Pedal clamping stagnation monitoring method for identifying braking intention based on multi-source data fusion

Abstract

The invention discloses a pedal clamping stagnation monitoring method for recognizing a braking intention based on multi-source data fusion, and belongs to the field of automobile braking. S1, pressure applied to a brake pedal, displacement of the brake pedal under applied pressure, displacement of an accelerator pedal and acceleration of a whole automobile are synchronously collected, and current driving environment information is obtained; s2, the pedal position change rate, the pedal force change rate, the accelerator opening degree change rate and the relative distance ratio of the current vehicle to the front vehicle are calculated; s3, generating a preliminary recognition intention by using a random forest; s4, generating a final recognition intention; and S5, judging whether clamping stagnation exists or not. According to the pedal clamping stagnation monitoring method for identifying the braking intention based on multi-source data fusion, multi-source data such as brake pedal displacement, pressure, accelerator state and environmental parameters are fused, and mechanisms such as double-model decision, dynamic threshold adjustment and multi-rule weighted judgment are combined; accuracy, real-time performance and environmental adaptability of brake pedal clamping stagnation monitoring are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of automotive braking, and particularly to a pedal jamming monitoring method for identifying braking intention based on multi-source data fusion. Background Art

[0002] Currently, intelligent driving technology has become increasingly mature and stable, and more and more vehicles are equipped with intelligent driving functions. There are also various ways to implement pedal jamming detection. Currently, the positions of most pedals are monitored in real time by position sensors to calculate whether the pedal is jammed according to thresholds. It has the advantages of simple implementation and low cost, and can be applied to the application scenarios of most vehicles. However, once the controller fails to recognize pedal jamming, it will have a negative impact on the vehicle's driving braking and affect the user experience.

[0003] Moreover, using a position sensor to judge whether the pedal is jammed requires high precision, reliability, and stability of the sensor itself. If the sensor is accidentally damaged or the signal is interfered, it will cause abnormal input of the pedal source information for braking, affecting the braking performance of the whole vehicle and even endangering the driver's personal safety. Summary of the Invention

[0004] The purpose of the present invention is to provide a pedal jamming monitoring method for identifying braking intention based on multi-source data fusion to solve the above technical problems.

[0005] To achieve the above purpose, the present invention provides a pedal jamming monitoring method for identifying braking intention based on multi-source data fusion, including the following steps: S1. Synchronously collect the pressure applied to the brake pedal, the displacement of the brake pedal under the applied pressure, the displacement of the accelerator pedal, and the vehicle acceleration, and obtain the current driving environment information based on a vision camera and a lidar. S2. Calculate the pedal position change rate, the pedal force change rate, the throttle opening change rate, and the relative distance ratio between the current vehicle and the vehicle in front based on the collected pressure applied to the brake pedal and the displacement of the brake pedal under the applied pressure. S3. Generate a preliminary recognition intention using a random forest based on the brake pedal displacement, the brake pedal pressure, the vehicle acceleration, the throttle opening change rate, the relative distance ratio between the current vehicle and the vehicle in front, the pedal force change rate, and the current driving environment information. S4. Input the preliminary recognition intention into a neural network and optimize the preliminary recognition intention using the neural network classification cross-entropy function to generate a final recognition intention. S5. Judge whether there is jamming based on the brake pedal displacement, the accelerator pedal displacement, the throttle opening change rate, and the final recognition intention.

[0006] Preferably, the current driving environment information described in step S1 includes lane lines, traffic signs, signal lights, the distance information between the current vehicle and the vehicle in front, the relative speed information between the current vehicle and the vehicle in front, and the road gradient information.

[0007] Preferably, the pedal position change rate described in step S2 , the pedal force change rate , the throttle opening change rate and the relative distance ratio between the current vehicle and the vehicle in front are calculated as follows: (1); (2); (3); (4); In the formula, represents the displacement of the brake pedal from the initial position to the current position within the time period ; represents the pressure applied by the driver on the brake pedal within the time period ; represents the displacement of the throttle pedal from the initial position to the current position within the time period ; represents the minimum value; represents the distance between the current vehicle and the vehicle in front; represents the relative speed between the current vehicle and the vehicle in front.

[0008] Preferably, in step S3, the following are input into the random forest , represents the vehicle acceleration; the output categories are 0, 1, 2, where 0 represents no braking, 1 represents normal braking, and 2 represents emergency braking.

[0009] Preferably, the neural network classification cross-entropy function described in step S4 has the following expression: (5); In the formula, represents the true label of the sample; represents the probability that the model predicts the sample belongs to the th class; and the final recognition intention generated by the neural network , where represents the fusion weight, and

[0010] Preferably, in step S5, if any of the following conditions exist, it is determined that there is jamming: Condition 1: If there are both brake pedal displacement and accelerator pedal displacement simultaneously, it is determined as jamming; Condition 2: If the change rate of the throttle opening is opposite to the expected movement direction, a weight of 0.4 is added; If the growth rate of the brake master cylinder pressure is less than the set threshold, a weight of 0.4 is added; If there is abnormal fluctuation in the current of the brake electronic pedal motor, a weight of 0.2 is added; If the sum of the weights added above is greater than 0.6, it is determined that there is jamming; Condition 3: If there is , it is determined as jamming; Among them, represents the adjusted threshold of the change rate of the brake pedal position; represents the adjusted threshold of the change rate of the pedal force; and both represent the environmental correction coefficient; represents and; Value range: ; and for dry and smooth road surface: ; for wet and slippery road surface: ; for ice and snow road surface: ; for gravel road surface: ; Value range: ; and for normal driving: ; for emergency braking: ; for heavy-duty vehicles: .

[0011] Preferably, in step S5, the threshold of the change rate of the brake pedal position and the threshold of the change rate of the pedal force are adjusted according to the current driving environment information: (6); (7); In the formula, Adjustment change amount.

[0012] Preferably, after step S5, there is also step S6. When it is determined that there is jamming in the brake pedal, the jamming signal is transmitted to the vehicle controller through the CAN signal, and the vehicle controller prompts the driver of the pedal jamming through voice or instrument to achieve early warning.

[0013] Therefore, the present invention adopts the above-mentioned method for monitoring pedal jamming by identifying braking intention based on multi-source data fusion, and the beneficial effects are: 1. Multi-source data fusion improves accuracy: Integrate multi-source data such as brake pedal displacement, pressure, throttle status, and environmental parameters to comprehensively capture brake-related information, avoid the limitations of single data, and improve the accuracy of brake intention recognition and jamming monitoring; 2. Dual-model decision-making enhances reliability: Adopt dual models of random forest and neural network for processing, combine their advantages, and make decisions through result fusion to enhance the reliability and robustness of brake intention recognition under complex working conditions; 3. Dynamic thresholds adapt to complex working conditions: Dynamically adjust thresholds such as displacement rate and force change rate based on environmental perception to adapt to different driving scenarios (such as emergency braking, bumpy roads), reduce false positives and missed detections, and improve the adaptability of jamming detection; 4. Multi-rule weighted judgment for comprehensive detection: Through multi-parameter weighted analysis, kinematic threshold matching and other multi-rule fusions, comprehensively identify pedal jamming from different dimensions (such as pressure change, motor status), and improve the detection coverage rate.

[0014] The technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Description of the Drawings

[0015] Figure 1 It is a flowchart of a pedal jamming monitoring method for recognizing brake intention based on multi-source data fusion of the present invention. Detailed Embodiments

[0016] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the embodiments of the present invention, and are not used to limit the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of this application. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions throughout.

[0017] It should be noted that the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0018] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0019] As Figure 1As shown in the figure, a pedal jamming monitoring method for identifying braking intention based on multi-source data fusion includes the following steps: S1. Synchronously collect the pressure applied to the brake pedal, the displacement of the brake pedal under the applied pressure, the displacement of the accelerator pedal, and the vehicle acceleration, and obtain the current driving environment information based on a vision camera and a lidar; In this embodiment, the electronic service brake collects the brake pedal position information once every 5 ms, and simultaneously collects the pressure information at the pedal end once every 1 ms.

[0020] The current driving environment information described in step S1 includes lane lines, traffic signs, signal lights, the distance information between the current vehicle and the vehicle in front, the relative speed information between the current vehicle and the vehicle in front, and the road gradient information.

[0021] S2. Calculate the pedal position change rate, the pedal force change rate, the throttle opening change rate, and the relative distance ratio between the current vehicle and the vehicle in front based on the collected pressure applied to the brake pedal and the displacement of the brake pedal under the applied pressure; The pedal position change rate described in step S2 , the pedal force change rate , the throttle opening change rate , and the relative distance ratio between the current vehicle and the vehicle in front are calculated by the following formulas: (1); (2); (3); (4); In the formulas, represents the displacement of the brake pedal from the initial position to the current position during the time period ; represents the pressure applied by the driver to the brake pedal during the time period ; represents the displacement of the accelerator pedal from the initial position to the current position during the time period ; represents the minimum value; represents the distance between the current vehicle and the vehicle in front; represents the relative speed between the current vehicle and the vehicle in front.

[0022] S3. Generate a preliminary identification intention using a random forest based on the brake pedal displacement, the brake pedal pressure, the vehicle acceleration, the throttle opening change rate, the relative distance ratio between the current vehicle and the vehicle in front, the pedal force change rate, and the current driving environment information; In step S3, input , Represents the vehicle acceleration; the output categories are 0, 1, 2, where 0 represents no braking, 1 represents normal braking, and 2 represents emergency braking.

[0023] S4. Input the preliminary recognition intention into the neural network, and use the neural network classification cross-entropy function to optimize the preliminary recognition intention to generate the final recognition intention; The neural network classification cross-entropy function described in step S4 The expression is as follows: (5); In the formula, Represents the true label of the sample; Represents the probability that the model predicts the sample belongs to the th class; And the final recognition intention generated by the neural network , Represents the fusion weight, And Respectively represent the random forest prediction probability and the neural network prediction probability. The purpose of the fusion weight is to be more biased towards Or .

[0024] S5. Based on the brake pedal displacement, accelerator pedal displacement, accelerator opening change rate, and the final recognition intention, determine whether there is jamming.

[0025] In step S5, if any of the following situations occur, it is determined that there is jamming: Situation 1: If both the brake pedal displacement and the accelerator pedal displacement exist, it is determined as jamming; Situation 2: If the accelerator opening change rate is opposite to the expected movement direction, add a weight of 0.4; If the growth rate of the brake master cylinder pressure is less than the set threshold, add a weight of 0.4; If there is an abnormal fluctuation in the current of the brake electronic pedal motor, add a weight of 0.2; If the sum of the weights added above is greater than 0.6, it is determined that there is jamming; Situation 3: If there is , it is determined as jamming; Among them, Represents the adjusted threshold of the brake pedal position change rate; Represents the adjusted threshold of the pedal force change rate; And Both represent the environmental correction coefficient; Represents and; Value range: ; And for dry and smooth road surface: ; For wet and slippery road surface: ; Icy and snowy road surface: ; Gravel road surface: ; Value range: ; And normal driving: ; Emergency braking: ; Heavy-duty vehicle: . It can be seen that in Case 3, when , it indicates that the pedal displacement rate is extremely low and there may be jamming; when , it indicates that the driver steps on the pedal forcefully but the displacement does not increase synchronously (the force increases but the displacement is slow), and there may be jamming.

[0026] Preferably, in step S5, adjust the braking pedal position change rate threshold and the pedal force change rate threshold according to the current driving environment information: (6); (7); In the formula, Adjustment change amount. In this embodiment, it is possible to set the threshold to increase by +0.1 for congested road surfaces to reduce the system alarm condition; in the case of a heavier load, the threshold is -0.1 to improve the system sensitivity.

[0027] Preferably, after step S5, there is also step S6. When it is determined that the braking pedal is jammed, transmit the jamming signal to the vehicle controller through the CAN signal, and the vehicle controller prompts the driver that the pedal is jammed through voice or instrument to achieve early warning.

[0028] Finally, it should be noted that: The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that: They can still modify the technical solutions of the present invention or make equivalent replacements, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A pedal jamming monitoring method for identifying braking intention based on multi-source data fusion, characterized in that: It includes the following steps: S1. Synchronously collect the pressure applied to the brake pedal, the displacement of the brake pedal under the applied pressure, the displacement of the accelerator pedal, and the vehicle acceleration, and obtain the current driving environment information based on a vision camera and a lidar; S2. Calculate the pedal position change rate, the pedal force change rate, the throttle opening change rate, and the relative distance ratio between the current vehicle and the vehicle ahead based on the collected pressure applied to the brake pedal and the displacement of the brake pedal under the applied pressure; S3. Generate a preliminary recognition intention using a random forest based on the brake pedal displacement, the brake pedal pressure, the vehicle acceleration, the throttle opening change rate, the relative distance ratio between the current vehicle and the vehicle ahead, the pedal force change rate, and the current driving environment information; S4. Input the preliminary recognition intention into a neural network, and optimize the preliminary recognition intention using the neural network classification cross-entropy function to generate a final recognition intention; S5. Judge whether there is jamming based on the brake pedal displacement, the accelerator pedal displacement, the throttle opening change rate, and the final recognition intention; 2. The pedal jamming monitoring method for identifying braking intention based on multi-source data fusion according to claim 1, wherein: The current driving environment information described in step S1 includes lane lines, traffic signs, signal lights, the distance information between the current vehicle and the vehicle ahead, the relative speed information between the current vehicle and the vehicle ahead, and the road gradient information; 3. A pedal jamming monitoring method for identifying braking intention based on multi-source data fusion according to claim 1, characterized in that: The pedal position change rate described in step S2 , the pedal force change rate , the throttle opening change rate and the relative distance ratio between the current vehicle and the vehicle ahead are calculated as follows: （1）； （2）； （3）； （4）； In the formula, represents the time period and is the displacement of the brake pedal from the initial position to the current position within the time period; represents the time period and is the pressure applied by the driver to the brake pedal within the time period; represents the time period and is the displacement of the accelerator pedal from the initial position to the current position within the time period; represents the minimum value; represents the distance between the current vehicle and the vehicle in front; represents the relative speed between the current vehicle and the vehicle in front.

4. The pedal jamming monitoring method for braking intention recognition based on multi-source data fusion according to claim 3, characterized in that: In step S3, input into the random forest , which represents the vehicle acceleration; the output categories are 0, 1, and 2, where 0 represents no braking, 1 represents normal braking, and 2 represents emergency braking.

5. The pedal jamming monitoring method for identifying braking intention based on multi-source data fusion according to claim 4, wherein: The neural network classification cross-entropy function described in step S4 The expression is as follows: （5）； wherein, represents the true label of the sample; represents the probability that the model predicts the sample belongs to the th class; And the final recognition intention generated by the neural network , represents the fusion weight, and respectively represent the random forest prediction probability and the neural network prediction probability.

6. The pedal jamming monitoring method for identifying braking intention based on multi-source data fusion according to claim 5, wherein: In step S5, if any of the following situations occur, it is judged that there is jamming: Situation 1: If there are both brake pedal displacement and accelerator pedal displacement, it is judged as jamming; Situation 2: If the throttle opening change rate is opposite to the expected movement direction, a weight of 0.4 is added; If the growth rate of the brake master cylinder pressure is less than the set threshold, a weight of 0.4 is added; If there is abnormal fluctuation in the brake electronic pedal motor current, a weight of 0.2 is added; If the sum of the above added weights is greater than 0.6, it is determined that there is jamming; Case 3: If there exists , it is judged as stuck. Among them, represents the threshold value of the change rate of the adjusted brake pedal position; represents the threshold value of the change rate of the adjusted pedal force; and both represent the environmental correction coefficient; represents and; Value range: ; and for dry and smooth road surface: ; for wet and slippery road surface: ; for ice and snow road surface: ; for gravel road surface: ; Value range: ; and normal driving: ; emergency braking: ; heavy-duty vehicle: .

7. A pedal jamming monitoring method for identifying braking intention based on multi-source data fusion according to claim 6, characterized in that: In step S5, adjust the brake pedal position change rate threshold and the pedal force change rate threshold according to the current driving environment information; （6）； （7）； In the formula, Adjustment variation.

8. A pedal jamming monitoring method for identifying braking intention based on multi-source data fusion according to claim 1, characterized in that: After step S5, there is also step S6. When it is judged that the brake pedal is jammed, transmit the jamming signal to the vehicle controller through the CAN signal, and the vehicle controller prompts the driver that the pedal is jammed through voice or instrument to achieve early warning.

Images