Methods, devices and electronic equipment for determining ASIL level information of autonomous vehicles

Abstract

This disclosure provides a method, apparatus, and electronic device for determining the ASIL level information of an autonomous vehicle, relating to the field of vehicle technology, particularly to intelligent transportation and autonomous driving within vehicle technology. The specific implementation includes: acquiring historical information of the autonomous vehicle within a preset driving area, and collecting controllability information of the autonomous vehicle's safety redundancy system to avoid risks; calculating target information based on the historical information; and determining the ASIL level information of the autonomous vehicle based on the target information and the controllability information.

Description

Technical Field

[0001] This disclosure relates to the field of vehicle technology, and more particularly to the fields of intelligent transportation and autonomous driving in vehicle technology, specifically to a method, apparatus, and electronic device for determining the ASIL level information of an autonomous vehicle. Background Technology

[0002] Currently, it is often necessary to assess the Automotive Safety Integrity Level (ASIL) information of a vehicle. In the current process of determining ASIL information, each piece of information is usually determined based on experience, and then the ASIL level information is determined based on different information. However, the determination results of each piece of information vary greatly in different scenarios. Summary of the Invention

[0003] This disclosure provides a method, apparatus, and electronic device for determining the ASIL level information of an autonomous vehicle.

[0004] According to a first aspect of this disclosure, a method for determining the ASIL level information of an autonomous vehicle is provided, comprising:

[0005] Acquire historical information of autonomous vehicles within a preset driving area, and collect controllability information of the autonomous vehicle's safety redundancy system to avoid risks;

[0006] Calculate the target information based on the historical information;

[0007] Based on the target information and the controllability information, the ASIL level information of the autonomous vehicle is determined.

[0008] According to a second aspect of this disclosure, an autonomous driving method is provided, comprising:

[0009] Acquire ASIL level information of autonomous vehicles, wherein the ASIL level information is determined based on target information and controllability information, the target information is calculated based on the historical information of the autonomous vehicle in a preset driving area, and the controllability information is the controllability information of the autonomous vehicle's safety redundancy system to avoid risks.

[0010] Automated driving is performed based on the ASIL level information.

[0011] According to a third aspect of this disclosure, an apparatus for determining the ASIL level information of an autonomous vehicle is provided, comprising:

[0012] The first acquisition module is used to acquire historical information of the autonomous vehicle within a preset driving area, and to collect controllability information of the autonomous vehicle's safety redundancy system to avoid risks.

[0013] The calculation module is used to calculate the target information based on the historical information;

[0014] The determination module is used to determine the ASIL level information of the autonomous vehicle based on the target information and the controllability information.

[0015] According to a fourth aspect of this disclosure, an autonomous driving device is provided, comprising:

[0016] The second acquisition module is used to acquire ASIL level information of autonomous vehicles. The ASIL level information is determined based on target information and controllability information. The target information is calculated based on the historical information of the autonomous vehicle in a preset driving area. The controllability information is the controllability information of the autonomous vehicle's safety redundancy system to avoid risks.

[0017] An autonomous driving module is used to perform autonomous driving based on the ASIL level information.

[0018] According to a fifth aspect of this disclosure, an electronic device is provided, comprising:

[0019] At least one processor; and

[0020] A memory that is communicatively connected to at least one processor; wherein,

[0021] The memory stores instructions that can be executed by at least one processor to enable the at least one processor to perform either the first aspect or the second aspect.

[0022] According to a sixth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions for causing a computer to perform any one of the methods of the first or second aspect.

[0023] According to a seventh aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements any one of the methods of the first or second aspect.

[0024] In this embodiment of the disclosure, target information can be calculated based on historical information of the autonomous vehicle within a preset driving area. Since the target information calculated based on historical information usually conforms to the patterns within the preset driving area, the target information is more consistent with the patterns within the preset driving area, thus achieving higher accuracy of the target information. Then, the ASIL level information of the autonomous vehicle is determined based on the target information and the controllability information of the safety redundancy system of the autonomous vehicle to avoid risks, thereby enhancing the accuracy of the ASIL level information and making the ASIL level information more targeted for risk assessment within the preset driving area.

[0025] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating the method for determining the ASIL level information of an autonomous vehicle according to an embodiment of this disclosure.

[0027] Figure 2 This is a flowchart illustrating the autonomous driving method provided in an embodiment of this disclosure;

[0028] Figure 3 This is a schematic diagram of the structure of the ASIL level information determination device for autonomous vehicles provided in this embodiment of the present disclosure;

[0029] Figure 4 This is a schematic diagram of the structure of the autonomous driving device provided in the embodiments of this disclosure;

[0030] Figure 5 This is a schematic block diagram of an example electronic device used to implement embodiments of the present disclosure. Detailed Implementation

[0031] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0032] See Figure 1 , Figure 1 A flowchart illustrating a method for determining the ASIL level information of an autonomous vehicle provided in this disclosure is shown below. Figure 1 As shown, the method for determining the ASIL level information of an autonomous vehicle includes the following steps:

[0033] Step S101: Obtain historical information of the autonomous vehicle within the preset driving area, and collect controllability information of the autonomous vehicle's safety redundancy system to avoid risks.

[0034] The preset driving area can refer to an area where autonomous vehicles frequently travel, or it can refer to an area to be surveyed, or it can refer to an area with high demand for vehicles.

[0035] The specific types of historical information are not limited here. For example, historical information may include at least one of the following: the number of autonomous vehicles, the number of collisions involving autonomous vehicles, the severity of the collisions involving autonomous vehicles, the frequency of collisions involving autonomous vehicles, and the time taken to resolve the damage caused by collisions involving autonomous vehicles.

[0036] It should be noted that different preset driving areas correspond to different historical information, meaning that the preset driving areas and historical information are one-to-one.

[0037] In addition, the aforementioned historical information can be collected by roadside equipment set up within a preset driving area.

[0038] Among them, autonomous vehicles can be equipped with a safety redundancy system. When the main system of an autonomous vehicle fails, the safety redundancy system can take over the autonomous vehicle. The controllability information of the safety redundancy system in avoiding risks can be understood as the takeover capability of the safety redundancy system in taking over the autonomous vehicle. The takeover capability of the safety redundancy system in taking over the autonomous vehicle can be divided into multiple levels.

[0039] For example, the takeover capability of a safety redundancy system in an autonomous vehicle can be divided into four levels: C0, C1, C2, and C3. C0 means the safety redundancy system can detect the risks of the autonomous vehicle (which may include system malfunctions or abnormal behavior) and completely avoid them, thus preventing collisions. C1 means the safety redundancy system can detect the risks and implement a minimum-risk strategy to reduce them, but cannot guarantee complete avoidance. C2 means the safety redundancy system can detect the risks but lacks the ability to autonomously reduce them. C3 means the safety redundancy system cannot detect the risks. In other words, the controllability information gradually decreases from C0 to C3.

[0040] Step S102: Calculate the target information based on the historical information.

[0041] The specific method for calculating target information based on historical information is not limited here. As an optional implementation method, a portion of information with a relevance greater than a first preset threshold to the autonomous vehicle can be extracted from the historical information. Then, feature extraction is performed on the aforementioned portion of information to calculate the target information. Similarly, feature extraction can also be performed on the controllability information. In this way, the features corresponding to the target information and the features corresponding to the controllability information can be input into the information determination model to determine the ASIL level information, thereby improving the intelligence level of the ASIL level information determination method and increasing the speed of ASIL level information determination.

[0042] It should be noted that the aforementioned information determination model can be a pre-trained neural network model used to determine ASIL level information. Specifically, the training method can be to extract features corresponding to the target information and features corresponding to the controllability information from the sample data, and input these features into the neural network model for training. When the error between the output ASIL level information and the actual value of the ASIL level information determined by the target information and controllability information in the sample data is less than a preset difference, the neural network can be determined as the information determination model.

[0043] As another optional implementation method, this implementation method differs from the above implementation method in that the confidence level of the aforementioned partial information can be greater than the second preset threshold. This ensures that the extracted partial information has a high correlation with the autonomous vehicle and also a high confidence level. Then, feature extraction is performed on the aforementioned partial information to obtain target information, thereby making the reliability of the finally calculated target information higher and reducing the consumption of computing resources.

[0044] The specific type of target information is not limited here. As an optional implementation method, the target information includes at least one of the following: risk severity information and probability information of exposure to risk.

[0045] Among them, risk severity information can also be called severity information, and the probability information of exposure to risk can also be called exposure probability information.

[0046] The risk severity information can be divided into four levels: S0, S1, S2 and S3. S0 is no harm, S1 is minor or limited harm, S2 is serious or life-threatening harm to passengers in the autonomous vehicle (who can survive), and S3 is harm that endangers the lives of the aforementioned passengers or fatal harm. That is, the harm (i.e. severity) gradually increases from S0 to S3.

[0047] The probability information of exposure to risk can be divided into five levels: E0, E1, E2, E3 and E4. E0 is almost impossible, E1 is very low probability, E2 is low probability, E3 is moderate probability, and E4 is high probability. That is, the probability gradually increases from E0 to E4.

[0048] This increases the diversity of target information, making the determination of ASIL level information more diverse and flexible, and improving the accuracy of the determination results.

[0049] It should be noted that the method for determining ASIL level information based on risk severity information, probability information of exposure to risk, and controllability information can be found in Table 1.

[0050] Table 1 ASIL Level Information Determination Table

[0051]

[0052] See Table 1, where QM stands for Quality Management, meaning there is no risk to worry about; A, B, C, and D represent ASIL level information, with ASIL levels increasing from A to D, indicating a higher level of risk; E1, E2, E3, and E4 represent the probability of exposure to risk; S1, S2, and S3 represent the severity of risk; and C1, C2, and C3 represent the controllability information.

[0053] It should be noted that the target information may also include at least one of the first target information and the second target information. The first target information may be equal to the product of the risk severity information and the scenario coefficient, and the second target information may be equal to the product of the probability information of exposure to the risk and the scenario coefficient.

[0054] The specific scene coefficients mentioned above are not limited here. Different scenes correspond to different scene coefficients. For example, scenes with more pedestrians and vehicles and narrower roads have higher scene coefficients, while scenes with fewer pedestrians and vehicles and wider roads have lower scene coefficients.

[0055] As an optional implementation, the risk severity information is determined based on at least one of the radial relative velocity in the collision direction, the collision risk test level of the autonomous vehicle, and the collision severity attribute coefficient. This approach offers greater flexibility in determining the risk severity information, and when determined based on multiple pieces of information, the result is more accurate, thereby further improving the accuracy of the determined ASIL level information.

[0056] As an optional implementation, the probability information of exposure to risk includes at least one of the following: scene frequency information and scene collision risk frequency information, wherein the scene frequency information is determined based on the continuous running time of the scene or the number of times the scene occurs, and the scene collision risk frequency information is determined based on the number of times the scene collision risk occurs.

[0057] The frequency information of a scene is determined based on the duration of the scene or the number of times the scene occurs. The specific method for determining the frequency information of a scene is as follows: The value corresponding to the duration of the scene or the number of times the scene occurs can be converted into the frequency information of the scene. The larger the value of the duration of the scene or the number of times the scene occurs, the larger the frequency information of the scene. Conversely, the smaller the value of the duration of the scene or the number of times the scene occurs, the smaller the frequency information of the scene.

[0058] The specific method for determining the collision risk frequency information of a scene based on the number of times a collision risk occurs in the scene can be described as follows: the more times a collision risk occurs in a scene, the greater the collision risk frequency information of the scene; conversely, the fewer times a collision risk occurs in a scene, the smaller the collision risk frequency information of the scene.

[0059] It should be noted that the unit of collision risk frequency information for a scenario can be kilometers per collision risk in that scenario. In other words, the collision risk frequency information for a scenario can be understood as the distance traveled by the autonomous vehicle each time a collision risk occurs in that scenario.

[0060] In this embodiment of the disclosure, the probability information of exposure to risk includes at least one of the following: scene frequency information and scene collision risk frequency information. In this way, since the probability information of exposure to risk includes a variety of information, it can reflect the possibility of exposure to risk in multiple dimensions, and thus make the ASIL level information determined based on the probability information of exposure to risk more accurate.

[0061] As an optional implementation, the frequency information of the scenario includes at least one of the following: first information and second information, wherein the first information is equal to the ratio of the duration of the scenario to the total operating time of the autonomous vehicle, and the second information is equal to the ratio of the duration of the scenario to the number of times the scenario occurs.

[0062] The duration of a scene refers to the duration from its appearance to its end, while the total operating time of an autonomous vehicle can refer to the total duration from its start to its stop.

[0063] For example, the above scenario could refer to a scenario where an autonomous vehicle is located at an intersection. In this case, the first piece of information could be equal to the ratio of the total time the autonomous vehicle spends at the intersection to the total operating time of the autonomous vehicle.

[0064] For example, the above scenario could refer to a situation where it rains while the autonomous vehicle is in motion. In this case, the first piece of information could be equal to the ratio of the duration of the rain while the autonomous vehicle is in motion to the total operating time of the autonomous vehicle.

[0065] Here, the scene maintenance distance refers to the distance traveled by the autonomous vehicle from the appearance to the end of the scene, and the scene occurrence count refers to the number of times the scene occurs within a certain period of time, or the scene occurrence count refers to the number of times the scene occurs within a certain travel distance. It should be noted that the unit of the second information can be kilometers per occurrence.

[0066] For example, the scenario could refer to an autonomous vehicle entering a lane when construction equipment appears in the lane. In this case, the second piece of information could be the ratio of the distance of the construction equipment in the lane when the autonomous vehicle enters the lane to the number of times obstacles appear in the lane when the autonomous vehicle enters the lane. The obstacles could be construction equipment or objects such as rocks.

[0067] In this embodiment of the disclosure, since the frequency information of the scene includes at least one of the following: first information and second information, that is, there are many types of frequency information of the scene, so that the frequency information of the scene can be reflected in multiple dimensions, and the ASIL level information determined based on the frequency information of the scene can be more accurate.

[0068] It should be noted that the specific scenarios in this disclosure are not limited here. For example, the scenario may refer to the scenario of an autonomous vehicle passing through an intersection, the scenario of an autonomous vehicle driving in the rain, the scenario of an autonomous vehicle driving in a lane with obstacles, and the scenario of an autonomous vehicle colliding with another vehicle at an intersection.

[0069] Step S103: Determine the ASIL level information of the autonomous vehicle based on the target information and the controllability information.

[0070] Among them, the target information and controllability information can be normalized, and the normalized target information and controllability information are assigned corresponding scores respectively.

[0071] The above normalization process can be understood as transforming the target information and controllability information into data of the same dimension. This makes it easier to determine the ASIL level information based on the target information and controllability information, thus improving computational efficiency.

[0072] As an optional implementation, determining the ASIL level information of the autonomous vehicle based on the target information and the controllability information includes:

[0073] Calculate the sum of the risk severity information, the probability information of exposure to the risk, and the controllability information of avoiding the risk, and determine the ASIL level information corresponding to the sum.

[0074] Specifically, the correspondence between the sum of risk severity information, probability of exposure to risk, and controllability of risk avoidance information and ASIL level information can be preset. For example: if the sum of the above risk severity information, probability of exposure to risk, and controllability of risk avoidance information falls within the first interval, then the ASIL level information can be determined as level A; if the sum of the above risk severity information, probability of exposure to risk, and controllability of risk avoidance information falls within the second interval, then the ASIL level information can be determined as level B; if the sum of the above risk severity information, probability of exposure to risk, and controllability of risk avoidance information falls within the third interval, then the ASIL level information can be determined as level C; if the sum of the above risk severity information, probability of exposure to risk, and controllability of risk avoidance information falls within the fourth interval, then the ASIL level information can be determined as level D.

[0075] In this embodiment of the disclosure, the sum of risk severity information, probability information of exposure to risk, and controllability information of risk avoidance is calculated, and the corresponding ASIL level information is determined. In this way, the ASIL level information can be accurately and quickly determined based on the correspondence between the sum of risk severity information, probability information of exposure to risk, and controllability information of risk avoidance and the ASIL level information.

[0076] As an optional implementation, determining the ASIL level information of the autonomous vehicle based on the target information and the controllability information includes:

[0077] Calculate the weighted sum of the risk severity information, the probability information of exposure to the risk, and the controllability information of avoiding the risk, and determine the ASIL level information corresponding to the weighted sum.

[0078] When calculating the weighted sum of risk severity information, probability information of exposure to risk, and controllability information of risk avoidance, the weight corresponding to each piece of information can be different, and the weight corresponding to each piece of information can be dynamically adjusted.

[0079] For example, if any one of the following information—risk severity, probability of exposure to risk, or controllability of risk avoidance—gradually increases over several consecutive periods, the weight of that information can be increased. This increases the weight of that information in determining the ASIL level, making the ASIL level information more clearly reflect this increasing trend and further enhancing the accuracy of the ASIL level information.

[0080] In this embodiment of the disclosure, ASIL level information is determined by weighting and summing risk severity information, probability information of exposure to risk, and controllability information of risk avoidance, thereby increasing the diversity and flexibility of the method for determining ASIL level information. At the same time, since the weights of the above information can be adjusted, the accuracy of ASIL level information can be further enhanced.

[0081] It should be noted that, as an optional implementation method, after determining the ASIL level information of the autonomous vehicle, the autonomous vehicle can also be identified as the autonomous driving strategy corresponding to the ASIL level information.

[0082] Different ASIL levels correspond to different autonomous driving strategies. When the ASIL level is high, it indicates that the autonomous vehicle is in a high-risk situation. In this case, the autonomous driving strategy prioritizes safety, which means the autonomous driving strategy can be low-speed. Similarly, when the ASIL level is low, it indicates that the autonomous vehicle is in a low-risk situation. In this case, the autonomous driving strategy prioritizes efficiency, which means the autonomous driving strategy can be high-speed.

[0083] In this way, by determining ASIL level information and then using that information to determine the corresponding autonomous driving strategy, the autonomous driving strategy of autonomous vehicles becomes more flexible and intelligent.

[0084] It should be noted that after determining the ASIL level information of an autonomous vehicle, the ASIL level information can be shared with servers, other electronic devices, or other autonomous vehicles, thereby enhancing the sharing effect of the ASIL level information. At the same time, when the ASIL level information is shared with other autonomous vehicles, other autonomous vehicles can determine the corresponding autonomous driving strategy based on the ASIL level information, thereby enhancing the adjustment effect of the autonomous driving strategy.

[0085] In this embodiment of the disclosure, through steps S101 to S103, target information can be calculated based on the historical information of the autonomous vehicle in the preset driving area. Since the target information calculated based on the historical information usually conforms to the pattern in the preset driving area, the target information is more consistent with the pattern in the preset driving area, thus the accuracy of the target information is high. Then, based on the target information and the controllability information of the safety redundancy system of the autonomous vehicle to avoid risks, the ASIL level information of the autonomous vehicle is determined, thereby enhancing the accuracy of the ASIL level information and making the ASIL level information more targeted for risk assessment in the preset driving area.

[0086] See Figure 2 , Figure 2 A flowchart of an autonomous driving method provided in this disclosure embodiment, such as Figure 2 As shown, the autonomous driving method includes the following steps:

[0087] Step S201: Obtain ASIL level information of the autonomous vehicle, wherein the ASIL level information is determined based on target information and controllability information. The target information is calculated based on the historical information of the autonomous vehicle in the preset driving area, and the controllability information is the controllability information of the autonomous vehicle's safety redundancy system to avoid risks.

[0088] The ASIL level information, target information, controllability information, preset driving area, historical information, and safety redundancy system can all be found in the relevant descriptions in the above embodiments, and will not be repeated here.

[0089] Step S202: Perform autonomous driving based on the ASIL level information.

[0090] For details on autonomous driving based on ASIL level information, please refer to the relevant descriptions of adjusting autonomous driving strategies based on ASIL level information in the above embodiments.

[0091] In addition, when autonomous driving is based on ASIL level information, the ASIL level information can also be displayed on the display screen of the autonomous vehicle, thereby alerting pedestrians and other vehicles in the vicinity and reducing the possibility of collision risk for the autonomous vehicle.

[0092] In this embodiment of the disclosure, by steps S201 to S202, the ASIL level information of the autonomous vehicle can be obtained, and autonomous driving can be performed based on the ASIL level information, thereby enhancing the flexibility and intelligence of the autonomous driving of the autonomous vehicle.

[0093] See Figure 3 , Figure 3 This is a schematic diagram of the structure of an ASIL level information determination device for an autonomous vehicle provided in an embodiment of this disclosure, as shown below. Figure 3 As shown, the ASIL level information determination device 300 for autonomous vehicles includes:

[0094] The first acquisition module 301 is used to acquire historical information of the autonomous vehicle in a preset driving area, and to collect controllability information of the autonomous vehicle's safety redundancy system to avoid risks.

[0095] Calculation module 302 is used to calculate target information based on the historical information;

[0096] The determination module 303 is used to determine the ASIL level information of the autonomous vehicle based on the target information and the controllability information.

[0097] Optionally, the target information includes at least one of the following: risk severity information and probability information of exposure to the risk.

[0098] Optionally, the determining module 303 is further configured to:

[0099] Calculate the sum of the risk severity information, the probability information of exposure to the risk, and the controllability information of risk avoidance, and determine the ASIL level information corresponding to the sum; or

[0100] Calculate the weighted sum of the risk severity information, the probability information of exposure to the risk, and the controllability information of avoiding the risk, and determine the ASIL level information corresponding to the weighted sum.

[0101] Optionally, the probability information of exposure to risk includes at least one of the following: scene frequency information and scene collision risk frequency information, wherein the scene frequency information is determined based on the continuous running time of the scene or the number of times the scene occurs, and the scene collision risk frequency information is determined based on the number of times the scene collision risk occurs.

[0102] Optionally, the frequency information of the scenario includes at least one of the following: first information and second information, wherein the first information is equal to the ratio of the duration of the scenario to the total operating time of the autonomous vehicle, and the second information is equal to the ratio of the duration of the scenario to the number of times the scenario occurs.

[0103] Optionally, the risk severity information is determined based on at least one of the radial relative velocity in the collision direction, the collision risk test level of the autonomous vehicle, and the collision severity attribute coefficient.

[0104] The ASIL level information determination device 300 for autonomous vehicles provided in this disclosure can implement all the processes implemented in the embodiments of the ASIL level information determination method for autonomous vehicles, and can achieve the same beneficial effects. To avoid repetition, it will not be described again here.

[0105] See Figure 4 , Figure 4 This is a schematic diagram of the structure of an autonomous driving device provided in an embodiment of the present disclosure, such as... Figure 4 As shown, the autonomous driving device 400 includes:

[0106] The second acquisition module 401 is used to acquire ASIL level information of the autonomous vehicle. The ASIL level information is determined based on target information and controllability information. The target information is calculated based on the historical information of the autonomous vehicle in a preset driving area. The controllability information is the controllability information of the autonomous vehicle's safety redundancy system to avoid risks.

[0107] The autonomous driving module 402 is used to perform autonomous driving based on the ASIL level information.

[0108] The autonomous driving device 400 provided in this disclosure can implement all the processes implemented in the autonomous driving method embodiments and can achieve the same beneficial effects. To avoid repetition, it will not be described again here.

[0109] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0110] Figure 5 A schematic block diagram of an example electronic device 500 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0111] like Figure 5As shown, device 500 includes a computing unit 501, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 502 or a computer program loaded from storage unit 508 into random access memory (RAM) 503. RAM 503 may also store various programs and data required for the operation of device 500. The computing unit 501, ROM 502, and RAM 503 are interconnected via bus 504. Input / output (I / O) interface 505 is also connected to bus 504.

[0112] Multiple components in device 500 are connected to I / O interface 505, including: input unit 506, such as keyboard, mouse, etc.; output unit 507, such as various types of monitors, speakers, etc.; storage unit 508, such as disk, optical disk, etc.; and communication unit 509, such as network card, modem, wireless transceiver, etc. Communication unit 509 allows device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0113] The computing unit 501 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as the ASIL level information determination method for an autonomous vehicle or an autonomous driving method. For example, in some embodiments, the ASIL level information determination method for an autonomous vehicle or an autonomous driving method can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed on device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by the computing unit 501, one or more steps of the ASIL level information determination method for an autonomous vehicle or an autonomous driving method described above can be performed. Alternatively, in other embodiments, the computing unit 501 may be configured by any other suitable means (e.g., by means of firmware) to perform an ASIL level information determination method or an autonomous driving method for an autonomous vehicle.

[0114] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0115] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0116] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0117] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0118] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0119] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0120] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0121] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for determining the ASIL level information of an autonomous vehicle, comprising: The system acquires historical information about autonomous vehicles within a preset driving area, and collects controllability information about the safety redundancy system of the autonomous vehicles to avoid risks. The historical information includes at least the number of autonomous vehicles and the number of collision accidents involving autonomous vehicles. Calculate the target information based on the historical information; Based on the target information and the controllability information, the safety integrity level (ASIL) of the autonomous vehicle is determined.

2. The method according to claim 1, wherein, The target information includes at least one of the following: Information on the severity of the risk and the probability of exposure to the risk.

3. The method according to claim 2, wherein, Determining the ASIL level information of the autonomous vehicle based on the target information and the controllability information includes: Calculate the sum of the risk severity information, the probability information of exposure to the risk, and the controllability information of risk avoidance, and determine the ASIL level information corresponding to the sum; or Calculate the weighted sum of the risk severity information, the probability information of exposure to the risk, and the controllability information of avoiding the risk, and determine the ASIL level information corresponding to the weighted sum.

4. The method according to claim 2, wherein, The probability information of exposure to risk includes at least one of the following: scene frequency information and scene collision risk frequency information, wherein the scene frequency information is determined based on the duration of the scene's continuous operation or the number of times the scene occurs, and the scene collision risk frequency information is determined based on the number of times the scene's collision risk occurs.

5. The method according to claim 4, wherein, The frequency information of the scenario includes at least one of the following: first information and second information, wherein the first information is equal to the ratio of the duration of the scenario to the total operating time of the autonomous vehicle, and the second information is equal to the ratio of the duration of the scenario to the number of times the scenario occurs.

6. The method according to any one of claims 2 to 5, wherein, The risk severity information is determined based on at least one of the radial relative velocity in the direction of collision, the collision risk test level of the autonomous vehicle, and the collision severity attribute coefficient.

7. An autonomous driving method, comprising: The ASIL level information of the autonomous vehicle is obtained, wherein the ASIL level information is determined based on target information and controllability information. The target information is calculated based on the historical information of the autonomous vehicle in a preset driving area. The controllability information is the controllability information of the safety redundancy system of the autonomous vehicle to avoid risks. The historical information includes at least the number of autonomous vehicles and the number of collision accidents involving autonomous vehicles. Automated driving is performed based on the ASIL level information.

8. An ASIL level information determination device for an autonomous vehicle, comprising: The first acquisition module is used to acquire historical information of autonomous vehicles within a preset driving area, and to collect controllability information of the safety redundancy system of the autonomous vehicles to avoid risks. The historical information includes at least the number of autonomous vehicles and the number of collision accidents involving autonomous vehicles. The calculation module is used to calculate the target information based on the historical information; The determination module is used to determine the ASIL level information of the autonomous vehicle based on the target information and the controllability information.

9. The apparatus according to claim 8, wherein, The target information includes at least one of the following: Information on the severity of the risk and the probability of exposure to the risk.

10. The apparatus according to claim 9, wherein, The determining module is further configured to: Calculate the sum of the risk severity information, the probability information of exposure to the risk, and the controllability information of avoiding the risk, and determine the ASIL level information corresponding to the sum; or Calculate the weighted sum of the risk severity information, the probability information of exposure to the risk, and the controllability information of avoiding the risk, and determine the ASIL level information corresponding to the weighted sum.

11. The apparatus according to claim 9, wherein, The probability information of exposure to risk includes at least one of the following: scene frequency information and scene collision risk frequency information, wherein the scene frequency information is determined based on the duration of the scene's continuous operation or the number of times the scene occurs, and the scene collision risk frequency information is determined based on the number of times the scene's collision risk occurs.

12. The apparatus according to claim 11, wherein, The frequency information of the scenario includes at least one of the following: first information and second information, wherein the first information is equal to the ratio of the duration of the scenario to the total operating time of the autonomous vehicle, and the second information is equal to the ratio of the duration of the scenario to the number of times the scenario occurs.

13. The apparatus according to any one of claims 9 to 12, wherein, The risk severity information is determined based on at least one of the radial relative velocity in the direction of collision, the collision risk test level of the autonomous vehicle, and the collision severity attribute coefficient.

14. An autonomous driving device, comprising: The second acquisition module is used to acquire ASIL level information of autonomous vehicles. The ASIL level information is determined based on target information and controllability information. The target information is calculated based on the historical information of the autonomous vehicle in a preset driving area. The controllability information is the controllability information of the safety redundancy system of the autonomous vehicle to avoid risks. The historical information includes at least the number of autonomous vehicles and the number of collision accidents involving autonomous vehicles. An autonomous driving module is used to perform autonomous driving based on the ASIL level information.

15. An electronic device comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to perform the method of any one of claims 1-6, or enable the at least one processor to perform the method of claim 7.

16. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6, or the computer instructions are used to cause the computer to perform the method according to claim 7.

17. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-6, or, when executed by a processor, implements the method according to claim 7.

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