Multi-level scene coverage simulation test method and related device

Abstract

The invention belongs to the technical field of simulation testing, and provides a multi-level scene coverage simulation testing method and a related device, and the method comprises the steps: carrying out the value combination of a road parameter, an environment parameter, an information parameter and an interaction parameter of scene coverage, and obtaining a scene space; constructing a test scene according to the test requirement and the scene space; and carrying out reproduction test on the test scene on the bench test system to obtain a test evaluation result of the intelligent driving system. The method integrates the advantages of a three-pillar verification method, is popular, can solve the bottleneck problem of the current expected function safety test demand, can achieve the high scene coverage of the intelligent driving system test, and also can improve the expected function safety performance and reliability of the intelligent driving system test.

Description

Technical Field

[0001] The present invention relates to the technical field of simulation testing, and in particular to a multi-level scenario coverage simulation testing method and related devices. Background Art

[0002] As the functional architecture of autonomous driving systems becomes more complete, the analysis of functional safety issues caused by failure risks covered by the international standard ISO 26262 can no longer meet the safety analysis requirements of highly complex systems. Safety risks caused by the absence of system failures are becoming more and more important. ISO / PAS21448 attributes such issues to intended functional safety (Safety Of The Intended Functionality, SOTIF) and gives a detailed definition. It points out that the goal of conducting intended functional safety activities is to ensure that there are no unreasonable risks caused by performance limitations that affect specific system behaviors or reasonably foreseeable human misuse.

[0003] According to the definition and division of the expected functional safety field, from the spatial (ODD boundary) dimension, SOTIF focuses on the boundary limitations of system performance and the safety risks caused by human misuse. From the time dimension, the use time limitation is also an important measure of limitation, and the reliability problems caused by the passage of time should also be taken into account. Therefore, to conduct a comprehensive expected functional safety test on the functions of the autonomous driving system, it is necessary to solve the problems of the two dimensions of safety and reliability.

[0004] In order to comprehensively test the expected functional safety of the intelligent driving system, it is necessary to combine simulation testing, field testing and actual road testing, which will consume a lot of time and economic costs.

[0005] Simulation testing: The test efficiency is high, the scenario coverage is high, and the test controllability is strong. However, the credibility of the test data and the confidence level of the scenarios are low, which is always a major constraint in simulation testing.

[0006] Field testing: The scenario is relatively controllable, the test risk is low, and the confidence level is higher than simulation, but there are key issues such as low efficiency of scenario layout and low coverage.

[0007] Actual road testing: There is no scenario confidence, and the scenario coverage can be extremely high. However, there are problems such as high test risk, extremely low test efficiency, and scenario coverage must be exchanged for an equal amount of cost.

[0008] Moreover, among the above three methods, only the actual road test can take into account the reliability test, while the simulation and field test can test the SOTIF safety performance, and it is difficult to test and verify the reliability of the intelligent driving system. Therefore, it is urgent to design a better way that can integrate the advantages of the three methods, avoid the disadvantages, and truly achieve high-scenario coverage and efficient intelligent driving system expected functional safety testing from the two dimensions of safety and reliability to meet the high-speed iteration and application requirements of intelligent driving technology. Summary of the invention

[0009] In view of the defects in the prior art, the present invention provides a testing method with multi-level scenario coverage simulation to improve the expected functional safety performance and reliability of intelligent driving system testing.

[0010] A multi-level scenario coverage simulation testing method, comprising:

[0011] The road parameters, environment parameters, information parameters and interaction parameters of the scene coverage are combined to obtain the scene space;

[0012] Construct test scenarios based on test requirements and scenario space;

[0013] The test scenarios are reproduced on the test bench system to obtain test evaluation results of the intelligent driving system.

[0014] Furthermore, the road parameters are simulated using a road load simulation system, which includes a road structure, a road surface and road appendages, wherein:

[0015] Road structures include ordinary straight roads, bridges, tunnels, toll booths, and intersections;

[0016] The road surface includes road conditions, adhesion, road markings and road markings;

[0017] Road accessories include speed bumps, steel plates and other construction accessories.

[0018] Furthermore, the environmental parameters are simulated by an environmental simulation system, which includes a weather simulation module and a light simulation module, wherein:

[0019] The weather simulation module is used to simulate weather information, including rain, water accumulation, snow, snow accumulation, fog, other particulate matter, wind level and wind direction;

[0020] The lighting simulation module is used to simulate lighting information, which includes lighting intensity, lighting direction and color temperature.

[0021] Furthermore, the information parameters are simulated using a diversified variable information system, which includes signs and traffic lights, wherein:

[0022] Signs and signs include warning signs, prohibition signs, instruction signs, wayfinding signs, tourist area signs, road construction signs, pole forms and sign deployment locations;

[0023] Traffic lights include signal light style, light pole form and signal light deployment location.

[0024] Furthermore, the interactive parameters are simulated using a high-fidelity interactive system, which includes traffic participants and dynamic scenes, where:

[0025] Traffic participants include adults, children, baby carriages, cars, medium-sized buses, large buses, small trucks, medium-sized trucks, large trucks, two-wheelers, three-wheelers, small special-shaped objects, medium special-shaped objects and large special-shaped objects;

[0026] Dynamic scenarios include natural driving scenarios, dangerous edge scenarios, and accident scenarios.

[0027] Furthermore, scenario coverage also includes human misuse, which includes missed hearing, lack of comprehension, misunderstanding and incorrect operation by drivers and passengers.

[0028] Furthermore, it also includes using a scene automation orchestration system to construct a test scene according to test requirements and scene space.

[0029] Furthermore, the test scenario is reproduced on the bench test system to obtain the test evaluation results of the intelligent driving system, including:

[0030] The test scenario is reproduced on the bench test system using an all-weather test method to obtain scenario test data;

[0031] Use scene data automatic extraction technology to eliminate redundant data of scene test data and obtain key parameters of scene test of intelligent driving system;

[0032] Perform feature analysis on key parameters of scenario tests on the intelligent driving system to obtain test evaluation results of the intelligent driving system.

[0033] A test device for multi-level scenario coverage simulation, characterized by comprising:

[0034] A combination unit is used to combine the road parameters, environmental parameters, information parameters and interaction parameters of the scene coverage to obtain the scene space;

[0035] A construction unit is used to construct a test scenario according to test requirements and scenario space;

[0036] The test unit is used to reproduce the test scenarios on the test bench system to obtain the test evaluation results of the intelligent driving system.

[0037] A terminal, characterized in that it includes a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute any one of the multi-level scenario coverage simulation test methods described. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the specific embodiments of the present invention, the following will briefly introduce the drawings required for the specific embodiments or the prior art description. In all the drawings, each element or part is not necessarily drawn according to the actual scale.

[0039] Figure 1 A flow chart of a test method for multi-level scenario coverage simulation provided by an embodiment of the present invention;

[0040] Figure 2 A schematic diagram of the structure of a test device for multi-level scene coverage simulation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0041] The following embodiments of the technical solution of the present invention are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.

[0042] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application should have the common meanings understood by those skilled in the art to which the present invention belongs.

[0043] In one embodiment, a multi-level scenario coverage simulation test method is provided, which can not only achieve high scenario coverage of the intelligent driving system test, but also improve the expected functional safety performance and reliability of the intelligent driving system test. Figure 1 As shown, the method includes:

[0044] 1. Combine the road parameters, environmental parameters, information parameters and interaction parameters of the scene coverage to obtain the scene space;

[0045] Road parameters are the basis of vehicle operation and the carrier of scene occurrence. Different road parameters can cause performance limitations in perception, decision-making, control, execution, etc., and are one of the necessary elements of the performance test of the intended functional safety (SOTIF). The road parameters are decomposed and classified into three types of road parameters: road structure, pavement, and attachments. The road parameters and their meanings are shown in Table 1.

[0046] Table 1

[0047]

[0048] Preferably, in order to achieve coverage of road parameters, a highly flexible combined road load simulation system is constructed.

[0049] Among them, the road structure in the road parameters can be independently simulated by collecting road spectrum information and load information through four-wheel independent hubs according to actual natural driving.

[0050] The road surface parameters include road conditions, adhesion, road signs and road markings. Road signs and road markings affect the lane recognition of the camera. The detachable flexible lane line rapid deployment technology is adopted to realize the deployment of diversified lane road signs and road markings.

[0051] The road appendages in the road parameters are real movable road appendages, which can be deployed according to the real scene placement method to achieve the reproduction of the road appendages on the bench test system.

[0052] Environmental parameters are important factors that affect the perception system of the intelligent driving system. The performance limitations of the perception system are also the key to the research on the expected functional safety performance. Therefore, the triggering conditions of environmental parameters are the core link of the expected functional safety test. However, judging from the current testing status at home and abroad, the "weather-dependent" test that relies on natural weather changes is difficult to achieve high coverage and high efficiency. Some test sites have built environmental simulation systems, but have not achieved quantitative control and comprehensive simulation of environmental-triggered price adjustments.

[0053] Therefore, preferably, the environmental parameters in the present invention are simulated by an environmental simulation system, which includes a weather simulation module and a light simulation module. The weather simulation module is used to simulate weather information, and the light simulation module is used to simulate light information.

[0054] The weather information and light information are further decomposed into categories and parameters as shown in Table 2.

[0055] Table 2

[0056]

[0057] Weather information refers to meteorological parameters such as rain, snow, fog, and wind. Weather information such as rain, snow, fog, and wind have different degrees of impact on the cameras, millimeter-wave radars, lidar, etc. of intelligent driving vehicles. Eight parameters that will affect the perception system in the intelligent driving system have been selected.

[0058] Lighting information describes the lighting conditions in which the driving scene of the intelligent driving car operates. It is related to the time when the scene runs (natural lighting during the day and outdoors, artificial lighting indoors and at night), and will have a significant impact on the camera of the intelligent driving car.

[0059] For weather information, a controllable environmental simulation system is built on the basis of the road load simulation system to cover various environmental parameters of the scene. Unlike other rain and fog simulation systems, the environmental simulation system can realize quantitative simulation and stepless adjustment of the triggering conditions of weather information such as rain, snow, fog, and particulate matter. At the same time, the target rainfall / snow amount / particulate matter concentration can also be quantitatively calibrated, pre-set, quickly matched, and applied in the scene. The environmental simulation system also includes omnidirectional wind ducts that can simulate multi-wind direction environmental conditions, quickly adapt to various environmental parameters involved in the expected functional safety test, and solve the current pain point of low efficiency in intelligent driving system testing.

[0060] In terms of lighting information, a variable track light simulator can be used in conjunction with a multi-parameter lighting simulation system to adjust the intensity, direction, and color temperature of the light. It can simulate the solar altitude angle, solar azimuth angle, and lighting effects of different weather conditions at different times and geographical locations under natural lighting conditions during the day; as well as street lights of the same color, brightness, and orientation under night conditions; while considering the rapid changes in lighting conditions under tunnels, occlusions, and other terrains, a controllable and variable lighting simulation system can be used to simulate the lighting conditions at tunnel entrances and exits, light groups in long tunnels, and light-blocking terrains, fully verifying the "glare" and other testing problems in the field of limitations of the perception system of the expected functional safety intelligent driving system.

[0061] Preferably, the information parameters are simulated using a diversified variable information system, which includes signs and traffic lights.

[0062] Information parameters refer to the information environment composed of signs, signboards and traffic lights. The information environment of intelligent driving is an indispensable link.

[0063] The information parameters are decomposed into two categories: signs and traffic lights, as shown in Table 3.

[0064] Table 3

[0065]

[0066] Traffic markings also belong to traffic information, but they have been classified as road parameters in the previous article and are covered in the road load simulation system, so they will not be repeated here.

[0067] In order to cover the values ​​of information parameters, variable LED traffic sign screens can be used in combination with various forms of combined poles. The database of signs, signs and traffic lights is based on GB 5768.1 and EU standards, covering the full range of standard traffic sign data in China and the EU, as well as single-column, multi-column, single-cantilever and multi-cantilever types, which can be flexibly combined in the scene. The sign installation position reserved by the road load simulation system can realize the rapid installation of light poles, signal poles, gantries and other carriers, and achieve wide coverage of the information parameter deployment location.

[0068] Road parameters, environmental parameters, and information parameters constitute the background and carrier of scene operation. The evolution process of the scene itself is the core of the dynamic development of the scene. Parameters related to the dynamic interaction of the scene are classified as "interaction parameters."

[0069] Preferably, the interaction parameters can be decomposed into two parts: one is the subject of the interaction relationship, that is, the traffic participant, and the other is how to interact, that is, the dynamic scene (scene story line). The traffic participant is the protagonist of the dynamic scene story line.

[0070] The categories of traffic participants are not divided into fine-grained categories according to common automobile classifications, but are divided according to the specification classifications mostly adopted by the perception systems of intelligent driving systems in the industry.

[0071] For dynamic scenes, we divide them into three categories according to the intensity of scene interaction: natural driving scenes, dangerous edge scenes, and accident scenes. Each scene has several subcategories, as shown in Table 4.

[0072] Table 4

[0073]

[0074]

[0075] For the simulation of pedestrians (including adults and children) and two-wheeled vehicles among traffic participants, multi-specification road pedestrian, bicycle, and motorcycle models are used (to ensure the authenticity of visual appearance and radar monitoring echo properties), and multi-directional detachable modular mobile tracks are used to achieve movement. For other types of traffic participants, simulated target fake car models are used to simulate targets of different sizes. For special-shaped targets among traffic participants, target accessories such as shielding tarpaulins and multi-form cargo are equipped, and used in conjunction with ordinary targets, various typical special-shaped targets can be combined.

[0076] The laboratory's bench test system realizes high-speed motion: The scenes of high-speed motion and interaction in the traffic environment are, in essence, the relative motion relationship between traffic participants. Therefore, in the bench test system, it is only necessary to accurately calculate the relative motion parameters of the traffic participants in the scene to simulate the situation when the real high-speed scene occurs, and it can greatly reduce the experimental risks, enhance the controllability of the test, save resources and improve efficiency.

[0077] Preferably, the road parameters, environmental parameters, information parameters and interaction parameters of the scene coverage are combined to obtain the scene space, including: building a specific test scene, using existing technical means to classify and count the key parameters of the road parameters, environmental parameters, information parameters and interaction parameters in different scene coverages, obtaining parameter distribution, and then confirming the parameter values ​​of the road parameters, environmental parameters, information parameters and interaction parameters of the scene coverage.

[0078] During the test, it is only necessary to randomly combine and determine the values ​​of the road parameters, environmental parameters, information parameters, and interaction parameters of the scene coverage (since the selected types and values ​​are all reasonable results after screening and analysis), and then they can be combined into a scene space.

[0079] Preferably, the scenario coverage also includes human misuse, which includes missed hearing, lack of comprehension, misunderstanding and incorrect operation by the driver and passengers.

[0080] Misuse by drivers and passengers, such as missed listening, lack of understanding, misunderstanding, and incorrect operation, will lead to SOTIF risks. Therefore, human misuse is also a parameter level that must be considered in SOTIF performance testing. The bench test system in the laboratory converts high-speed motion into relative motion, reduces scene risks, and has efficient controllability of parameters, so that real drivers and passengers can take the tested intelligent driving vehicle to conduct a series of human misuse experiments in a safe environment, further ensuring the scene coverage of the test.

[0081] 2. Build test scenarios based on test requirements and scenario space.

[0082] Preferably, the method further includes using a scene automation arrangement system to construct a test scene according to test requirements and scene space.

[0083] 3. Reproduce the test scenario on the test bench system to obtain the test evaluation results of the intelligent driving system.

[0084] The test scenarios are reproduced on the bench test system to obtain the test evaluation results of the intelligent driving system, including:

[0085] The test scenario is reproduced on the bench test system using an all-weather test method to obtain scenario test data;

[0086] Use scene data automatic extraction technology to eliminate redundant data of scene test data and obtain key parameters of scene test of intelligent driving system;

[0087] Perform feature analysis on key parameters of scenario tests on the intelligent driving system to obtain test evaluation results of the intelligent driving system.

[0088] In addition to using the above high-coverage test scenarios to test the expected functional safety performance of the intelligent driving system, the present invention also focuses on the reliability of the intelligent driving system. For example, perception systems such as cameras will creep in their originally calibrated positions under long-term vibrations. As time goes by and the environment changes, the camera lens will have a weakened photosensitivity. The present invention adopts all-weather testing, automated scene arrangement and automated data extraction technology to achieve efficient reliability testing.

[0089] All-weather testing: The controllability of the scenarios in the bench test system in the laboratory is guaranteed. There is no need for test personnel to monitor 24 hours a day, nor is there any need to worry about experimental fatigue safety like in actual road testing. 24-hour uninterrupted testing can be achieved regardless of day or night.

[0090] Construction and execution of test scenarios: In addition to all-weather uninterrupted testing, if the intelligent driving vehicle under test is running in an environment without a test scenario, reliability issues cannot be exposed. Therefore, a scene automation orchestration system is adopted to carry out automation advancement within a reasonable range. The road load simulation system, environmental simulation system, diversified variable information system and high-simulation interactive system work together to simulate real dynamic traffic flow, making the intelligent driving vehicle under test just like running in a real road environment for a long time. At the same time, the changes in weather environment and quantitative control of severe weather simulation according to time and weather presets can accelerate the stimulation of system reliability risks.

[0091] Automatic scene data extraction technology: All-weather testing will reproduce the test scenarios on the bench test system, and the scenario test data obtained will generate a large amount of redundant data, which will cause difficulties in processing. Therefore, an automatic scene data extraction technology is designed to intercept the key parameters of the scenario test of the intelligent driving vehicle under test during the test scenario operation period, perform feature analysis, and obtain the test evaluation results of the intelligent driving system. The test evaluation results are automatically output, which greatly reduces the difficulty of data processing.

[0092] The present invention designs a testing method with multi-level scenario coverage simulation, which realizes comprehensive and efficient expected functional safety and reliability testing of the intelligent driving system using a laboratory bench test system with an environmental simulation system, a diversified variable information system and a high-simulation interactive system.

[0093] Compared with the three-pillar test method commonly used for intelligent driving systems, it has the following advantages:

[0094] (1) Compared with simulation tests, this application significantly improves the experimental confidence while ensuring scene coverage.

[0095] (2) Compared with closed field testing, this application has extremely high scene controllability, reduces testing risks, and improves the efficiency of manual scene placement.

[0096] (3) Compared with actual road testing, this application significantly reduces the risk and uncontrollability of the test while ensuring the confidence of the scenario, and takes the approach of actively simulating scenarios to shorten the time required for sufficient testing.

[0097] In addition, in addition to testing the expected functional safety, this application can also use all-weather testing methods to fully verify the reliability of the intelligent driving system in a safe and controllable manner. This application integrates the advantages of the three-pillar verification method, draws on the strengths of many others, and can solve the current bottleneck problem of expected functional safety testing needs. It can not only achieve high scenario coverage of intelligent driving system testing, but also improve the expected functional safety performance and reliability of intelligent driving system testing.

[0098] In one embodiment, Figure 2 As shown, a test device for multi-level scenario coverage simulation is provided, comprising:

[0099] A combination unit is used to combine the road parameters, environmental parameters, information parameters and interaction parameters of the scene coverage to obtain the scene space;

[0100] A construction unit is used to construct a test scenario according to test requirements and scenario space;

[0101] The test unit is used to reproduce the test scenarios on the test bench system to obtain the test evaluation results of the intelligent driving system.

[0102] In one embodiment, a terminal is also provided, including a processor, an input device, an output device and a memory, wherein the processor, input device, output device and memory are interconnected, wherein the memory is used to store a computer program, and the computer program includes program instructions, and the processor is configured to call the program instructions to execute a test method for multi-level scenario coverage simulation described in the aforementioned embodiment.

[0103] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0104] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A test method for multi-level scenario coverage simulation, characterized in that: include: The road parameters, environment parameters, information parameters and interaction parameters of the scene coverage are combined to obtain the scene space; Construct a test scenario according to the test requirements and the scenario space; The test scenario is reproduced on the bench test system to obtain the test evaluation results of the intelligent driving system.

2. A multi-level scenario coverage simulation test method as claimed in claim 1, characterized in that: The road parameters are simulated by a road load simulation system, which includes a road structure, a road surface and road appendages, wherein: The road structure includes ordinary straight roads, bridges, tunnels, toll booths and intersections; The road surface includes road conditions, adhesion, road surface markings and road surface markings; The road accessories include speed bumps, steel plates and other construction accessories.

3. A multi-level scenario coverage simulation test method as claimed in claim 1, characterized in that: The environmental parameters are simulated by an environmental simulation system, which includes a weather simulation module and a light simulation module, wherein: The weather simulation module is used to simulate weather information, and the weather information includes rain, water accumulation, snow, snow accumulation, fog, other particulate matter, wind level and wind direction; The illumination simulation module is used to simulate illumination information, and the illumination information includes illumination intensity, illumination direction and color temperature.

4. A multi-level scenario coverage simulation test method as claimed in claim 1, characterized in that: The information parameters are simulated using a diversified variable information system, which includes signs and traffic lights, wherein: The signs and signage include warning signs, prohibition signs, instruction signs, wayfinding signs, tourist area signs, road construction signs, pole forms and sign deployment locations; The traffic light includes a light style, a light pole form and a light deployment location.

5. A multi-level scenario coverage simulation test method as claimed in claim 1, characterized in that: The interaction parameters are simulated using a high-simulation interaction system, which includes traffic participants and dynamic scenes, wherein: The traffic participants include adults, children, baby carriages, cars, medium-sized buses, large buses, small trucks, medium-sized trucks, large trucks, two-wheeled vehicles, three-wheeled vehicles, small special-shaped objects, medium special-shaped objects and large special-shaped objects; The dynamic scenes include natural driving scenes, dangerous edge scenes and accident scenes.

6. A multi-level scenario coverage simulation test method as claimed in claim 1, characterized in that: The scenario coverage also includes human misuse, which includes missed hearing, lack of comprehension, misunderstanding and incorrect operation by drivers and passengers.

7. A multi-level scenario coverage simulation test method as claimed in claim 1, characterized in that: It also includes using a scene automation orchestration system to construct a test scene according to the test requirements and scene space.

8. A multi-level scenario coverage simulation test method as claimed in claim 1, characterized in that: The test scenario is reproduced on the bench test system to obtain the test evaluation results of the intelligent driving system, including: The test scenario is reproduced on the bench test system using an all-weather test method to obtain scenario test data; The scene data automatic extraction technology is used to eliminate redundant data of the scene test data to obtain the scene test key parameters of the intelligent driving system; The key parameters of the scenario test of the intelligent driving system are characterized and analyzed to obtain the test evaluation results of the intelligent driving system.

9. A test device for multi-level scene coverage simulation, characterized in that: include: A combination unit is used to combine the road parameters, environmental parameters, information parameters and interaction parameters of the scene coverage to obtain the scene space; A construction unit, used to construct a test scene according to the test requirements and the scene space; The test unit is used to reproduce the test scenario on the bench test system to obtain the test evaluation results of the intelligent driving system.

10. A terminal, characterized in that: The method comprises a processor, an input device, an output device and a memory, wherein the processor, the input device, the output device and the memory are interconnected, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute the method according to any one of claims 1 to 8.

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