Freight heavy truck driving parameter design method, system, equipment and medium

Abstract

The invention provides a freight heavy truck driving parameter design method, system and device and a medium, and belongs to the technical field of intelligent driving, and the method comprises the steps: S1, classifying the operation scenes of a freight heavy truck, and collecting the driving parameter data of a driver in each operation scene; s2, preprocessing the collected driving parameter data to obtain a driving parameter sample set; and S3, analyzing the driving parameter sample set by adopting a clustering algorithm, and extracting typical driving parameters in each operation scene. Accurate matching of the driving parameters and the freight scene is achieved, and adaptability, safety and efficiency of the intelligent driving system in a complex freight environment are improved.

Description

Technical Field

[0001] This invention belongs to the field of intelligent driving technology, and in particular relates to a method, system, device and medium for designing driving parameters for heavy-duty freight trucks. Background Technology

[0002] In recent years, with the rapid development of the logistics and transportation industry, heavy-duty trucks have played a vital role in road transportation. The introduction of intelligent driving technology has provided a new technological path to improve freight efficiency, ensure driving safety, and reduce operating costs. In the commercial vehicle sector, especially in the scenario of heavy-duty trucks, the large variations in load, diverse types of goods, and complex road conditions place higher demands on vehicle planning and control algorithms.

[0003] In existing technologies, control parameters are initially set based on historical data, but these parameters are fixed and lack the ability to deeply explore and dynamically adapt to multi-dimensional driving characteristics, resulting in poor adaptability of driving parameters and high safety risks.

[0004] Therefore, the present invention provides a method, system, device and medium for designing driving parameters for heavy-duty freight trucks. Summary of the Invention

[0005] This invention provides a method, system, device, and medium for designing driving parameters for heavy-duty freight trucks, in order to at least solve the problems of poor scenario adaptability, insufficient control precision, and high safety risks caused by the fixed parameters in existing intelligent driving systems.

[0006] In a first aspect, embodiments of this application provide a method for designing driving parameters for heavy-duty freight trucks, the method comprising: Step S1: Classify the operating scenarios of heavy-duty freight trucks and collect driving parameter data of drivers in each operating scenario; Step S2: Preprocess the collected driving parameter data to obtain a driving parameter sample set; Step S3: Use clustering algorithms to analyze the driving parameter sample set and extract typical driving parameters for each operating scenario.

[0007] Further, step S3: Analyze the driving parameter sample set using a clustering algorithm to extract typical driving parameters for each operating scenario, specifically including: Step S31: Traverse the number of clusters from K=2 to K=8 and calculate the silhouette coefficient corresponding to each K value; Step S32: Select the K value corresponding to the largest silhouette coefficient, and use this K value as the optimal number of clusters; Step S33: Randomly select the driving parameter sample with the optimal number of clusters from the driving parameter sample set as the initial cluster centers; Step S34: Calculate the distance from each driving parameter sample to each initial cluster center using Manhattan distance, and assign each driving parameter sample to the nearest initial cluster center to form K clusters; Step S35: Within each cluster, select the driving parameter sample with the smallest sum of Manhattan distances from all driving parameter samples within the cluster to the initial cluster center as the new cluster center; Step S36: Repeat steps S33-S37 until the cluster centers change no further or the number of iterations reaches a preset threshold, then stop the iteration and output the final cluster centers; Step S37: Calculate the Davies-Bouldin index of the final cluster centers. When the Davies-Bouldin index is less than 1.2, the clustering is confirmed to be effective, and the typical driving parameters for each operating scenario are output.

[0008] Further, in step S31, the cluster sizes K=2 to K=8 are traversed, and the silhouette coefficient corresponding to each K value is calculated, the expression of which is:

[0009] in, For driving parameter samples The profile coefficient, For driving parameter samples The average distance to other driving parameter samples within the cluster. For driving parameter samples The average distance to the most recent heterogeneous driving parameter sample.

[0010] Further, in step S34, the Manhattan distance is used to calculate the distance from each driving parameter sample to each cluster center, and its expression is:

[0011] in, For driving parameter sample feature vectors, The feature vector of the cluster center. The number of feature parameters, Indicates the first Each driving parameter sample feature parameter value, Indicates the first The feature parameter values ​​of each cluster center.

[0012] Further, in step S37, the Davies-Bouldin index of the final cluster centers is calculated, and its expression is:

[0013] in, The Davies-Bouldin index represents the final cluster centers; Indicates the total number of clusters; Indicates the first Cluster and the first Similarity between clusters; This indicates that for each cluster Find the cluster that is most similar to it. ,Right now The largest cluster is then selected, and the average of the maximum similarity values ​​of all clusters is taken.

[0014] Furthermore, the first Cluster and the first The expression for the similarity between clusters is:

[0015] in, Indicates the first The average distance from all driving parameter sample points in each cluster to the final cluster center; Indicates the first The average distance from all driving parameter sample points in each cluster to the final cluster center; Indicates the first Cluster and the first The distance between the cluster centers of each cluster.

[0016] Furthermore, in step S3, the method further includes: The deviation between actual driving parameters and typical driving parameters is calculated using the mean square error:

[0017] in, These are the actual feature parameter values. These are typical driving parameter values. The number of feature parameters; When the deviation percentage exceeds 10%, steps S33-S37 are repeated. The formula for calculating the deviation percentage is as follows:

[0018] in, Indicates the first The center point of each cluster.

[0019] Secondly, embodiments of this application also provide a system applied to a method for designing driving parameters for a heavy-duty freight truck as described in the above aspects, the system comprising: The data acquisition module is used to classify the operating scenarios of heavy-duty freight trucks and collect driving parameter data of drivers in each operating scenario. The data processing module is used to preprocess the collected driving parameter data to obtain a driving parameter sample set; The typical driving parameter acquisition module is used to analyze the driving parameter sample set using a clustering algorithm and extract typical driving parameters for various operating scenarios.

[0020] Thirdly, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the steps of the freight truck driving parameter design method as described in the preceding aspects.

[0021] Fourthly, a storage medium storing a computer program that, when executed by a processor, implements the steps of the freight heavy-duty truck driving parameter design method as described in the preceding aspects.

[0022] As can be seen from the above technical solutions, the present invention has the following advantages: The driving parameter design method for heavy-duty freight trucks provided in this application can achieve refined classification of heavy-duty freight truck operation scenarios and collect representative driving parameter data for each scenario, thereby improving the relevance and effectiveness of data collection.

[0023] This application uses a clustering algorithm to analyze a sample set of driving parameters, which can automatically uncover typical driving characteristics in various operating scenarios, avoiding the subjectivity and limitations of manually setting parameters and improving the precision of control.

[0024] This application embeds typical driving parameters from various operating scenarios into the planning and control algorithm of heavy-duty freight trucks, achieving dynamic matching between driving style and scenario requirements. It can automatically switch or adjust control parameters according to actual operating conditions, thereby maintaining the adaptability and stability of control in complex and ever-changing freight environments, and improving driving safety and operational efficiency. Attached Figure Description

[0025] To more clearly illustrate the technical solution of this application, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart of the method for designing driving parameters for heavy-duty freight trucks according to the present invention. Detailed Implementation

[0027] To make the purpose, features, and advantages of this application more apparent and understandable, specific embodiments and accompanying drawings will be used to clearly and completely describe the technical solution protected by this application. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments in this patent, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this patent.

[0028] This application provides a method, system, device, and medium for designing driving parameters for heavy-duty freight trucks, addressing the urgent technical problem of achieving precise matching between driving parameters and freight scenarios to improve the adaptability, safety, and efficiency of intelligent driving systems in complex freight environments.

[0029] The technical solutions proposed in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0030] Figure 1 This is a flowchart illustrating a method for designing driving parameters for a heavy-duty freight truck, as provided in an embodiment of this application. Figure 1 As shown in the figure, the method for designing driving parameters for a heavy-duty freight truck provided in this application embodiment specifically includes the following steps: Step S1: Classify the operating scenarios of heavy-duty freight trucks and collect driving parameter data of drivers in each operating scenario; In step S1, the operational scenarios include at least: empty vehicle, fully loaded, dangerous goods transportation, easily rolling goods transportation, cold chain transportation, light-weight transportation, and oversized transportation; wherein, empty vehicle is defined as a transportation state where the weight of the goods is 0; fully loaded is defined as the weight of the goods reaching 80% or more of the vehicle's rated load capacity; dangerous goods transportation is defined as the transportation of goods that are flammable, explosive, toxic, corrosive, or radioactive; easily rolling goods transportation is defined as the transportation of steel coils or drummed goods; cold chain transportation is defined as the transportation of fresh food and pharmaceuticals; light-weight transportation is defined as the transportation of goods whose weight is less than 50% of the vehicle's rated load capacity; and oversized transportation is defined as the transportation of goods that exceed conventional standards in terms of size and weight.

[0031] It should be noted that three types of sensors and data acquisition units are installed on freight trucks corresponding to different operating scenarios: environmental perception, vehicle status, and cargo status. Environmental perception sensors include LiDAR, forward-looking camera, and millimeter-wave radar; vehicle status sensors include IMU inertial navigation, steering wheel angle sensor, throttle opening sensor, brake pedal travel sensor, gear position sensor, and load sensor; cargo status sensors include hazardous materials pressure sensor and volatile goods displacement sensor; the data acquisition unit uses a multi-core MCU to support parallel processing of sensor data, and uses a memory card with more than 1TB of storage space to meet 24-hour continuous data recording.

[0032] Step S2: Preprocess the collected driving parameter data to obtain a driving parameter sample set; The preprocessing includes: Data denoising: Kalman filtering is used to eliminate noise from sensors such as IMU and radar. The filter gain is set to 0.3 to ensure that the accuracy of acceleration data is ≤0.05m / s².

[0033] Timestamp alignment: Align parameters such as LiDAR, camera, throttle / brake, etc. to the 1ms level using GPS timestamps to avoid data timing deviations affecting analysis results; Feature parameter extraction: Eight core driving feature parameters were extracted from the processed data, including ACC following distance d (unit: s), throttle opening change rate α (unit: % / s), braking deceleration a (unit: m / s²), shift interval t (unit: s), steering angular velocity ω (unit: rad / s), fuel consumption rate q (unit: L / 100km), shift shock j (unit: m / s³), and following speed fluctuation σ (unit: km / h).

[0034] Step S3: Use clustering algorithms to analyze the driving parameter sample set and extract typical driving parameters for each operating scenario; In an exemplary embodiment, step S3: Analyze the driving parameter sample set using a clustering algorithm to extract typical driving parameters for each operating scenario, specifically including: Step S31: Traverse the number of clusters from K=2 to K=8 and calculate the silhouette coefficient corresponding to each K value; Step S32: Select the K value corresponding to the largest silhouette coefficient, and use this K value as the optimal number of clusters; Step S33: Randomly select the driving parameter sample with the optimal number of clusters from the driving parameter sample set as the initial cluster centers; Step S34: Calculate the distance from each driving parameter sample to each initial cluster center using Manhattan distance, and assign each driving parameter sample to the nearest initial cluster center to form K clusters; Step S35: Within each cluster, select the driving parameter sample with the smallest sum of Manhattan distances from all driving parameter samples within the cluster to the initial cluster center as the new cluster center; Step S36: Repeat steps S33-S37 until the cluster centers remain unchanged for three consecutive iterations or the number of iterations reaches a preset threshold. Stop the iteration and output the final cluster centers. Step S37: Calculate the Davies-Bouldin index of the final cluster centers. When the Davies-Bouldin index is less than 1.2, the clustering is confirmed to be effective, and the typical driving parameters for each operating scenario are output.

[0035] It should be noted that the following steps are included before step S31: For each type of operation scenario, select more than 50 professional drivers with more than 5 years of freight driving experience, and collect driving parameters under real road conditions (highway, national road, provincial road). Collect at least 1,000 sets of valid samples for each type of scenario (each set of samples contains 8 core feature parameters for 10 consecutive minutes). Min-Max normalization is used to map driving parameters to the [0,1] interval, eliminating the influence of differences in the dimensions of different parameters on the clustering results. The expression is as follows:

[0036] in, For standardized driving parameters, For driving parameters, The minimum value of the driving parameter sample. This represents the maximum value of the driving parameter sample.

[0037] According to another embodiment of the present invention, in step S31, the number of clusters K=2 to K=8 is traversed, and the silhouette coefficient corresponding to each K value is calculated, the expression of which is:

[0038] in, For driving parameter samples The profile coefficient, For driving parameter samples The average distance to other driving parameter samples within the cluster. For driving parameter samples The average distance to the most recent heterogeneous driving parameter sample.

[0039] According to an embodiment of this application, in step S34, the distance from each driving parameter sample to each cluster center is calculated using Manhattan distance, and its expression is:

[0040] in, For driving parameter sample feature vectors, The feature vector of the cluster center. The number of feature parameters, Indicates the first Each driving parameter sample feature parameter value, Indicates the first The feature parameter values ​​of each cluster center.

[0041] In one embodiment, in step S37, the Davies-Bouldin index of the final cluster centers is calculated, and its expression is:

[0042]

[0043] in, The Davies-Bouldin index represents the final cluster centers; Indicates the total number of clusters; Indicates the first Cluster and the first Similarity between clusters; This indicates that for each cluster Find the cluster that is most similar to it. ,Right now The largest cluster is ultimately the average of the maximum similarity values ​​of all clusters. Indicates the first The average distance from all driving parameter sample points in a cluster to the final cluster center reflects the compactness within the cluster. The smaller the value, the more concentrated the driving parameter sample points are within the cluster; Indicates the first The average distance from all driving parameter sample points in a cluster to the final cluster center reflects the compactness within the cluster. The smaller the value, the more concentrated the driving parameter sample points are within the cluster; Indicates the first Cluster and the first The distance between the cluster centers of a cluster reflects the degree of separation between clusters. The larger the value, the more significant the differences between clusters.

[0044] The typical driving parameters of various operating scenarios are embedded into the planning and control algorithm of freight heavy trucks; the corresponding typical driving parameters are automatically matched.

[0045] As an example, the planning and control algorithm includes using adaptive cruise control to maintain following distance, the expression of which is:

[0046] in, The following distance is obtained from clustering, in seconds. Current vehicle speed This refers to the safety redundancy distance. The safety redundancy distance is set according to the road type. In the case of highways, the safety redundancy distance is set to 10 meters, and in the case of national / provincial highways, the safety redundancy distance is set to 5 meters.

[0047] The planning and control algorithm also includes using the shift intervals obtained from clustering. As a shift trigger threshold, shifting is delayed to reduce impact; the shift trigger threshold for dangerous goods transportation is: ≥2.5s.

[0048] Based on shift shock Controlling the clutch engagement speed ensures smooth gear shifting. Its expression is: The unit is m / s Maximum deceleration limit: The actual braking deceleration must not exceed the clustering parameter. 1.1 times (safety factor); Braking advance compensation:

[0049] When the lidar detects a distance of ≤ to the vehicle in front When the braking is triggered in advance, in the case of transporting easily rolling goods, Δt=1.2s, to prevent the goods from shifting.

[0050] It should be further noted that, in step S3, the method further includes: The deviation between actual driving parameters and typical driving parameters is calculated using the mean square error:

[0051] in, These are the actual feature parameter values. These are typical driving parameter values. The number of feature parameters; When the deviation percentage exceeds 10%, steps S33-S37 are repeated. The formula for calculating the deviation percentage is as follows:

[0052] in, Indicates the first The center point of each cluster.

[0053] The continuous optimization iteration cycle is 3 months, and each iteration requires the collection of no less than 1,000 kilometers of real vehicle test data.

[0054] For example, when transporting gasoline (which is a flammable and explosive hazardous material) in a tanker truck, the route is "expressway + national highway" (average daily mileage of 500km). The core requirements are: smooth braking (to prevent tank swaying and leakage), smooth gear shifting, no cargo safety risks, and compliance with the relevant provisions of the "Regulations on the Safety Management of Road Transport of Dangerous Goods".

[0055] The front of the vehicle is equipped with a 16-line lidar and a forward-looking camera; a pressure sensor is installed on the top of the cargo box; an IMU, steering wheel angle sensor, throttle opening sensor, brake pedal travel sensor, and gear position sensor are installed in the cab; the data acquisition unit uses an NXP S32G399 MCU and the memory card has a capacity of 1TB.

[0056] Fifty drivers with over five years of experience driving tanker trucks were selected. Driving parameters were collected under highway and national road conditions. Each driver collected 20 sets of valid data (10 minutes per set), for a total of 1000 samples. Kalman filtering was used to denoise the IMU acceleration data, and 1ms-level timestamps were used to align with LiDAR, throttle, and braking parameters. Eight core feature parameters (e.g., d: 3-4s, a: 0.3-0.8m / s², t: 2.0-3.5s, etc.) were extracted and then standardized using Min-Max.

[0057] Traversing k=2-8, the silhouette coefficient is largest (0.85) when k=2, so k=2 is determined; the initial cluster centers are selected from sample A (d=3.5s, a=0.5m / s², t=2.8s) and sample B (d=3.2s, a=0.6m / s², t=2.5s). After 30 iterations, the cluster centers are stable, and two sets of typical parameters are output: Cluster 1 (safety priority type): d=3.6±0.2s, a=0.45±0.03m / s², t=3.0±0.2s, j=0.20±0.02m / s³; Cluster 2 (stable safety type): d=3.3±0.2m, a=0.55±0.04m / s², t=2.6±0.15s, j=0.23±0.03m / s³; DBI is calculated to be 1.05<1.2, so the clustering result is valid.

[0058] Set d=3-3.5s, follow vehicle speed fluctuation σ≤5km / h, when the speed change of the vehicle in front is ≥5km / h, the throttle opening change rate α=5% / s, to avoid rapid acceleration; Set t≥2.5s, j≤0.25m / s³, and set the clutch engagement speed to 0.8m / s to reduce shift shock; Set a≤0.6m / s², brake advance Δt=1.4s, brake is triggered when the following distance ≤d-Δt×current speed, and the brake master cylinder pressure adjustment rate ≤0.2MPa / s.

[0059] After driving 5,000 km on highways and national roads, the oil tank pressure fluctuation was ≤0.1MPa (no risk of leakage), the fuel consumption rate was reduced, the braking distance was shortened compared to the existing solution, and no cargo safety issues occurred. In heavy rain scenarios (visibility 50m), the ACC following distance control deviation is ≤1m, and the braking deceleration is stable at 0.45-0.55m / s², meeting safety requirements; in slope scenarios (slope 10%), the shift logic adapts to slope loads without shift jerking.

[0060] For example, transporting steel coils with a diameter of 1.5m and a weight of 5 tons (easily rolling goods), with the main route being national highways (average daily mileage of 300km), the core requirements are: preventing the steel coils from rolling and shifting, and smooth braking and steering.

[0061] Traversing k=2-8, the contour coefficient is the largest (0.82) when k=3, DBI=1.1, and three sets of typical parameters are output: Stationary type: a = 0.4 m / s², ω = 0.4 rad / s, d = 35 ± 1.3 m; High-efficiency and stable type: a=0.45m / s², ω=0.5rad / s, d=32±1.1m; Safety type: a=0.38m / s², ω=0.35rad / s, d=38±1.5m; The braking response module is set to a≤0.5m / s², and the steering angular velocity ω≤0.6rad / s; the ACC module is set to follow-speed fluctuation σ≤3km / h to avoid sudden speed changes that could cause the steel coil to sway.

[0062] Verification results: The vehicle traveled 5,000 km without any collisions or damage to the steel coils; the steering and braking control were smooth, and the driver comfort score was greater than 7 out of 10; the fuel consumption rate was reduced, meeting operational requirements.

[0063] This invention also provides a driving parameter design system for heavy-duty freight trucks. The following are embodiments of the driving parameter design system for heavy-duty freight trucks provided in this disclosure. This driving parameter design system for heavy-duty freight trucks belongs to the same inventive concept as the driving parameter design methods for heavy-duty freight trucks in the above embodiments. For details not described in detail in the embodiments of the driving parameter design system for heavy-duty freight trucks, please refer to the embodiments of the driving parameter design methods for heavy-duty freight trucks described above.

[0064] The system includes: The data acquisition module is used to classify the operating scenarios of heavy-duty freight trucks and collect driving parameter data of drivers in each operating scenario. The data processing module is used to preprocess the collected driving parameter data to obtain a driving parameter sample set; The typical driving parameter acquisition module is used to analyze the driving parameter sample set using a clustering algorithm and extract typical driving parameters for various operating scenarios.

[0065] The method for designing driving parameters for heavy-duty freight trucks provided in this application can be applied to electronic devices. Those skilled in the art will understand that the electronic device structure involved in the embodiments of this invention does not constitute a limitation on the electronic device. An electronic device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. In the embodiments of this invention, the electronic device includes, but is not limited to, laptop computers, desktop computers, workbenches, 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, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of this application described and / or claimed herein.

[0066] Electronic devices may include processors, external memory interfaces, internal memory, universal serial bus (USB) interfaces, charging management modules, power management modules, batteries, wireless communication modules, audio modules, speakers, microphones, sensor modules, buttons, cameras, displays, and SIM card interfaces, etc.

[0067] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the electronic device. In other embodiments of this application, the electronic device may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0068] A processor may include one or more processing units, such as: a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). Different processing units may be independent devices or integrated into one or more processors.

[0069] The processor can serve as the nerve center and command center of an electronic device. The controller can generate operation control signals based on the instruction opcode and timing signals to control the fetching and execution of instructions.

[0070] The processor may also include memory for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. This memory can store instructions or data that the processor has just used or that are used repeatedly. If the processor needs to use the instruction or data again, it can retrieve it directly from this memory. This avoids repeated accesses, reduces processor latency, and thus improves system efficiency.

[0071] An external storage interface (ESI) can be used to connect external memory cards, such as microSD cards, to expand the storage capacity of electronic devices. The external memory card communicates with the processor through the ESI to perform data storage functions, such as saving music and video files on the external memory card.

[0072] Internal memory can be used to store computer executable program code, which includes instructions. The processor executes various functional applications and data processing of electronic devices by running the instructions stored in internal memory. Internal memory can include a program storage area and a data storage area. Internal memory can include high-speed random access memory, and can also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc.

[0073] Wireless communication functionality in electronic devices can be achieved through antennas, wireless communication modules, modem processors, and baseband processors.

[0074] Wireless communication modules can provide solutions for wireless communication applications in electronic devices, including wireless local area networks (WLANs) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies.

[0075] Electronic devices can implement audio functions through audio modules, speakers, receivers, microphones, headphone jacks, and application processors.

[0076] Electronic devices can achieve shooting functions through ISPs, cameras, video codecs, GPUs, displays, and application processors.

[0077] Electronic devices can achieve display functions through GPUs, displays, and application processors.

[0078] A GPU is a microprocessor for image processing, connected to the display screen and application processor. GPUs are used to perform mathematical and geometric calculations for graphics rendering. A processor may include one or more GPUs, which execute program instructions to generate or modify display information.

[0079] A display screen is used to display images, videos, etc. A display screen includes a display panel.

[0080] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0081] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of devices, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0082] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, 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 couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, apparatuses, or units, or they may be electrical, mechanical, or other forms of connection.

[0083] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a full understanding of embodiments of the invention. However, those skilled in the art will recognize that the technical solutions of the invention can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of the invention.

[0084] The aforementioned electronic device implements the following steps in the freight heavy-duty truck driving parameter design method of this application: Step S1: classifying the operating scenarios of freight heavy-duty trucks and collecting driving parameter data of drivers under each operating scenario; Step S2: preprocessing the collected driving parameter data to obtain a driving parameter sample set; Step S3: using a clustering algorithm to analyze the driving parameter sample set and extracting typical driving parameters under each operating scenario; it can achieve refined classification of freight heavy-duty truck operating scenarios and collect representative driving parameter data for each scenario, thereby improving the targeting and effectiveness of data collection.

[0085] The storage medium provided in this application stores a program product capable of implementing a method for designing driving parameters for heavy-duty freight trucks.

[0086] The design method for driving parameters of heavy-duty freight trucks includes: classifying the operating scenarios of heavy-duty freight trucks and collecting driving parameter data of drivers in each operating scenario; preprocessing the collected driving parameter data to obtain a driving parameter sample set; and using a clustering algorithm to analyze the driving parameter sample set and extract typical driving parameters for each operating scenario.

[0087] The driving parameter design method for heavy-duty freight trucks provided in this application can achieve refined classification of heavy-duty freight truck operation scenarios and collect representative driving parameter data for each scenario, thereby improving the relevance and effectiveness of data collection.

[0088] This application uses a clustering algorithm to analyze a sample set of driving parameters, which can automatically uncover typical driving characteristics in various operating scenarios, avoiding the subjectivity and limitations of manually setting parameters and improving the precision of control.

[0089] This application embeds typical driving parameters from various operating scenarios into the planning and control algorithm of heavy-duty freight trucks, achieving dynamic matching between driving style and scenario requirements. It can automatically switch or adjust control parameters according to actual operating conditions, thereby maintaining the adaptability and stability of control in complex and ever-changing freight environments, and improving driving safety and operational efficiency.

[0090] In some possible implementations, the method for designing driving parameters for heavy-duty freight trucks disclosed herein can be implemented as a program product comprising program code that, when run on a terminal device, causes the terminal device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure.

[0091] The storage medium disclosed herein may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may 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 (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable 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.

[0092] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0093] Any changes, modifications, substitutions, and variations made to the embodiments without departing from the principles and spirit of the present invention still fall within the protection scope of the present invention.

Claims

1. A method for designing driving parameters for heavy-duty freight trucks, characterized in that, The method includes: Step S1: Classify the operating scenarios of heavy-duty freight trucks and collect driving parameter data of drivers in each operating scenario; Step S2: Preprocess the collected driving parameter data to obtain a driving parameter sample set; Step S3: Use clustering algorithms to analyze the driving parameter sample set and extract typical driving parameters for each operating scenario.

2. The method as described in claim 1, characterized in that, Step S3: Analyze the driving parameter sample set using a clustering algorithm to extract typical driving parameters for each operating scenario, specifically including: Step S31: Traverse the number of clusters from K=2 to K=8 and calculate the silhouette coefficient corresponding to each K value; Step S32: Select the K value corresponding to the largest silhouette coefficient, and use this K value as the optimal number of clusters; Step S33: Randomly select the driving parameter sample with the optimal number of clusters from the driving parameter sample set as the initial cluster centers; Step S34: Calculate the distance from each driving parameter sample to each initial cluster center using Manhattan distance, and assign each driving parameter sample to the nearest initial cluster center to form K clusters; Step S35: Within each cluster, select the driving parameter sample with the smallest sum of Manhattan distances from all driving parameter samples within the cluster to the initial cluster center as the new cluster center; Step S36: Repeat steps S33-S35 until the cluster centers change no further or the number of iterations reaches a preset threshold. Stop the iteration and output the final cluster centers. Step S37: Calculate the Davies-Bouldin index of the final cluster centers. When the Davies-Bouldin index is less than 1.2, the clustering is confirmed to be effective, and the typical driving parameters for each operating scenario are output.

3. The method as described in claim 2, characterized in that, In step S31, the cluster sizes K=2 to K=8 are traversed, and the silhouette coefficient corresponding to each K value is calculated. Its expression is: in, For driving parameter samples The profile coefficient, For driving parameter samples The average distance to other driving parameter samples within the cluster. For driving parameter samples The average distance to the most recent heterogeneous driving parameter sample.

4. The method as described in claim 2, characterized in that, In step S34, the Manhattan distance is used to calculate the distance from each driving parameter sample to each cluster center, and its expression is: in, For driving parameter sample feature vectors, The feature vector of the cluster center. The number of feature parameters, Indicates the first Each driving parameter sample feature parameter value, Indicates the first The feature parameter values ​​of each cluster center.

5. The method as described in claim 2, characterized in that, In step S37, the Davies-Bouldin index of the final cluster centers is calculated, and its expression is: in, The Davies-Bouldin index represents the final cluster centers; Indicates the total number of clusters; Indicates the first Cluster and the first Similarity between clusters; This indicates that for each cluster Find the cluster that is most similar to it. ,Right now The largest cluster is then selected, and the average of the maximum similarity values ​​of all clusters is taken.

6. The method as described in claim 1, characterized in that, No. Cluster and the first The expression for the similarity between clusters is: in, Indicates the first The average distance from all driving parameter sample points in each cluster to the final cluster center; Indicates the first The average distance from all driving parameter sample points in each cluster to the final cluster center; Indicates the first Cluster and the first The distance between the cluster centers of each cluster.

7. The method as described in claim 6, characterized in that, In step S3, the method further includes: The deviation between actual driving parameters and typical driving parameters is calculated using the mean square error: in, These are the actual feature parameter values. These are typical driving parameter values. The number of feature parameters; When the deviation percentage exceeds 10%, steps S33-S37 are repeated. The formula for calculating the deviation percentage is as follows: in, Indicates the first The center point of each cluster.

8. A system applied to the design method for driving parameters of heavy-duty freight trucks as described in any one of claims 1-7, characterized in that, The system includes: The data acquisition module is used to classify the operating scenarios of heavy-duty freight trucks and collect driving parameter data of drivers in each operating scenario. The data processing module is used to preprocess the collected driving parameter data to obtain a driving parameter sample set; The typical driving parameter acquisition module is used to analyze the driving parameter sample set using a clustering algorithm and extract typical driving parameters for various operating scenarios.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the freight heavy truck driving parameter design method as described in any one of claims 1-7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the freight heavy truck driving parameter design method as described in any one of claims 1-7.

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