Intelligent driving system reliability assessment method and device, vehicle, storage medium and product

Abstract

The invention discloses an intelligent driving system reliability evaluation method and device, a vehicle, a storage medium and a product, and relates to the technical field of intelligent driving, and the method comprises the steps: obtaining a plurality of pieces of dimension information affecting the reliability of an intelligent driving system under the condition that the intelligent driving function of the vehicle is monitored to be started; the multi-dimension information comprises front road information, current weather information, front real-time road condition information, driver current state information and vehicle current state information; based on a preset scoring rule, scoring each piece of dimension information to obtain a reliability score corresponding to each piece of dimension information; and determining a reliability evaluation result of the intelligent driving system based on all the reliability scores. According to the method, the reliability of the intelligent driving system can be intelligently evaluated, so that the trust degree of a driver on the intelligent driving system and the driving safety are improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent driving technology, and in particular to an intelligent driving system reliability assessment method, device, vehicle, storage medium and product. Background Art

[0002] Advanced intelligent driving features are increasingly being used in vehicles, providing driver assistance and enhancing driving convenience. However, in practice, most intelligent driving systems only offer real-time assisted driving capabilities and lack future reliability assessments and early warning mechanisms. This makes it difficult for drivers to understand the reliability of the automated driving system in advance. This lack of transparency not only reduces driver trust in the intelligent driving system but can also prevent drivers from taking control of the vehicle at critical moments, increasing driving risks. Summary of the Invention

[0003] The main purpose of this application is to provide a method, device, vehicle, storage medium and product for evaluating the reliability of an intelligent driving system, aiming to solve the technical problem in related technologies that the intelligent driving system lacks reliability evaluation, affecting the driver's system trust and driving safety.

[0004] To achieve the above objectives, this application proposes a method for evaluating the reliability of an intelligent driving system, which includes:

[0005] When the vehicle's intelligent driving function is activated, multiple dimensions of information that affect the reliability of the intelligent driving system are obtained; the multiple dimensions of information include road ahead information, current weather information, real-time road condition information, driver's current status information, and vehicle's current status information;

[0006] Based on the preset scoring rules, score each dimension of information separately to obtain the reliability score corresponding to each dimension of information;

[0007] Based on all reliability scores, the reliability evaluation results of the intelligent driving system are determined.

[0008] In one embodiment, after the step of determining a reliability evaluation result of the intelligent driving system based on all the reliability scores, the method further includes:

[0009] Based on the reliability evaluation results, intelligent driving reliability prompt information is generated and output; the form of the intelligent driving reliability prompt information includes at least one of voice prompts, text prompts and steering wheel vibration prompts.

[0010] In one embodiment, the step of generating and outputting intelligent driving reliability prompt information based on the reliability evaluation result includes:

[0011] Based on the reliability assessment results, the reliability level of the intelligent driving system is determined; the reliability level is strong reliability, medium reliability or weak reliability;

[0012] When the reliability level is strong, generate and output prompt information indicating that the intelligent driving function is in normal use;

[0013] When the reliability level is medium reliability, generating and outputting attention maintenance prompt information;

[0014] When the reliability level is weak reliability, vehicle takeover prompt information is generated and output.

[0015] In one embodiment, the step of generating and outputting intelligent driving reliability prompt information based on the reliability evaluation result includes:

[0016] If the reliability evaluation result is greater than the preset evaluation threshold, the intelligent driving reliability prompt information output operation will not be performed;

[0017] When the reliability evaluation result is less than or equal to the preset evaluation threshold, the intelligent driving reliability prompt information is output.

[0018] In one embodiment, the road ahead information includes at least one of a roundabout, a U-turn, an unprotected left turn, a large curvature, a road width, a tunnel, and a slope;

[0019] Current weather information including visibility information;

[0020] The real-time road condition information ahead includes at least one of a traffic accident, congestion, traffic light, ramp entry, merging into the main road, lane change, vehicle speed change, and toll booth;

[0021] Driver's current state information including the driver's mental state; and / or

[0022] The vehicle's current status information includes vehicle speed information and vehicle maintenance information; each different forward road information, current weather information, forward real-time road condition information, driver's current status information and vehicle's current status information are all set with corresponding reliability scores.

[0023] In one embodiment, the step of determining a reliability evaluation result of the intelligent driving system based on all reliability scores includes:

[0024] Perform a weighted summation of all reliability scores to determine the reliability evaluation result of the intelligent driving system.

[0025] In addition, to achieve the above objectives, the present application also proposes an intelligent driving system reliability assessment device, which includes:

[0026] An information acquisition module is used to obtain multiple dimensions of information that affect the reliability of the intelligent driving system when the intelligent driving function of the vehicle is activated; the multiple dimensions of information include road ahead information, current weather information, real-time road condition information, driver's current status information, and vehicle current status information;

[0027] The scoring module is used to score each dimension of information based on preset scoring rules to obtain the reliability score corresponding to each dimension of information;

[0028] The reliability determination module is used to determine the reliability evaluation result of the intelligent driving system based on all reliability scores.

[0029] In addition, to achieve the above-mentioned purpose, the present application also proposes a vehicle, which includes: a memory, a processor, and a computer program stored in the memory and runnable on the processor, and the computer program is configured to implement the steps of the intelligent driving system reliability assessment method as described above.

[0030] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the intelligent driving system reliability evaluation method as described above are implemented.

[0031] In addition, to achieve the above-mentioned purpose, the present application also proposes a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the above-mentioned intelligent driving system reliability assessment method.

[0032] One or more technical solutions proposed in this application have at least the following technical effects:

[0033] The intelligent driving system reliability assessment method proposed in this application can comprehensively consider multi-dimensional information that affects the reliability of the intelligent driving system, such as the road ahead information, current weather information, real-time road condition information ahead, the driver's current status information, and the vehicle's current status information, when the vehicle turns on the intelligent driving function, and score each dimensional information to obtain the corresponding reliability score. The reliability assessment result of the intelligent driving system is determined based on all reliability scores, and abstract factors such as the driving environment and vehicle status are converted into specific scores, making the reliability assessment more quantitative and objective, providing the driver with a comprehensive and accurate reliability assessment, facilitating the driver's understanding of the system reliability status, thereby enhancing their trust in the intelligent driving system, and can remind the driver to take over the vehicle in time at critical moments to reduce driving risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0036] Figure 1 A flowchart of the first embodiment of the intelligent driving system reliability assessment method provided in this application;

[0037] Figure 2 This is a detailed flowchart of step S400;

[0038] Figure 3 This is a schematic diagram of an example vehicle HMI interface;

[0039] Figure 4 This is a schematic diagram of the module structure of the intelligent driving system reliability assessment device according to an embodiment of the present application;

[0040] Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the intelligent driving system reliability assessment method in the embodiment of the present application.

[0041] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0042] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0043] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0044] The main solution of the embodiment of the present application is: when monitoring that the vehicle's intelligent driving function is turned on, obtain multiple dimensions of information that affect the reliability of the intelligent driving system; the multiple dimensions of information include road information ahead, current weather information, real-time road condition information ahead, driver's current status information and vehicle current status information; based on preset scoring rules, score each dimension of information separately to obtain the reliability score corresponding to each dimension of information; based on all reliability scores, determine the reliability evaluation result of the intelligent driving system.

[0045] Advanced intelligent driving features are increasingly being used in vehicles, providing driver assistance and improving driving convenience. However, in practice, most intelligent driving systems only offer real-time assisted driving capabilities and lack future reliability assessments and early warning mechanisms. This makes it difficult for drivers to understand the reliability of the automated driving system in advance. This lack of transparency not only reduces driver trust in the intelligent driving system but can also prevent drivers from taking control of the vehicle at critical moments, increasing driving risks.

[0046] This application provides a solution that can comprehensively consider multi-dimensional information such as road information ahead, current weather information, real-time road condition information ahead, driver's current status information, and vehicle's current status information that affect the reliability of the intelligent driving system when the vehicle turns on the intelligent driving function, score each dimensional information to obtain a corresponding reliability score, and determine the reliability evaluation result of the intelligent driving system based on all reliability scores, converting abstract factors such as driving environment and vehicle status into specific scores, making the reliability evaluation more quantitative and objective, and providing the driver with a comprehensive and accurate reliability evaluation, so that the driver can understand the system reliability status and enhance their trust in the intelligent driving system. It can also remind the driver to take over the vehicle in time at critical moments to reduce driving risks.

[0047] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, etc., or an electronic device capable of implementing the above functions. The following uses the vehicle controller as an example to illustrate this embodiment and the following embodiments.

[0048] Based on this, the embodiment of the present application provides a method for evaluating the reliability of an intelligent driving system, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the intelligent driving system reliability assessment method of this application.

[0049] In this embodiment, the intelligent driving system reliability assessment method may include steps S100 to S300:

[0050] Step S100, when monitoring that the vehicle's intelligent driving function is turned on, obtain multiple dimensions of information that affect the reliability of the intelligent driving system; the multiple dimensions of information include road information ahead, current weather information, real-time road condition information ahead, driver's current status information, and vehicle current status information.

[0051] Step S200: Scoring each dimension of information based on a preset scoring rule to obtain a reliability score corresponding to each dimension of information.

[0052] Step S300: Determine the reliability evaluation result of the intelligent driving system based on all reliability scores.

[0053] Specifically, during the operation of a vehicle's intelligent driving system, its reliability may be affected by a combination of factors. In order to help the driver accurately understand the reliability of the intelligent driving system, it is possible to obtain multiple dimensional information affecting the reliability of the intelligent driving system in real time when monitoring that the vehicle's intelligent driving function is turned on, so as to conduct a comprehensive assessment of the reliability of the intelligent driving system; among them, the multiple dimensional information may include but is not limited to the vehicle's front road information, current weather information, real-time road condition information ahead, driver's current status information, and vehicle current status information, etc. In actual applications, the above information can be obtained through various sensors installed on the vehicle and external data sources; for example, the type and road conditions of the road ahead can be identified through the vehicle's image acquisition device, and radar and lidar can assist in judging the curvature of the road and the distance to obstacles; the vehicle's meteorological sensors can sense the current weather conditions, or obtain real-time weather information from the meteorological department through the vehicle's Internet of Vehicles function; the vehicle's traffic information receiving system can receive real-time road condition information from the traffic management department, and can also obtain the road conditions encountered by the vehicle ahead through vehicle-to-vehicle communication (V2V); the vehicle's driver monitoring system (such as a camera monitoring the driver's facial expressions, eye closure frequency, etc.) can determine the driver's current status; the vehicle's own fault diagnosis system can detect the operating status of various vehicle components, etc.

[0054] Based on preset scoring rules, each dimension of information is scored to obtain a corresponding reliability score for each dimension of information. In one feasible embodiment, road ahead information includes at least one of roundabouts, U-turns, unprotected left turns, sharp curvatures, road width, tunnels, and slopes; current weather information includes visibility information; real-time road condition information includes at least one of traffic accidents, congestion, traffic lights, ramp entry, merging into the main road, lane changes, speed changes, and toll booths; driver's current state information includes the driver's mental state; and / or vehicle's current state information includes speed information and vehicle maintenance information; each of the different road ahead information, current weather information, real-time road condition information, driver's current state information, and vehicle's current state information is assigned a corresponding reliability score. It should be noted that the preset scoring rules may include the correspondence between different dimensional information and reliability scores, which may be a reliability score pre-assigned to different dimensional information based on relevant industry standards or expert experience. The reliability scores corresponding to different dimensional information may be the same or different. The vehicle controller can determine the specific reliability score corresponding to the dimensional information based on the monitored dimensional information and the preset scoring rules.

[0055] For example, after the vehicle turns on the intelligent driving function, when it detects the presence of roundabouts and large curvatures on the road ahead of the vehicle, and at the same time obtains the road width and slope information ahead, the reliability scores corresponding to the roundabout, large curvature, road width and slope information can be superimposed to obtain the reliability score corresponding to the road ahead information; similarly, the reliability scores corresponding to the current weather information, real-time road conditions ahead, the driver's current status information and the vehicle's current status information can also be superimposed and calculated according to the above method.

[0056] After determining the reliability scores corresponding to the road ahead information, current weather information, real-time road condition information ahead, driver's current status information, and vehicle's current status information, all reliability scores can be combined to determine the final reliability evaluation result of the intelligent driving system. In a feasible implementation, step S300 may specifically include: taking a weighted sum of all reliability scores to determine the reliability evaluation result of the intelligent driving system. That is, according to the importance of each dimensional information (the degree of influence on the reliability of the intelligent driving system), a corresponding weight can be set for each dimensional information, and the score of each dimension can be multiplied by the weight and summed up. The final total reliability score is the reliability evaluation result of the intelligent driving system. When the influence of the above-mentioned multiple dimensional information is comparable, the above-mentioned reliability scores can also be directly added and calculated to obtain the reliability evaluation result of the intelligent driving system for the driver's reference.

[0057] During driving, the driver can send instructions to the vehicle (such as issuing a voice command of "output the current reliability assessment result"). After receiving the driver's instruction, the vehicle can display the reliability assessment result of the current intelligent driving system on the on-board display screen, or the vehicle can directly broadcast the reliability assessment result by voice. In order to further simplify user operations and provide a more intelligent reliability assessment experience, in a feasible embodiment, step S300 can also include step S400 to automatically output reliability prompt information, so that the driver can promptly obtain the reliability information of the intelligent driving system:

[0058] Step S400: Generate and output intelligent driving reliability prompt information based on the reliability evaluation result; the form of the intelligent driving reliability prompt information includes at least one of voice prompt, text prompt and steering wheel vibration prompt.

[0059] Specifically, after determining the reliability evaluation result of the intelligent driving system, the vehicle can automatically generate and output intelligent driving reliability prompt information, which can include the current reliability status and driving suggestions, etc.; the form of intelligent reliability prompt information can include but is not limited to voice prompts, text prompts and steering wheel vibration prompts. For example, when the reliability evaluation result of the current intelligent driving system is low, the vehicle can output a text sign through the on-board display screen that says "The current intelligent driving system reliability is low, please pay more attention and prepare to take over the vehicle" as a reminder. At the same time, the above text content can be output in the form of voice to prevent the driver from not noticing the above text information in time due to inattention, which affects driving safety; while performing voice and text reminders, prompts can also be given through steering wheel vibration to allow the driver to concentrate. In a feasible embodiment, the above step S400 can specifically include steps S410 to S440, such as Figure 2 As shown, Figure 2 This is a detailed flow chart of step S400:

[0060] Step S410: Determine the reliability level of the intelligent driving system based on the reliability evaluation result; the reliability level is strong reliability, medium reliability or weak reliability.

[0061] Step S420: When the reliability level is strong reliability, generate and output prompt information indicating that the intelligent driving function is in normal use.

[0062] Step S430: When the reliability level is medium, generate and output attention maintenance prompt information.

[0063] Step S440: When the reliability level is weak reliability, generate and output vehicle takeover prompt information.

[0064] Usually, multiple reliability levels (such as strong reliability, medium reliability or weak reliability) can be pre-set. Different reliability levels correspond to different total reliability score ranges. The specific reliability level of the current intelligent driving system can be determined according to the total reliability score corresponding to the reliability assessment result; thus, targeted prompts can be given to the driver according to different reliability levels; when the reliability level is strong reliability, it indicates that the intelligent driving system can be used safely at this time, so it can generate and output prompt information for normal use of the intelligent driving function to inform the driver that the intelligent driving function can be used for driving with confidence; when the reliability level is medium reliability, it generates and outputs attention maintenance prompt information to inform the driver that the current reliability is average and a certain amount of attention needs to be maintained to deal with emergencies; when the reliability level is weak reliability, it generates and outputs vehicle takeover prompt information to remind the driver that the current reliability is weak and that attention needs to be increased to prepare to take over the vehicle. The prompt information corresponding to the above different levels can be output in the form of text or voice. In addition, the signal strength icon can be output on the vehicle HMI interface (Human-Machine Interface) according to different reliability levels, such as Figure 3 As shown, Figure 3 This is a schematic diagram of an example vehicle HMI interface. Figure 3 Icon A is the signal strength icon used to represent the reliability of the intelligent driving system. Strong reliability can display 3 bars of signal strength, medium reliability can display 2 bars of signal strength, and weak reliability can display 1 bar of signal strength. The visualized signal strength icon can help the driver understand the current reliability of the intelligent driving system more intuitively, and ensure that the driver has a certain psychological expectation of the status of the intelligent driving system in the future.

[0065] Alternatively, in another feasible implementation, step S400 may further specifically include steps S450 to S460, selectively providing reliability prompts to avoid unnecessary prompt interference to the driver:

[0066] Step S450: When the reliability evaluation result is greater than the preset evaluation threshold, the intelligent driving reliability prompt information output operation is not performed.

[0067] Step S460: When the reliability evaluation result is less than or equal to the preset evaluation threshold, output intelligent driving reliability prompt information.

[0068] Specifically, a reliability assessment threshold can be set in advance based on the actual performance and safety requirements of the intelligent driving system. After completing the reliability assessment of the intelligent driving system, the reliability assessment result can be compared with the preset assessment threshold; if the reliability assessment result is greater than the preset assessment threshold, it indicates that the vehicle's intelligent driving system is in a reliable state. At this time, the output operation of the intelligent driving reliability prompt information can be omitted, that is, no prompt about the intelligent driving reliability is issued to the driver, so as to avoid unnecessary reminders to the driver when the reliability of the intelligent driving system is high, thereby reducing the driver's distraction and improving driving comfort and concentration. Only when the reliability assessment result is less than or equal to the preset assessment threshold will the intelligent driving reliability prompt information be output to promptly remind the driver to pay attention to the reliability issues of the intelligent driving system to ensure driving safety. For example, when the reliability is less than the preset assessment threshold, a voice prompt can be issued, "The current reliability is low, please hold the steering wheel tightly and look straight ahead, and drive carefully" to improve the driver's concentration. Alternatively, when the reliability is lower than the preset assessment threshold, the driver's current state can be further monitored. If the driver is detected to have incorrect behaviors such as improper sitting posture, not looking straight ahead, or taking both hands off the steering wheel, an alarm prompt can be issued. By comparing the reliability assessment result with the preset assessment threshold, it is decided whether to output the prompt information, thereby realizing effective monitoring and reminder of the reliability of the intelligent driving system. This mechanism not only avoids unnecessary interference, but also can remind the driver in time when necessary, improving the safety and reliability of the intelligent driving system, but also improving the driver's user experience and trust in the intelligent driving system.

[0069] It can be understood that the intelligent driving system reliability assessment method provided in the embodiment of the present application can comprehensively consider multi-dimensional information such as the road ahead information, current weather information, real-time road condition information ahead, driver's current status information, and vehicle's current status information that affect the reliability of the intelligent driving system when the vehicle turns on the intelligent driving function, score each dimensional information to obtain the corresponding reliability score, and determine the reliability assessment result of the intelligent driving system based on all reliability scores, and convert abstract factors such as driving environment and vehicle status into specific scores, so that the reliability assessment is more quantitative and objective, providing the driver with a comprehensive and accurate reliability assessment, facilitating the driver to understand the system reliability status, so as to enhance their trust in the intelligent driving system, and can remind the driver to take over the vehicle in time at critical moments to reduce driving risks.

[0070] To help understand the intelligent driving system reliability assessment method provided in the first embodiment of the present application, the following example is given for illustration, specifically:

[0071] All possible scenarios that may affect the intelligent driving system can be exhaustively enumerated, and preset reliability scores can be assigned in advance to the road ahead information, current weather information, real-time road condition information, driver's current status information, and vehicle's current status information. For the road ahead information, its reliability score is denoted as a. Among them, the reliability score corresponding to the roundabout is -1, the reliability score corresponding to the U-turn is -1, the reliability score corresponding to the unprotected left turn is -1, the reliability score corresponding to the large curvature is -0.5, and the reliability score corresponding to the road width is - 1, -0.5, 0, 0.5, or 1 (the wider the road, the higher the corresponding reliability score); the reliability score corresponding to a tunnel is -1; the reliability score corresponding to a slope is -1, -0.5, or 0 (the flatter the slope, the higher the corresponding reliability score); in actual vehicle operation, if any of the above scenarios exists, the reliability score a corresponding to the road information ahead is the superposition of the scores of the above scenarios. For example, if there is a U-turn ahead (-1) and the road width is narrow (-1), the reliability score corresponding to the road information ahead is a = -1 - 1 = -2.

[0072] The reliability score of the current weather information is denoted as b. Current weather information can include rain, snow, and fog, and different weather information is assigned a preset reliability score based on visibility conditions. Rain is assigned a reliability score of -1, snow is assigned a reliability score of -2, and fog is assigned a reliability score of -3.

[0073] For the real-time road condition information ahead, its reliability score is denoted as c; among them, the reliability score corresponding to a traffic accident is -3, the reliability score corresponding to congestion is -2, the reliability score corresponding to a red light is -1, the reliability score corresponding to a green light is 0, the reliability score corresponding to entering the ramp is -1, the reliability score corresponding to merging into the main road is -1, the reliability score corresponding to changing lanes is -1, the reliability score corresponding to speed change is -1, 0 or 1 (for example, the speed exceeds the speed limit or the speed changes too quickly is -1, the speed remains within the speed limit and changes smoothly is 0, and the speed is below the speed limit and changes smoothly is 1). The reliability score corresponding to a toll station is -1 or 0 (for example, if there is a toll station ahead is -1, and if there is no toll station is 0).

[0074] For the driver's current status information, its reliability score is recorded as d; the driver's mental state can be obtained through DMS (Driver Monitor System), and different mental states are assigned different reliability scores. For example, the driver is in an extremely tired state, such as driving for a long time without rest, feeling unwell, etc., and the intelligent driving system detects that the driver's fatigue level is high (such as the driver yawns frequently and closes his eyes frequently through the camera), and the corresponding reliability score is -2; the driver is in a fatigued state, but has not reached the level of extreme fatigue, and the intelligent driving system detects that the driver's fatigue level is moderate (such as the driver yawns occasionally and is not focused through the camera), and the corresponding reliability score is -1; the driver's mental state is normal, and the intelligent driving system detects that the driver is focused and has no signs of fatigue, and the corresponding reliability score is 0; the driver's mental state is good, and the intelligent driving system detects that the driver is highly focused and responsive, and the corresponding reliability score is 1.

[0075] For the vehicle's current status information, its reliability score is denoted as e; the reliability score corresponding to the vehicle speed can be -2, -1, 0, 1, or 2 (the higher the speed, the lower the reliability score), and the reliability score corresponding to the vehicle maintenance information can be -1, 0, or 1 (for example, if the vehicle has unresolved maintenance issues, the corresponding reliability score is -1; if the vehicle's maintenance status is normal, the corresponding reliability score is 0; if the vehicle's maintenance status is good, with all key systems in optimal condition, the corresponding reliability score is 1).

[0076] Based on the above-mentioned preset reliability scores and the actual vehicle information collected, the actual reliability scores a, b, c, d and e corresponding to the road ahead information, current weather information, real-time road condition information ahead, driver's current status information and vehicle's current status information can be determined respectively; thereby, the total reliability score M=a+b+c+d+e corresponding to the reliability evaluation result of the current vehicle intelligent driving system can be determined.

[0077] The vehicle's reliability level is determined based on the total reliability score. When M is less than -5, the reliability level is weak. In this case, 1 grid of signal strength can be displayed, and a prompt message can be output to remind the user to pay more attention and prepare to take over the vehicle. When -5≤M≤-3, the reliability level is medium, indicating that the current intelligent driving system has average reliability. In this case, 2 grids of signal strength can be displayed, and a corresponding prompt message can be output to remind the user to maintain a certain level of attention. When M is greater than -3, the reliability is strong. In this case, 3 grids of signal strength can be displayed, and the driver is prompted that the current intelligent driving system is highly reliable and can drive with confidence.

[0078] It should be noted that the above examples are only used to understand this application and do not constitute a limitation on the reliability assessment method of the intelligent driving system of this application. More simple transformations based on this technical concept are all within the scope of protection of this application.

[0079] This application also provides a device for evaluating the reliability of an intelligent driving system. Figure 4 , the intelligent driving system reliability evaluation device includes:

[0080] An information acquisition module 10 is configured to acquire, when detecting that the intelligent driving function of the vehicle is enabled, information from multiple dimensions that may affect the reliability of the intelligent driving system; the information from multiple dimensions includes information about the road ahead, current weather conditions, real-time road conditions ahead, the driver's current state, and the vehicle's current state.

[0081] Scoring module 20, used to score each dimension of information based on preset scoring rules to obtain the reliability score corresponding to each dimension of information;

[0082] The reliability determination module 30 is used to determine the reliability evaluation result of the intelligent driving system based on all reliability scores.

[0083] The intelligent driving system reliability assessment device provided in this application adopts the intelligent driving system reliability assessment method in the above-mentioned embodiment, which can solve the technical problem that the intelligent driving system in the related art lacks reliability assessment, affecting the driver's system trust and driving safety. Compared with the related art, the beneficial effects of the intelligent driving system reliability assessment device provided in this application are the same as the beneficial effects of the intelligent driving system reliability assessment method provided in the above-mentioned embodiment, and the other technical features of the above-mentioned intelligent driving system reliability assessment device are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0084] The present application provides a vehicle, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the intelligent driving system reliability assessment method in the above-mentioned embodiment one.

[0085] Reference below Figure 5, which shows a schematic structural diagram of a vehicle suitable for implementing the embodiments of the present application. In the embodiments of the present application, the device for implementing the intelligent driving system reliability assessment method in the above-mentioned embodiment 1 may also include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (such as in-vehicle navigation terminals), and fixed terminals such as desktop computers. Figure 5 The vehicle structure shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0086] like Figure 5 As shown, the vehicle may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for vehicle operation. Processing device 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems may be connected to I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speakers, and vibrator; storage device 1003 including, for example, a magnetic tape or hard disk; and communication device 1009. The communication device 1009 can allow the vehicle to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a vehicle with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have instead.

[0087] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0088] The vehicle provided in this application uses the intelligent driving system reliability assessment method in the above-mentioned embodiment, which can solve the technical problem of the lack of reliability assessment of the intelligent driving system in the related art, which affects the driver's system trust and driving safety. Compared with the related art, the beneficial effects of the vehicle provided in this application are the same as the beneficial effects of the intelligent driving system reliability assessment method provided in the above-mentioned embodiment, and the other technical features of the vehicle are the same as those disclosed in the method of the previous embodiment, which will not be repeated here.

[0089] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0090] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0091] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the intelligent driving system reliability assessment method in the above-mentioned embodiment.

[0092] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0093] The computer-readable storage medium may be included in the vehicle, or may exist independently without being installed in the vehicle.

[0094] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the vehicle, the vehicle: obtains multiple dimensional information that affects the reliability of the intelligent driving system when it detects that the vehicle's intelligent driving function is turned on; the multiple dimensional information includes road information ahead, current weather information, real-time road condition information ahead, driver's current status information and vehicle current status information; based on preset scoring rules, each dimensional information is scored separately to obtain the reliability score corresponding to each dimensional information; based on all reliability scores, the reliability evaluation result of the intelligent driving system is determined.

[0095] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0096] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0097] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0098] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., a computer program) for executing the above-mentioned intelligent driving system reliability assessment method. It can solve the technical problem in the related art that the intelligent driving system lacks reliability assessment, which affects the driver's system trust and driving safety. Compared with the related art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the intelligent driving system reliability assessment method provided in the above embodiment, and will not be repeated here.

[0099] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned intelligent driving system reliability assessment method.

[0100] The computer program product provided in this application can address the technical problem in related technologies where intelligent driving systems lack reliability assessment, which affects drivers' trust in the system and driving safety. Compared with related technologies, the beneficial effects of the computer program product provided in this application are the same as those of the intelligent driving system reliability assessment method provided in the above embodiment, and will not be elaborated here.

[0101] The above descriptions are only some embodiments of the present application and do not limit the scope of protection. All equivalent structural transformations made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the scope of protection.

Claims

1. A method for evaluating the reliability of an intelligent driving system, characterized in that: The intelligent driving system reliability evaluation method includes: When the intelligent driving function of the vehicle is detected to be turned on, multiple dimensions of information that affect the reliability of the intelligent driving system are obtained; the multiple dimensions of information include road ahead information, current weather information, real-time road condition information ahead, driver current status information, and vehicle current status information; Scoring each dimension information based on a preset scoring rule to obtain a reliability score corresponding to each dimension information; Based on all the reliability scores, the reliability evaluation result of the intelligent driving system is determined.

2. The intelligent driving system reliability assessment method according to claim 1, characterized in that: After the step of determining a reliability evaluation result of the intelligent driving system based on all the reliability scores, the method further includes: Based on the reliability evaluation result, intelligent driving reliability prompt information is generated and output; the form of the intelligent driving reliability prompt information includes at least one of voice prompt, text prompt and steering wheel vibration prompt.

3. The intelligent driving system reliability assessment method according to claim 2, characterized in that: The step of generating and outputting intelligent driving reliability prompt information based on the reliability evaluation result includes: Determining a reliability level of the intelligent driving system based on the reliability evaluation result; the reliability level is strong reliability, medium reliability, or weak reliability; When the reliability level is strong, generating and outputting prompt information indicating that the intelligent driving function is in normal use; When the reliability level is medium reliability, generating and outputting attention maintenance prompt information; When the reliability level is weak reliability, vehicle takeover prompt information is generated and output.

4. The intelligent driving system reliability assessment method according to claim 2, characterized in that: The step of generating and outputting intelligent driving reliability prompt information based on the reliability evaluation result includes: If the reliability evaluation result is greater than the preset evaluation threshold, the intelligent driving reliability prompt information output operation is not performed; When the reliability evaluation result is less than or equal to a preset evaluation threshold, the intelligent driving reliability prompt information is output.

5. The intelligent driving system reliability assessment method according to claim 1, wherein: The road ahead information includes at least one of a roundabout, a U-turn, an unprotected left turn, a large curvature, a road width, a tunnel, and a slope; The current weather information includes visibility information; The real-time road condition information ahead includes at least one of traffic accidents, congestion, traffic lights, entering ramps, merging into main roads, lane changes, vehicle speed changes, and toll booths; The driver's current state information includes the driver's mental state; and / or The vehicle current status information includes vehicle speed information and vehicle maintenance information; each different forward road information, current weather information, forward real-time road condition information, driver current status information and vehicle current status information are all set with corresponding reliability scores.

6. The intelligent driving system reliability assessment method according to any one of claims 1 to 5, characterized in that: The step of determining a reliability evaluation result of the intelligent driving system based on all the reliability scores includes: A weighted sum is taken of all the reliability scores to determine a reliability evaluation result of the intelligent driving system.

7. A reliability assessment device for an intelligent driving system, characterized in that: The intelligent driving system reliability evaluation device includes: An information acquisition module is used to obtain multiple dimensions of information that affect the reliability of the intelligent driving system when the intelligent driving function of the vehicle is detected to be turned on; the multiple dimensions of information include road ahead information, current weather information, real-time road condition information ahead, driver current status information, and vehicle current status information; A scoring module is used to score each dimension information based on a preset scoring rule to obtain a reliability score corresponding to each dimension information; The reliability determination module is used to determine the reliability evaluation result of the intelligent driving system based on all the reliability scores.

8. A vehicle, characterized in that: The vehicle includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the intelligent driving system reliability assessment method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the intelligent driving system reliability assessment method as described in any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product includes a computer program, which, when executed by a processor, implements the steps of the intelligent driving system reliability assessment method as described in any one of claims 1 to 6.

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