Method for road condition recognition of a commercial vehicle, related controller and computer program product

Abstract

The application provides a method for identifying road conditions based on actual values of operating parameters of a commercial vehicle. The method comprises the following steps: obtaining actual values of each of the operating parameters; performing data processing on each actual value to obtain a processed value; calling the processed value for each of a plurality of selected parameters determined for each of a plurality of preset set road conditions, and comparing the processed value with a related threshold value; performing logical analysis based on the comparison result; and determining whether the commercial vehicle is in a target set road condition based on the result of the logical analysis. With the method for identifying road conditions of a commercial vehicle according to the application, the current road condition on which the vehicle is running can be determined more accurately without causing unnecessary cost increase, thereby providing a good basis for vehicle operation control based on the identification result, so as to improve the driving performance of the vehicle. The application also relates to a controller and a computer program product related to the above method.

Description

Technical Field

[0001] This invention relates to a road condition recognition method for commercial vehicles, specifically, a method for recognizing road conditions based on actual values ​​of vehicle operating parameters. Furthermore, this invention also relates to a controller and computer program product related to implementing this method. Background Technology

[0002] Electric drive axles (EVs) are considered an ideal drive system for electric commercial vehicles due to their advantages such as high response accuracy, fast response speed, and convenient real-time vehicle dynamics control. However, this arrangement increases the unsprung mass of the vehicle. When traversing rough roads, this can lead to vehicle vibration and other unforeseen problems, increasing the difficulty of longitudinal and stability control. Currently, road condition recognition is not integrated into the operational control strategies of existing commercial vehicles due to various limitations. Therefore, strategies for controlling the operation of commercial vehicles based on specific road conditions are gradually gaining traction.

[0003] In vehicle control technology, road condition recognition is a crucial and relatively complex component. Currently, road condition recognition is mostly implemented in passenger vehicles. In existing passenger vehicles, road condition recognition is typically achieved using technologies such as machine vision and infrared imaging. The implementation of these technologies requires sensing elements and controllers with extremely high computing power to process the relevant information obtained from these sensing elements. This obviously increases the manufacturing cost of commercial vehicles.

[0004] Therefore, for commercial vehicles, there is a need to provide an improved control strategy to optimize road condition (especially rough road conditions) identification without incurring unnecessary cost increases, thereby estimating the roughness of the road profile relatively accurately, so as to provide input for the dynamic control of vehicle operation and improve the driving performance of the vehicle. Summary of the Invention

[0005] To address at least one of the aforementioned technical problems, the present invention provides a method for identifying road conditions based on the actual values ​​of operating parameters of a commercial vehicle. The method includes the following steps: an acquisition step: acquiring the actual values ​​of each parameter in the operating parameters at regular time intervals; a data processing step: performing data processing on each of the actual values ​​to obtain a processed value; a retrieval step: for each selected parameter determined from the operating parameters among a plurality of pre-set road conditions, retrieval the processed value of the selected parameter, and comparing the processed value with a relevant threshold for a target road condition stored in a database of the commercial vehicle; a logical analysis step: performing logical analysis based on the comparison results; and a determination step: determining whether the commercial vehicle is in the target road condition based on the results of the logical analysis.

[0006] The present invention also relates to a controller comprising: at least one processor, and a memory coupled to said at least one processor, said memory storing computer program instructions, wherein said at least one processor is configured to execute said computer program instructions to implement the above-described method.

[0007] The present invention further relates to a computer program product, wherein the computer program product stores computer program instructions configured to implement the above-described method when executed by a computer.

[0008] The method for identifying road conditions using actual values ​​of commercial vehicle operating parameters according to the present invention can more accurately determine the current road conditions under which the vehicle is operating without incurring unnecessary cost increases. This provides a good foundation for vehicle operation control based on the identification results, thereby improving the vehicle's driving performance. Attached Figure Description

[0009] Figure 1 A simplified flowchart of a method for identifying road conditions based on actual values ​​of operating parameters of commercial vehicles according to the present invention is shown.

[0010] Figure 2 It shows according to Figure 1 A simplified flowchart of the data analysis process shown in the diagram. Detailed Implementation

[0011] As described in the background section, passenger vehicle recognition technology typically employs image recognition to determine various factors affecting vehicle operation. These include the presence of pedestrians in the vehicle's expected travel area, their direction and speed, the presence and size of obstacles, and road surface smoothness. In contrast, in commercial vehicle applications, the issues of pedestrians and obstacles are usually not considered; instead, the focus is more on the road conditions themselves, such as traction and their impact on braking distance.

[0012] For commercial vehicles, directly applying existing road condition recognition systems from passenger cars to commercial vehicles typically requires collecting road surface images from cameras, infrared imagers, and other human visual recognition devices, or gathering acceleration information from the vehicle's suspension, tires, controllers, and other locations to estimate road contour information. However, many of the sensors required by the human visual technologies used in these recognition methods need to be used in conjunction with separate controllers. This inevitably necessitates adding acceleration sensors to the commercial vehicle's suspension and many other locations, thus unavoidably increasing vehicle costs. This is clearly uneconomical.

[0013] Furthermore, due to the different application scenarios of passenger cars and commercial vehicles, the complexity of the objects and road conditions that need to be identified in vehicle operation control differs significantly between the two, which directly affects the accuracy and effectiveness of vehicle operation control. Therefore, directly applying existing control strategies used for passenger cars to commercial vehicles is problematic.

[0014] Therefore, this invention abandons the road condition recognition technology used in passenger vehicles and adopts a completely new road condition recognition / determination logic. In short, this invention mainly considers obtaining the actual values ​​(also known as instantaneous values ​​or measured values) of the vehicle's operating parameters and determining the road conditions based on these actual values.

[0015] Specifically, the road condition recognition method according to the present invention mainly includes the following aspects, see below. Figure 1 .

[0016] First, several road conditions are pre-defined (hereinafter referred to as "defined road conditions"). For example, defined road conditions can be divided into high-adhesion road conditions (e.g., concrete or asphalt pavement), medium-adhesion road conditions (e.g., gravel pavement), slippery road conditions (e.g., muddy pavement), and icy / snowy road conditions based on ground adhesion. These defined road conditions are manually set road condition judgment benchmarks in vehicle operation control, and there may be differences between them and the actual road conditions the vehicle travels on. When judging different defined road conditions, it is usually not necessary to consider all vehicle operating parameters, but only to judge a few selected parameters (hereinafter referred to as "selected parameters") from the vehicle's operating parameters. (See step S1)

[0017] As those skilled in the art will understand, these road condition classifications are not static but can be modified according to actual needs. Furthermore, the selection parameters for the same road condition are determined by technicians before the vehicle is delivered for use. This parameter determination can be done individually for a single commercial vehicle or as a group of commercial vehicles. A "group of commercial vehicles" can be grouped according to at least one specific factor, which may include the user type, task type, usage scenario, and specific user needs. In other words, the determination of these parameters is highly flexible, and the number of selected parameters is unlimited. However, the more parameters selected, the lower the probability of meeting the target road condition (this will be detailed below). The "target road condition" refers to the road condition in which the vehicle is determined to be in a specific road condition based on the selected parameters in the operating parameters; this specific road condition is then defined as the target road condition (i.e., the road condition judgment criterion mentioned earlier). Generally, the road conditions within the vehicle control system are simultaneously recognized as target road conditions by the vehicle processing unit for judgment. In other words, the control unit within the vehicle control system will simultaneously perform logical analysis and judgment based on the selected parameters chosen for the target road conditions.

[0018] The operating parameters of the aforementioned vehicles include, but are not limited to, vehicle speed, slip ratio, throttle opening, brake opening, x-axis acceleration, y-axis acceleration, and z-axis acceleration.

[0019] From the start of vehicle operation, the actual value of each of the above operating parameters is acquired at regular intervals (or time intervals) and stored in the vehicle's storage unit (see step S2). This time interval is typically 10ms.

[0020] These operating parameters are obtained using existing devices or layouts in commercial vehicles, without requiring additional components. For example, the vehicle's (actual) speed and tire speed can be acquired using output shaft speed sensors and wheel speed sensors, respectively; throttle and brake openings can be acquired using an ADC sampling circuit; and accelerations in the x, y, and z directions can be acquired using MEMS sensors built into the vehicle's automatic transmission control unit (TCU). Based on the acquired vehicle speed and tire speeds, the slip ratio is calculated by the in-vehicle processor, which can be calculated using the following formula:

[0021] s = [(v T -v) / v]*100%

[0022] In the formula:

[0023] s—slip ratio;

[0024] v T —Tire speed; and

[0025] v — Vehicle speed.

[0026] As mentioned above, the MEMS sensors built into the TCU can collect the acceleration and angular velocity of each axis in the vehicle's Cartesian coordinate system. Taking the z-axis acceleration as an example, the z-axis acceleration can provide real-time feedback on the vehicle's bumpiness on the road surface. When the bumpiness is significant, the vehicle's z-axis velocity will change significantly. Therefore, the input variance information can be used to determine the vehicle's road condition. When using the z-axis acceleration variance value to determine the degree of road bumpiness, the variance value can only reflect the degree of bumpiness over time for the entire road segment, and cannot detect the time when the vehicle enters or exits rough roads.

[0027] Therefore, the inventors envisioned calculating the variance of acceleration in the z-direction over a period of time to promptly identify rough road conditions and avoid unexpected retraction of judgments due to sudden acceleration on rough roads. The sampling data range and judgment threshold should be reasonably calibrated based on actual vehicle data.

[0028] Therefore, after obtaining the corresponding operating parameters, a processing unit, such as the vehicle's TCU or other processor, performs data processing on the actual values ​​of each operating parameter to obtain processed values ​​(see step S3). This data processing includes calculating the mean, mean square error, and filtered values ​​of the actual values ​​over a period of time. This period can be set to 1-3 seconds, but the invention is not limited to this. As those skilled in the art will understand, technicians can reconfigure these settings based on the actual application of the commercial vehicle or the specific needs of the user. All obtained processed values ​​are stored in the vehicle's storage unit.

[0029] When determining the target road condition in the set road condition, the processing values ​​of the selected parameters initially selected by the technician for the target road condition are compared (see step S4) and logically analyzed (see step S5) to determine whether the commercial vehicle is currently in the target road condition (see step S6).

[0030] The following explanation uses the determination of medium-grade road conditions (e.g., gravel road surface) as an example. (See [link to relevant documentation]). Figure 2 First, taking the vehicle speed, z-axis acceleration, slip ratio, and throttle opening—the vehicle operating parameters pre-selected by the technician for identifying intermediate road conditions—as the selected parameters, the processing unit performs the following operations:

[0031] 1. Retrieve the average vehicle speed stored in the storage unit (see step S40) and determine whether it meets condition 1: the average vehicle speed > threshold 1 and is less than threshold 2 (see step S41);

[0032] 2. Retrieve the vehicle speed mean squared error value stored in the storage unit (see step S42) and determine whether it meets condition 2: vehicle speed mean squared error value > threshold 3 (see step S43);

[0033] 3. Call the mean z-direction acceleration stored in the storage unit (see step S44), and determine whether it meets condition 3: the mean square error of z-direction acceleration > threshold 4 (see step S45);

[0034] 4. Retrieve the throttle opening mean square error value stored in the storage unit (see step S46) and determine whether it meets condition 4: throttle opening mean square error value > threshold 5 (see step S47);

[0035] 5. Call the slip ratio filter value stored in the storage unit (see step S48) and determine whether it meets condition 5: slip ratio filter value > threshold 6 and less than threshold 7 (see step S49).

[0036] It should be noted that operations 1-5 are not numbered in order of operation. Generally, they can be performed simultaneously, or they can be performed in a different order than the operations 1-5 listed herein. Furthermore, thresholds 1-7 are relevant limit values ​​set by technicians and pre-stored in the vehicle's storage unit, and they can be changed according to the actual application of the commercial vehicle or the specific requirements of the user.

[0037] Subsequently, the processing unit performs logical analysis on the determination results of conditions 1-5 (see step S5). For example, it performs an AND logical analysis on all the determination results of conditions 1-5. In other words, only when all conditions 1-5 are determined to be true can it be determined that the commercial vehicle is currently in the intermediate road condition of the set road condition (see step S6).

[0038] As mentioned above, different parameters can be selected for different road conditions, and there is no limit to the number of parameters that can be selected. The more parameters selected, the lower the probability of determining that the vehicle is in that road condition, which also reduces the likelihood of using a control strategy tailored to that road condition to control the vehicle's operation. Therefore, the determination of the types and number of selected parameters needs to be done by technical personnel, usually based on experimental data from before the vehicle is delivered for use or the specific needs of the user.

[0039] Different control strategies are configured within the vehicle control unit for different road conditions. Let's take a slippery road condition (e.g., a muddy road) as an example. When a commercial vehicle is determined to be in a slippery road condition, the control strategy for this condition takes effect (see step S7). For example, this control strategy imposes an upper limit on the driver's torque input value (e.g., throttle opening). For instance, in this road condition, the effective torque value should not exceed 300N. Therefore, even if the driver accelerates sharply, providing a torque input value of 1000N, under the action of this control strategy, the actual effective torque value produced will only be 300N, thus preventing the vehicle from experiencing more adverse driving conditions in this slippery road condition. In contrast, if the driver lightly accelerates, providing only 100N of torque input, the control strategy will not intervene in the driver's driving behavior, as it only imposes a limit on the upper limit.

[0040] Although several embodiments of the invention have been described with reference to the accompanying drawings, it will be understood by those skilled in the art that various modifications can be made to the above embodiments without departing from the scope defined by the appended claims. The above embodiments are provided merely as examples to illustrate the technical solutions of the invention and are not intended to limit the scope of protection of the invention. Features or elements described in one embodiment may be incorporated into another embodiment unless they contradict existing features or elements in the other embodiment. Furthermore, the specific wording of features and the possible use of reference numerals in the appended claims are not intended to limit the scope of protection claimed.

Claims

1. A method for identifying road conditions based on actual values ​​of operating parameters of commercial vehicles, the method comprising the following steps: Acquisition steps: Acquire the actual values ​​of each parameter in the running parameters at regular time intervals; Data processing steps: Perform data processing on each of the actual values ​​to obtain the processed value; Invocation steps: For each selected parameter determined from the operating parameters among a number of preset road conditions, the processing value of the selected parameter is invoked, and the processing value is compared with the relevant threshold for the target preset road condition stored in the database of the commercial vehicle. Logical analysis steps: Perform logical analysis based on the results of the comparison; as well as Determination Step: Based on the results of the logical analysis, determine whether the commercial vehicle is in the target road condition.

2. The method according to claim 1, wherein, The method further includes: Setting steps: Before the invocation step, several preset road conditions are preset, and for each of the preset road conditions, the selected parameter is determined from the running parameters.

3. The method according to claim 2, wherein, The method further includes: Implementation steps: After the determination step, if the determination result is yes, then the control strategy corresponding to the target road condition is invoked to intervene in the current driving behavior of the commercial vehicle driver; and if the determination result is no, then no intervention is applied to the current driving behavior.

4. The method according to claim 3, wherein, The time interval is 10ms; and / or The database exists either in the vehicle hardware of the commercial vehicle or in the cloud.

5. The method according to any one of claims 1-4, wherein, The operating parameters are at least one of the following: vehicle speed, slip ratio, throttle opening, brake opening, x-axis acceleration, y-axis acceleration, and z-axis acceleration of the commercial vehicle.

6. The method according to any one of claims 1-4, wherein, The data processing includes calculating the mean, standard deviation, and filtered value of each of the operating parameters.

7. The method according to claim 6, wherein, The mean and the mean squared error are the mean and mean squared error of the actual values ​​of the operating parameters obtained over a period of time, and the period of time can be set based on experimental data of the commercial vehicle before delivery and use.

8. The method according to claim 7, wherein, The time period is 1-3 seconds.

9. The method according to any one of claims 1-4, wherein, The relevant thresholds are set based on experimental data of the commercial vehicle before it is delivered for use.

10. The method according to any one of claims 2-4, wherein, The setup steps are completed before the commercial vehicle is delivered for use, and / or The setup steps are performed individually for a single commercial vehicle, or in groups for a group of commercial vehicles; and / or The logical analysis includes AND logic.

11. The method according to claim 10, wherein, The group of commercial vehicles is formed by grouping the commercial vehicles according to at least one of the specific factors, including the user type, task type, usage scenario, and special user needs of the commercial vehicles.

12. A controller, comprising: At least one processor, and A memory coupled to the at least one processor, the memory storing computer program instructions, The at least one processor is configured to execute the computer program instructions to implement the method according to any one of claims 1 to 11.

13. A computer program product, wherein, The computer program product stores computer program instructions configured to implement the method according to any one of claims 1-11 when executed by a computer.

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