Vehicle driving risk degree estimation method, device and equipment, and storage medium

Abstract

The application provides a vehicle driving risk degree estimation method, device and equipment, and a storage medium. The method comprises the following steps: obtaining driving information of a vehicle during driving for a period of time, wherein the driving information comprises a proportion of an automatic driving time length in the period of time and a frequency of switching from automatic driving to manual driving in the period of time; determining a first risk degree of automatic driving of the vehicle according to the proportion of the automatic driving time length in the period of time; determining a second risk degree of automatic driving of the vehicle according to the frequency of switching from automatic driving to manual driving in the period of time; and determining a risk degree of automatic driving of the vehicle according to the first risk degree and the second risk degree. The application can perform safety testing on a vehicle with an automatic driving function, and improve the testing effect of the vehicle with the automatic driving function.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and in particular to a method, device and equipment, and storage medium for estimating the risk of vehicle driving, which can be applied to scenarios such as ports, highways, logistics, mines, closed parks or urban traffic. Background Technology

[0002] Autonomous driving mode is an intelligent vehicle system that enables driverless operation through an onboard computer system. It relies on the collaborative efforts of artificial intelligence, visual computing, radar, monitoring devices, and global positioning systems to allow the computer to automatically and safely operate the motor vehicle without any human intervention.

[0003] Before vehicles leave the factory, it is necessary to assess and predict their safety level regarding autonomous driving, in order to inspect and repair vehicles that do not meet the factory requirements. How to conduct safety tests on vehicles with autonomous driving capabilities and improve the effectiveness of such tests remains a question that needs to be considered. Summary of the Invention

[0004] This application provides a method, apparatus, device, and storage medium for estimating the driving hazard of a vehicle, used to conduct safety tests on vehicles with autonomous driving functions, thereby improving the testing results of vehicles with autonomous driving functions.

[0005] On the one hand, this application provides a method for estimating the driving hazard of a vehicle, the method comprising:

[0006] The vehicle obtains driving information during a certain period of time, including: the percentage of time spent in autonomous driving during the period of time, and the frequency of switching from autonomous driving to manual driving during the period of time.

[0007] The first level of danger of vehicle autonomous driving is determined based on the proportion of the autonomous driving time within the specified time period.

[0008] The second degree of danger of the vehicle's autonomous driving is determined based on the frequency of switching from autonomous driving to manual driving within the aforementioned period of time.

[0009] The risk level of the vehicle's autonomous driving is determined based on the first risk level and the second risk level.

[0010] In one embodiment, determining the first level of danger of vehicle autonomous driving based on the proportion of autonomous driving time within the time period includes:

[0011] When the proportion of the autonomous driving time within the specified time period is less than a preset proportion, the vehicle driving state is determined to be the first state.

[0012] Based on the first state and the constraint relationship between the vehicle driving state and the vehicle driving hazard level, the first hazard level of the vehicle's autonomous driving is determined.

[0013] In one embodiment, determining the second level of danger of the vehicle's autonomous driving based on the frequency of switching from autonomous driving to manual driving within the specified time period includes:

[0014] When the frequency is greater than the preset frequency, the vehicle driving state is determined to be the second state;

[0015] The second level of danger for the autonomous driving of the vehicle is determined based on the second state and the constraint relationship between the vehicle driving state and the vehicle driving hazard level.

[0016] In one embodiment, the driving information further includes autonomous driving level information, and determining the first level of danger of the vehicle's autonomous driving based on the proportion of autonomous driving time within the specified time period includes:

[0017] The vehicle driving status is determined based on the autonomous driving level information and the proportion of autonomous driving time within the specified time period.

[0018] The first level of danger for autonomous driving is determined based on the vehicle driving state and the constraint relationship between the vehicle driving state and the vehicle driving hazard level.

[0019] In one embodiment, determining the first level of danger of vehicle autonomous driving based on the autonomous driving level information and the proportion of autonomous driving time within the time period includes:

[0020] When the autonomous driving level information is Level 1 autonomous driving, and the proportion of the autonomous driving time within the time period is less than the first preset proportion, the vehicle driving state is determined to be the third state.

[0021] When the autonomous driving level information is Level 2 autonomous driving, and the proportion of the autonomous driving time within the time period is less than the second preset proportion, the vehicle driving state is determined to be the fourth state.

[0022] When the autonomous driving level information is Level 3 autonomous driving, and the proportion of autonomous driving time within the specified time period is less than a third preset proportion, the vehicle driving state is determined to be the fifth state.

[0023] In one embodiment, the driving information further includes the type of autonomous driving function. When the autonomous driving level information is Level 1 autonomous driving, and the proportion of autonomous driving time within the specified time period is less than a first preset proportion, determining the vehicle driving state as a third state includes:

[0024] When the autonomous driving level information is Level 1 autonomous driving, the autonomous driving function types include a first preset function type, and the autonomous driving time accounts for less than the first preset percentage within the time period, the vehicle driving state is determined to be the third state.

[0025] The method further includes:

[0026] When the autonomous driving level information is Level 1 autonomous driving, the autonomous driving function types include a second preset function type, and the autonomous driving time accounts for less than a first preset percentage within the time period, the vehicle driving state is determined to be the sixth state.

[0027] In one embodiment, determining the vehicle driving state to be in the fourth state when the autonomous driving level information is level two autonomous driving and the proportion of autonomous driving time within the time period is less than a second preset proportion includes:

[0028] When the autonomous driving level information is Level 2 autonomous driving, the autonomous driving function types include a third preset function type, and the autonomous driving time accounts for less than the second preset percentage within the time period, the vehicle driving state is determined to be the fourth state.

[0029] The method further includes:

[0030] When the autonomous driving level information is Level 2 autonomous driving, the autonomous driving function types include a fourth preset function type, and the autonomous driving time accounts for less than a second preset percentage within the time period, the vehicle driving state is determined to be the seventh state.

[0031] In one embodiment, determining the vehicle driving state to the fifth state when the autonomous driving level information is Level 3 autonomous driving and the proportion of autonomous driving time within the time period is less than a third preset proportion includes:

[0032] When the autonomous driving level information is Level 3 autonomous driving, the autonomous driving function types include a fifth preset function type, and the autonomous driving time accounts for less than the third preset percentage within the time period, the vehicle driving state is determined to be the fifth state.

[0033] The method further includes:

[0034] When the autonomous driving level information is Level 3 autonomous driving, the autonomous driving function types include a sixth preset function type, and the autonomous driving time accounts for less than a third preset percentage within the time period, the vehicle driving state is determined to be the eighth state.

[0035] On the other hand, this application provides a vehicle driving hazard estimation device, comprising:

[0036] The acquisition module is used to acquire driving information of the vehicle during a period of time. The driving information includes: the proportion of autonomous driving time during the period of time, and the frequency of switching from autonomous driving to manual driving during the period of time.

[0037] The processing module is used to determine the first degree of danger of the vehicle's autonomous driving based on the proportion of the autonomous driving time within the time period.

[0038] The processing module is also used to determine the second degree of danger of the vehicle's autonomous driving based on the frequency of switching from autonomous driving to manual driving within the specified time period;

[0039] The processing module is also used to determine the risk level of the vehicle's autonomous driving based on the first risk level and the second risk level.

[0040] On the other hand, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0041] The memory stores computer-executed instructions;

[0042] The processor executes computer execution instructions stored in the memory to implement the vehicle driving hazard estimation method as described in the first aspect.

[0043] On the other hand, this application provides a computer-readable storage medium storing computer-executable instructions that, when executed, cause a computer to perform the vehicle driving hazard estimation method as described in the first aspect.

[0044] On the other hand, this application provides a computer program product, including a computer program that, when executed by a processor, implements the vehicle driving hazard estimation method as described in the first aspect.

[0045] The vehicle driving hazard estimation method provided in this application acquires driving information of a vehicle during a certain period of time. This driving information includes the proportion of autonomous driving time within the time period and the frequency of switching from autonomous driving to manual driving within the time period. A first hazard level of the vehicle's autonomous driving is determined based on the proportion of autonomous driving time within the time period; a longer proportion indicates a higher safety factor for autonomous driving and a lower hazard level. A second hazard level of the vehicle's autonomous driving is determined based on the frequency of switching from autonomous driving to manual driving within the time period. A higher frequency of switching from autonomous driving to manual driving indicates a lower safety factor for autonomous driving and a higher hazard level. Determining the hazard level of the vehicle's autonomous driving based on the first and second hazard levels allows for a more comprehensive and accurate understanding of the safety level of autonomous driving, improving the testing effectiveness for vehicles with autonomous driving capabilities. Attached Figure Description

[0046] Figure 1 A schematic diagram illustrating an application scenario of the vehicle driving hazard estimation method provided in this application;

[0047] Figure 2 A flowchart illustrating a vehicle driving hazard estimation method provided in one embodiment of this application;

[0048] Figure 3 A schematic diagram illustrating the interaction between an in-vehicle system and a cloud server, provided as an embodiment of this application;

[0049] Figure 4 A schematic diagram of a vehicle driving hazard estimation device provided in one embodiment of this application;

[0050] Figure 5 A schematic diagram of an electronic device provided for one embodiment of this application. Detailed Implementation

[0051] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0052] Autonomous driving mode is an intelligent vehicle system that enables driverless operation through an onboard computer system. It relies on the collaborative efforts of artificial intelligence, computer vision, radar, monitoring devices, and a global positioning system to allow the computer to automatically and safely operate the vehicle without any human intervention. Before a vehicle leaves the factory, its safety level for autonomous driving needs to be assessed and predicted to allow for the repair of vehicles that do not meet factory requirements.

[0053] Generally, autonomous driving functions are tested manually by driving the vehicle. This method is subjective and not very accurate. How to conduct safety tests on vehicles with autonomous driving capabilities remains a question that needs to be considered.

[0054] Based on this, this application provides a method for estimating the driving hazard of a vehicle, including acquiring driving information of the vehicle during a certain period of time. This driving information includes: the proportion of autonomous driving time during that period, and the frequency of switching from autonomous driving to manual driving during that period. A first hazard level of the vehicle's autonomous driving is determined based on the proportion of autonomous driving time during that period. A longer proportion indicates a higher safety factor for autonomous driving and a lower hazard level. A second hazard level of the vehicle's autonomous driving is determined based on the frequency of switching from autonomous driving to manual driving during that period. A higher frequency of switching from autonomous driving to manual driving indicates a lower safety factor for autonomous driving and a higher hazard level. Determining the hazard level of the vehicle's autonomous driving based on the first and second hazard levels allows for a more comprehensive and accurate understanding of the safety level of autonomous driving, improving the testing effectiveness for vehicles with autonomous driving capabilities.

[0055] The vehicle driving hazard estimation method provided in this application is applied to electronic devices, such as computers, backend servers, and cloud servers. Figure 1 This diagram illustrates one application of the vehicle driving hazard estimation method. In the diagram, a cloud server acquires driving information of the vehicle over a period of time through the in-vehicle system. This information includes the percentage of time spent in autonomous driving within that period and the frequency of switching from autonomous to manual driving during that period. A first level of hazard for autonomous driving is determined based on the percentage of time spent in autonomous driving within that period. A second level of hazard for autonomous driving is determined based on the frequency of switching from autonomous to manual driving during that period. The final hazard level for autonomous driving is determined based on both the first and second hazard levels.

[0056] Please see Figure 2 One embodiment of this application provides a method for estimating the driving hazard of a vehicle, including:

[0057] S210, acquire driving information of the vehicle during a period of time, including: the percentage of time spent in autonomous driving during that period, and the frequency of switching from autonomous driving to manual driving during that period.

[0058] It can randomly acquire driving information of a vehicle over a period of time, or it can acquire driving information of a vehicle over a preset time period. The preset time period is, for example, a time when the weather conditions are unfavorable, such as cloudy or foggy days.

[0059] The driving information of the vehicle during a certain period of time refers to the driving information of the vehicle after the autonomous driving function is activated during a certain period of time.

[0060] This driving information includes the percentage of time spent in autonomous driving within that time period. When the vehicle's autonomous driving function is activated, the higher the percentage of time spent in autonomous driving within that time period, the less time requires human intervention, which indicates a higher safety level for the vehicle's autonomous driving.

[0061] S220 determines the first level of danger of vehicle autonomous driving based on the proportion of the autonomous driving time within that period.

[0062] When a vehicle's autonomous driving function is activated, the larger the proportion of time spent in autonomous driving within that timeframe, the less time requires human intervention, indicating a higher safety level for the vehicle's autonomous driving. Therefore, the primary level of danger associated with autonomous driving can be determined based on the proportion of time spent in autonomous driving within that timeframe.

[0063] In an optional embodiment, when the proportion of autonomous driving time within a certain period is less than a preset proportion, the vehicle driving state is determined to be a first state. This first state, for example, is a state where the vehicle cannot normally activate its autonomous driving function.

[0064] After determining the vehicle's driving state, the constraint relationship between the vehicle's driving state and its driving hazard level is obtained. This constraint relationship limits the driving hazard level corresponding to the vehicle's driving state and can be set by the tester according to actual needs. For example, if more attention is paid to the percentage of time spent in autonomous driving, the hazard level corresponding to the vehicle state obtained based on the percentage of time spent in autonomous driving can be set to high hazard.

[0065] Based on the first state and the constraint relationship between the vehicle driving state and the vehicle driving hazard level, the first hazard level of the vehicle's autonomous driving is determined.

[0066] In an optional embodiment, the driving information further includes autonomous driving level information, and the vehicle driving state can be determined based on the autonomous driving level information and the proportion of autonomous driving time within that period. Then, a first level of danger for the vehicle's autonomous driving is determined based on the vehicle driving state and the constraint relationship between the vehicle driving state and the vehicle driving hazard level.

[0067] For example, the autonomous driving level information is L0-L5.

[0068] L0 (No Automation): The car is fully operated by a human driver, with the assistance of warning and protection systems during driving.

[0069] L1 (Driver Support): Provides driver support for one of the following actions through the driving environment: steering wheel operation and acceleration / deceleration. All other driving actions are performed by a human driver.

[0070] L2 (Partial Automation): Provides driving support for multiple operations of steering wheel and acceleration / deceleration through the driving environment, while other driving actions are performed by a human driver.

[0071] Level 3 (Conditional Automation): All driving operations are performed by the autonomous driving system. The human driver provides appropriate responses based on system requests.

[0072] Level 4 (High Automation): All driving operations are performed by an autonomous driving system. Human drivers are not necessarily required to respond to all system requests, depending on road and environmental conditions.

[0073] Level 5 (Fully Automated): All driving operations are performed by an autonomous driving system. A human driver takes over when possible. It can drive in all road and environmental conditions.

[0074] When the autonomous driving level information is Level 1 autonomous driving, and the proportion of autonomous driving time within a certain period is less than a first preset proportion, the vehicle driving state is determined to be in the third state. When the autonomous driving level information is Level 2 autonomous driving, and the proportion of autonomous driving time within a certain period is less than a second preset proportion, the vehicle driving state is determined to be in the fourth state. When the autonomous driving level information is Level 3 autonomous driving, and the proportion of autonomous driving time within a certain period is less than a third preset proportion, the vehicle driving state is determined to be in the fifth state.

[0075] The first level of autonomous driving is, for example, Level 1; the second level of autonomous driving is, for example, Level 2; and the third level of autonomous driving is, for example, Level 3.

[0076] The first preset percentage can be less than the second preset percentage. For example, the first preset percentage might be 60%, and the second preset percentage might be 80%. Therefore, at Level 1, if the autonomous driving time percentage is below 60%, the vehicle's driving status can be determined. At Level 2, if the autonomous driving time percentage is below 80%, the vehicle's driving status can be determined. Level 2 corresponds to a higher level of autonomous driving intelligence, allowing the vehicle to perform more autonomous driving functions. Therefore, when the autonomous driving time percentage is below 80%, it is necessary to confirm the vehicle's driving status to assess the level of hazard.

[0077] The second preset percentage can be less than the third preset percentage. For example, the second preset percentage might be 80%, and the third preset percentage might be 90%. At Level 2, if the autonomous driving time percentage is below 80%, the vehicle's driving status can be determined. At Level 3, if the autonomous driving time percentage is below 90%, the vehicle's driving status can be determined. Level 3 corresponds to a higher level of intelligence in autonomous driving, allowing it to perform more autonomous driving functions. Therefore, when the autonomous driving time percentage is below 90%, it is necessary to confirm the vehicle's driving status to assess the level of hazard.

[0078] In an optional embodiment, the driving information may also include the type of autonomous driving function.

[0079] For example, the types of autonomous driving functions corresponding to Level 0 include Lane Departure Warning (LDW) and Forward Collision Warning (FCW).

[0080] For example, the types of autonomous driving functions corresponding to L1 include Adaptive Cruise (ACC) and Automatic Emergency Braking (AEB).

[0081] For example, the autonomous driving functions corresponding to L2 include High-speed Automatic Cruise (HWA), Traffic Jam Assist (TJA), and Pilot Assisted Driving (ICA).

[0082] For example, the autonomous driving functions corresponding to L3 include Autopilot function on highway (HWP) and Autopilot in traffic jams (TJP).

[0083] For example, Level 4 autonomous driving functions include Highway Automated Driving (HWP), Traffic Jam Automated Driving (TJP), and Autonomous Valet Parking (AVP).

[0084] Specifically, when determining the vehicle's driving state to be in the third state, it can be when the autonomous driving level information is Level 1 autonomous driving, the autonomous driving function types include a first preset function type, and the autonomous driving time accounts for less than the first preset percentage within a certain period. For example, Level 1 autonomous driving is L1, and the first preset function type is, for example, adaptive cruise control (ACC). Based on this, when the autonomous driving time accounts for less than the first preset percentage within a certain period, the vehicle's driving state is determined to be in the third state.

[0085] When the autonomous driving level information is Level 1 autonomous driving, the autonomous driving function types include a second preset function type, and the autonomous driving time accounts for a less than a first preset percentage within a certain period, the vehicle driving state is determined to be in the sixth state. For example, Level 1 autonomous driving is L1, and the second preset function type is, for example, Automatic Emergency Braking (AEB). Based on this, if the autonomous driving time accounts for a less than a first preset percentage within a certain period, the vehicle driving state is determined to be in the third state; otherwise, the vehicle driving state is determined to be in the sixth state.

[0086] If the first preset function is set to be more important than the second preset function, then the third state is worse than the sixth state, and the danger level of the vehicle's autonomous driving corresponding to the third state is higher than the danger level of the vehicle's autonomous driving corresponding to the sixth state.

[0087] Specifically, when determining the vehicle's driving state to be in the fourth state, it can be when the autonomous driving level information is Level 2 autonomous driving, the autonomous driving function types include a third preset function type, and the autonomous driving time accounts for a less than a second preset percentage within a certain period. For example, Level 2 autonomous driving, and the second preset function type is, for example, Highway Adaptive Cruise Control (HWA). Based on this, the vehicle's driving state is determined to be in the fourth state when the autonomous driving time accounts for a less than the second preset percentage within that period.

[0088] When the autonomous driving level information is Level 2 autonomous driving, the autonomous driving function types include a third preset function type, and the autonomous driving time accounts for less than a second preset percentage within a certain period, the vehicle driving state is determined to be the seventh state. For example, Level 2 autonomous driving, and the third preset function type is Traffic Jam Assist (TJA). Based on this, when the autonomous driving time accounts for less than a second preset percentage within a certain period, the vehicle driving state is determined to be the seventh state.

[0089] If the third preset function is set to be more important than the fourth preset function, then the fourth state is worse than the seventh state, and the danger level of the vehicle's autonomous driving corresponding to the fourth state is higher than that of the vehicle's autonomous driving corresponding to the seventh state.

[0090] Specifically, when determining the vehicle's driving state to be in the fifth state, it can be when the autonomous driving level information is Level 3 autonomous driving, the autonomous driving function types include the fifth preset function type, and the autonomous driving time accounts for less than the third preset percentage within a certain period. For example, Level 3 autonomous driving is L3, and the fifth preset function type is, for example, Highway Automated Driving (HWP). Based on this, the vehicle's driving state is determined to be in the fifth state when the autonomous driving time accounts for less than the third preset percentage within that period.

[0091] When the autonomous driving level information is Level 3 autonomous driving, the autonomous driving function types include the sixth preset function type, and the autonomous driving time accounts for less than the third preset percentage within a certain period, the vehicle driving state is determined to be the eighth state. For example, Level 3 autonomous driving is L3, and the sixth preset function type is, for example, Traffic Jam Automated Driving (TJP). Based on this, when the autonomous driving time accounts for less than the third preset percentage within a certain period, the vehicle driving state is determined to be the eighth state.

[0092] The fifth preset function can be set to be more important than the sixth preset function. In this case, the fifth state is worse than the eighth state, and the danger level of the vehicle's autonomous driving corresponding to the fifth state is higher than that of the vehicle's autonomous driving corresponding to the eighth state.

[0093] The driving information may also include other information, such as the number of times manual intervention was required within the specified period, the number of autonomous driving functions, and the importance of the autonomous driving functions. The specific settings can be configured according to actual needs, and this embodiment does not impose any limitations.

[0094] S230 determines the second level of danger of the vehicle's autonomous driving based on the frequency of switching from autonomous driving to manual driving within this period.

[0095] In an optional embodiment, when determining the second level of danger of the vehicle's autonomous driving based on the frequency of switching from autonomous driving to manual driving within a certain period, if the frequency is greater than a preset frequency, the vehicle's driving state is determined to be a second state. This second state, for example, is a state in which the vehicle cannot normally use its autonomous driving function.

[0096] When more attention is paid to the frequency of switching from autonomous driving to manual driving, the second state is worse than the first state.

[0097] After determining the vehicle's driving state, the constraint relationship between the vehicle's driving state and its driving hazard level is obtained. This constraint relationship limits the driving hazard level corresponding to the vehicle's driving state and can be set by the tester according to actual needs. For example, if more attention is paid to the percentage of time spent in autonomous driving, the hazard level corresponding to the vehicle state obtained based on the percentage of time spent in autonomous driving can be set to high hazard.

[0098] Based on the second state and the constraint relationship between the vehicle driving state and the vehicle driving hazard level, the second hazard level of the vehicle's autonomous driving is determined.

[0099] When you want to focus more on the percentage of time spent in autonomous driving mode, you can set the first level of danger to be greater than the second level of danger.

[0100] S240, determine the risk level of the vehicle's autonomous driving based on the first risk level and the second risk level.

[0101] For example, the first hazard level and the second hazard level can be directly summed to determine the hazard level of the vehicle's autonomous driving. Alternatively, the first hazard level and the second hazard level can be weighted and summed to determine the hazard level of the vehicle's autonomous driving. The weights of the first hazard level and the second hazard level can be set according to actual needs, and this embodiment does not impose any limitations.

[0102] In some optional embodiments, the degree of vehicle driving danger can also be determined by comprehensively considering information such as the proportion of autonomous driving time within a certain period, the frequency of switching from autonomous driving to manual driving within that period, autonomous driving level information, and the types of autonomous driving functions.

[0103] Please see Figure 3 The in-vehicle system or mobile phone can send a hazard level notification request to this electronic device to request the vehicle's driving hazard level, assisting the driver in paying more attention to the vehicle's autonomous driving process. This electronic device can also send more specific information about highly hazard autonomous driving functions or durations to the in-vehicle system or mobile phone, further assisting the driver in focusing on the functions used in the vehicle's autonomous driving or the duration of autonomous driving operation.

[0104] In summary, this embodiment provides a method for estimating vehicle driving risk. It acquires driving information of the vehicle over a period of time, including the percentage of time spent in autonomous driving and the frequency of switching from autonomous to manual driving during that period.

[0105] For example, the first degree of danger of autonomous driving is determined based on the proportion of autonomous driving time within that period. The longer the proportion, the higher the safety factor of autonomous driving and the lower the degree of danger of autonomous driving.

[0106] For example, the second level of danger of the vehicle's autonomous driving is determined based on the frequency of switching from autonomous to manual driving within a certain period. The higher the frequency of switching from autonomous to manual driving, the lower the safety factor of autonomous driving and the higher the level of danger of the vehicle's autonomous driving.

[0107] For example, determining the risk level of autonomous driving of a vehicle based on the first risk level and the second risk level can provide a more comprehensive and accurate understanding of the safety level of autonomous driving, thereby improving the testing effectiveness for vehicles with autonomous driving capabilities.

[0108] In addition, the method provided in this embodiment can also provide a certain reference for estimating vehicle insurance costs and maintenance costs. For example, when the risk level of autonomous driving of the vehicle is high, the vehicle's insurance cost can be increased; when the risk level of autonomous driving of the vehicle is low, the vehicle's insurance cost can be decreased. More specifically, different insurance cost levels can be set according to different risks of autonomous driving of the vehicle, such as setting a first insurance cost level for a first risk level and a second insurance cost level for a second risk level, etc.

[0109] For example, a pre-defined rule for setting vehicle insurance premiums is used, which sets the premiums based on the level of autonomous driving. Autonomous driving levels L0 and L1 correspond to premium 1, while levels L3 and L4 correspond to premium 2. Premium 3 is greater than premium 2; that is, as the level of autonomous driving increases, the required premium decreases. The premiums corresponding to different levels of autonomous driving can be further subdivided, but this embodiment does not limit this.

[0110] For example, the insurance premium is set based on the number of times manual intervention is required after the vehicle enters autonomous driving mode. The more times manual intervention occurs during autonomous driving, the more corner cases the vehicle encounters, indicating that the autonomous driving model used by the vehicle is not stable enough. Therefore, when manual intervention occurs, the cloud retrieves the road scene information and performs a scene assessment. If it is a corner case, the manual intervention is deemed valid. In this case, the more manual interventions, the lower the insurance premium.

[0111] For example, this insurance premium setting rule determines the premium based on information about the vehicle's autonomous driving functions. This information includes the types of autonomous driving functions the vehicle can perform, the number of autonomous driving functions, and the importance of each function. Autonomous driving functions can be categorized according to their importance, with different types of autonomous driving functions corresponding to different insurance premiums.

[0112] For example, if a vehicle has Level 4 autonomous driving capabilities, the calculated insurance cost is cost 1 if the vehicle has HWP (Hardware Driver Program) functionality. The calculated insurance cost is cost 2 if the vehicle has TJP (Traffic Driver Assistance Program) functionality. The calculated insurance cost is cost 3 if the vehicle has AVP (Automated Driver Assistance Program) functionality.

[0113] When cost 1 is less than cost 2, and cost 2 is less than cost 3, the vehicle is considered safer to drive when the HWP function is activated. When cost 1 is greater than cost 2, and cost 2 is greater than cost 3, the vehicle is considered less safe to drive at high speeds when the HWP function is activated than when manually driving at high speeds.

[0114] It should be noted that the more autonomous driving functions a vehicle has, the lower the calculated insurance premium will be. For example, if a vehicle has HWP, TJP, and AVP functions simultaneously, the calculated insurance premium will be lower than any of the following three costs: Cost 1, Cost 2, and Cost 3.

[0115] For example, this insurance premium setting rule determines the premium based on the duration of autonomous driving data collected while the vehicle is on the road. For instance, first, the total driving time is determined; then, the time window for autonomous driving is identified within this total time, denoted as the first duration; and finally, the time window for manual driving is identified, denoted as the second duration. The insurance premium corresponding to the first duration is denoted as the first cost, and the insurance premium corresponding to the second duration is denoted as the second cost. The first cost is less than the second cost. The insurance premium for vehicle driving is calculated as: Insurance Premium = First Cost * First Duration + Second Cost * Second Duration.

[0116] For example, insurance premiums can be set by combining the collected information on the duration of autonomous driving and the number of times manual intervention was required after the vehicle activated autonomous driving mode. When setting the premium, weights are assigned to the duration of autonomous driving and the number of manual interventions. For instance, the weight corresponding to the autonomous driving level is A1, and the corresponding insurance premium cost is B1. The weight corresponding to the autonomous driving function is A2, and the corresponding insurance premium cost is B2. The weight corresponding to the duration of autonomous driving function use is A3, and its corresponding insurance premium cost is B3. The weight corresponding to the number of manual interventions is A4, and its corresponding insurance premium cost is B4. Then the total insurance premium X is: X = A1*B1 + A2*B2 + A3*B3 + A4*B4.

[0117] For example, insurance premiums can also be calculated based on the version information after an over-the-air (OTA) technology update. A higher version number generally results in a lower insurance premium. The insurance premium can be calculated only based on upgrades to core components; upgrades to infotainment systems will not affect the premium calculation.

[0118] Please see Figure 4 An embodiment of this application also provides a vehicle driving hazard estimation device 10, comprising:

[0119] The acquisition module 11 is used to acquire driving information of the vehicle during a period of time. The driving information includes: the proportion of autonomous driving time during the period of time, and the frequency of switching from autonomous driving to manual driving during the period of time.

[0120] Processing module 12 is used to determine the first level of danger of vehicle autonomous driving based on the proportion of the autonomous driving time within the time period.

[0121] The processing module 12 is also used to determine the second level of danger of the vehicle's autonomous driving based on the frequency of switching from autonomous driving to manual driving within the time period.

[0122] The processing module 12 is also used to determine the risk level of the vehicle's autonomous driving based on the first risk level and the second risk level.

[0123] The processing module 12 is specifically used to determine the vehicle driving state as a first state when the proportion of the autonomous driving time in the time period is less than a preset proportion; and to determine the first degree of danger of the vehicle's autonomous driving based on the first state and the constraint relationship between the vehicle driving state and the vehicle driving danger.

[0124] The processing module 12 is specifically used to determine the vehicle driving state as the second state when the frequency is greater than the preset frequency; and to determine the second degree of danger of the vehicle's autonomous driving based on the second state and the constraint relationship between the vehicle driving state and the vehicle driving danger.

[0125] The driving information also includes autonomous driving level information. Specifically, the processing module 12 is used to determine the vehicle driving state based on the autonomous driving level information and the proportion of autonomous driving time within the time period; and to determine the first degree of danger of the vehicle's autonomous driving based on the vehicle driving state and the constraint relationship between the vehicle driving state and the vehicle driving hazard.

[0126] Specifically, the processing module 12 is used to determine the vehicle driving state as the third state when the autonomous driving level information is Level 1 autonomous driving and the proportion of autonomous driving time in the period is less than a first preset proportion; to determine the vehicle driving state as the fourth state when the autonomous driving level information is Level 2 autonomous driving and the proportion of autonomous driving time in the period is less than a second preset proportion; and to determine the vehicle driving state as the fifth state when the autonomous driving level information is Level 3 autonomous driving and the proportion of autonomous driving time in the period is less than a third preset proportion.

[0127] The driving information also includes the types of autonomous driving functions. Specifically, the processing module 12 is used to determine the vehicle driving state as the third state when the autonomous driving level information is Level 1 autonomous driving, the types of autonomous driving functions include a first preset type, and the proportion of autonomous driving time within a certain period is less than a first preset proportion. The processing module 12 is also used to determine the vehicle driving state as the sixth state when the autonomous driving level information is Level 1 autonomous driving, the types of autonomous driving functions include a second preset type, and the proportion of autonomous driving time within a certain period is less than a first preset proportion.

[0128] Specifically, the processing module 12 is used to determine the vehicle driving state as the fourth state when the autonomous driving level information is Level 2 autonomous driving, the autonomous driving function types include the third preset function type, and the autonomous driving time accounts for less than the second preset percentage within a certain period. The processing module 12 is also used to determine the vehicle driving state as the seventh state when the autonomous driving level information is Level 2 autonomous driving, the autonomous driving function types include the fourth preset function type, and the autonomous driving time accounts for less than the second preset percentage within a certain period.

[0129] Specifically, the processing module 12 is used to determine the vehicle driving state as the fifth state when the autonomous driving level information is Level 3 autonomous driving, the autonomous driving function types include a fifth preset function type, and the autonomous driving time accounts for less than a third preset percentage within a certain period. The processing module 12 is also used to determine the vehicle driving state as the eighth state when the autonomous driving level information is Level 3 autonomous driving, the autonomous driving function types include a sixth preset function type, and the autonomous driving time accounts for less than a third preset percentage within a certain period.

[0130] Please see Figure 5 This application also provides an electronic device 20, which includes a processor 21 and a memory 22 communicatively connected to the processor 21. The memory 22 stores computer-executable instructions, and the processor 21 executes the computer-executable instructions stored in the memory to implement the vehicle driving hazard estimation method provided in any of the above embodiments.

[0131] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed, cause the computer-executable instructions to be executed by a processor to implement the vehicle driving hazard estimation method provided in any of the preceding embodiments.

[0132] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the vehicle driving hazard estimation method as provided in any of the above embodiments.

[0133] It should be noted that the aforementioned computer-readable storage media can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM), etc. It can also be various electronic devices that include one or any combination of the above-mentioned memories, such as mobile phones, computers, tablet devices, personal digital assistants, etc.

[0134] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0135] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0136] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0137] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0138] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0139] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0140] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for estimating the risk of driving a vehicle, characterized in that, The method includes: The vehicle obtains driving information during a certain period of time, including: the percentage of time spent in autonomous driving during the period of time, and the frequency of switching from autonomous driving to manual driving during the period of time. The first level of danger of vehicle autonomous driving is determined based on the proportion of the autonomous driving time within the specified time period. The second degree of danger of the vehicle's autonomous driving is determined based on the frequency of switching from autonomous driving to manual driving within the aforementioned period of time. The risk level of the vehicle's autonomous driving is determined based on the first risk level and the second risk level; The determination of the first level of danger of vehicle autonomous driving based on the proportion of autonomous driving time within the specified time period includes: When the proportion of the autonomous driving time within the specified time period is less than a preset proportion, the vehicle driving state is determined to be the first state. Based on the first state and the constraint relationship between the vehicle driving state and the vehicle driving hazard level, the first hazard level of the vehicle's autonomous driving is determined; The determination of the second level of danger of the vehicle's autonomous driving based on the frequency of switching from autonomous driving to manual driving within the aforementioned period includes: When the frequency is greater than the preset frequency, the vehicle driving state is determined to be the second state; The second level of danger for the autonomous driving of the vehicle is determined based on the second state and the constraint relationship between the vehicle driving state and the vehicle driving hazard level.

2. The method according to claim 1, characterized in that, The driving information also includes autonomous driving level information, and determining the first level of danger of vehicle autonomous driving based on the proportion of autonomous driving time within the specified time period includes: The vehicle driving status is determined based on the autonomous driving level information and the proportion of autonomous driving time within the specified time period. The first level of danger for autonomous driving is determined based on the vehicle driving state and the constraint relationship between the vehicle driving state and the vehicle driving hazard level.

3. The method according to claim 2, characterized in that, The determination of the first level of danger of vehicle autonomous driving based on the autonomous driving level information and the proportion of autonomous driving time within the time period includes: When the autonomous driving level information is Level 1 autonomous driving, and the proportion of the autonomous driving time within the time period is less than the first preset proportion, the vehicle driving state is determined to be the third state. When the autonomous driving level information is Level 2 autonomous driving, and the proportion of the autonomous driving time within the time period is less than the second preset proportion, the vehicle driving state is determined to be the fourth state. When the autonomous driving level information is Level 3 autonomous driving, and the proportion of autonomous driving time within the specified time period is less than a third preset proportion, the vehicle driving state is determined to be the fifth state.

4. The method according to claim 3, characterized in that, The driving information also includes the type of autonomous driving function. When the autonomous driving level information is Level 1 autonomous driving, and the proportion of autonomous driving time within the specified time period is less than a first preset proportion, determining the vehicle driving state as a third state includes: When the autonomous driving level information is Level 1 autonomous driving, the autonomous driving function types include a first preset function type, and the autonomous driving time accounts for less than the first preset percentage within the time period, the vehicle driving state is determined to be the third state. The method further includes: When the autonomous driving level information is Level 1 autonomous driving, the autonomous driving function types include a second preset function type, and the autonomous driving time accounts for less than a first preset percentage within the time period, the vehicle driving state is determined to be the sixth state.

5. The method according to claim 4, characterized in that, When the autonomous driving level information is Level 2 autonomous driving, and the proportion of autonomous driving time within the specified time period is less than a second preset proportion, determining the vehicle driving state to be in the fourth state includes: When the autonomous driving level information is Level 2 autonomous driving, the autonomous driving function types include a third preset function type, and the autonomous driving time accounts for less than the second preset percentage within the time period, the vehicle driving state is determined to be the fourth state. The method further includes: When the autonomous driving level information is Level 2 autonomous driving, the autonomous driving function types include a fourth preset function type, and the autonomous driving time accounts for less than a second preset percentage within the time period, the vehicle driving state is determined to be the seventh state.

6. The method according to claim 5, characterized in that, When the autonomous driving level information is Level 3 autonomous driving, and the proportion of autonomous driving time within the specified time period is less than a third preset proportion, determining the vehicle driving state to the fifth state includes: When the autonomous driving level information is Level 3 autonomous driving, the autonomous driving function types include a fifth preset function type, and the autonomous driving time accounts for less than the third preset percentage within the time period, the vehicle driving state is determined to be the fifth state. The method further includes: When the autonomous driving level information is Level 3 autonomous driving, the autonomous driving function types include a sixth preset function type, and the autonomous driving time accounts for less than a third preset percentage within the time period, the vehicle driving state is determined to be the eighth state.

7. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes the computer execution instructions stored in the memory to implement the vehicle driving hazard estimation method as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when executed, cause the computer to perform the vehicle driving hazard estimation method as described in any one of claims 1 to 6.

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