An intelligent early warning method, system and device for driving risks considering vehicle interaction

Abstract

The present invention discloses an intelligent early warning method, system and device for driving risks considering vehicle interaction. The method includes the following steps: establishing a driving risk assessment model; collecting historical vehicle trajectory data, extracting key features for clustering, dividing the clustering results into risk levels, and determining the warning thresholds corresponding to each risk level; obtaining the real-time vehicle motion state information of the vehicle itself and the vehicle in front in real time to obtain the real-time risk level; and sending corresponding preset warning information to the driver according to the real-time risk level. The present invention uses the driving risk assessment model to accurately and real-time evaluate the potential collision risk of the vehicle, combines a large amount of historical vehicle trajectory data, determines the warning thresholds for different risk levels, provides effective warning suggestions in different risk scenarios, and better assists the driver to cope with potential risks. The present invention can improve the accuracy and real-time performance of risk assessment, significantly enhance the driving safety in complex traffic environments, and reduce the accident risk.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic warning, and in particular, to an intelligent warning method, system and device for driving risk considering vehicle interaction. Background Art

[0002] With the popularization of modern transportation means and the increasing complexity of road traffic, the complex and changeable traffic flow environment poses higher requirements for driving safety. Traditional driving safety warning systems often rely on drivers' experience and simple judgments of vehicle sensors, and it is difficult to comprehensively and accurately evaluate potential collision risks. In order to improve driving safety, a more scientific and accurate risk assessment method is urgently needed. The existing driving safety warning methods mainly include the following several ways:

[0003] 1. A driving safety warning method based on directly discriminating risks by sensors, such as detecting obstacles and road surface coverings in front through a driving safety warning device, and calculating the recommended speed according to the distance from the obstacles; or obtaining the position information of surrounding vehicles through image data, and generating a warning message when the distance between vehicles is less than a preset distance. This type of method is simple to implement and has high warning efficiency, but it is difficult to be used for warning in complex traffic scenarios and emergencies. At the same time, sensors are difficult to perceive potential collision risks and have a certain lag.

[0004] 2. A driving safety warning method based on risk indicators and prediction models, such as comprehensively obtaining the driving risk value by predicting the severity of accidents between bus vehicles and surrounding vehicles and the relative probability of accidents occurring, and evaluating the risk in combination with the driver's risk feedback degree; or evaluating the coupling risk of people, vehicles and roads by constructing the collision accident probability of pedestrians and vehicles and the probability of skidding and rollover accidents. If the risk assessment result exceeds the threshold, a warning is issued. This type of method can accurately evaluate risks and issue warnings in a timely manner, but the models of this type of method are relatively complex and the required calculation cost is relatively high.

[0005] In summary, the existing driving safety warning methods mainly directly discriminate risks based on sensors or evaluate risks based on risk indicators and prediction models and conduct driving safety warnings. However, these methods are difficult to accurately identify risks with simple and fast methods in the complex and changeable road traffic environment. Summary of the Invention

[0006] The present invention aims to solve at least one of the technical problems existing in the prior art. For this purpose, the present invention proposes an intelligent warning method for driving risk considering vehicle interaction, which can accurately simulate the dynamic relationship between vehicles and can achieve more accurate and effective driving safety warnings in complex traffic environments.

[0007] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0008] An intelligent early warning method for driving risks considering vehicle interaction, comprising the following steps: S1, establishing a driving risk assessment model; S2, collecting historical vehicle trajectory data, extracting key features representing risks in the historical trajectory data for clustering, dividing the clustering results into different risk levels, and determining the corresponding early warning thresholds for each risk level according to the average risk values of the samples of each risk level; S3, obtaining the real-time vehicle motion state information of the vehicle itself and the vehicle in front in real time, calculating the real-time vehicle risk value according to the driving risk assessment model, and judging the risk level corresponding to the real-time vehicle risk value according to the early warning thresholds of each risk level to obtain the real-time risk level; S4, sending the corresponding preset early warning information to the driver according to the real-time risk level.

[0009] Further, the driving risk assessment model f(s) is as follows:

[0010]

[0011] where s e is the critical safety distance between the vehicle itself and the vehicle in front; s is the actual distance between the vehicle itself and the vehicle in front; D is the minimum potential energy at the balance key length s e ; α is the vibration frequency response coefficient.

[0012] Further, the calculation formula of s e is as follows:

[0013] In the formula, SSD p is the safe stopping distance of the vehicle in front, SSD f is the safe stopping distance of the vehicle itself; h is the center distance between the vehicle in front and the vehicle itself; v p and v f are the speeds of the vehicle in front and the vehicle itself respectively; L p and L f are the lengths of the vehicle in front and the vehicle itself respectively; a p and a f are the maximum decelerations of the vehicle in front and the vehicle itself respectively; PRT is the perception reaction time.

[0014] Further, in the step S2, the key features representing risks include: the speed of the vehicle itself, the speed of the vehicle in front, the distance and speed difference between the vehicle itself and the vehicle in front, and the risk value calculated according to the driving risk assessment model.

[0015] Further, in the step S2, the risks are first divided into k levels, and then the early warning thresholds for each risk level are determined respectively; the corresponding early warning thresholds for each risk level are determined according to the average risk values of the samples of each risk level, and are specifically carried out through the following formula: where, R kis the warning threshold for the k-th level of risk; n k is the number of samples for the k-th level of risk; r i is the risk value of the i-th sample in the k-th level of risk.

[0016] Further, in the step S2, a clustering algorithm is used to cluster the historical trajectory data, where the number of clusters is k.

[0017] Further, in the step S3, the real-time vehicle motion state information includes the vehicle type of the host vehicle, the static information of the vehicle length of the host vehicle, the speed of the host vehicle, the vehicle type of the preceding vehicle, the static information of the vehicle length of the preceding vehicle, the speed of the preceding vehicle, and the vehicle distance.

[0018] Further, the warning information includes the display information on the in-vehicle display screen and the voice prompt information of the voice broadcast system; the lower the warning threshold, the higher the corresponding prompt frequency and intensity of the risk level.

[0019] The present invention also provides an intelligent transportation Internet of Things warning system for driving collision prevention risks considering vehicle interaction, including: a risk assessment model establishment module for establishing a driving risk assessment model; a clustering module for collecting historical vehicle trajectory data, extracting key features representing risks in the historical trajectory data for clustering, and dividing the clustering results into different risk levels; a threshold determination module for determining the corresponding warning thresholds for each risk level according to the average risk values of the samples of each risk level; a data acquisition module for real-time obtaining the real-time vehicle motion state information of the host vehicle and the preceding vehicle; a risk assessment module for calculating the real-time vehicle risk value according to the real-time vehicle motion state information, and judging the risk level corresponding to the real-time vehicle risk value according to the warning thresholds of each risk level to obtain the real-time risk level; and a warning release module for sending the corresponding preset warning information to the driver according to the real-time risk level.

[0020] The present invention also proposes an electronic device, including a processor and a memory, where the memory is used to store a computer program, and the processor is used to execute the computer program to execute the intelligent warning method for driving risks considering vehicle interaction.

[0021] The present invention has the following beneficial effects:

[0022] By using the driving risk assessment model, the potential collision risk of the vehicle can be accurately and real-time evaluated. Combining a large amount of historical vehicle trajectory data, the warning thresholds for different risk levels are determined, and effective warning suggestions are provided in different risk scenarios to better assist the driver in coping with potential risks. The present invention can improve the accuracy and real-time performance of risk assessment, significantly enhance the driving safety in complex traffic environments, and reduce the accident risk.

[0023] In addition to the purposes, features, and advantages described above, the present invention has other purposes, features, and advantages. The following will further describe the present invention in detail with reference to the drawings. Description of the Drawings

[0024] The drawings forming a part of this application are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0025] Figure 1 is the overall flowchart of the method of the present invention;

[0026] Figure 2 is the schematic diagram of the system of the present invention;

[0027] Figure 3 is the schematic diagram of the driving risk assessment model based on molecular dynamics provided by an embodiment of the present invention.

[0028] Figure 4 is the risk clustering result graph of historical trajectory data provided by an embodiment of the present invention.

[0029] Figure 5 is the risk value curve graph of sample vehicles provided by an embodiment of the present invention. Detailed Description of the Embodiments

[0030] 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.

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0032] It should be noted that all directional indications (such as up, down, left, right, front, back...) in the embodiments of the present invention are only used to explain the relative positional relationship and movement conditions between components in a specific posture (as shown in the drawings). If the specific posture changes, the directional indications will also change accordingly.

[0033] In addition, the descriptions involving "first", "second", etc. in the present invention are for descriptive purposes only, and should not be construed as indicating or implying their relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions between various embodiments may be combined with each other, but it must be based on the ability of those of ordinary skill in the art to implement. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0034] Please refer to Figure 1 , a driving risk intelligent early warning method considering vehicle interaction in a preferred embodiment provided by the present invention includes steps S1, S2, S3, and S4.

[0035] S1, establish a driving risk assessment model; refer to Figure 3 , which is a schematic diagram of a driving risk assessment model based on molecular dynamics. Since the molecular interactions in a microscopic particle system and the interaction behaviors between vehicles in the macroscopic world have systematic similarities, vehicles are regarded as molecules, and the interaction relationship between vehicles is described using molecular interaction forces. By improving the molecular interaction force function, a driving risk assessment model is obtained. By introducing the principle of molecular dynamics, the dynamic relationship between vehicles can be accurately simulated, and more accurate and effective driving safety early warning can be realized in a complex traffic environment.

[0036] S2, collect historical vehicle trajectory data, extract the key features characterizing risks in the historical trajectory data for clustering, divide the clustering results into different risk levels, and determine the corresponding early warning thresholds for each risk level according to the average risk values of the samples of each risk level. Refer to Figure 4 , which is a graph of the risk clustering results of historical trajectory data provided by an embodiment of the present invention.

[0037] S3, obtain the real-time vehicle motion state information of the host vehicle and the vehicle ahead in real time, calculate the real-time vehicle risk value according to the driving risk assessment model, and determine the risk level corresponding to the real-time vehicle risk value according to the early warning thresholds of each risk level to obtain the real-time risk level; specifically, the real-time vehicle motion state information of the host vehicle and the vehicle ahead can be collected in real time through sensing devices such as GPS, radar, and cameras, and the data is transmitted to the central processor for the processor to perform data preprocessing. The driving risk assessment model and the thresholds are preset in the processor, and the processor calculates the vehicle risk value to obtain the real-time vehicle risk value of the vehicle, and obtains the real-time risk level according to the threshold discrimination. Refer to Figure 5 , which is a graph of the risk value of a sample vehicle in an embodiment of the present invention. The real-time vehicle risk value is obtained and the curve shown in Figure 5 is obtained according to the change of the real-time vehicle risk value with driving time.

[0038] S4. Issue the corresponding preset warning information to the driver according to the real-time risk level. The warning information is generated by the processor according to the real-time risk level to generate the corresponding safety warning information. The warning information needs to be preset, and specifically, the warning information can be issued to the driver in a timely manner through the display screen or voice prompt.

[0039] An evaluation model for driving risk is obtained by improving the molecular interaction force function. The driving risk evaluation model is expressed as f(s), and the function value is the driving risk value. When f(s) < 0, it indicates that the actual distance between the vehicle and the vehicle in front is less than the critical safety distance, and there is a collision risk. Specifically, the driving risk evaluation model f(s) is as follows:

[0040]

[0041] where s e is the critical safety distance between the vehicle and the vehicle in front; s is the actual distance between the vehicle and the vehicle in front; D is the minimum potential energy at the equilibrium bond length s e i.e., the energy required for bond breakage, which reflects the potential risk when the vehicle and the vehicle in front maintain the critical safety distance, and can be understood as the potential risk coefficient when the vehicle and the vehicle in front maintain the critical safety distance; α is the vibration frequency response coefficient, which is a parameter related to the vibration frequency; e is the natural constant, i.e., the base of the natural logarithm. D and α are constants. For example, in this embodiment, D takes the value of 3.2 and α takes the value of 0.38.

[0042] In a specific embodiment of the present invention, where s e The calculation formula is as follows:

[0043]

[0044]

[0045]

[0046] In the formula, SSD p is the safe stopping distance of the vehicle in front, SSD f is the safe stopping distance of the vehicle; h is the center distance between the vehicle in front and the vehicle; v p and v f are the speeds of the vehicle in front and the vehicle respectively; L p and L f are the lengths of the vehicle in front and the vehicle respectively; a p and a f are the maximum decelerations of the vehicle in front and the vehicle respectively, and are taken according to the maximum decelerations specified for different vehicle types in relevant standards; PRT is the perception reaction time. In a specific embodiment of the present invention, PRT can take the value of 1.5 s.

[0047] In some embodiments of the present invention, in the step S2, the key features characterizing the risk include: the speed of the host vehicle, the speed of the leading vehicle, the distance and speed difference between the host vehicle and the leading vehicle, and the risk value calculated based on the driving risk assessment model. In a specific embodiment of the present invention, examples of the extracted key features are as follows in the table:

[0048]

[0049] In some embodiments of the present invention, in the step S2, the warning threshold corresponding to each risk level is determined according to the average risk value of the samples of each risk level, specifically through the following formula:

[0050]

[0051] where R k is the warning threshold for the k-th level of risk; n k is the number of samples for the k-th level of risk; r i is the risk value of the i-th sample in the k-th level of risk. Specifically, the extracted feature values are standardized, and the number of clusters is set to 3, k = 1, 2, 3. In step S2, a clustering algorithm is specifically used to cluster the historical trajectory data, and the obtained clustering results are divided into low risk, medium risk, and high risk. k = 1, 2, 3 respectively represent low risk, medium risk, and high risk. The clustering algorithm can specifically adopt the K-Means clustering algorithm or other clustering algorithms.

[0052] In a specific embodiment of the present invention, the warning thresholds for each level of risk obtained by clustering are as follows in the table:

[0053] Risk level Warning threshold Low risk 0.51 Medium risk -0.10 High risk -7.72

[0054] When the real-time vehicle risk value is between 0.51 and -0.10, it is evaluated as low risk. When the real-time vehicle risk value is between -0.10 and -7.72, it is evaluated as medium risk. When the real-time vehicle risk value is less than -7.72, it is evaluated as high risk. When the real-time vehicle risk value is equal to the warning threshold, it is evaluated as the risk level corresponding to the warning threshold. For example, when the real-time vehicle risk value is 0.51, it is evaluated as low risk, and so on.

[0055] In a specific embodiment of the present invention, in the step S3, the real-time vehicle motion state information includes the type of the host vehicle, the static information of the length of the host vehicle, the speed of the host vehicle, the type of the leading vehicle, the static information of the length of the leading vehicle, the speed of the leading vehicle, and the vehicle distance. According to the type of the host vehicle, the length L f and the maximum deceleration a f of the host vehicle can be obtained. According to the type of the leading vehicle, the length L pand the maximum deceleration a p Furthermore, based on the static information of the vehicle length of this vehicle, the speed of this vehicle, the static information of the vehicle length of the preceding vehicle, and the speed of the preceding vehicle, the real-time vehicle risk value can be obtained according to the driving risk assessment model. In addition, each sensor stores the obtained real-time vehicle motion state information in the processor, and the processor integrates the original data and performs data preprocessing such as removing noise and outliers. The data preprocessing specifically includes missing value completion, duplicate data removal, noise processing, and outlier detection and processing.

[0056] Missing value completion: If there are missing values, for vehicle type and vehicle length, the previous adjacent record can be used for filling or incomplete records can be deleted; for the speed of this vehicle, the speed of the preceding vehicle, and the vehicle distance, the mean value of the two adjacent points before and after is used to complete the missing record.

[0057] Duplicate data removal: Delete duplicate records and retain only unique records to ensure data independence.

[0058] Noise processing: For speed data, the Savitzky-Golay filtering method is used to smooth the noise and reduce data fluctuations; this method is based on local polynomial least squares fitting, where the fitting polynomial p(j) is used to fit the data points within the sliding window, and the least squares method determines the coefficients a of the polynomial by minimizing the sum of the squared residuals ε N to determine the coefficients a of the polynomial j specifically through the following formula:

[0059]

[0060]

[0061] where a n is the polynomial coefficient; N is the order of the polynomial, n is the term of the polynomial, representing the power of j in the polynomial; j is the position index within the sliding window, and j n represents the nth power of the position index j; ε N is the sum of the squared residuals; x[j] is the original data point at position j within the window; M is half the width of the sliding window, and the window size is 2M + 1; p(j) is the fitting value of the fitting polynomial at n; in some embodiments, N = 3 and M = 3 are taken.

[0062] Outlier detection and processing: The standard deviation method is used to detect outliers. When |x - μ| > 3σ, that is, when the difference between the data x and the mean μ exceeds 3 standard deviations σ, it is regarded as an outlier, and the mean value of the two adjacent points before and after the missing value is used for completion.

[0063] In a specific embodiment of the present invention, the warning information includes the display information of the in-vehicle display screen and the voice prompt information of the voice broadcast system; the lower the warning threshold, the higher the corresponding prompt frequency and intensity of the risk level, that is, as the risk level increases, the prompt frequency and intensity are enhanced.

[0064] In a specific embodiment of the invention, the warnings are as shown in the following table:

[0065]

[0066] Referring to Figure 2 , the present invention also proposes an intelligent transportation Internet of Things warning system for driving collision prevention risks considering vehicle interaction, including a risk assessment model establishment module, a clustering module, a threshold determination module, a data acquisition module, and a risk assessment module.

[0067] The risk assessment model establishment module is used to establish a driving risk assessment model.

[0068] The clustering module is used to collect historical vehicle trajectory data, extract key features representing risks in the historical trajectory data for clustering, and divide the clustering results into different risk levels.

[0069] The threshold determination module is used to determine the corresponding warning thresholds for each risk level according to the average risk values of the samples of each risk level, and transmit the warning thresholds of each level of risk to the risk assessment module.

[0070] The data acquisition module is used to obtain the real-time vehicle motion state information of the vehicle itself and the vehicle in front in real time, GPS collects the position and speed of the vehicle itself in real time, the vehicle-mounted radar collects the speed and relative position of the vehicle in front in real time, and the camera identifies the vehicle type and vehicle length of the vehicle in front.

[0071] The risk assessment module is used to calculate the real-time vehicle risk value according to the real-time vehicle motion state information, and judge the risk level corresponding to the real-time vehicle risk value according to the warning thresholds of each risk level to obtain the real-time risk level, and transmit the real-time risk level to the warning release module.

[0072] The warning release module is used to issue the corresponding preset warning information to the driver according to the real-time risk level.

[0073] In order to improve the quality of data, an intelligent transportation Internet of Things warning system for driving collision prevention risks considering vehicle interaction further includes a data processing module. The information collected by the data acquisition module is transmitted to the data processing module. The data processing module integrates and stores the received original data, and performs data cleaning to provide high-quality data input for subsequent risk assessment.

[0074] The present invention also provides an electronic device, including a processor and a memory. The memory is used to store a computer program, and the processor is used to execute the computer program to perform the intelligent warning method for driving risks considering vehicle interaction.

[0075] It should be understood that, in the embodiments of the present invention, the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A part of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0076] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An intelligent early warning method for driving risk considering vehicle interaction, characterized in that: The steps include: S1, establish a driving risk assessment model; S2, collect historical vehicle trajectory data, extract key features representing risks from the historical trajectory data for clustering, divide the clustering results into different risk levels, and determine the corresponding warning thresholds for each risk level based on the average risk value of samples of each risk level; S3, real-time acquisition of vehicle motion status information of the vehicle and the preceding vehicle, and calculation of the real-time vehicle risk value according to the driving risk assessment model, and determination of the risk level corresponding to the real-time vehicle risk value according to the early warning thresholds of various risk levels, so as to obtain the real-time risk level; S4, issuing corresponding preset warning information to the driver according to the real-time risk level; The driving risk assessment model f(s) is as follows: where s e is the critical safety distance between the vehicle and the vehicle in front; s is the actual distance between the vehicle and the vehicle in front; D is the equilibrium bond length s e The potential energy is the minimum at ; α is the vibration frequency response coefficient; In step S2, the warning threshold corresponding to each risk level is determined according to the average risk value of samples of each risk level, which is specifically performed by the following formula: Among them, R k is the warning threshold of the k-th level risk; n k is the number of samples of the k-th risk level; r i is the risk value of the i-th sample in the k-th risk level.

2. The intelligent early warning method for driving risk considering vehicle interaction according to claim 1 is characterized in that: where s e The calculation formula is as follows: In the formula, SSD p is the safe stopping distance of the vehicle ahead, SSD f is the safe stopping distance of the vehicle; h is the center distance between the front vehicle and the vehicle; v p and v f are the speeds of the preceding vehicle and the vehicle itself; L p and L f are the lengths of the preceding vehicle and the vehicle itself respectively; a p and a f are the maximum decelerations of the preceding vehicle and the vehicle itself respectively; PRT is the perception reaction time.

3. The intelligent early warning method for driving risk considering vehicle interaction according to claim 1 is characterized in that: In step S2, the key features characterizing the risk include: the speed of the vehicle, the speed of the preceding vehicle, the distance and speed difference between the vehicle and the preceding vehicle, and the risk value calculated based on the driving risk assessment model.

4. The intelligent early warning method for driving risk considering vehicle interaction according to claim 1 is characterized in that: In step S2, a clustering algorithm is used to cluster the historical trajectory data.

5. The intelligent early warning method for driving risk considering vehicle interaction according to claim 1 is characterized in that: In step S3, the real-time vehicle motion state information includes the vehicle type of the vehicle, static information of the vehicle length of the vehicle, the speed of the vehicle, the vehicle type of the preceding vehicle, static information of the vehicle length of the preceding vehicle, the speed of the preceding vehicle, and the vehicle spacing.

6. The intelligent early warning method for driving risk considering vehicle interaction according to claim 1 is characterized in that: The warning information includes display information on the vehicle display screen and voice prompt information from the voice broadcast system; the risk level with a lower warning threshold has a higher corresponding prompt frequency and intensity.

7. An intelligent traffic Internet of Things warning system for traffic collision avoidance risk considering vehicle interaction, used to execute the intelligent warning method for traffic risk considering vehicle interaction as claimed in any one of claims 1 to 6, characterized in that: include: Risk assessment model building module, used to build a driving risk assessment model; The clustering module is used to collect historical vehicle trajectory data, extract key features representing risks from the historical trajectory data for clustering, and divide the clustering results into different risk levels; A threshold determination module is used to determine the corresponding warning thresholds for each risk level according to the average risk value of samples of each risk level; Data acquisition module, used to obtain real-time vehicle motion status information of the vehicle and the preceding vehicle; The risk assessment module is used to calculate the real-time vehicle risk value according to the real-time vehicle motion state information, and determine the risk level corresponding to the real-time vehicle risk value according to the warning thresholds of various risk levels to obtain the real-time risk level; The warning release module is used to release corresponding preset warning information to the driver according to the real-time risk level.

8. An electronic device comprising a processor and a memory, wherein the memory is used to store a computer program, wherein: The processor is used to execute the computer program to execute the intelligent warning method for driving risk considering vehicle interaction as described in any one of claims 1 to 6.

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