Traffic violation reporting method and device, vehicle, program product and storage medium

Abstract

The present disclosure provides a traffic violation reporting method and device, a vehicle, a program product and a storage medium, and relates to the technical field of vehicle intelligent control. The traffic violation reporting method comprises the following steps: after detecting a user login operation, encrypting and binding a user account, vehicle identity information and vehicle location information to form a set identity token, wherein the vehicle identity information comprises a vehicle identification code and / or a hardware identifier of a vehicle machine; obtaining violation evidence data; assembling the violation evidence data and the set identity token to form a structured evidence package, and sending the structured evidence package to a traffic management government affairs platform. The reporting method of the present disclosure can trace back to the reporting subject, the reporting vehicle, the reporting location and the reporting equipment for each report, and can give the reporting evidence a legal basis. Meanwhile, the reporting method realizes an integrated closed-loop process from evidence collection to reporting, and improves the reporting efficiency.

Description

Technical Field

[0001] This disclosure relates to the field of vehicle intelligent control technology, and in particular to methods, devices, vehicles, program products and storage media for reporting traffic violations. Background Technology

[0002] Currently, reporting traffic violations mainly relies on users manually taking photos and videos using dashcams or mobile phones, then logging into government service platforms such as "Traffic Management 12123" to fill in the information and upload it. While some solutions can achieve automatic recognition and uploading from vehicle cameras, the following problems still exist: First, existing solutions lack a strong binding mechanism between user accounts and vehicle information, making it difficult to accurately trace the reported evidence to specific people, vehicles, times, locations, and devices, resulting in insufficient legal effect; second, the evidence collection and reporting processes are separated, requiring users to switch between multiple systems, leading to low automation and affecting reporting efficiency. Summary of the Invention

[0003] This disclosure provides a method, device, vehicle, program product, and storage medium for reporting traffic violations, which can improve the driver's interactive experience while ensuring safety.

[0004] Firstly, this disclosure provides a method for reporting traffic violations, applied to vehicles. The method includes: after detecting a user login operation, encrypting and binding the user account, vehicle identity information, and vehicle location information to form a collective identity token, wherein the vehicle identity information includes the vehicle identification code and / or vehicle hardware identifier; obtaining violation evidence data; assembling the violation evidence data and the collective identity token to form a structured evidence package; and sending the structured evidence package to the traffic management government affairs platform.

[0005] The traffic violation reporting method disclosed herein encrypts and binds user accounts, vehicle identity information, and vehicle location information on the vehicle-mounted terminal to form a collective identity token. This token allows each report to be traced back to the specific reporting entity, vehicle, location, and device, giving the evidence a legal basis. Simultaneously, the violation evidence data is assembled with the collective identity token into a structured evidence package and directly sent to the traffic management government platform, realizing an integrated closed-loop process from evidence collection to reporting, thereby improving reporting efficiency.

[0006] In some embodiments of this disclosure, obtaining violation evidence data includes: monitoring the current driving scenario according to an enabled monitoring strategy; when a violation is detected, collecting audiovisual evidence data containing the violating vehicle and the violation; generating violation evidence data based on the audiovisual evidence data; the monitoring strategy includes at least one of a stored custom strategy, a system-built-in strategy, and a temporary custom strategy.

[0007] This disclosure introduces monitoring strategies into the process of acquiring violation evidence data, ensuring that the system only performs violation monitoring and evidence collection in driving scenarios that match the enabled strategy, remaining silent in incompatible scenarios. The monitoring strategies support multiple sources, including stored custom strategies, system-built-in strategies, and temporary user-defined strategies. Users can pre-configure or adjust applicable monitoring strategies according to their individual needs. Through this strategy-driven on-demand activation mechanism, the reporting function shifts from "passively all-on" to "actively on-demand enabled," giving users fine-grained control over when and where to activate the reporting function. This reduces privacy risks associated with continuous monitoring in unnecessarily scenarios and significantly reduces the collection of invalid evidence inconsistent with user intent, thereby reducing disruptions in subsequent user confirmation processes and improving user acceptance and overall user experience of the reporting function.

[0008] In some embodiments of this disclosure, the monitoring strategy defines the following rules: violation monitoring area, violation monitoring period, road type to which the monitoring strategy applies, violation type, and evidence collection quality. The road type includes one or more of the following: highway, urban expressway, ordinary road, school perimeter, and construction road.

[0009] In this disclosure, the monitoring strategy defines multi-dimensional rules, including the violation monitoring area, monitoring time period, applicable road type, violation type, and evidence collection quality. Road types include one or more of the following: highways, urban expressways, ordinary roads, roads near schools, and roads under construction. In related technologies, the triggering conditions for reporting functions are usually relatively simple; users can only choose to enable or disable them, and cannot finely configure them according to different driving scenarios. This leads to invalid monitoring being triggered in inappropriate scenarios. For example, users on highways are more concerned about the use of emergency lanes, while on urban commuting roads they are more concerned about lane changes and cutting in front of other vehicles over solid lines. However, existing solutions cannot differentiate settings for different road types; the system applies the same monitoring strategy to all violation types, which reduces the match with the user's true reporting intent and increases the probability of false alarms for irrelevant violation types. This disclosure introduces a multi-dimensional rule-limiting mechanism, allowing users to configure different violation monitoring types, monitoring time periods, and monitoring areas for different road types, and to set quality thresholds for evidence collection. This flexible combination of "scenario and rule" allows the reporting strategy to be accurately adapted to diverse driving scenarios. It ensures high-sensitivity monitoring in key scenarios that users care about, while effectively excluding irrelevant monitoring in non-focused scenarios. This transforms the reporting function from a "one-size-fits-all" approach to "scenario-based customization," significantly improving the accuracy of reporting and the flexibility of user configuration.

[0010] In some embodiments of this disclosure, generating violation evidence data based on audiovisual evidence data includes: performing privacy compliance processing on the audiovisual evidence data to blur the license plate numbers and pedestrian facial information of non-violation targets in the audiovisual evidence data, thereby forming violation evidence data.

[0011] This disclosure achieves local obfuscation on the vehicle's in-vehicle terminal, retaining only identifiable information of the violating target. This ensures that the evidence package reported to the government platform meets privacy protection requirements at the source, reducing public concerns about participating in reporting violations.

[0012] In some embodiments of this disclosure, generating violation evidence data based on audiovisual evidence data includes: analyzing the video stream in the audiovisual evidence data, automatically extracting at least one frame of image that can characterize the key stage of the traffic violation as a key frame, and associating and binding the time, geographical location and vehicle driving status information when the traffic violation occurred with the key frame to form violation evidence data.

[0013] This disclosure analyzes video streams in audiovisual evidence data, automatically extracting at least one frame representing a key stage of a traffic violation as a keyframe. The time, geographical location, and vehicle status information of the traffic violation are then linked and bound to the keyframe to form violation evidence data. By automatically capturing keyframes and binding precise spatiotemporal and vehicle status metadata, the generated evidence data is more standardized and complete, facilitating review and adoption by government platforms and simplifying the manual extraction and filling process for users.

[0014] In some embodiments of this disclosure, the method further includes: displaying the currently enabled monitoring policy mode in the form of a visual icon.

[0015] This disclosure provides real-time visual prompts for the currently effective monitoring strategy mode via icons on the vehicle's infotainment interface. Users can easily grasp the system's current monitoring status and applicable strategy mode without needing to navigate through complex settings menus. These visual icons dynamically update as strategies change; when the vehicle enters different road types or during different time periods, triggering automatic strategy switching, the icons change synchronously to reflect the currently effective strategy. Through this real-time visual prompt mechanism, the system monitoring status remains continuously transparent to the user, eliminating user concerns arising from unseen status, enhancing user perception and control of system behavior, and helping users switch or supplement monitoring strategies promptly when necessary. This improves the transparency of human-computer interaction and user confidence.

[0016] In some embodiments of this disclosure, the method further includes: after obtaining violation evidence data, outputting confirmation information on whether to report; after receiving a user's confirmation report instruction on the violation evidence data, performing the steps of assembling the violation evidence data and the aggregated identity token into a structured evidence package, and sending the structured evidence package to the traffic management government affairs platform.

[0017] This disclosure adds a user confirmation step after evidence generation, explicitly giving users the final decision-making power over reporting. After reviewing the evidence, users can choose to confirm the report or abandon it. This step forms a "human-machine collaboration" quality control node in the technical process, effectively intercepting invalid reports caused by system misidentification, and ensuring that every successfully submitted report has been informed and consented to by the user. This strengthens the authenticity and accountability of reporting, while also enhancing users' trust in the system's operating logic and their confidence in using it.

[0018] In some embodiments of this disclosure, the method further includes: recording confirmation records of confirming or canceling reports of all violation evidence data; and adjusting the reporting sensitivity of the monitoring strategy corresponding to the violation evidence data based on the confirmation records.

[0019] In this disclosure, the system records confirmation records for all violation evidence data, including both confirmed and cancelled reports, and adjusts the reporting sensitivity of the monitoring strategy corresponding to the violation evidence data based on these confirmation records. This disclosure introduces an adaptive adjustment mechanism based on user feedback, recording and quantifying each user's confirmation or cancellation operation as a feedback signal. When a user's cumulative cancellation operations in a specific scenario reach a certain level, the system automatically reduces the monitoring sensitivity parameter for that scenario, thereby reducing the frequency of subsequent monitoring triggers in that scenario; conversely, for scenarios where users frequently confirm reports, the sensitivity is maintained or appropriately increased. Through this closed-loop "monitoring-feedback-adjustment" mechanism, the monitoring strategy of the reporting system is upgraded from user-preset static rules to an adaptive strategy that can be dynamically optimized based on user behavior. This allows the system's operating logic to gradually align with users' personalized reporting habits, significantly reducing the frequency of invalid interruptions while ensuring effective reporting coverage, achieving a personalized intelligent reporting experience, and enhancing the system's long-term usability and user stickiness.

[0020] In some embodiments of this disclosure, the method further includes: acquiring environmental information of the vehicle, the environmental information including at least one of natural environment, road environment and traffic flow conditions; and determining an activated monitoring strategy based on the vehicle's environmental information.

[0021] In this disclosure, the system acquires vehicle environmental information and determines the activated monitoring strategy based on this information. The environmental information includes at least one of the natural environment, road environment, and traffic flow conditions. In existing technologies, the monitoring strategies for reporting functions typically rely solely on user-preset static rules or single-dimensional scenario judgments, failing to incorporate real-time changes in external environmental factors into the strategy decision-making process. This static decision-making mechanism results in a lack of adaptive adjustment capabilities when facing dynamically changing driving environments. For example, in adverse weather conditions such as rain, snow, and fog, the image clarity captured by the vehicle-mounted camera significantly decreases. If the system still uses the evidence quality standards under clear weather conditions for identification and collection, it will generate a large amount of invalid evidence that cannot be accepted by the government platform due to image blur. Similarly, in slow-moving traffic scenarios, frequent close-range lane changes and cutting in line constitute violations, but if the system monitors with the sensitivity of normal roads, it may generate continuous alarm pushes, severely interfering with the driver. This disclosure introduces at least one of the environmental information—natural environment, road environment, and traffic flow conditions—into the dynamic determination process of the monitoring strategy, enabling the system to automatically match or switch to an appropriate monitoring strategy when environmental conditions change. For example, in rainy night scenarios, strategy parameters with higher tolerance for ambiguous evidence can be automatically activated, or in congested scenarios, monitoring sensitivity can be adjusted to address frequent lane-cutting behavior. Through the aforementioned environmental perception and strategy linkage technologies, the monitoring strategy is upgraded from a user-preset static configuration to an intelligent strategy that can be dynamically adjusted in real time according to the external environment. This ensures the effectiveness and usability of evidence collection under different environmental conditions, while effectively controlling a large amount of invalid monitoring and user disturbance caused by system incompatibility in harsh environments or complex traffic flows. It achieves scenario-adaptive optimization of the reporting function, further improving the system's intelligence level and long-term operational reliability.

[0022] In some embodiments of this disclosure, the method further includes: collecting input information to generate a temporary defined strategy; or collecting input information and storing the input information as a custom strategy; or collecting input information, generating a temporary defined strategy, and storing the temporary defined strategy as a custom strategy.

[0023] Secondly, this disclosure provides a traffic violation reporting device for use in vehicles. The traffic violation reporting device includes: a binding module, used to encrypt and bind the user account, vehicle identity information, and vehicle location information after detecting a user login operation, forming a collective identity token, wherein the vehicle identity information includes a vehicle identification number and / or a vehicle hardware identifier; an acquisition module, used to acquire violation evidence data; and a sending module, used to assemble the violation evidence data and the collective identity token into a structured evidence package, and send the structured evidence package to the traffic management government affairs platform.

[0024] The traffic violation reporting device provided in this public disclosure encrypts and binds user accounts, vehicle identity information, and vehicle location information at the vehicle terminal to form a collective identity token. This token allows each report to be traced back to the specific reporting entity, vehicle, location, and device, giving the evidence a legal basis. At the same time, the violation evidence data is assembled with the collective identity token into a structured evidence package and sent directly to the traffic management government platform, realizing an integrated closed-loop process from evidence collection to reporting and improving reporting efficiency.

[0025] Thirdly, this disclosure provides a control device, including: a memory and a processor; the memory and the processor are coupled; the memory is used to store a computer program; when the processor executes the computer program, it implements the traffic violation reporting method of any of the above embodiments.

[0026] Fourthly, this disclosure provides a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the traffic violation reporting method of any of the above embodiments.

[0027] Fifthly, this disclosure provides a computer program product including computer program instructions that, when executed by a processor, implement the traffic violation reporting method of any of the above embodiments.

[0028] Sixthly, this disclosure provides a vehicle including the traffic violation reporting device in any of the preceding embodiments; or the control device in any of the preceding embodiments; or the computer-readable storage medium in any of the preceding embodiments; or the computer program product in any of the preceding embodiments. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in this disclosure, the accompanying drawings used in some embodiments of this disclosure will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings.

[0030] Figure 1 A flowchart illustrating a traffic violation reporting method provided in some embodiments of this disclosure; Figure 2 A flowchart illustrating a traffic violation reporting method provided for other embodiments of this disclosure; Figure 3 This is a diagram illustrating the overall architecture of a traffic violation reporting system provided in some embodiments of this disclosure. Figure 4 This is a schematic diagram of the structure of a traffic violation reporting device provided in some embodiments of the present disclosure; Figure 5This is a schematic diagram of the structure of a control device provided in some embodiments of the present disclosure; Figure 6 This is a diagram showing the structure of scene intelligent monitoring rule parameters provided in some embodiments of this disclosure; Figure 7 This is a configuration flowchart for in-vehicle infotainment system control - scenario-based intelligent control provided in some embodiments of this disclosure. Detailed Implementation

[0031] The technical solutions of this disclosure will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0032] It should be noted that, in this disclosure, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this disclosure should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0033] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0034] In the description of this disclosure, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. "And / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" means one or more, and "more than one" means two or more.

[0035] As mentioned in the background technology, public participation in reporting traffic violations mainly relies on dashcam or mobile phone footage, which is then manually uploaded through government service platforms such as "Traffic Management 12123". Among related technologies, traffic violation monitoring systems based on intelligent recognition identify violations through in-vehicle cameras and automatically upload them to the cloud for processing. While this solution achieves automatic identification and uploading, it still faces the following technical challenges: 1) Lack of a strong binding mechanism between identity and vehicle; 2) Users cannot flexibly control the reporting function; 3) Lack of scenario-based intelligent control strategies.

[0036] In summary, the relevant technologies have the following defects and shortcomings: 1. The identity authentication and vehicle binding mechanism is weak, making it difficult to guarantee the authenticity and traceability of reported behavior; 2. Users have no choice in controlling the process, and the system's automatic monitoring throughout the entire process may infringe on user privacy and affect user experience; 3. The system has limited intelligence and cannot flexibly adjust the reporting strategy according to actual driving scenarios; 4. The overall solution lacks a closed-loop design for the collaboration of "people-vehicle-road-cloud".

[0037] Based on the above issues, this disclosure aims to provide a mechanism (Super ID) to enhance the binding of identity and vehicle, ensuring the authenticity, traceability, and legal liability of reported behavior; allowing users to selectively enable or disable the violation photography and reporting functions according to driving scenarios, thereby enhancing users' privacy control; implementing scenario-based intelligent reporting strategies, dynamically adjusting system behavior based on information such as road type and geographical location; and constructing a closed-loop reporting system that coordinates "people-vehicle-road-cloud" to improve reporting efficiency and user acceptance.

[0038] This disclosure provides a method for reporting traffic violations, applied to a vehicle's infotainment system. Specifically, it applies to the in-vehicle infotainment unit within the infotainment system.

[0039] This disclosure provides a method for reporting traffic violations. It involves encrypting and binding user accounts, vehicle identity information, and vehicle location information to form a collective identity token. Based on the monitoring strategy enabled by the user, it performs scenario-based violation monitoring and evidence collection. After completing privacy compliance processing locally, and with user confirmation, the structured evidence package is reported to the traffic management government platform. Simultaneously, the reporting sensitivity of the monitoring strategy is adaptively adjusted based on user feedback. The following provides a detailed description of each step.

[0040] like Figure 1 and Figure 2 As shown, the methods for reporting traffic violations include: S102, after detecting a user login operation, encrypts and binds the user account, vehicle identity information and vehicle location information to form a collective identity token.

[0041] The vehicle identification information includes the vehicle identification number and / or the vehicle's hardware identifier.

[0042] After entering the vehicle, the driver activates the vehicle's infotainment system. The screen displays an entry point for the "Traffic Violation Co-governance Function." The driver clicks to enter, selects "Traffic Management 12123 Account Login," and completes facial recognition verification via the vehicle's built-in camera.

[0043] This step can be performed through the identity authentication module configured in the vehicle's infotainment system, specifically the Super ID management module. After successfully verifying the user account, the Super ID management module automatically reads the vehicle identification code (VIN) via the CAN bus, obtains the vehicle's hardware identifier, and collects the current GPS positioning information as the vehicle's location information. Subsequently, the Super ID management module performs multi-layered encryption and binding of the user account, VIN, vehicle hardware identifier, and vehicle location information, generating a one-time or short-term valid aggregate identity token.

[0044] A unified identity token is a dynamic credential generated on the vehicle's infotainment system by binding a user's personal government account, vehicle identification number, vehicle hardware identifier, and real-time location information through multiple encryption methods. It serves as the smallest unit of responsibility for tracing subsequent reports. Its core function is to ensure that every report can be accurately traced to a specific person, vehicle, time, location, and device, thus providing a legal basis for the evidence of the report.

[0045] A vehicle infotainment system hardware identifier refers to a hardware identifier inherent in the vehicle infotainment system itself or its internal components that can uniquely identify the vehicle infotainment system and indirectly assist in identifying the vehicle's identity. For example, the vehicle infotainment system hardware identifier can be any one or any combination of the following: the International Mobile Equipment Identity (IMEI) of the vehicle communication module, the chip serial number of the vehicle processor, and the key identifier of the vehicle security chip; it can also be a vehicle infotainment system fingerprint generated by hashing multiple of the above hardware identifiers.

[0046] like Figure 1 and Figure 2 As shown, the methods for reporting traffic violations include: S104, obtaining evidence data of violations.

[0047] like Figure 2 As shown, S104 includes the following sub-steps: S1042, monitoring the current driving scenario according to the enabled monitoring policy.

[0048] The monitoring strategy includes at least one of the following: stored custom strategies, system-built-in strategies, and temporary custom strategies. For example... Figure 6 As shown, the monitoring strategy defines the following rules: violation monitoring area, violation monitoring time period, road type to which the monitoring strategy applies, violation type, and evidence collection quality. Road types include one or more of the following: highways, urban expressways, ordinary roads, roads around schools, and roads under construction.

[0049] After the driver completes identity verification, such as Figure 7As shown, the vehicle's infotainment system guides the driver in configuring monitoring strategies. In the settings interface, the system offers multi-dimensional rule options for users to freely combine, including: a master switch to enable or disable the automatic reporting function; scenario selection, providing road type options such as "urban roads (including main roads and expressways)," "highways," and "rural roads"; time rules, allowing users to set specific time periods; and sensitive area exclusion, where users can manually select prohibited areas on the map. Furthermore, users can select the types of violations to monitor for different road types, such as focusing on emergency lane violations on highways and lane-changing violations by crossing solid lines on urban roads, and setting a minimum evidence clarity requirement as an evidence collection quality threshold.

[0050] This step can be executed through the policy configuration module configured in the vehicle's infotainment system. Specifically, the policy configuration module can be a scenario-based policy execution module. The policy configuration module also supports users selecting built-in system policies or temporary custom policies. An example of a built-in system policy is the "Sensitive Mode Around Schools During Peak Hours," which users can enable with a single click. Temporary custom policies allow users to quickly set monitoring rules for the current trip before driving, without needing to modify previously stored custom policies.

[0051] In this embodiment, the driver selects only "Urban Roads" based on their commuting needs, sets the time to all day, and excludes the area within 500 meters of their home address as a prohibited zone in the sensitive area exclusion section, saving this as a custom policy. This custom policy is the currently enabled monitoring policy.

[0052] A monitoring strategy refers to a set of rules that users can freely combine and set through the vehicle's visual interface to limit the conditions for enabling the violation monitoring function. The monitoring strategy defines multiple dimensions of rules, including the violation monitoring area, the violation monitoring time period, the road type to which the monitoring strategy applies, the type of violation, and the quality of evidence collection, ensuring that the activation conditions of the reporting function are precisely matched with the user's actual needs.

[0053] In some possible implementations, S104 further includes: prior to S1042, acquiring environmental information of the vehicle and determining an activated monitoring strategy based on the vehicle's environmental information. The environmental information includes at least one of the natural environment, road environment, and traffic flow conditions.

[0054] This step can be performed through the environmental perception module configured in the vehicle's infotainment system. The environmental perception module accesses data from the vehicle's rain sensor, light sensor, and weather forecast to determine the natural environment (e.g., rain, snow, fog, day / night); it acquires road environment data (e.g., construction zones, school zones) through GPS and high-precision map data; and it senses surrounding vehicle density through cameras, combining this with the vehicle's driving status to determine traffic flow conditions (e.g., congestion / smooth traffic). Based on this environmental information, the environmental perception module automatically adjusts the parameters of the currently enabled monitoring strategy. For example, it automatically increases tolerance for ambiguous evidence in rainy night scenarios and adjusts monitoring sensitivity for frequent lane-cutting behavior in congested scenarios.

[0055] In related technologies, the monitoring strategies for reporting functions typically rely solely on user-preset static rules, failing to incorporate real-time changes in external environmental factors into the strategy decision-making process. This disclosure, by acquiring environmental information such as the vehicle's natural environment, road conditions, and traffic flow, and dynamically determining the monitoring strategy to be activated based on this information, enables the system to automatically match or switch to appropriate strategy parameters when environmental conditions change, ensuring the effectiveness and usability of evidence collected under different environmental conditions.

[0056] Once the monitoring policy is enabled, the vehicle system displays the currently enabled monitoring policy mode in the form of a visual icon.

[0057] This step can be performed through the visual prompt module configured in the vehicle's infotainment system. This module displays the currently active monitoring policy in real-time using a simple icon on the vehicle's interface. For example, when the vehicle enters an urban expressway and the user has enabled the urban road policy, the interface displays an "Urban Road Mode Activated" icon. If the environmental perception module detects a rainy night scenario and automatically adjusts the policy parameters, the icon switches to "Rainy Night Mode Activated." When the vehicle enters a user-selected restricted area or a road type where monitoring is not enabled, the icon remains dormant. This visual icon updates dynamically with policy changes, allowing the user to be aware of the system's current monitoring status at any time.

[0058] The driver enters an urban expressway. The vehicle's infotainment system uses GPS positioning and high-precision map data to obtain information about the current road type. The policy configuration module confirms that the current road type is "urban expressway," which falls within the coverage area of ​​the user's enabled "urban road" policy. Furthermore, the current time period is within the user-defined all-day time period, and the current geographical location is not within the user-selected prohibited area. Therefore, the system determines that the current driving scenario meets the enabled monitoring policy requirements.

[0059] In some possible implementations, such as Figure 2 As shown, S104 also includes: S1044, when a violation is detected, collecting audiovisual evidence data containing the violating vehicle and the violation.

[0060] After determining that the current driving scenario meets the monitoring policy, the system automatically activates the violation detection engine and begins to analyze the video stream data and vehicle driving data collected by the vehicle's forward-facing camera in real time. When a black sedan in an adjacent lane suddenly changes lanes to the right in the solid line area and squeezes past the vehicle, the violation detection engine identifies the "crossing the solid line to change lanes" violation within a preset time, and the confidence level reaches or exceeds the preset threshold.

[0061] After the violation detection engine confirms the violation, the system triggers an automatic evidence collection process. The system retrieves the video stream from the video cache, from the first preset duration before the black sedan begins to cross the line to the second preset duration after it completes the lane change.

[0062] This step can be performed through the evidence collection module configured in the vehicle's infotainment system. Specifically, the evidence collection module can be a photo or video module. When a traffic violation is detected, the photo or video module is used to automatically collect audiovisual evidence data from the video cache, including the offending vehicle and the complete process before and after the violation.

[0063] In some possible implementations, such as Figure 2 As shown, S104 also includes: S1046, generating violation evidence data based on audiovisual evidence data.

[0064] Specifically, S1046 includes at least one of the following processes: First, the video stream in the audiovisual evidence data is analyzed, and at least one frame that can represent the key stage of the traffic violation is automatically extracted as a key frame. The time, geographical location and vehicle driving status information when the traffic violation occurred are associated and bound with the key frame to form violation evidence data.

[0065] This step can be performed through the keyframe extraction module configured in the vehicle's infotainment system. The keyframe extraction module automatically extracts at least one frame from the audiovisual evidence data that represents a key stage of the violation. Specifically, the algorithm automatically analyzes the video and selects several of the most representative images. For example: the first image shows the moment the front wheel crosses the solid line before the lane change, with the license plate clearly visible; the second image shows the most dangerous intermediate state in the relationship between the two vehicles; and the third image is the rear view of the vehicle within its lane after the lane change, including complete rear-end information and environmental details. Subsequently, the system obtains the precise UTC time, latitude and longitude coordinates, vehicle speed, and heading from the data fusion center and associates this metadata with the selected keyframe images and video stream.

[0066] Traditional reporting methods require users to manually capture key frames from videos or fill in time and location information item by item, which is cumbersome and prone to errors. This disclosure automatically captures key frames and binds them with precise spatiotemporal and vehicle status metadata, eliminating the need for manual capture or filling in. This reduces the user's workload and ensures that the reported evidence meets the traffic management platform's review requirements in terms of format and information completeness, thereby improving the standardization and acceptance rate of evidence.

[0067] Secondly, the audiovisual evidence data is processed in accordance with privacy regulations to blur the license plate numbers of non-violation targets and the facial information of pedestrians in the audiovisual evidence data, thereby forming evidence data of violations.

[0068] This step can be performed through the privacy processing module configured in the vehicle's infotainment system. The privacy processing module uses the vehicle's computing power to analyze the video and images frame by frame. Using license plate recognition and facial recognition technologies, it automatically mosaics and blurs the license plate numbers of all vehicles in the image except the target violating vehicle, as well as the facial information of pedestrians, forming violation evidence data that has been processed in accordance with privacy regulations.

[0069] Existing reporting methods typically upload raw videos containing third-party vehicle license plates and pedestrian faces directly, potentially infringing on the privacy rights of unrelated third parties and posing legal compliance risks. This disclosure addresses this by performing blurring processing locally on the vehicle's infotainment system. During the evidence generation stage, the license plates and faces of non-violation targets are automatically blurred using mosaic techniques, retaining only the identifiable information of the violation target. This processing is completed locally on the vehicle's infotainment system, eliminating the need to upload raw data to the cloud. This aligns with the principle of data minimization for personal information protection and eliminates user concerns about potential privacy violations during reporting, thereby increasing the social acceptance of the reporting function.

[0070] In some possible implementations, such as Figure 1 and Figure 2 As shown, methods for reporting traffic violations also include: S106: After obtaining the violation evidence data, output confirmation information on whether to report; after receiving the user's confirmation report instruction on the violation evidence data, assemble the violation evidence data and the aggregated identity token into a structured evidence package, and send the structured evidence package to the traffic management government affairs platform.

[0071] After the privacy compliance process is completed, the vehicle's infotainment screen displays a non-modal pop-up window asking whether to report the violation. The message might include something like, "A lane change violation involving crossing a solid line has been detected. Evidence is ready. Do you want to report it?" At the same time, a blurred thumbnail of the scene is displayed for the user to preview the evidence.

[0072] This step can be performed through the confirmation interaction module configured in the vehicle's infotainment system. This module outputs confirmation information after obtaining evidence of the violation and receives confirmation or cancellation commands from the user.

[0073] After reviewing the evidence, the driver can issue a confirmation and report command for the violation evidence data by using the voice command "Confirm Report" or by tapping the "Confirm" button on the screen.

[0074] After receiving a user's confirmation and reporting instruction for violation evidence data, the vehicle system will assemble the violation evidence data, which has been processed for privacy compliance, with the aggregate identity token generated in S102 to form a structured evidence package.

[0075] This step can be performed through the evidence assembly module configured in the vehicle's infotainment system. The evidence assembly module is used to assemble violation evidence data and aggregated identity tokens into a structured evidence package that conforms to the interface specifications of the traffic management government affairs platform.

[0076] A structured evidence package refers to a complete report data unit formed by assembling illegal audiovisual evidence data, keyframe images, spatiotemporal metadata, and aggregated identity tokens according to the data format specified by the government platform. It includes two parts: evidence content and traceability credentials, and can be directly received and processed by the government platform.

[0077] After assembly, the system encrypts the structured evidence package using a secure transport layer protocol via the reporting interface module. Then, through the vehicle's built-in cellular network, it calls the dedicated application programming interface provided by the traffic management government platform to send the structured evidence package to the platform. Upon successful transmission, the vehicle's screen displays the message "Report submitted successfully, thank you for your participation."

[0078] In related technologies, the evidence collection and reporting processes are separated, requiring users to manually export evidence and then log in to the government affairs platform to upload it item by item, resulting in a long and inefficient workflow. This disclosure, by adding a user confirmation step, gives the final decision-making power over the report to the user, thus preventing invalid reports caused by system misidentification and ensuring that every successfully submitted report has been approved by the user. Simultaneously, the violation evidence data and the aggregated identity token are automatically assembled into a structured evidence package on the vehicle's in-vehicle system and sent directly to the government affairs platform via an encrypted communication link. This achieves an integrated closed-loop process from identification, evidence collection, privacy processing, user confirmation to reporting, requiring only one confirmation operation to complete the entire process, significantly improving reporting efficiency and user experience.

[0079] In some possible implementations, such as Figure 2 As shown, methods for reporting traffic violations also include: S108, record confirmation records for all violation evidence data, including whether a report is confirmed or canceled; based on the confirmation records, adjust the reporting sensitivity of the monitoring strategy corresponding to the violation evidence data.

[0080] The system records the user's confirmation or cancellation of the report each time the user confirms or cancels the report, and generates a confirmation record.

[0081] This step can be performed through the behavior recording module configured in the vehicle's infotainment system. The behavior recording module stores all confirmation records locally in the vehicle and adjusts the reporting sensitivity of the monitoring policy based on the cumulative number of confirmation records. User-defined monitoring policies and adjustment parameters generated based on confirmation records are both stored and executed locally in the vehicle and are not uploaded to the cloud server.

[0082] Specifically, if a user cancels more than a preset threshold in a driving scenario corresponding to a specific geographical location, the behavior recording module automatically lowers the monitoring sensitivity parameter for that specific geographical location. This prevents the vehicle's infotainment system from repeatedly generating violation evidence data and pushing confirmation requests to the user in the vicinity of that location, thus avoiding repeated disturbances. Conversely, for scenarios where users frequently confirm reports, the behavior recording module maintains or appropriately increases the monitoring sensitivity to ensure the coverage of effective reports.

[0083] In related technologies, monitoring strategies operate with fixed parameters once set, failing to detect changes in a user's true reporting intent across different scenarios. This disclosure introduces an adaptive adjustment mechanism based on user feedback. The system dynamically adjusts monitoring sensitivity parameters for corresponding scenarios based on accumulated user confirmation or cancellation records. Through this closed-loop "monitoring-feedback-adjustment" mechanism, the system strategy is upgraded from static rules to dynamic strategies that can self-optimize with user behavior. This allows the reporting function to gradually align with individual user habits, reducing the frequency of invalid interruptions and achieving a personalized intelligent reporting experience. Simultaneously, all strategy parameters and learning models are stored and executed locally on the vehicle's infotainment system, preventing sensitive information from being uploaded to the cloud and protecting user privacy.

[0084] This embodiment also provides a manual emergency reporting mode as a supplement. During driving, if the driver witnesses an extremely dangerous or clear violation by a vehicle ahead, such as malicious cutting off another vehicle or throwing objects, even if the current driving scenario does not meet any enabled monitoring policies, the driver can still manually trigger an emergency report via a custom shortcut key on the steering wheel or a voice command. Upon receiving the manual trigger command, the vehicle's infotainment system immediately executes the entire process from S104 to S106: evidence collection, keyframe extraction, privacy compliance processing, user confirmation, and encrypted reporting.

[0085] The automatic monitoring strategy mainly covers user-preset scenarios, but serious violations that occur suddenly while driving on the road can happen in any scenario. This disclosure, in addition to the strategy-driven automatic monitoring mechanism, retains a manual triggering channel as a supplement, enabling the reporting system to operate in both "strategy-driven automatic monitoring" and "user-driven manual triggering" modes. This ensures intelligent, on-demand monitoring in daily scenarios while also covering the immediate evidence collection needs for sudden serious violations, thus expanding the system's application flexibility.

[0086] like Figure 3 As shown, the overall architecture of the traffic violation reporting system disclosed herein consists of three parts: the vehicle-mounted terminal system 310, the cloud-based government affairs platform 320, and the secure communication link 330. The core modules of the vehicle-mounted terminal system are described in detail below.

[0087] I. Super ID Identity Authentication and Binding Module After users log in to their personal government affairs account on the vehicle's infotainment system, the system binds the account with the vehicle's VIN code, the vehicle's hardware ID, and the vehicle's real-time location information to generate a one-time or short-term valid "super ID," ensuring that every report can be traced back to the specific vehicle and the responsible person.

[0088] This module corresponds to the identity authentication module described in embodiment S102 above. In its specific implementation, after the user logs into their "Traffic Management 12123" account and completes facial recognition verification via the vehicle's built-in camera, the Super ID management module automatically reads the VIN code and current GPS location information from the vehicle's CAN bus. Combined with the vehicle's hardware identifier, this information is bound together using multiple encryption algorithms to generate a unified identity token. This token serves as a traceable credential for the reported behavior, persisting throughout all subsequent evidence collection and reporting stages. This ensures that each report can be accurately traced to the specific person, vehicle, time, location, and device, achieving the legal effect of an electronic record.

[0089] II. Scenario-based Intelligent Control Module This module aims to achieve precise, scenario-based control of the intelligent reporting function through the deep integration of multi-dimensional perception and user-defined strategies, avoiding accidental triggering or resource waste in unnecessary scenarios, while enhancing user trust and controllability of the function.

[0090] This module corresponds to the policy configuration module and environment awareness module described in embodiment S1042 above, and specifically includes the following sub-modules: (I) Multi-dimensional scene perception and fusion judgment Dynamic road type recognition: In addition to GPS positioning, the system combines road signs (such as speed limit signs and school zone signs), high-precision map data (number of lanes, whether it is a highway / urban expressway), and traffic sign recognition results to perform composite verification of the current driving scenario. For example, if a road segment is located by GPS, but the camera recognizes a "school zone" sign, the system will automatically trigger school zone-specific strategies (such as speed reduction and increased sensitivity).

[0091] Real-time environment and state awareness: By integrating vehicle rain sensor, light sensor, and weather forecast data, weather (rain, snow, fog) and lighting conditions (day / night) are determined and used as weighting factors for scene strategies. For example, in rainy night scenarios, the tolerance for behaviors such as crossing solid lines can be lowered (i.e., more sensitive), but the threshold for judging ambiguous evidence is correspondingly increased.

[0092] Multi-dimensional judgment: Combining the vehicle's driving status (speed, turn signals, acceleration) and the density of surrounding vehicles (sensed by cameras), the system determines the current traffic flow status (congested / smooth). For example, in congested areas, frequent lane changes and cutting in may trigger reports, but if the system detects that the vehicle is in an emergency avoidance state, it will automatically suppress false alarms.

[0093] (ii) User-editable multi-dimensional rule customization Users can freely combine rules in the vehicle's infotainment settings through a visual interface (such as a condition selector), supporting dimensions including: Time: Specific time periods (such as peak school hours), weekdays, holidays.

[0094] Areas: Geofencing (custom-drawn areas), frequently used routes (such as commuting routes), and unfamiliar areas.

[0095] Road types: expressways, urban expressways, ordinary roads, roads around schools, construction zones, etc.

[0096] Violation type: You can select the type of violation to monitor for different scenarios (e.g., focus on occupying the emergency lane on highways, and focus on cutting in line by crossing solid lines on urban roads).

[0097] Evidence collection quality: Set minimum evidence clarity requirements (such as license plate pixel threshold).

[0098] (III) Scene Templates and Sharing The system comes pre-loaded with common scenario strategy templates (such as "Sensitive mode around schools during peak hours"), which users can enable with one click. Users can also share custom strategies (after anonymization) to the community to form a strategy ecosystem.

[0099] (iv) Adaptive strategies and user feedback-based learning The system records users' confirmation / cancellation behavior regarding report pop-ups in different scenarios. For example, if a user frequently cancels reports at a certain intersection, the system will reduce sensitivity in the vicinity of that location.

[0100] This mechanism corresponds to the behavior recording module described in embodiment S108 above. With user authorization, anonymized user policy data can be uploaded to the cloud for analysis and optimization of the general scenario model, and then distributed to the vehicle to achieve a balance between personalized experiences and overall performance improvement.

[0101] (v) Localization and secure execution of scenario strategies Local storage and execution: All user-defined strategies and learning models are stored and run locally in the vehicle system, avoiding the uploading of sensitive information to the cloud and protecting user privacy.

[0102] Avoid conflicts: When multiple rules are triggered simultaneously (such as the "City Roads" rule and the "School Zone" rule), they are processed according to the user-defined priority to avoid conflicts.

[0103] Real-time policy visualization: The vehicle's infotainment system displays the currently active policy using a simple icon (such as "School Area Mode Activated"). This function corresponds to the visualization prompt module described in embodiment S1042 above. When the vehicle enters different road types or triggers automatic policy switching at different times, the icon updates synchronously, allowing the user to perceive the current monitoring status of the system at any time.

[0104] III. Privacy Compliance Handling The "Privacy Processing Module" uses onboard computing power to analyze evidence videos and images frame by frame. It uses license plate recognition and facial recognition technology to automatically blur the license plates of all vehicles and the faces of pedestrians in the image, except for the target vehicle that violated the law.

[0105] This module corresponds to the privacy processing module described in embodiment S1046 above. In its specific implementation, the privacy processing module is activated during the evidence generation stage, performing frame-by-frame analysis on the collected audiovisual evidence data, automatically identifying and blurring the license plate numbers of non-violation targets and the facial information of pedestrians. This processing is entirely completed locally on the vehicle's infotainment system, without needing to upload the raw data to the cloud. This not only complies with the principle of data minimization for personal information protection but also eliminates users' concerns that their reporting behavior may infringe on the privacy of others.

[0106] IV. System Workflow Based on the above modules, the overall workflow of this public method for reporting traffic violations is as follows: User logs in and completes super ID binding → The system determines whether to enable violation monitoring for the current road type based on user settings → If enabled, violations are identified in real time → Photos or videos are automatically generated → After user confirmation, the system automatically reports to the government affairs platform.

[0107] This workflow upgrades smart cars into traceable, user-controllable, and privacy-friendly trusted mobile government service terminals. Through a strong super ID binding mechanism, it addresses the weaknesses in the identity and vehicle traceability chain and the insufficient legal validity of reports in existing reporting schemes. Through user-editable scenario-based intelligent control and local adaptive learning, it solves the problems of disconnect between system behavior and user intent, lack of user privacy control, and significant false alarm interference. Through local privacy ambiguity processing, it addresses compliance issues regarding evidence potentially infringing on the privacy of unrelated third parties. Overall, it achieves the technical effects of enhancing the legal validity of reporting evidence, granting users granular control for privacy protection, and providing an adaptive intelligent reporting experience.

[0108] Furthermore, the specific implementation of the technical solution disclosed herein will be further explained below in conjunction with typical application scenarios.

[0109] Application Example 1: Reporting in Core Urban Road Scenarios Based on Collective Identity Tokens This is a typical application scenario, and the specific process is as follows.

[0110] Initial Binding: Driver Xiao Wang enters the vehicle and starts the central control system. The screen displays the "Traffic Violation Co-governance Function" entry. Xiao Wang clicks to enter, selects "Traffic Management 12123 Account Login," and completes facial recognition verification through the vehicle's built-in camera. The system's background Super ID management module runs: After successful account verification, it automatically reads the VIN code from the vehicle's CAN bus, the current GPS location, and combines it with the vehicle's hardware identifier to generate a collective identity token through multi-layered encryption binding.

[0111] Scene strategy configuration: The system guides Xiao Wang through the reporting settings. The settings interface offers several options: a main function switch to enable or disable automatic reporting; and scene selection, where users can check the boxes for... The map selection criteria are: "Urban roads (including main roads and expressways)," "□ Expressways," and "□ Rural roads." The time frame can be set to "07:00-22:00 daily." Sensitive area exclusions can be manually selected on the map, with a 500-meter radius around the "home address" and "company address" designated as a restricted area. Xiao Wang, a commuter, primarily wants to report violations on urban roads, so he only selected "Urban roads," set the time to all day, and saved the settings.

[0112] Driving Monitoring and Triggering: Xiao Wang drives onto an urban expressway. The vehicle scenario policy execution module, through GPS and high-precision map data, confirms that the current road type is "urban expressway" and matches the rules already enabled by the user. Subsequently, the AI ​​violation recognition engine is activated and begins to analyze the forward-facing camera video stream and vehicle data in real time. When a black sedan in the adjacent lane suddenly changes lanes to the right in the solid line area, squeezing Xiao Wang's vehicle, the AI ​​engine identifies the "crossing the solid line to change lanes" behavior within 0.3 seconds, with a confidence level of 92%.

[0113] Automatic evidence collection: The photo or video module is triggered. It immediately retrieves a 15-second (1080P) video stream from the cache, from 10 seconds before the black sedan begins crossing the line to 5 seconds after completing the lane change. Simultaneously, the algorithm automatically analyzes this 15-second video, selecting three of the most representative JPEG images: the first shows the moment the car's front wheel crosses the solid line before the lane change, with the license plate clearly visible; the second shows the most dangerous intermediate state in the relationship between the two vehicles; and the third is a rear view of the car within its lane after the lane change, including the complete rear end and surrounding environment. Subsequently, the module obtains the precise UTC time, latitude and longitude, vehicle speed, and heading information at the time of the violation from the data fusion center and associates it with the keyframe images.

[0114] Privacy compliance processing: After generating complete evidence materials, the privacy processing module is activated. It uses the vehicle's computing power to analyze the 15-second video and 3 images frame by frame, and uses license plate recognition and facial recognition technology to automatically blur the license plates of all vehicles and the faces of pedestrians in the scene, except for the target violating vehicle (black sedan).

[0115] User Confirmation and Reporting: After processing, a non-modal pop-up window appears in the lower right corner of the vehicle's screen: "A lane-changing violation involving crossing a solid line has been detected. Evidence is ready. Do you want to report it?" A blurred thumbnail of the scene is also displayed. Xiao Wang, looking ahead, simply needs to say "Confirm Reporting" or tap the "Confirm" button on the screen.

[0116] Encrypted transmission: The reporting interface module encrypts a structured evidence package containing video, images, and metadata (including a collection of identity tokens) using the TLS 1.3 protocol. This package is then uploaded via the vehicle's built-in 4G / 5G cellular network, calling a dedicated API provided by the government platform. The vehicle's infotainment system displays "Report submitted successfully, thank you for your participation."

[0117] Application Example 2: Manually Triggering Emergency Reporting Mode Even if automatic reporting is not enabled in a specific scenario, the system retains the ability to "actively collect evidence." While driving, if the driver witnesses an extremely dangerous or clear violation by a vehicle ahead (such as deliberately cutting off another vehicle or throwing objects), they can manually trigger the report using a custom shortcut key on the steering wheel or a voice command (such as "report the vehicle ahead").

[0118] Upon receiving the instruction, the system immediately activates the photo and video modules, recording a 30-second high-definition video before and after the instruction. It then automatically performs keyframe extraction, data binding, and privacy processing, before displaying a confirmation interface for the user to submit their report. This mode expands the system's application flexibility, enabling the reporting system to operate in both "strategy-driven automatic monitoring" and "user-driven manual triggering" modes. This ensures intelligent, on-demand monitoring in daily scenarios while also covering the immediate evidence collection needs for sudden and serious violations.

[0119] Figure 4 This is a schematic diagram of a traffic violation reporting device provided in this embodiment. The traffic violation reporting device is applied to a vehicle and can execute the traffic violation reporting method provided in the above method embodiment.

[0120] This disclosure provides a traffic violation reporting device, which can be applied to vehicles, such as... Figure 4 As shown, the traffic violation reporting device 400 includes: a binding module 410, used to encrypt and bind the user account, vehicle identity information, and vehicle location information after detecting a user login operation, forming a collective identity token, wherein the vehicle identity information includes a vehicle identification code and / or a vehicle hardware identifier; an acquisition module 420, used to acquire violation evidence data; and a sending module 430, used to assemble the violation evidence data and the collective identity token into a structured evidence package, and send the structured evidence package to the traffic management government affairs platform.

[0121] The traffic violation reporting device provided in this public disclosure encrypts and binds user accounts, vehicle identity information, and vehicle location information at the vehicle terminal to form a collective identity token. This token allows each report to be traced back to the specific reporting entity, vehicle, location, and device, giving the evidence a legal basis. At the same time, the violation evidence data is assembled with the collective identity token into a structured evidence package and sent directly to the traffic management government platform, realizing an integrated closed-loop process from evidence collection to reporting and improving reporting efficiency.

[0122] When implementing the functions of the integrated modules described above in hardware, this disclosure provides a possible structure for the traffic violation reporting device involved in the above embodiments. For example... Figure 5 As shown, the control device 500 includes a processor 502 and a bus 504. Optionally, the traffic violation reporting device may further include a memory 501; optionally, the traffic violation reporting device 500 may further include a communication interface 503. Optionally, the memory 501 and the processor 502 are coupled; the memory 501 is used to store a computer program; when the processor 502 executes the computer program, it implements the traffic violation reporting method of any of the above embodiments.

[0123] Processor 502 may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with embodiments of this disclosure. Processor 502 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with embodiments of this disclosure. Processor 502 may also be a combination that implements computing functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0124] Communication interface 503 is used to connect to other devices via a communication network. This communication network can be Ethernet, wireless access network, wireless local area network (WLAN), etc.

[0125] The memory 501 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), disk storage medium or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0126] In one possible implementation, the memory 501 can exist independently of the processor 502. The memory 501 can be connected to the processor 502 via a bus 504 and is used to store instructions or program code. When the processor 502 calls and executes the instructions or program code stored in the memory 501, it can implement the traffic violation reporting method provided in this embodiment. In another possible implementation, the memory 501 can also be integrated with the processor 502.

[0127] Bus 504 can be an extended industry standard architecture (EISA) bus, etc. Bus 504 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0128] Some embodiments of this disclosure provide a computer-readable storage medium (e.g., a non-transitory computer-readable storage medium) storing computer program instructions that, when executed by a processor, implement the traffic violation reporting method of any of the above embodiments.

[0129] Exemplary examples of computer-readable storage media may include, but are not limited to: magnetic storage devices (e.g., hard disks, floppy disks, or magnetic tapes), optical discs (e.g., compact disks (CDs), digital versatile disks (DVDs), etc.), smart cards, and flash memory devices (e.g., erasable programmable read-only memory (EPROMs), cards, sticks, or key drives, etc.). The various computer-readable storage media described in this disclosure may represent one or more devices and / or other machine-readable storage media for storing information. The term "machine-readable storage medium" may include, but is not limited to, wireless channels and various other media capable of storing, containing, and / or carrying instructions and / or data.

[0130] This disclosure provides a computer program product containing computer instructions. When the computer program instructions are executed by a processor, they implement the traffic violation reporting method of any of the above embodiments.

[0131] This disclosure provides a vehicle, including a traffic violation reporting device as described in any of the preceding embodiments; or a control device as described in any of the preceding embodiments; or a computer-readable storage medium as described in any of the preceding embodiments; or a computer program product as described in any of the preceding embodiments.

[0132] The vehicles provided in this disclosure may be passenger vehicles or freight vehicles, and may be electric vehicles or hybrid vehicles. This disclosure does not limit the specific purpose or power type of the vehicles, and can be selected according to actual needs.

[0133] A vehicle consists of a body and wheels. The body is used for passengers and for carrying goods, while the wheels are mounted underneath the body to support it and allow it to roll on the road, thus enabling the vehicle to move.

[0134] In some possible examples, the vehicle is equipped with a control system, which typically adopts a layered distributed architecture. From the bottom layer to the top layer, it can be roughly divided into a perception layer, a control layer, a coordination layer, and an interaction layer. The layers communicate with each other through an in-vehicle network.

[0135] The perception layer mainly consists of various sensors distributed inside and outside the vehicle, including but not limited to external environment cameras, millimeter-wave radar, lidar, ultrasonic sensors, in-vehicle driver monitoring cameras, microphone arrays, and various vehicle status sensors (such as wheel speed sensors, inertial measurement units, temperature sensors, etc.). The perception layer is responsible for collecting multi-dimensional data such as the vehicle's own operating status, driver behavior, and external driving environment in real time.

[0136] The control layer consists of dozens to hundreds of electronic control units (ECUs), distributed across multiple functional domains including powertrain, chassis, body, intelligent driving, and infotainment. Each ECU embeds real-time control software that performs closed-loop control of the vehicle's actuators based on preset control strategies or upper-level commands, and generates corresponding alarm signals when abnormal conditions are detected. Typical ECUs include the engine control unit, transmission control unit, brake control unit, steering control unit, vehicle stability control unit, airbag control unit, intelligent driving domain controller, and in-vehicle infotainment unit.

[0137] The coordination layer typically exists in the form of a domain controller or a central computing platform, responsible for cross-domain data fusion, global state management, and collaborative decision-making. The coordination layer centrally processes and schedules the sensing data and control commands that were originally scattered across various functional domains, connecting downwards to various electronic control units and supporting human-machine interaction functions upwards.

[0138] The interaction layer mainly includes in-cabin display devices (such as instrument panel, central control screen, head-up display), voice interaction system, haptic feedback device, etc., which are responsible for presenting vehicle status, warning information and driving suggestions to the driver in the form of visual, auditory or tactile, while receiving the driver's touch, voice and other input commands.

[0139] Data transmission and interaction between different layers are achieved through the vehicle bus network. Common vehicle bus protocols include CAN (Controller Area Network), CAN FD (CAN with Flexible Data-Rate), LIN (Local Interconnect Network), FlexRay, and vehicle Ethernet, which supports high-bandwidth data transmission. Among these, CAN and CAN FD buses are widely used for communication in real-time control domains such as powertrain and chassis, while vehicle Ethernet is gradually being applied to high-bandwidth sensor data transmission in the intelligent driving domain and multimedia interaction scenarios in the cockpit domain.

[0140] In some possible examples, the cockpit domain controller, as the core of the in-vehicle computing platform, is configured with a processor and memory to execute the traffic violation reporting method provided in the embodiments of this disclosure.

[0141] For example, the traffic violation reporting device of this disclosure is arranged in an in-vehicle infotainment unit.

[0142] For example, the in-vehicle infotainment unit is equipped with multiple functional modules for performing the various method steps.

[0143] The above are merely specific embodiments of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any changes or substitutions within the technical scope disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A method for reporting traffic violations, characterized in that, Applied to vehicles, the method includes: After detecting a user login operation, the user account, vehicle identity information and vehicle location information are encrypted and bound to form a collective identity token. The vehicle identity information includes the vehicle identification code and / or the vehicle hardware identifier. Obtain evidence of violations; The violation evidence data and the aggregated identity tokens are assembled into a structured evidence package, which is then sent to the traffic management government affairs platform.

2. The method for reporting traffic violations according to claim 1, characterized in that, The data used to obtain evidence of violations includes: The current driving scenario is monitored according to the enabled monitoring strategy; When violations are detected, audiovisual evidence data containing information about the violating vehicle and the violation will be collected. Based on the aforementioned audiovisual evidence data, generate violation evidence data; The monitoring strategy includes at least one of the following: stored custom strategy, system built-in strategy, and temporary custom strategy.

3. The method for reporting traffic violations according to claim 2, characterized in that, The monitoring strategy defines the following rules: violation monitoring area, violation monitoring time period, road type to which the monitoring strategy applies, violation type, and evidence collection quality. The road type includes one or more of the following: highway, urban expressway, ordinary road, road near school, and road under construction.

4. The method for reporting traffic violations according to claim 2, characterized in that, The step of generating violation evidence data based on the audiovisual evidence data includes: The audiovisual evidence data is processed for privacy compliance to obscure the license plate numbers of non-violation targets and the facial information of pedestrians in the audiovisual evidence data, thereby forming violation evidence data; and / or The video stream in the audiovisual evidence data is analyzed, and at least one frame that can characterize the key stage of the traffic violation is automatically extracted as a key frame. The time, geographical location and vehicle driving status information when the traffic violation occurred are associated and bound with the key frame to form the violation evidence data.

5. The method for reporting traffic violations according to claim 2, characterized in that, The method further includes: The currently enabled monitoring policy mode is displayed in the form of visual icons.

6. The method for reporting traffic violations according to any one of claims 1 to 5, characterized in that, The method further includes: After obtaining the evidence data of the violation, output a confirmation message indicating whether to report it; After receiving a user's confirmation and reporting instruction regarding the violation evidence data, the step of assembling the violation evidence data and the aggregated identity token into a structured evidence package and sending the structured evidence package to the traffic management government affairs platform is executed.

7. The method for reporting traffic violations according to claim 6, characterized in that, The method further includes: Record the confirmation of all reported violations or the cancellation of reports; Based on the confirmation records, adjust the reporting sensitivity of the monitoring strategy corresponding to the violation evidence data.

8. The method for reporting traffic violations according to claim 2, characterized in that, The method further includes: Acquire vehicle environmental information, wherein the environmental information includes at least one of natural environment, road environment, and traffic flow conditions; The monitoring strategy to be activated is determined based on the vehicle's environmental information.

9. A traffic violation reporting device, characterized in that, Applied to vehicles, the traffic violation reporting device includes: The binding module is used to encrypt and bind the user account, vehicle identity information and vehicle location information after detecting a user login operation, forming a collective identity token. The vehicle identity information includes the vehicle identification code and / or the vehicle hardware identifier. The acquisition module is used to acquire evidence data of violations. The sending module is used to assemble the violation evidence data and the aggregated identity token into a structured evidence package, and send the structured evidence package to the traffic management government affairs platform.

10. A control device, characterized in that, include: Memory and processor; the memory and processor are coupled; The memory is used to store computer programs; When the processor executes the computer program, it implements the traffic violation reporting method as described in any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, include: The computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the traffic violation reporting method as described in any one of claims 1 to 8.

12. A computer program product, characterized in that, include: The computer program product includes computer program instructions that, when executed by a processor, implement the traffic violation reporting method as described in any one of claims 1 to 8.

13. A vehicle, characterized in that, include: The traffic violation reporting device as described in claim 9; or The control device as described in claim 10; or The computer-readable storage medium as claimed in claim 11; or The computer program product as described in claim 12.

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