up time, current driving torque, vehicle speed, and road feature description information are then input into a pre-trained multi-dimensional coupled load analysis model to obtain second load information. To improve the accuracy of the second load information and reduce noise interference, multiple pieces of second load information can be calculated within a set time window using the above method, and noise interference can be filtered out using a Kalman filter algorithm.

[0058] For example, the second load information is Kalman filtered using the following formula:

[0059]

[0060] Where m(k) represents the second load information at time k, f r (k) represents the rolling resistance coefficient at time k, m(k-1) represents the second load information at time k-1, and f r (k-1) represents the rolling resistance coefficient at time k-1, w(k-1) represents the process noise at time k-1, and v(k) represents the observation noise at time k. Both the process noise and the observation noise follow a zero-mean Gaussian distribution. Let θ represent the observation at time k. k T represents the road gradient angle at time k. d (k) represents the driving torque at time k, and h(·) represents the observation function.

[0061] In some embodiments, driving control of the vehicle based on the second load information includes:

[0062] The values ​​of the PID control parameters and braking parameters are determined based on the second load information and the feature description information of the road; the vehicle is accelerated based on the values ​​of the PID control parameters, and the vehicle is braked based on the braking parameters.

[0063] The determination of the PID control parameters and braking parameters based on the second load information and the feature description information of the road can be achieved by searching a preset mapping relationship or by using a large model.

[0064] The step of accelerating the vehicle based on the values ​​of the PID control parameters includes:

[0065] In response to receiving an acceleration command from the upper-level planning module, the system determines the error between the vehicle's current speed and the target speed issued by the upper-level planning module; substitutes the values ​​of the PID control parameters and the error into the PID control calculation formula to obtain the target value; and requests a drive torque of the target value from the drive system.

[0066] The specific formula for PID control can be expressed as follows:

[0067]

[0068] Among them, T cmd (k) represents the target value at time k, and e(k) represents the error between the vehicle speed at time k and the target speed issued by the upper-level planning module, which can also be called the speed tracking error. f The compensation term, K, is obtained by searching from pre-calibrated relationships based on road feature description information. P K i K d These are the values ​​of the proportional coefficient, integral coefficient, and derivative coefficient in the PID control parameters. In some embodiments, in response to receiving a braking command from the upper-level planning module, the vehicle is braked according to the braking parameters. By determining the braking parameters based on the second load information and the road characteristic description information, the braking parameters can be better adapted to the actual load of the vehicle and the current road conditions, thereby making the vehicle's braking effect smoother and more energy-efficient while ensuring safety.

[0069] In summary, the vehicle's drive torque and braking pressure are adjusted through the drive-by-wire execution layer to achieve smooth vehicle operation. Specifically, control commands generated based on the values ​​of the PID control parameters and braking parameters are transmitted in real-time to the drive-by-wire execution layer via the CAN bus. The drive-by-wire execution layer uses PID and fuzzy control algorithms to dynamically adjust the output torque and braking pressure of the drive motor, achieving smooth operation under different load conditions. The fuzzy control algorithm employs a dual-input, three-output structure. The dual inputs are the speed error e and the speed error change rate ec, and the three outputs are the correction values ​​ΔKp, ΔKi, and ΔKd for the PID parameters. The membership function of the fuzzy control algorithm uses a triangular distribution, the fuzzy inference uses the Mamdani algorithm, and the declarative method uses the centroid method.

[0070] For the initial start-up phase, drive parameters are determined based on the feature description information and the first load information. Control commands are generated based on these drive parameters and transmitted in real-time to the drive-by-wire execution layer via the CAN bus. The drive-by-wire execution layer dynamically adjusts the output torque of the drive motor to achieve a smooth start for the vehicle, such as completing a start within 0.8 seconds when the vehicle is unloaded and smoothly transitioning within 1.5 seconds when the vehicle is fully loaded. In some embodiments, after determining the second load information of the vehicle, the method further includes: determining whether the second load information is the same as the first load information. If they are different, the first load information is updated to the second load information to prepare for the next start-up phase after the vehicle stops, ensuring the effectiveness of the vehicle's start-up control each time.

[0071] The vehicle control method provided in this embodiment determines the driving parameters for vehicle start-up by combining the characteristic description information of the road where the vehicle is located and the vehicle's first load information. This achieves the purpose of using dynamic driving parameters to control the vehicle's start-up, better adapting to the random and changing vehicle state, thereby improving the vehicle's stability during the start-up phase and avoiding unnecessary energy consumption, thus achieving rational utilization of resources. Furthermore, after the vehicle starts, the second load information of the vehicle is determined based on the actual start-up information, and the vehicle's driving control is performed based on the second load information, ensuring the stability and safety of the vehicle during driving.

[0072] Based on the above embodiments, the vehicle control method also includes a safety protection mechanism to trigger an alarm and decelerate when the actual load of the vehicle is determined to be abnormal (e.g., the second load information shows that the vehicle is overloaded). Specifically, when the safety protection layer determines that the vehicle load exceeds a set threshold (e.g., 110% of the maximum allowable load), it immediately triggers three levels of protection measures. The first level of protection measures includes a local audible and visual alarm, a red warning light on the dashboard, and a buzzer to alert the driver to the abnormal situation. The second level of protection measures involves cloud data uploading, uploading alarm data packets to the cloud management platform in real time. The alarm data packets contain 23 parameters, including timestamps, geographical location, and load curves. The third level of protection measures involves graded deceleration, executing a graded deceleration strategy, such as a smooth stop with a deceleration of 0.5g, while simultaneously activating hazard lights to warn following vehicles.

[0073] The vehicle control method provided in this embodiment determines the actual load of the vehicle through software, reducing the reliance on hardware modification. In the fields of logistics trailers, commercial vehicles, and heavy trucks, the hardware modification cost of adapting weight sensors is high, and the accuracy cost is high and easily restricted by the installation location, making it impossible to reflect the dynamic load changes of the vehicle in real time.

[0074] By incorporating environmental perception data during autonomous driving, the effectiveness and adaptability of the vehicle load, driving parameters, and braking parameters calculated in this disclosure are significantly improved compared to conventional load estimation algorithms.

[0075] By dynamically adjusting the output torque and braking pressure of the drive motor through PID and fuzzy control algorithms, smooth driving under different load conditions can be achieved.

[0076] By determining the driving parameters for the start-up phase based on environmental perception data, experimental data show that a start-up can be completed within 0.8s under no-load conditions and a smooth transition within 1.5s under full load conditions, reducing the start-up impact by 60% compared to the traditional fixed parameter control method.

[0077] By combining a multi-dimensional coupled load analysis model based on torque, speed, and slope, along with a Kalman filter algorithm, a load identification accuracy within ±5% is achieved. Compared to traditional weighing sensor solutions, the cost is reduced by 40%, while also avoiding hardware installation location limitations.

[0078] By establishing a three-tiered safety protection mechanism, effective measures can be taken promptly in the event of abnormal load. Real-vehicle testing has verified that this system improves the starting safety of autonomous vehicles under complex conditions by 40%, effectively reducing the risk of safety accidents caused by abnormal load.

[0079] By employing an adaptive control strategy based on load conditions, the vehicle can operate within its optimal efficiency range under varying load conditions. Test data shows that compared to traditional fixed-parameter control methods, the system can reduce energy consumption by 8-15%, thereby reducing carbon emissions.

[0080] Figure 2 This is a schematic diagram of the structure of a vehicle control device according to an embodiment of this disclosure. Figure 2 As shown, the vehicle control device includes: an acquisition module 210 for acquiring feature description information of the road where the vehicle is located and first load information of the vehicle; a first determination module 220 for determining drive parameters based on the feature description information and the first load information; a first control module 230 for performing start control on the vehicle based on the drive parameters; and a second determination module 240 for determining second load information of the vehicle based on actual start information; and a second control module 250 for performing driving control on the vehicle based on the second load information, wherein the actual start information includes start time, start speed, and start drive torque.

[0081] Specifically, the first determining module 220 is used to: use the feature description information and the first load information as query fields to find the matching driving parameters from the first mapping relationship; or, input the feature description information and the first load information into a trained intelligent model, and determine the driving parameters through the intelligent model.

[0082] The second determining module 240 includes: a first determining unit, configured to, in response to the first detection of vehicle speed information, determine the vehicle speed information as the vehicle speed at the start, determine the current driving torque as the driving torque at the start, and determine the start-up time based on the current time; and a second determining unit, configured to determine the second load information of the vehicle based on the road feature description information, the vehicle speed at the start, the driving torque at the start, and the start-up time.

[0083] Furthermore, the second determining unit is specifically used to: input the feature description information of the road, the vehicle speed at the start, the driving torque at the start, and the start time into a pre-trained multidimensional coupled load analysis model to obtain the second load information.

[0084] In some implementations, the first control module 230 is specifically used to control the drive torque according to the drive parameters in response to receiving an acceleration command from the upper-level planning module.

[0085] In some embodiments, the second control module 250 includes: a determining unit, configured to determine the values ​​of PID control parameters and braking parameters based on the second load information and the feature description information of the road; and a control unit, configured to perform acceleration control on the vehicle based on the values ​​of the PID control parameters and braking control on the vehicle based on the braking parameters.

[0086] Specifically, the control unit is used to: respond to receiving an acceleration command from the upper-level planning module, determine the error between the vehicle's current speed and the target speed issued by the upper-level planning module; substitute the values ​​of the PID control parameters and the error into the PID control calculation formula to obtain the target value; and request a drive torque of the target value from the drive system.

[0087] In some embodiments, the system further includes an update module, configured to, after determining the second load information of the vehicle, determine whether the second load information is the same as the first load information, and if they are not the same, update the first load information to the second load information.

[0088] The vehicle control device provided in this embodiment determines the driving parameters for vehicle start-up by combining the characteristic description information of the road where the vehicle is located and the vehicle's first load information. This achieves the purpose of using dynamic driving parameters to control the vehicle's start-up, better adapting to the random and changing vehicle state, thereby improving the vehicle's stability during the start-up phase and avoiding unnecessary energy consumption, thus achieving rational utilization of resources. Furthermore, after the vehicle starts, the second load information of the vehicle is determined based on the actual start-up information, and the vehicle's driving control is performed based on the second load information, ensuring the stability and safety of the vehicle during driving.

[0089] The vehicle control device provided in this disclosure embodiment can execute the steps in the vehicle control method provided in this disclosure method embodiment, and has the execution steps and beneficial effects, which will not be repeated here.

[0090] Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of this disclosure. See below for details. Figure 3 It shows a schematic diagram of a structure suitable for implementing the electronic device 300 in the embodiments of this disclosure. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0091] like Figure 3 As shown, the electronic device 300 may include a processing device 301, a ROM 302, a RAM 303, a bus 304, an input / output (I / O) interface 305, an input device 306, an output device 307, a storage device 308, and a communication device 309. The processing device (e.g., a central processing unit, a graphics processor, etc.) 301 can perform various appropriate actions and processes to implement the methods of the embodiments described herein, based on a program stored in the read-only memory (ROM) 302 or a program loaded from the storage device 308 into the random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the electronic device 300. The processing device 301, the ROM 302, and the RAM 303 are interconnected via the bus 304. The input / output (I / O) interface 305 is also connected to the bus 304.

[0092] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts, thereby implementing the vehicle control method as described above. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 309, or installed from a storage device 308, or installed from a ROM 302. When the computer program is executed by the processing device 301, it performs the functions defined in the methods of embodiments of this disclosure.

[0093] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0094] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: acquire feature description information of the road where the vehicle is located and first load information of the vehicle; determine driving parameters based on the feature description information and the first load information; perform start-up control on the vehicle based on the driving parameters; determine second load information of the vehicle based on actual start-up information; and perform driving control on the vehicle based on the second load information, wherein the actual start-up information includes start-up time, start-up speed, and start-up driving torque.

[0095] Optionally, when one or more of the above-described procedures are executed by the electronic device, the electronic device may also perform other steps described in the above embodiments.

[0096] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include, based on electrical connections of one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0097] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

Claims

1. A vehicle control method, characterized in that, include: Obtain feature description information of the road where the vehicle is located and the vehicle's initial load information; The driving parameters are determined based on the feature description information and the first load information; The vehicle is controlled to start based on the driving parameters, and the second load information of the vehicle is determined based on the actual start information. The vehicle is then controlled to drive based on the second load information. The actual start information includes start time, vehicle speed at start, and drive torque at start.

2. The vehicle control method according to claim 1, characterized in that, Determining the driving parameters based on the feature description information and the first load information includes: Using the feature description information and the first load information as query fields, the matching drive parameters are searched from the first mapping relationship; Alternatively, the feature description information and the first load information can be input into a trained intelligent model, and the driving parameters can be determined by the intelligent model.

3. The vehicle control method according to claim 1, characterized in that, Determining the second load information of the vehicle based on the actual start-up information includes: In response to the first detection of vehicle speed information, the vehicle speed information is determined as the vehicle speed at the start, and the current driving torque is determined as the driving torque at the start, and the start time is determined according to the current time. The second load information of the vehicle is determined based on the feature description information of the road, the vehicle speed at the start, the driving torque at the start, and the start time.

4. The vehicle control method according to claim 1, characterized in that, The step of controlling the vehicle to start based on the driving parameters includes: In response to receiving an acceleration command from the upper-level planning module, the drive torque is controlled according to the drive parameters; The step of controlling the vehicle's movement based on the second load information includes: The values ​​of the PID control parameters and braking parameters are determined based on the second load information and the feature description information of the road. The vehicle is accelerated based on the values ​​of the PID control parameters, and the vehicle is braked based on the braking parameters.

5. The vehicle control method according to claim 4, characterized in that, The step of accelerating the vehicle based on the values ​​of the PID control parameters includes: In response to receiving an acceleration command from the upper-level planning module, determine the error between the vehicle's current speed and the target speed issued by the upper-level planning module; Substitute the values ​​of the PID control parameters and the error into the PID control calculation formula to obtain the target value; Request a drive torque of the target value from the drive system.

6. The vehicle control method according to claim 1, characterized in that, After determining the second load information of the vehicle, the method further includes: Determine whether the second load information is the same as the first load information. If they are not the same, update the first load information to the second load information.

7. The vehicle control method according to claim 3, characterized in that, The step of determining the second load information of the vehicle based on the feature description information of the road, the vehicle speed at startup, the driving torque at startup, and the startup time includes: The feature description information of the road, the vehicle speed at the start, the driving torque at the start, and the start time are input into a pre-trained multidimensional coupled load analysis model to obtain the second load information.

8. A vehicle control device, characterized in that, include: The acquisition module is used to acquire feature description information of the road where the vehicle is located and the vehicle's first load information; The first determining module is used to determine the driving parameters based on the feature description information and the first load information; The first control module is used to perform start control on the vehicle based on the driving parameters; And a second determining module, used to determine the second load information of the vehicle based on the actual starting information; The second control module is used to control the vehicle's driving based on the second load information, wherein the actual start-up information includes start-up time, vehicle speed at start-up, and drive torque at start-up.

9. An electronic device, characterized in that, The electronic device includes: one or more processors; a storage device for storing one or more programs; and when the one or more programs are executed by the one or more processors, the one or more processors perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1-7.

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