Intelligent driving assistance system and method, electronic equipment and computer readable medium

Abstract

The invention provides an intelligent driving assistance system and method, and belongs to the field of intelligent driving, the driving data of a vehicle is collected in real time through a data acquisition module, a driving behavior analysis engine analyzes the driving data to evaluate the safety of the driving behavior, and a suggestion generator generates a driving suggestion according to the driving behavior. The report generator integrates driving data and driving suggestions and generates a driving behavior analysis report, the voice interaction module converts the analysis report into voice information and broadcasts the voice information to a driver in real time, and the display module provides a user interface so that the driver can check the analysis report. Compared with the prior art, more advanced data acquisition and analysis technologies and a machine learning algorithm are integrated to achieve deep understanding and personalized feedback of driving behaviors, key parameters in the driving process can be recorded and analyzed, and through the data, the system can evaluate the safety of the driving behaviors, help drivers to optimize driving habits and improve driving safety.

Description

Technical Field

[0001] This invention relates to the field of intelligent driving technology, and in particular to an intelligent driving assistance system, method, electronic device, and computer-readable medium. Background Technology

[0002] Intelligent driving assistance systems, as a key component of modern automotive technology innovation, are gradually changing the way we drive. These systems utilize various advanced sensors integrated into vehicles, such as radar, LiDAR, cameras, and ultrasonic sensors, to monitor the vehicle's surroundings in real time. Through these sensors, the systems can track other vehicles, pedestrians, traffic signals, and road conditions, thereby providing a range of safety assistance functions.

[0003] However, despite significant advancements in real-time monitoring and warning technologies, existing systems still fall short in terms of in-depth analysis of driving behavior and personalized feedback. Most systems, when issuing warnings, do not adequately consider the driver's specific behavioral patterns, habits, and preferences. For example, in the event of emergency braking or approaching an obstacle, the system may simply issue a warning or automatically brake without providing in-depth analysis and suggestions on how to improve driving skills and avoid similar situations.

[0004] Furthermore, existing systems typically lack the ability to track and evaluate driving behavior over the long term, failing to provide users with comprehensive reports and improvement suggestions regarding their driving habits. This means that drivers may not fully understand the impact of their driving behavior on vehicle safety and efficiency, thus missing opportunities to improve driving skills and reduce the chances of accidents. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art, and proposes an intelligent driving assistance system and method.

[0006] In a first aspect, embodiments of the present invention provide an intelligent driving assistance system, comprising:

[0007] The data acquisition module is used to collect vehicle driving data in real time;

[0008] A driving behavior analysis engine is used to analyze the driving data to assess the safety of driving behavior;

[0009] The suggestion generator generates driving suggestions based on driving behavior.

[0010] A report generator is used to integrate driving data and driving suggestions to generate driving behavior analysis reports.

[0011] The display module provides a user interface for users to view the analysis report.

[0012] In some embodiments, the driving data includes, but is not limited to, speed, acceleration, braking force, steering angle, driving time, and GPS location information.

[0013] In some embodiments, the driving behavior analysis engine uses machine learning algorithms to analyze the collected data to assess the safety of driving behavior and give a driving behavior score accordingly.

[0014] In some embodiments, the machine learning algorithm includes support vector machines, random forests, or convolutional neural networks.

[0015] In some embodiments, a voice interaction module is also included. The voice interaction module converts the analysis report into voice information using text-to-speech technology or natural language processing technology and broadcasts it to the user in real time through the vehicle's audio system. The voice interaction module also supports voice recognition, allowing users to query report details through voice commands.

[0016] In some embodiments, a data storage module is also included for storing and managing data and reports collected during driving.

[0017] In some embodiments, an open API is also included, which allows third-party developers and services to access and extend the functionality of the system.

[0018] Secondly, embodiments of the present invention also provide an assistance method for the aforementioned intelligent driving assistance system, comprising the following steps:

[0019] Collect vehicle driving data in real time;

[0020] The driving data is analyzed to assess the safety of driving behavior;

[0021] Generate driving suggestions based on driving behavior;

[0022] Integrate driving data and driving suggestions to generate driving behavior analysis reports.

[0023] Thirdly, embodiments of the present invention also provide an electronic device, comprising:

[0024] One or more processors;

[0025] Memory, used to store one or more programs;

[0026] When the one or more programs are executed by the one or more processors, the one or more processors implement the method.

[0027] Fourthly, embodiments of the present invention also provide a computer-readable medium storing a computer program, which, when executed by a processor, implements the steps in the method.

[0028] The intelligent driving assistance system provided by this invention includes a data acquisition module for real-time collection of vehicle driving data, a driving behavior analysis engine for analyzing the driving data to assess the safety of driving behavior, a suggestion generator for generating driving suggestions based on driving behavior, a report generator for integrating driving data and driving suggestions to generate a driving behavior analysis report, a voice interaction module for converting the analysis report into voice information and broadcasting it to the user in real time, and a display module for providing a user interface for viewing the analysis report. This intelligent driving assistance system integrates more advanced data acquisition and analysis technologies, as well as machine learning algorithms, to achieve a deeper understanding of driving behavior and personalized feedback. These systems should be able to record and analyze key parameters during the driving process. Through this data, the system can assess the safety of driving behavior and generate customized improvement suggestions to help drivers optimize their driving habits and improve driving safety. By feeding these analysis results and suggestions back to the user through voice or other interactive methods, the intelligent driving assistance system can provide a richer and more personalized driving experience, further improving driving safety and efficiency. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the structure of an embodiment of the intelligent driving assistance system of the present invention;

[0030] Figure 2 This is a schematic diagram illustrating the operation of an embodiment of the driving behavior analysis engine of the present invention;

[0031] Figure 3 This is a flowchart illustrating the steps of one embodiment of the intelligent driving assistance method of the present invention;

[0032] Figure 4 This is a schematic diagram of the structure of an embodiment of the electronic device of the present invention. Detailed Implementation

[0033] To enable those skilled in the art to better understand the technical solutions of the present invention, exemplary embodiments of the present invention are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0034] Where there is no conflict, the various embodiments of the present invention and the features thereof may be combined with each other.

[0035] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0036] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used herein, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Terms such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0037] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having the meaning consistent with their meaning in the context of the relevant art and the invention, and will not be interpreted as having an idealized or overly formal meaning unless expressly so defined herein.

[0038] In the technical solution of this invention, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information all comply with relevant laws and regulations and do not violate public order and good morals. The use of user data in this technical solution follows relevant national laws and regulations (e.g., the "Information Security Technology - Personal Information Security Specification"). For example: appropriate measures are taken for personal information access control; restrictions are imposed on the display of personal information; the purpose of using personal information does not exceed the scope of direct or reasonable association; and explicit identity targeting is eliminated when using personal information to avoid precisely locating a specific individual.

[0039] In related technologies, Adaptive Cruise Control (ACC) uses radar or cameras to monitor road conditions ahead, track changes in the speed of the vehicle ahead in real time, and automatically adjust the vehicle's speed to maintain a safe distance. This system can significantly reduce the driver's workload when driving on highways. However, ACC systems are designed to provide assistance in ideal road conditions such as highways, and their algorithms and sensor configurations may not fully consider urban congestion or complex traffic environments. In these environments, the system may not respond flexibly or accurately due to decreased sensor recognition accuracy or insufficient algorithm adaptability. Furthermore, because ACC systems generally adopt a standardized design, they may not consider the individualized needs of different drivers regarding distance and response time. While this simplification reduces system complexity, it also limits its ability to meet the preferences of different drivers. Existing systems use general algorithms and fail to personalize adjustments based on each driver's unique driving style and habits. This results in the system being unable to provide customized feedback and suggestions to the driver.

[0040] To address at least one of the technical problems existing in the aforementioned related technologies, the present invention provides an intelligent driving assistance system. Figure 1 This is a schematic diagram of the structure of an intelligent driving assistance system provided in an embodiment of the present invention.

[0041] like Figure 1 As shown, the intelligent driving assistance system includes a data acquisition module 10, a driving behavior analysis engine 20, a suggestion generator 30, a report generator 40, a voice interaction module 50, and a display module 60. The specific implementation methods of each component are described in detail below.

[0042] The data acquisition module 10 is used to collect vehicle driving data in real time.

[0043] The data acquisition module 10 is responsible for collecting vehicle driving data in real time, including but not limited to speed, acceleration, braking force, steering angle, driving time, GPS location information, etc.

[0044] It is understandable that, in addition to using the vehicle's own sensors, the data acquisition module 10 could consider integrating sensor data from mobile devices (such as smartphones), such as accelerometers and GPS, to assist or enhance the acquisition of driving data.

[0045] It should be noted that the data acquisition module 10 is used to acquire information from the vehicle's CAN bus. The CAN bus is the primary data source, providing a wealth of information about the vehicle's internal systems. For example, vehicle speed is obtained from wheel speed sensors or transmission output; acceleration / deceleration is typically obtained by calculating the rate of change of vehicle speed or by directly reading from the vehicle's longitudinal acceleration sensor (if available); braking force is obtained through a brake pedal position sensor or a master cylinder pressure sensor. Steering angle is obtained from a steering wheel angle sensor.

[0046] Specifically, the data acquisition module 10 is used to acquire data from the GPS / GNSS receiver. For example, latitude and longitude coordinates are used to provide location information, including altitude, speed, and vehicle heading.

[0047] The data acquisition module 10 is also used to acquire data from the inertial measurement unit, such as three-axis acceleration, to provide more accurate acceleration information, especially lateral acceleration (which is crucial for cornering analysis); and a three-axis gyroscope to measure the rate of change of vehicle attitude.

[0048] The data acquisition module 10 may include an on-board data recorder that connects to the CAN bus via an OBD-II interface and integrates a GPS module, which is low-cost and easy to deploy, or it may include an on-board infotainment system / vehicle networking module that can directly provide some data through a built-in T-Box or IVI system.

[0049] The driving behavior analysis engine 20 is used to analyze the driving data to assess the safety of driving behavior.

[0050] In this embodiment, the driving behavior analysis engine 20 uses machine learning algorithms to perform in-depth analysis of the collected data, assess the safety of driving behavior, and assign a driving behavior score accordingly. This engine can identify potentially dangerous behaviors such as rapid acceleration, rapid deceleration, and sharp turns, and assess the driver's compliance with traffic rules.

[0051] Please see Figure 2 The workflow of the driving behavior analysis engine 20 is as follows: it processes the collected raw data, extracts features from the processed data, analyzes the data after feature extraction using machine learning algorithms, and then calculates the driving behavior score based on the results of the analysis engine to generate a report or alert.

[0052] Specifically, the driving behavior analysis engine 20's data processing flow includes: receiving real-time data streams from the acquisition module, cleaning the data (e.g., handling missing values ​​by interpolation or labeling), smoothing noise (e.g., using moving averages or Kalman filtering), detecting and removing obvious outliers, and data alignment (ensuring timestamp synchronization).

[0053] Furthermore, the driving behavior analysis engine 20 extracts features from the processed data, including basic features such as raw values ​​(current speed, current acceleration, etc.); statistical features within a time window, such as the mean, maximum, minimum, variance, and standard deviation of speed, acceleration, or deceleration, or the count of specific events (such as the number of times acceleration > 0.3g) and quantiles (such as the 95th percentile velocity); rate of change / derivative features, such as acceleration (the first derivative of speed) and steering angular velocity (the first derivative of steering angle); and spatiotemporal features (combining GPS and map data). For example, it compares the speed at the current GPS location with the speed limit of the road segment in the electronic map to determine speeding; it combines GPS trajectory, timestamp, heading, and traffic light information (requires external data sources or advanced maps) to analyze red light violations / illegal turns; it identifies road types by highways, urban roads, and rural roads (affecting behavior assessment criteria); and it identifies turns by combining high lateral acceleration, high steering angular velocity, and the curvature of curves displayed on the GPS trajectory.

[0054] The driving behavior analysis engine 20 uses machine learning algorithms to perform in-depth analysis of the collected data, including dangerous behavior identification, such as identifying a single event (such as a sudden braking or a sharp turn); overall safety assessment, such as combining all behaviors over a period of time to give an overall safety score; and rule compliance assessment, such as assessing compliance with speed limits, traffic lights, and other rules.

[0055] Furthermore, commonly used model types include: logistic regression, support vector machine, decision tree / random forest / gradient boosting tree (XGBoost, LightGBM, CatBoost) and neural network.

[0056] In this embodiment, the model training and deployment are as follows:

[0057] Data labeling: Collect a large amount of driving trip data, and have experts label dangerous events and safety scores based on sensor data (especially video recordings) or explicit rules (such as acceleration >0.4g is defined as rapid acceleration).

[0058] Feature selection: Select the subset of features that are most useful for target prediction to improve model efficiency and generalization ability.

[0059] Model training and validation: Train the model on the labeled dataset and evaluate its performance (accuracy, recall, F1 score, AUC, RMSE, etc.) using methods such as cross-validation.

[0060] Model deployment: Deploy the trained model into the analytics engine for real-time stream processing (such as Apache Flink, Spark Streaming) or near real-time batch processing.

[0061] Model monitoring and updates: Continuously monitor the model's performance in the production environment, and periodically retrain or fine-tune the model with new data to adapt to changes (such as new vehicle models or new road conditions).

[0062] The driving behavior analysis engine 20 then assesses the safety of driving behavior based on the training and deployment results, which may include, for example, the following driving behaviors:

[0063] Rapid acceleration: Longitudinal acceleration > the set high threshold (e.g., 0.3g or 0.4g).

[0064] Rapid deceleration / emergency braking: Longitudinal deceleration < the set low threshold (e.g., -0.4g or -0.5g).

[0065] Sharp turns: Lateral acceleration > set threshold (e.g., 0.3g or 0.4g), or steering angular velocity > set threshold (e.g., 90° / s).

[0066] Speeding: Current speed > current road segment speed limit (requires high-precision map data).

[0067] Other dangerous behaviors include: frequent lane changes (rapid fluctuations in heading / steering angle), fatigued driving (abnormally increased minor speed / steering wheel corrections - requiring higher precision sensors and models), and distracted driving (which may require additional sensors such as cameras).

[0068] Rule compliance: Primarily relies on the matching analysis of GPS location and high-precision map (including information such as speed limits, traffic lights, and restricted areas), combined with vehicle actions (such as whether the vehicle slows down and stops before the stop line when the light is red).

[0069] Furthermore, driving behavior scores are calculated based on the above driving behaviors.

[0070] In this embodiment, the driving behavior score includes a basic method and a weighted summation:

[0071] For example, the basic method uses a point deduction system. For instance, a base score is set (e.g., 100 points). Each time a dangerous behavior or rule violation occurs, points are deducted based on its severity (the probability value or confidence level output by the model, or the intensity of the event itself). For example: minor speeding (<10km / h): -1 point / time; serious speeding (>20km / h): -5 points / time; rapid acceleration: -3 points / time; sudden braking: -4 points / time (assuming sudden braking is considered higher risk); sharp turns: -3 points / time; running a red light: -10 points / time.

[0072] For example, weighted aggregation mainly involves summing various risk indicators output by the analysis engine (such as the number of rapid accelerations, average overspeed value, maximum lateral G-force, etc.) or mapping them directly to a total score (0-100 points) through a regression model. The weights or model parameters are determined based on expert knowledge or data training.

[0073] Understandably, in practice, the standards and weights of driving behavior scoring can be adjusted according to the specific needs of different regions or users to adapt to different driving environments and cultures.

[0074] It is understood that this embodiment constructs a complete closed-loop system from the vehicle's underlying sensor data to high-level driving behavior assessment. Its core lies in utilizing machine learning, especially well-designed features and appropriate models, to automatically and intelligently identify hazardous patterns and assess safety from massive, multi-dimensional real-time data.

[0075] Suggestion generator 30 is used to generate driving suggestions based on driving behavior.

[0076] It's important to note that driving recommendations are organized by priority or issue category (e.g., "Acceleration and Deceleration," "Cornering Control," "Speed ​​Management," "Rules Compliance") to create a clear report. Each recommendation should be linked to a specific analyzed event (e.g., "A sudden acceleration was detected near [location] at [time], recommendation:...") to increase persuasiveness. The location of the problematic event is marked on the GPS track map, and the corresponding recommendation bubble is displayed. For complex maneuvers, short animation or video demonstration links can be provided (e.g., showing the difference between smooth acceleration and sudden acceleration). The report begins or ends with a brief summary of the trip (highlighting strengths and areas for improvement), and acknowledges and encourages safe driving behaviors.

[0077] The suggestion generator 30 system has a large built-in "issue-suggestion" mapping rule library. For example:

[0078] IF (Rapid acceleration event detected) THEN Recommendation = "Try to press the accelerator pedal more gently to avoid sudden, forceful acceleration. Anticipate road conditions and allow sufficient acceleration space."

[0079] IF (Speeding event AND Road type = Urban road) THEN Recommendation = "When driving on urban roads, please pay attention to speed limit signs. The current speed limit for this section is [speed limit value] km / h. Control your speed to ensure the safety of pedestrians and other vehicles."

[0080] IF (frequent sudden braking incidents) THEN Recommendation = "Frequent sudden braking indicates that the following distance may be too close or that the driver's anticipation is insufficient. Please maintain a greater safe following distance, observe the road conditions ahead in advance, and slow down by releasing the accelerator rather than applying the brakes."

[0081] IF (sharp turn event AND lateral G-force > threshold) THEN Recommendation = "Slow down in advance when cornering and maintain stable steering wheel operation. High-speed cornering can easily lead to loss of vehicle control."

[0082] Understandably, the suggestion generator acts as a catalyst, transforming cold, hard driving behavior data into something that enhances drivers' safety awareness and skills. Through precise problem diagnosis, contextualized suggestions, and a clear and user-friendly presentation, it guides drivers to recognize the risks in their driving habits and provides specific improvement paths. The suggestion generator provided in this embodiment significantly enhances the practical value and user engagement of the entire driving behavior analysis system, representing a crucial step in achieving a "safe driving closed loop." Its core lies in combining the precision of a rules engine with the flexibility of an (optional) AI model to output personalized, actionable, context-relevant, and easily acceptable safe driving guidance.

[0083] The report generator 40 is used to integrate driving data and driving suggestions to generate a driving behavior analysis report.

[0084] For example, the report may include details of each demerit point (e.g., 9 points for 3 instances of rapid acceleration, 5 points for 5 minutes of speeding), a list of specific hazardous events identified (time, location, type, intensity), safe driving advice, visualization charts (speed-time curves, acceleration-time curves, GPS track charts with hazardous points marked), and real-time feedback / alarms (optional), such as issuing immediate warnings to the driver via in-vehicle devices (e.g., screens, voice) in the event of extremely dangerous behavior (e.g., severe speeding, high risk of collision).

[0085] Understandably, the report generator 40 guides drivers to recognize the risks in their driving habits and provides specific improvement paths through accurate problem diagnosis, contextualized suggestions, and a clear and user-friendly presentation. The personalized suggestion generator, combined with the output of the driving behavior analysis engine, provides drivers with tailored improvement suggestions to help them better understand and improve their driving habits.

[0086] The voice interaction module 50 is used to convert the analysis report into voice information and broadcast it to the driver in real time.

[0087] In some embodiments, the voice interaction module converts the analysis report into voice information using text-to-speech technology or natural language processing technology, and broadcasts it to the driver in real time through the vehicle's audio system. The voice interaction module also supports voice recognition, allowing the driver to query report details via voice commands.

[0088] Understandably, the voice interaction module enables real-time risk warnings and immediate responses to high-risk behaviors (such as sudden braking and collision risks) without distracting the driver; it also provides a post-trip summary report and automatically outputs a driving score and improvement suggestions at the end of the trip; and it supports passive interaction, allowing the driver to actively obtain information through voice commands (such as "report report").

[0089] Display module 60 provides a user interface for the driver to view the analysis report.

[0090] It is understood that this embodiment can provide an intuitive user interface that transforms driving behavior analysis results (scores, events, suggestions) into a visual interface, enabling drivers to quickly obtain key information while driving and view in-depth reports after parking. The interface design will focus on ease of use and driving safety.

[0091] In some embodiments, a data storage module is also included for storing and managing data and reports collected during driving.

[0092] It's understandable that data can be stored locally or encrypted and uploaded to the cloud for analysis and storage, providing greater flexibility and scalability.

[0093] In some embodiments, an open API is also included, which allows third-party developers and services to access and extend the functionality of the system.

[0094] It is understood that the system integration and interface design ensure that the present invention can be seamlessly integrated with existing vehicle systems, thereby improving system compatibility and user convenience.

[0095] The intelligent driving assistance system provided by this invention integrates more advanced data acquisition and analysis technologies, as well as machine learning algorithms, to achieve a deeper understanding of driving behavior and personalized feedback. These systems should be able to record and analyze key parameters during the driving process. Through this data, the system can assess the safety of driving behavior and generate customized improvement suggestions to help drivers optimize their driving habits and improve driving safety. By providing these analysis results and suggestions to the driver in real time via voice or other interactive methods, the intelligent driving assistance system will be able to provide a richer and more personalized driving experience, further enhancing driving safety and efficiency.

[0096] Please see Figure 3 The present invention also provides an intelligent driving assistance method. Figure 3 A flowchart illustrating the steps of an intelligent in-vehicle multimedia recommendation method provided in this embodiment of the invention, applied to the intelligent driving assistance system provided in the above embodiment, specifically includes the following steps:

[0097] Step S10: Collect vehicle driving data in real time.

[0098] Specifically, the data acquisition module 10 is used to collect vehicle driving data in real time, including but not limited to speed, acceleration, braking force, steering angle, driving time, GPS location information, etc.

[0099] It is understandable that, in addition to using the vehicle's own sensors, the data acquisition module 10 could consider integrating sensor data from mobile devices (such as smartphones), such as accelerometers and GPS, to assist or enhance the acquisition of driving data.

[0100] Step S20: Analyze the driving data to assess the safety of driving behavior.

[0101] In this embodiment, a driving behavior analysis engine 20 utilizes machine learning algorithms to perform in-depth analysis of the collected data, assess the safety of driving behavior, and assign a driving behavior score accordingly. This engine can identify potentially dangerous behaviors such as rapid acceleration, rapid deceleration, and sharp turns, and assess the driver's compliance with traffic rules.

[0102] Step S30: Generate driving suggestions based on driving behavior.

[0103] It's important to note that driving recommendations are organized by priority or issue category (e.g., "Acceleration and Deceleration," "Cornering Control," "Speed ​​Management," "Rules Compliance") to create a clear report. Each recommendation should be linked to a specific analyzed event (e.g., "A sudden acceleration was detected near [location] at [time], recommendation:...") to increase persuasiveness. The location of the problematic event is marked on the GPS track map, and the corresponding recommendation bubble is displayed. For complex maneuvers, short animation or video demonstration links can be provided (e.g., showing the difference between smooth acceleration and sudden acceleration). The report begins or ends with a brief summary of the trip (highlighting strengths and areas for improvement), and acknowledges and encourages safe driving behaviors.

[0104] Step S40: Integrate driving data and driving suggestions to generate a driving behavior analysis report.

[0105] For example, the report may include details of each demerit point (e.g., 9 points for 3 instances of rapid acceleration, 5 points for 5 minutes of speeding), a list of specific hazardous events identified (time, location, type, intensity), safe driving advice, visualization charts (speed-time curves, acceleration-time curves, GPS track charts with hazardous points marked), and real-time feedback / alarms (optional), such as issuing immediate warnings to the driver via in-vehicle devices (e.g., screens, voice) in the event of extremely dangerous behavior (e.g., severe speeding, high risk of collision).

[0106] Step S50: Convert the analysis report into voice information and broadcast it to the driver in real time.

[0107] In some embodiments, the voice interaction module converts the analysis report into voice information using text-to-speech technology or natural language processing technology, and broadcasts it to the driver in real time through the vehicle's audio system. The voice interaction module also supports voice recognition, allowing the driver to query report details via voice commands.

[0108] Furthermore, it also includes the following steps: storing and managing data and reports collected during driving.

[0109] It's understandable that data can be stored locally or encrypted and uploaded to the cloud for analysis and storage, providing greater flexibility and scalability.

[0110] The intelligent driving assistance method provided by this invention integrates more advanced data acquisition and analysis technologies, as well as machine learning algorithms, to achieve a deeper understanding of driving behavior and personalized feedback. These systems should be able to record and analyze key parameters during the driving process. Through this data, the system can assess the safety of driving behavior and generate customized improvement suggestions to help drivers optimize their driving habits and improve driving safety. By providing these analysis results and suggestions to the driver in real time via voice or other interactive methods, the intelligent driving assistance system will be able to provide a richer and more personalized driving experience, further enhancing driving safety and efficiency.

[0111] Based on the same inventive concept, embodiments of the present invention also provide an electronic device. Figure 4 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Figure 4 As shown, an embodiment of the present invention provides an electronic device including: one or more processors 101, a memory 102, and one or more I / O interfaces 103. The memory 102 stores one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement any of the intelligent driving assistance methods described in the above embodiments; the one or more I / O interfaces 103 are connected between the processors and the memory, configured to enable information interaction between the processors and the memory.

[0112] The processor 101 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 102 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface (read / write interface) 103 is connected between the processor 101 and the memory 102, and can realize information interaction between the processor 101 and the memory 102, including but not limited to a data bus (Bus).

[0113] In some embodiments, the processor 101, memory 102, and I / O interface 103 are interconnected via bus 104, and thus connected to other components of the computing device.

[0114] In some embodiments, the one or more processors 101 include a field-programmable gate array.

[0115] This invention also provides a computer-readable medium. The computer-readable medium stores a computer program, which, when executed by a processor, implements the steps of any of the intelligent in-vehicle multimedia recommendation methods described in the above embodiments. The computer-readable storage medium can be volatile or non-volatile.

[0116] This invention also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code is run in the processor of an electronic device, the processor in the electronic device executes the above-described intelligent driving assistance method.

[0117] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer storage media (or non-transitory media) and communication media (or transient media).

[0118] As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable program instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0119] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0120] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via 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., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0121] The computer program product described herein can be implemented specifically through hardware, software, or a combination thereof. In one alternative embodiment, the computer program product is specifically embodied in a computer storage medium; in another alternative embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0122] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0123] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0124] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0125] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0126] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of the invention as set forth in the appended claims.

Claims

1. An intelligent driving assistance system, characterized in that, It includes: The data acquisition module is used to collect vehicle driving data in real time; A driving behavior analysis engine is used to analyze the driving data to assess the safety of driving behavior; The suggestion generator generates driving suggestions based on driving behavior. A report generator is used to integrate driving data and driving suggestions to generate driving behavior analysis reports. The display module provides a user interface for users to view the analysis report.

2. The intelligent driving assistance system according to claim 1, characterized in that, The driving data includes speed, acceleration, braking force, steering angle, driving time, and GPS location information.

3. The intelligent driving assistance system according to claim 1, characterized in that, The driving behavior analysis engine uses machine learning algorithms to analyze the collected data to assess the safety of driving behavior and give a driving behavior score accordingly.

4. The intelligent driving assistance system according to claim 3, characterized in that, The machine learning algorithms include support vector machines, random forests, or convolutional neural networks.

5. The intelligent driving assistance system according to claim 1, characterized in that, It also includes a voice interaction module, which uses text-to-speech technology or natural language processing technology to convert the analysis report into voice information and broadcast it to the user in real time through the vehicle's audio system. The voice interaction module also supports voice recognition, allowing users to query report details through voice commands.

6. The intelligent driving assistance system according to claim 1, characterized in that, It also includes a data storage module, which is used to store and manage data and reports collected during driving.

7. The intelligent driving assistance system according to claim 1, characterized in that, It also includes an open API, which allows third-party developers and services to access and extend the system's functionality.

8. An assistance method for an intelligent driving assistance system according to any one of claims 1-7, characterized in that, Includes the following steps: Collect vehicle driving data in real time; The driving data is analyzed to assess the safety of driving behavior; Generate driving suggestions based on driving behavior; Integrate driving data and driving suggestions to generate driving behavior analysis reports.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in claim 8.

10. A computer-readable medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method as described in claim 8.

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