Steering mode adaptive adjustment method based on accident prevention

Abstract

The invention is applicable to the technical field of automobiles, and provides an accident prevention-based steering mode adaptive adjustment method, which comprises the following steps of: respectively acquiring environmental information data around a vehicle under a natural driving condition and a narrow space scene driving condition; comprehensively analyzing the input environment information, and judging whether the driving mode needs to be adjusted in the environment defined by the environment information or not; and S2, setting two driving modes including a standard mode and a precise mode, and adjusting the driving mode of the vehicle based on a result output in the step S2. According to the method, the driving safety risk can be avoided from the subjective angle of the driver and the input angle of the operation amount of the driver.

Description

Technical Field

[0001] The present invention belongs to the technical field of automobiles, and particularly relates to a method for adaptively adjusting a steering mode based on accident prevention. Background Technique

[0002] In recent years, with the rapid development of autonomous driving and human-machine co-driving technologies, vehicle intelligence has become an important direction for the industry's development. Against this background, as relevant technologies are gradually applied industrially, the driving experience of drivers has become more convenient and efficient. However, this transformation has also led to higher requirements for vehicle handling accuracy and driving safety. In the face of this trend, researchers in the industry are deeply exploring driving mode switching strategies and risk prevention and control mechanisms under different working conditions, aiming to optimize the human-vehicle interaction and actively prevent risks through scientific design, so as to create a more intelligent and reliable driving experience for users while ensuring driving safety.

[0003] However, there are generally the following problems in the design of current vehicle driving safety assistance methods: The driving assistance methods currently adopted in the industry are generally passive emergency avoidance measures restricted by the traditional vehicle mechanical structure. For example, the AEB automatic emergency braking system. Such passive driving assistance methods cannot enable the vehicle to actively adjust the movement intensity to avoid risks actively. Similarly, restricted by the traditional vehicle mechanical structure, the operating habits and movement intensity characteristics of the vehicle and the driver cannot be adjusted, so different driving modes cannot be designed to help the driver complete more precise driving. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for adaptively adjusting a steering mode based on accident prevention, aiming to solve the problem of the current driving safety assistance method's passive trigger thinking mode. From the perspective of the movement intensity in vehicle driving, a method for adaptively adjusting a steering mode based on accident prevention is proposed, which is beneficial to providing a driving experience that better meets the driver's psychological expectations and a safer driving feeling.

[0005] The present invention is implemented as follows. A method for adaptively adjusting a steering mode based on accident prevention, the method includes: S1. Collect the environmental information data around the vehicle under natural driving conditions and narrow space scene driving conditions of the vehicle respectively; S2. Comprehensively analyze the input environmental information to determine whether the driving mode needs to be adjusted in the environment defined by the environmental information; S3. Set two driving modes: a standard mode and a precise mode, and adjust the driving mode of the vehicle based on the result output in step S2.

[0006] As a further solution of the present invention, the S1 specifically includes: Obtain the distances between the automatic parking ultrasonic radar and the obstacles on both left and right sides, and establish a coordinate system with time as the horizontal axis and the distance information from the obstacles as the vertical axis; Adopt the signal processing technology based on the Butterworth low-pass filter to smooth the time-varying distance information collected by the ultrasonic radar, eliminate the influence of the noise signal of the sensor, and the influence of the obstacles that suddenly appear within the detectable range of the ultrasonic radar on the overall distance judgment; Use forward-backward bidirectional filtering, filter the signal line forward, and then reverse the signal for backward filtering to eliminate the phase delay of the filtering and output a signal with zero phase distortion , and the mathematical process is expressed as: ; where represents the forward filtering operation, represents the backward filtering operation, is the time index, represents the sequence reversal operation, represents the sequence of the input data; Calculate the mean and variance of the distance information from the obstacles within the unit time window, and output the distance from the obstacles within the unit time.

[0007] Calculate the mean and variance of the distance information from the obstacles within the unit time window, and output the distance from the obstacles within the unit time.

[0008] As a further solution of the present invention, the Butterworth low-pass filter is specifically: For each sampling point of the time series, perform signal reconstruction by constructing a fourth-order Butterworth low-pass filter, and the mathematical expression is: ; where, represents the filter order, is the angular frequency corresponding to the cut-off frequency, and , is the cut-off frequency, , which is set according to the separation characteristics of the effective signal frequency band and the noise frequency band of the ultrasonic radar; Convert the analog filter into a digital filter by the bilinear transformation method.

[0009] Convert the analog filter into a digital filter by the bilinear transformation method.

[0010] As a further solution of the present invention, the S2 specifically includes: Traverse the received array information and perform a chi-square goodness-of-fit test on the data in the array. The basic formula for the chi-square goodness-of-fit test is: ; where is the observed frequency of the th category, represents the expected frequency of the th category, is the number of categories; Set the statistical test hypotheses, including: The null hypothesis : In the unit time window, the proportion of data with a distance less than 2.5 meters from the obstacle is not less than 90%; The alternative hypothesis : In the unit time window, the proportion of data with a distance less than 2.5 meters from the obstacle is less than 90%; Determine the degrees of freedom , and the formula is: ; Set the significance level , and use the built-in function of MATLAB to calculate the value. Among them, the value represents the probability of measuring the degree of conformity between the observed data and the null hypothesis. If the value is less than the preset significance level , then reject the null hypothesis , and accept the alternative hypothesis ; otherwise, accept the null hypothesis , and reject the alternative hypothesis .

[0011] As a further solution of the present invention, the S3 specifically includes: Respectively plot the corresponding relationship diagrams of the steering wheel steering torque and the lateral acceleration in the standard mode and the precise mode; Simulate the driving scenario and the parking scenario, and collect the steering wheel steering torque and the vehicle lateral acceleration; Fit the collected steering wheel steering torque and the vehicle lateral acceleration by the least squares method to generate the mapping curves of the standard mode and the precise mode; Based on the results output in the S2 step, select the current driving mode and adjust the input of the steering system in real time.

[0012] As a further solution of the present invention, it further includes: Continuously monitor the actual distance, steering torque, and lateral acceleration of the vehicle from the obstacle through in-vehicle sensors; Set an error threshold to determine whether the error between the actual distance and the filtered data exceeds the error threshold; If the error between the actual distance and the filtered data exceeds the error threshold, readjust the cut-off frequency and order of the Butterworth filter.

[0013] As a further solution of the present invention, the system for implementing the steering mode adaptive adjustment method based on accident prevention includes: An environmental information acquisition module, configured to collect the environmental information around the vehicle during driving, including but not limited to the distance from the vehicle itself as the main information, as well as the vehicle speeds and accelerations of surrounding vehicles; An accident prevention module, configured to analyze and synthesize the environmental information input by the environmental information acquisition module, and determine whether it is necessary to adjust the driving mode in the environment defined by the environmental information; A steering mode adaptive module, configured to parse the mapping curve of the driver's steering wheel torque and the vehicle lateral acceleration in the current environment according to the judgment result of the accident prevention module, and assist in adjusting the driving mode.

[0014] The beneficial effects of the present invention are: Based on the driving data of the driver under natural driving conditions and in low-speed parking or narrow space scenarios, a steering mode adaptive adjustment method is constructed with the environmental perception information acquisition module as the input, the accident prevention module as the judgment relay, and the steering mode adaptive module as the output. The selection of different driving modes is completed through the vehicle's own environmental perception and the driver's active switching. It can avoid driving safety risks from the perspectives of the driver's subjectivity and the input of the driver's operation amount. Description of the Drawings

[0015] Figure 1 It is a flowchart of a steering mode adaptive adjustment method provided by an embodiment of the present invention; Figure 2 It is a schematic diagram of vehicle detection; Figure 3 It is a graph showing the change of the unfiltered detection distance over time; Figure 4 It is a graph showing the change of the detection distance over time after Butterworth low-pass filtering; Figure 5 It is a graph showing the relationship between the steering wheel torque and the lateral acceleration. Detailed Embodiments

[0016] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0017] The present invention proposes a method for adaptively adjusting the steering mode based on accident prevention. By using the spatial information perception data in different scenarios, the input-output mapping relationships of the steering wheel torque and lateral acceleration in two different modes are constructed, laying a foundation for the design of the control strategy of the subjective driving safety assistance system. Under natural driving conditions and low-speed parking or driving in narrow spaces, the driver's sensitivity to operations is different. To construct an accident prevention method from the perspectives of driving input and vehicle movement intensity, corresponding data is collected through experiments, the characteristics of the spatial environment information are extracted, and the distance information of the lateral APA ultrasonic radar is selected and input into the accident prevention judgment relay module to complete the judgment of whether to switch modes. Then, through experimental data collection, the mapping data of the operation amount that the driver feels comfortable with under low-speed parking or in narrow spaces is fitted to obtain the steering wheel torque and lateral acceleration .

[0018] Figure 1 The flowchart of a method for adaptively adjusting the steering mode based on accident prevention provided by an embodiment of the present invention is as follows Figure 1 shown, and the method includes: S1. Under the natural driving conditions of the vehicle and the driving conditions in narrow space scenarios, collect the relevant data of the vehicle surrounding environment information respectively. In this method, at least L2-level assisted driving vehicles are selected, and the distances between the automatic parking ultrasonic radar (Automatic Parking Assistance, APA) and the surrounding environment (obstacles on the left and right sides) are obtained through the CAN FD bus. The detection distance in this embodiment is between 0.3m and 5m, as shown in the appendix Figure 2 shown. Its frequency is selected as 40kHz, and it is assumed that the distance update information frequency , and the time within the update frequency range is called the unit time . For intuitive expression, a coordinate system with time as the horizontal axis and distance information as the vertical axis is drawn, as shown in the appendix Figure 3 shown.

[0019] Since the purpose of this method is to provide a driving mode with accident prevention properties, the handling of emergencies can be ignored. Therefore, in the process of processing the graph line data, a smooth curve method is used for processing. This method uses the signal processing technology based on the Butterworth low-pass filter to smooth the time-varying distance information collected by the ultrasonic radar. The purpose is not only to eliminate the influence of the noise signal of the sensor, but also to eliminate the influence of suddenly appearing adjacent obstacles on the overall distance judgment. The design principle of the Butterworth low-pass filter is as follows The Butterworth filter is a filter with a maximally flat magnitude response, and its transfer function shows a monotonically decreasing characteristic in the passband. For each sampling point of the time series, signal reconstruction is achieved by constructing a fourth-order Butterworth low-pass filter, and its mathematical expression is: ; where represents the filter order, taking , is the angular frequency corresponding to the cut-off frequency, and , is the cut-off frequency, , which is determined according to the characteristics of the target signal. The analog filter is converted into a digital filter by the bilinear transformation method to ensure the phase linearity characteristic at a sampling frequency of 40 kHz.

[0020] The implementation method of zero-phase filtering is to avoid the phase delay phenomenon caused by traditional filtering. This method adopts the forward-backward two-way filtering technique (i.e., algorithm). This method performs two filtering processes on the signal through time reversal operation, making the group delay of the finally output filtered signal zero, and its mathematical process can be expressed as: ; where represents the forward filtering operation, represents the backward filtering operation. This technique effectively eliminates the phase distortion problem inherent in traditional causal filters and is particularly suitable for the autonomous driving scenario that requires strict maintenance of the time series synchronization.

[0021] In terms of the parameter optimization strategy, the key parameters of the filter are determined according to the following criteria: Cut-off frequency : Set according to the separation characteristic of the effective signal frequency band and the noise frequency band of the ultrasonic radar; Filter order : Achieve an engineering balance between the transition band steepness (-80 dB / decade) and the computational complexity; Stopband attenuation > 40 dB: Ensure sufficient suppression of high-frequency noise components.

[0022] The description of the technical effect is as shown in the appendix Figure 4 As shown, the distance signal after Butterworth filtering effectively suppresses the following interference components while retaining the original trend component (the slowly varying signal of 0.3 - 5 m): sensor quantization noise (>1 kHz high-frequency component); sudden electromagnetic interference (time-domain spike pulses); false distance jumps caused by multipath reflection.

[0023] The final output signal is obtained by calculating the unit time window Mean distance within and variance , providing safety decision-making inputs that comply with ISO 26262 standards for the accident prevention module. By calculating the mean and variance of the distance information within a unit time, the distance from the obstacle within the unit time is output as the judgment input for the accident prevention module.

[0024] S2. Comprehensively analyze the input environmental information to determine whether the driving mode needs to be adjusted in the environment defined by the environmental information; Traverse the received array information, perform a chi-square goodness-of-fit test on the data in the array, and utilize the properties of the chi-square test to avoid misadjustment of the driving mode due to setting a single threshold, reducing the computational complexity and improving the computational efficiency. The chi-square goodness-of-fit test is used to test whether the difference between the observed frequency and the expected frequency is significant, that is, whether it conforms to a specific distribution or hypothesis. The basic formula of the chi-square test is: ; where is the observed frequency of the th category, represents the expected frequency of the th category, is the number of categories; Set the statistical test hypotheses, including: Null hypothesis : The proportion of data with a distance from the obstacle less than 2.5 meters within a unit time window is not less than 90%; Alternative hypothesis : The proportion of data with a distance from the obstacle less than 2.5 meters within a unit time window is less than 90%; In addition, the degrees of freedom need to be determined. Since the number of parameters is zero, the degrees of freedom formula is: ; Set the significance level , which is selected as in this embodiment, and use the built-in function of MATLAB to calculate the value. If the value is less than the preset significance level, reject the null hypothesis , accept the alternative hypothesis , and consider that the proportion of distances less than 2.5 meters is less than 90%; otherwise, accept the null hypothesis , reject the alternative hypothesis , and consider that the proportion of data with a distance less than 2.5 meters is not less than 90%.

[0025] Due to the relatively high update frequency and to further reduce the possibility of misadjustment, it is set that when the null hypothesis is accepted for n consecutive times , the alternative hypothesis is rejected , the accident prevention judgment module outputs an instruction to change the driving mode from the normal driving mode to the precise driving mode.

[0026] The purpose of performing the chi-square test is to utilize the characteristics of the chi-square test to avoid misadjustment of the driving mode caused by setting a single threshold, while reducing the computational complexity and improving the computational efficiency. Multiple chi-square tests further reduce the possibility of misadjustment, and the principle is similar to the allocation of control rights during the process of human-machine co-driving. The realization of accident prevention is achieved by adjusting the relationship between the steering wheel torque and the lateral acceleration between different modes. Simply put, in a narrow and dense space, the driver does not need to worry about not being able to drive along the expected trajectory due to unstable steering wheel grip. A typical example can be understood as the straight driving section in the third subject of the driver's license exam. The adjustment mode can better help the driver complete the straight driving task.

[0027] S3. Set two driving modes, the standard mode and the precise mode, and based on the result output in step S2, adjust the driving mode of the vehicle.

[0028] The construction of the steering mode adaptive module requires respectively giving the corresponding relationship diagrams of the steering wheel steering torque and the lateral acceleration for the two modes (the standard mode and the precise mode). The significance of the precise mode is to provide the driver with a more delicate and safe operation mode for the vehicle being driven, so that the vehicle can still travel along the expected trajectory according to the driver's psychology when the driver enters different psychological stress environments. Through multiple experiments on the simulator in low-speed driving and parking scenarios, this method collects the corresponding data and fits the steering wheel torque and the vehicle lateral acceleration through mathematical methods, and determines the relationship curve between the two when the driver enters the precise driving mode (the standard mode curve (dashed line), the precise mode curve (solid line)). The comparison diagram of the two mode curves is shown in the appendix Figure 5 .

[0029] Judge whether the obstacle distance distribution conforms to the safety threshold through the chi-square test (such as the proportion of distances < 2.5 meters ≥ 90%). If it does not conform (that is the value < ), then trigger the precise mode, and reduce the potential collision risk caused by high-frequency operations by reducing the steering torque sensitivity, thereby achieving active accident prevention.

[0030] Through simulator tests, after the subjects used two different modes respectively in a narrow scenario, subjective inquiries and psychological tests were conducted on them. 80% of the subjects had higher confidence in the vehicle they were driving after using the precise mode. At the same time, by comparing the data collected by the simulator, the high-frequency components of the steering wheel angle operation amount and the steering wheel angle rate of the driver were significantly reduced in the precise mode, which verified the results of the subjective inquiries and psychological tests. The improvement of the driver's confidence in vehicle control and the feedback of relevant data proved that the adjustment of the mode had an obvious effect on accident prevention.

[0031] The steering mode adaptive adjustment method based on accident prevention further includes: Continuously monitor the actual distance, steering torque, and lateral acceleration between the vehicle and the obstacle through in-vehicle sensors; Set an error threshold to determine whether the error between the actual distance and the filtered data exceeds the error threshold; If the error between the actual distance and the filtered data exceeds the error threshold, re-adjust the cut-off frequency and order of the Butterworth filter.

[0032] As Figure 1 shown, a system for implementing the steering mode adaptive adjustment method based on accident prevention includes: An environmental information acquisition module, which is used to collect the environmental information around the vehicle during driving, including but not limited to the distance from the vehicle itself as the main information, the vehicle speed and its acceleration of surrounding vehicles; An accident prevention module, which is used to analyze and synthesize the environmental information input by the environmental information acquisition module to determine whether the driving mode needs to be adjusted in the environment defined by the environmental information; A steering mode adaptive module, which is used to parse the mapping curve of the driver's steering wheel torque and the vehicle lateral acceleration in the current environment according to the judgment result of the accident prevention module and assist in adjusting the driving mode.

[0033] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in this application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0034] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0035] The above embodiments merely represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims.

[0036] The above is only the preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for adaptively adjusting a steering mode based on accident prevention, characterized in that: The method comprises: S1. Collect environmental information data around the vehicle under natural driving conditions and narrow space scene driving conditions; S2, comprehensively analyzing the input environmental information to determine whether the driving mode needs to be adjusted in the environment defined by the environmental information; S3. Setting two driving modes: standard mode and precision mode, and adjusting the driving mode of the vehicle based on the result outputted in step S2.

2. The method according to claim 1, characterized in that The S1 specifically includes: Obtain the distance between the automatic parking ultrasonic radar and the obstacles on the left and right sides, and establish a coordinate system with time as the horizontal axis and the distance information to the obstacles as the vertical axis; The signal processing technology based on Butterworth low-pass filter is used to smooth the time-varying distance information collected by the ultrasonic radar, eliminating the influence of the sensor's noise signal and the influence of obstacles that suddenly appear within the ultrasonic radar's detectable range on the overall distance judgment; Use forward-backward bidirectional filtering to filter the signal line forward, then invert the signal for backward filtering, eliminate the phase delay of the filter, and output a signal with zero phase distortion , the mathematical process is expressed as: ； in represents the forward filtering operation, represents the backward filtering operation, is the time index, represents the sequence reversal operation, Represents a sequence of input data; Calculate the mean and variance of the distance information to the obstacle within the unit time window, and output the distance to the obstacle within the unit time.

3. The method according to claim 2, characterized in that The Butterworth low-pass filter is specifically: For each sampling point of the time series, the signal is reconstructed by constructing a fourth-order Butterworth low-pass filter, and its transfer function expression is: ； in, represents a complex frequency variable, represents the filter order, is the angular frequency corresponding to the cut-off frequency, and , is the cut-off frequency, , set according to the separation characteristics of the effective signal frequency band and the noise frequency band of the ultrasonic radar; The analog filter is converted into a digital filter by bilinear transformation method.

4. The method according to claim 2, characterized in that: The S2 specifically includes: The received array information is traversed, and a chi-square goodness-of-fit test is performed on the data in the array. The basic formula of the chi-square goodness-of-fit test is: ； in, is chi-square, It is The observed frequency of the category, Indicates The expected frequency of the category, is the number of categories; Set the statistical test hypothesis, including: Null hypothesis : Within the unit time window, the proportion of data with a distance of less than 2.5 meters from obstacles is not less than 90%; Alternative hypothesis : Within the unit time window, the proportion of data with a distance of less than 2.5 meters from obstacles is less than 90%; Determine the degrees of freedom , the formula is: ； Setting significance level , and use the MATLAB built-in function to calculate Value, where The value represents the probability of measuring the degree of consistency between the observed data and the original hypothesis. The value is less than the preset significance level , then reject the null hypothesis , accept the alternative hypothesis Otherwise, accept the null hypothesis , reject the alternative hypothesis .

5. The method according to claim 1, characterized in that The S3 specifically includes: Plot the steering wheel torque for standard mode and precision mode respectively and lateral acceleration The corresponding relationship diagram of Simulate driving and parking scenarios to collect steering wheel torque and vehicle lateral acceleration; The collected steering wheel torque and vehicle lateral acceleration are fitted by the least square method to generate mapping curves of the standard mode and the precise mode; Based on the result outputted by step S2, the current driving mode is selected and the input of the steering system is adjusted in real time.

6. The method according to claim 1, characterized in that Also includes: The actual distance between the vehicle and the obstacle, steering torque and lateral acceleration are continuously monitored through on-board sensors; Set the error threshold to determine whether the error between the actual distance and the filtered data exceeds the error threshold; If the error between the actual distance and the filtered data exceeds the error threshold, the cutoff frequency and order of the Butterworth filter are readjusted.

7. The method according to claim 1, characterized in that The system for implementing the steering mode adaptive adjustment method based on accident prevention includes: The environment information acquisition module is used to collect the environment information around the vehicle during driving, including but not limited to the distance from the vehicle as the main information and the speed and acceleration of surrounding vehicles; An accident prevention module, used to analyze and synthesize the environmental information input by the environmental information acquisition module, and determine whether the driving mode needs to be adjusted in the environment defined by the environmental information; The steering mode adaptive module is used to analyze the mapping curve of the driver's steering wheel steering torque and the vehicle's lateral acceleration in the current environment according to the judgment results of the accident prevention module, and assist in adjusting the driving mode.

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