Intelligent driving assistance control method and system based on millimeter wave radar

Abstract

The invention belongs to the technical field of driving management and control, and particularly relates to an intelligent driving auxiliary control method and system based on a millimeter wave radar, and the system comprises a millimeter wave radar detection module, a data fusion output module, a driving decision generation module, a driving auxiliary control module, an auxiliary control safety evaluation module and a vehicle-mounted display early warning module. According to the invention, a plurality of vehicle-mounted millimeter wave radar sensors are used for environment detection, radar detection information and monitoring data of a vehicle-mounted camera are subjected to multi-modal data fusion, an environment sensing result is generated, and a deep learning algorithm is used for analysis according to the environment sensing result to formulate a corresponding control strategy. The corresponding operation is executed based on the auxiliary control decision, the driving safety and the vehicle intelligence and automation level are remarkably improved, the auxiliary control safety and the radar cooperation operation reliability can be reasonably judged, a user can conveniently take corresponding processing measures in time, the driving safety is further improved, and the vehicle supervision difficulty is reduced.

Description

Technical Field

[0001] The present invention relates to the field of driving control technology, and specifically to an intelligent driving assistance control method and system based on millimeter-wave radar. Background Art

[0002] Intelligent driving assistance systems use onboard sensors to perceive the vehicle's surroundings in real time, analyze driver behavior and potential risks through algorithms, and provide assistance through audio and visual prompts or active control to improve driving safety and comfort. Chinese invention patent publication number CN114212097A discloses an intelligent driving assistance control system and control method. This technical solution combines a road accident risk map with vehicle motion to determine whether the vehicle is experiencing sudden acceleration, poor braking, or poor steering, and issues corresponding alerts to the driver when these situations occur. However, in actual application, the above-mentioned technical solution focuses on the detection and analysis of vehicle driving conditions, and is unable to effectively perceive the vehicle's surrounding environment and make corresponding auxiliary controls. It is also difficult to reasonably evaluate the safety of auxiliary controls and the reliability of vehicle monitoring operations. Users cannot make corresponding improvement and optimization measures in a timely manner, which is not conducive to improving driving safety and the level of vehicle intelligence and automation, and makes vehicle operation supervision difficult. In view of the above technical defects, a solution is now proposed. Summary of the Invention

[0003] The purpose of the present invention is to provide an intelligent driving assistance control method and system based on millimeter-wave radar, which solves the problems that the existing technology cannot effectively perceive the vehicle's surrounding environment and make corresponding auxiliary control, and it is difficult to reasonably evaluate the safety of auxiliary control and the reliability of vehicle monitoring operation, which is not conducive to improving driving safety and the level of vehicle intelligence and automation, and the difficulty of vehicle operation supervision.

[0004] To achieve the above object, the present invention provides the following technical solutions: The intelligent driving assistance control system based on millimeter-wave radar includes a millimeter-wave radar detection module, a data fusion output module, a driving decision generation module, a driving assistance control module, an auxiliary control safety assessment module, and an on-board display and warning module. The millimeter-wave radar detection module uses multiple on-board millimeter-wave radar sensors to obtain target distance, speed, angle, and altitude information, and sends the detection information to the data fusion output module. The data fusion output module performs multimodal data fusion on the detection information of the millimeter-wave radar detection module and the monitoring data of the on-board camera, and uses the principle of spatiotemporal synchronization to align the raw data of the same environmental information perceived by each sensor in time and space to generate environmental perception results, and outputs the environmental perception results to the driving decision generation module. The driving decision generation module uses a deep learning algorithm to analyze the environmental perception results provided by the data fusion output module to identify different types of obstacles and assess collision risks, and formulates corresponding auxiliary control strategies accordingly, and sends the auxiliary control strategies to the driving auxiliary control module. The driving auxiliary control module performs corresponding operations based on the auxiliary control decisions and sends the execution information to the on-board display and warning module for display; the auxiliary control safety assessment module judges the auxiliary control safety through analysis, and generates an auxiliary control safety signal or an auxiliary control risk signal accordingly, and sends the auxiliary control safety signal or auxiliary control risk signal to the on-board display and warning module.

[0005] Furthermore, the millimeter-wave radar detection module includes a forward radar and a corner radar. The forward radar is installed in the vehicle logo or front grille to detect obstacles in front of the vehicle; the corner radar is installed at the four corners of the car to monitor the blind spots on the side and rear of the vehicle.

[0006] Furthermore, the specific analysis process of the auxiliary control safety assessment module includes: The generation moment of the corresponding auxiliary control strategy is collected and marked as the first characteristic moment, and the moment when the driving assistance control module executes the corresponding operation is marked as the second characteristic moment, and the time difference between the second characteristic moment and the first characteristic moment is calculated to obtain a delay duration; the delay duration is numerically compared with a corresponding preset delay duration threshold, and if the delay duration exceeds the corresponding preset delay duration threshold, the corresponding delay duration is marked as a dangerous duration; The number of dangerous time durations per unit time is obtained and the ratio thereof with the number of delay time durations is calculated to obtain the dangerous time proportion value, which is then numerically compared with the preset dangerous time proportion threshold. If the dangerous time proportion value exceeds the preset dangerous time proportion threshold, an auxiliary control risk signal is generated; If the dangerous time occupation value does not exceed the preset dangerous time occupation threshold, the excess value of the dangerous time compared with the corresponding preset delay time threshold is marked as the time excess monitoring value, and the average of all the time excess monitoring values ​​in the unit time is calculated to obtain the time excess performance value, and the time excess monitoring value with the largest value in the unit time is marked as the time excess amplitude value; The auxiliary control effectiveness measurement value is obtained by weighted summing up the dangerous time occupancy value, the time excess performance value and the time excess amplitude value, and the auxiliary control effectiveness measurement value is numerically compared with the preset auxiliary control effectiveness measurement threshold. If the auxiliary control effectiveness measurement value exceeds the preset auxiliary control effectiveness measurement threshold, an auxiliary control risk signal is generated; if the auxiliary control effectiveness measurement value does not exceed the preset auxiliary control effectiveness measurement threshold, an auxiliary control safety signal is generated.

[0007] Furthermore, the auxiliary control safety assessment module is communicatively connected to the operation reliability assessment module, and the auxiliary control safety assessment module sends the auxiliary control safety signal to the operation reliability assessment module. When the operation reliability assessment module receives the auxiliary control safety signal, it analyzes the coordination status of all millimeter-wave radars on the vehicle to judge the operation reliability, and generates a reliability qualified signal or a reliability abnormality signal accordingly, and sends the reliability qualified signal or the reliability abnormality signal to the on-board display warning module.

[0008] Furthermore, the specific analysis process of the operation reliability assessment module is as follows: Obtain the radar monitoring and evaluation values ​​of all millimeter-wave radars, compare the radar monitoring and evaluation values ​​with the preset radar monitoring and evaluation threshold, and if the radar monitoring and evaluation value exceeds the preset radar monitoring and evaluation threshold, mark the corresponding millimeter-wave radar as a non-optimal radar; if the radar monitoring and evaluation value does not exceed the preset radar monitoring and evaluation threshold, use the current moment as the end moment and trace back to a set tracing period of length P1 to obtain all fault information of the corresponding millimeter-wave radar during the vehicle operation during the tracing period; All faults occurring in the corresponding millimeter-wave radar during vehicle operation are classified, the number of occurrences of the corresponding type of fault is marked as a radar fault detection value, a set of preset detection influence weight values ​​is set in advance for each type of fault, the radar fault detection value of the corresponding type of fault is multiplied by the corresponding preset detection influence weight value to obtain a radar fault analysis value, and the radar fault analysis values ​​of all types of faults occurring in the corresponding millimeter-wave radar during the retrospective period are summed to obtain a radar fault decision value; The radar fault decision value is numerically compared with the preset radar fault decision threshold. If the radar fault decision value exceeds the preset radar fault decision threshold, the corresponding millimeter-wave radar is marked as a non-optimal radar; if a non-optimal radar exists on the vehicle, a reliability anomaly signal is generated.

[0009] Furthermore, the operation reliability assessment module is communicated with the radar monitoring and evaluation module, which detects, evaluates and analyzes the quality status of all millimeter-wave radars on the vehicle one by one, thereby obtaining the radar monitoring and evaluation value of the corresponding millimeter-wave radar, and sending the radar monitoring and evaluation value of the corresponding millimeter-wave radar to the auxiliary control safety assessment module.

[0010] Furthermore, the specific analysis process of the radar monitoring and review module is as follows: The total operating time of the corresponding millimeter-wave radar in the historical stage is obtained and marked as the radar operating value. The temperature of the corresponding millimeter-wave radar and the humidity of the environment in which it is located are also collected. If the temperature of the corresponding millimeter-wave radar or the humidity of the environment in which it is located exceeds the corresponding preset threshold, it is determined that the corresponding millimeter-wave radar is in a high temperature and high humidity state. The total time that the corresponding millimeter-wave radar is in the high temperature and high humidity state in the historical stage is obtained and marked as the high temperature and high humidity condition value; The vibration amplitude and vibration frequency of the corresponding millimeter-wave radar are collected. If the vibration amplitude or vibration frequency of the corresponding millimeter-wave radar exceeds the corresponding preset threshold, the corresponding millimeter-wave radar is determined to be in an abnormal vibration state. The total duration of the corresponding millimeter-wave radar in the abnormal vibration state in the historical stage is obtained and marked as the mechanical abnormal vibration time condition value; And obtain the number of occurrences in which the corresponding millimeter-wave radar is in a high temperature and high humidity state for a single time or in an abnormal vibration state for a single time in the historical stage exceeds the corresponding preset duration threshold and mark it as a dangerous frequency; obtain the radar monitoring follow-up value of the corresponding millimeter-wave radar by weighted summation of the radar operation value, high temperature and high humidity condition value, mechanical abnormal vibration condition value and dangerous frequency.

[0011] Furthermore, if there is no non-optimal radar on the vehicle, a number of detection periods are set during the operation of the vehicle during the retrospective period. If a millimeter-wave radar fails during a corresponding detection period, the corresponding detection period is marked as an exploration period. The number of exploration periods in the retrospective period is obtained and the ratio is calculated with the total number of detection periods to obtain an exploration detection value. The exploration detection value is numerically compared with a preset exploration detection threshold. If the exploration detection value exceeds the preset exploration detection threshold, a reliability anomaly signal is generated. If the adventure detection value does not exceed the preset adventure detection threshold, the radar monitoring and follow-up evaluation values ​​of all millimeter-wave radars on the vehicle are averaged to obtain the radar quality evaluation value, and the radar fault decision values ​​of all millimeter-wave radars on the vehicle are averaged to obtain the radar abnormal evaluation value. The reliability characteristic coefficient is calculated by taking the weighted sum of the adventure detection value, the radar quality evaluation value, and the radar abnormal evaluation value. The reliability characteristic coefficient is numerically compared with the preset reliability characteristic coefficient threshold. If the reliability characteristic coefficient exceeds the preset reliability characteristic coefficient threshold, a reliability abnormality signal is generated; if the reliability characteristic coefficient does not exceed the preset reliability characteristic coefficient threshold, a reliability qualified signal is generated.

[0012] Furthermore, the present invention also proposes an intelligent driving assistance control method based on millimeter wave radar, comprising the following steps: Step 1: Use multiple vehicle-mounted millimeter-wave radar sensors to detect targets; Step 2: Perform multimodal data fusion based on millimeter-wave radar detection information and vehicle-mounted camera monitoring data and output environmental perception results; Step 3: Analyze the environmental perception results to formulate corresponding auxiliary control strategies; Step 4: Execute corresponding operations based on the auxiliary control decision; Step 5: Determine the safety of the auxiliary control and enable the vehicle-mounted display warning module to display and issue a warning when an auxiliary control risk signal is generated.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention utilizes multiple on-board millimeter-wave radar sensors for environmental detection, fuses radar detection information with on-board camera monitoring data through multimodal data, and generates environmental perception results. Based on these environmental perception results and using deep learning algorithms for analysis, corresponding control strategies are formulated. Based on the auxiliary control decision, corresponding operations are executed. Furthermore, an auxiliary control safety assessment module analyzes and determines auxiliary control safety. When an auxiliary control risk signal is generated, the user is prompted to pause driving and take appropriate investigation and handling measures, significantly improving driving safety and the level of vehicle intelligence and automation. 2. In the present invention, the auxiliary control safety signal is sent to the operation reliability evaluation module through the auxiliary control safety evaluation module. When the operation reliability evaluation module receives the auxiliary control safety signal, it analyzes the coordination status of all millimeter-wave radars on the vehicle to determine the operation reliability. When a reliability abnormality signal is generated, it reminds the user to drive cautiously and promptly inspect and repair the vehicle's millimeter-wave radar, further improving subsequent driving safety and significantly reducing the difficulty of vehicle supervision. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings; Figure 1 This is a system block diagram of Embodiment 1 of the present invention; Figure 2 This is a system block diagram of Embodiment 2 and Embodiment 3 of the present invention; Figure 3 This is a flow chart of the method of embodiment 4 of the present invention. DETAILED DESCRIPTION

[0015] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0016] Example 1: Figure 1 As shown, the intelligent driving assistance control system based on millimeter-wave radar proposed in the present invention includes a millimeter-wave radar detection module, a data fusion output module, a driving decision generation module, a driving assistance control module, an auxiliary control safety assessment module and an on-board display warning module; The millimeter-wave radar detection module uses multiple vehicle-mounted millimeter-wave radar sensors to obtain target distance, speed, angle and height information (millimeter-wave radar sensors transmit and receive millimeter-wave signals to perceive the vehicle's surrounding environment in real time, providing accurate target position, speed and angle information. 4D millimeter-wave radar can also output height information, construct a three-dimensional point cloud, and identify obstacles more accurately), and send the detection information to the data fusion output module; preferably, the millimeter-wave radar detection module includes a forward radar and a corner radar, wherein the forward radar is installed in the car logo or the front grille to detect obstacles in front of the vehicle; the corner radar is installed at the four corners of the car to monitor the blind spots on the side and rear of the vehicle.

[0017] The data fusion output module performs multimodal data fusion on the detection information of the millimeter-wave radar detection module and the monitoring data of the vehicle-mounted camera. It uses the principle of time-space synchronization to align the original data of the same environmental information perceived by each sensor in time and space, generates environmental perception results, and outputs the environmental perception results to the driving decision generation module.

[0018] The driving decision generation module uses a deep learning algorithm to analyze the environmental perception results provided by the data fusion output module to identify different types of obstacles and assess collision risks (that is, the fused environmental perception results are further processed, such as target detection, tracking and classification, and the collision risk is assessed and corresponding control strategies are formulated based on the processed information. For example, in adaptive cruise control, the vehicle speed is adjusted according to the distance and speed to the vehicle in front; in automatic emergency braking, whether to trigger emergency braking and the intensity of braking are determined according to the collision risk level). Based on this, corresponding auxiliary control strategies are formulated (such as adjusting the vehicle speed, triggering emergency braking, providing warning information, etc.), and the auxiliary control strategies are sent to the driving assistance control module. The driving assistance control module performs corresponding operations based on the auxiliary control decisions, and sends the execution information to the on-board display warning module for display, significantly improving driving safety and the vehicle's intelligence and automation levels.

[0019] The auxiliary control safety assessment module determines the auxiliary control safety through analysis and generates an auxiliary control safety signal or auxiliary control risk signal accordingly. The auxiliary control safety signal or auxiliary control risk signal is then sent to the vehicle display warning module for display. When the vehicle display warning module receives the auxiliary control risk signal, it issues a corresponding warning to remind the user to stop driving and take appropriate investigation and handling measures to ensure the efficiency of subsequent driving auxiliary control execution and further improve driving safety. The specific analysis process of the auxiliary control safety assessment module is as follows: The generation moment of the corresponding auxiliary control strategy is collected and marked as the first characteristic moment, and the moment when the driving auxiliary control module executes the corresponding operation is marked as the second characteristic moment, and the time difference between the second characteristic moment and the first characteristic moment is calculated to obtain the delay duration; the delay duration is numerically compared with the corresponding preset delay duration threshold. If the delay duration exceeds the corresponding preset delay duration threshold, it indicates that the execution of the corresponding auxiliary control strategy by the maze is slow, and the corresponding delay duration is marked as a dangerous duration; The number of dangerous time durations per unit time is obtained and the ratio thereof to the number of delay time durations is calculated to obtain a dangerous time proportion value, which is then numerically compared with a preset dangerous time proportion threshold. If the dangerous time proportion value exceeds the preset dangerous time proportion threshold, indicating that the driving assistance control execution efficiency per unit time is poor, an auxiliary control risk signal is generated; If the dangerous time occupation value does not exceed the preset dangerous time occupation threshold, the excess value of the dangerous time compared with the corresponding preset delay time threshold is marked as the time excess monitoring value, and the average of all the time excess monitoring values ​​in the unit time is calculated to obtain the time excess performance value, and the time excess monitoring value with the largest value in the unit time is marked as the time excess amplitude value; The auxiliary control effectiveness measurement value is calculated by weighted summing the dangerous time occupation value, the time excess performance value, and the time excess amplitude value. That is, the dangerous time occupation value, the time excess performance value, and the time excess amplitude value are respectively assigned corresponding preset weight coefficients, and the dangerous time occupation value, the time excess performance value, and the time excess amplitude value are respectively multiplied by the corresponding preset weight coefficients, and the sum of the three sets of product results is marked as the auxiliary control effectiveness measurement value. In addition, the larger the value of the auxiliary control effectiveness measurement value, the worse the overall performance of the driving assistance control execution efficiency per unit time. The auxiliary control effectiveness measurement value is numerically compared with the preset auxiliary control effectiveness measurement threshold. If the auxiliary control effectiveness measurement value exceeds the preset auxiliary control effectiveness measurement threshold, it indicates that the driving auxiliary control execution efficiency performance per unit time is generally poor, and an auxiliary control risk signal is generated; if the auxiliary control effectiveness measurement value does not exceed the preset auxiliary control effectiveness measurement threshold, it indicates that the driving auxiliary control execution efficiency performance per unit time is generally good, and an auxiliary control safety signal is generated.

[0020] Example 2: Figure 2As shown, the difference between this embodiment and the first embodiment is that the auxiliary control safety assessment module is communicatively connected to the operation reliability assessment module. The auxiliary control safety assessment module sends an auxiliary control safety signal to the operation reliability assessment module. When the operation reliability assessment module receives the auxiliary control safety signal, it analyzes the coordination status of all millimeter-wave radars on the vehicle to determine the operation reliability. Based on this, a reliability qualified signal or a reliability abnormality signal is generated and sent to the vehicle display warning module for display. When the vehicle display warning module receives the reliability abnormality signal, it issues a corresponding warning to remind the user to drive carefully and promptly inspect and repair the vehicle's millimeter-wave radar, further improving subsequent driving safety and significantly reducing the difficulty of vehicle supervision. The specific analysis process of the operation reliability evaluation module is as follows: Obtain the radar monitoring and evaluation values ​​of all millimeter-wave radars, and compare the radar monitoring and evaluation values ​​with the preset radar monitoring and evaluation thresholds. If the radar monitoring and evaluation value exceeds the preset radar monitoring and evaluation threshold, it indicates that the quality of the corresponding millimeter-wave radar is poor and is not conducive to ensuring its smooth and stable operation. In this case, the corresponding millimeter-wave radar will be marked as a non-optimal radar. If the radar monitoring review value does not exceed the preset radar monitoring review threshold, the current moment is used as the end moment and the review period is set as P1. Preferably, P1 = 15 days; all fault information of the corresponding millimeter-wave radar during the vehicle operation during the review period is obtained; All faults occurring in the corresponding millimeter-wave radar during vehicle operation are classified, and the number of occurrences of the corresponding type of fault is marked as a radar fault detection value. A set of preset detection influence weight values ​​greater than zero is set in advance for each type of fault. The higher the degree of adverse impact of the corresponding type of fault on the smooth and stable operation of the millimeter-wave radar, the greater the value of the preset detection influence weight value corresponding to it. The radar fault detection value of the corresponding type of fault is multiplied by the corresponding preset detection influence weight value to obtain a radar fault analysis value, and the radar fault analysis values ​​of all types of faults occurring in the corresponding millimeter-wave radar during the retrospective period are summed to obtain a radar fault decision value. The radar fault decision value is numerically compared with the preset radar fault decision threshold. If the radar fault decision value exceeds the preset radar fault decision threshold, it indicates that the operating performance of the corresponding millimeter-wave radar is poor, and the corresponding millimeter-wave radar is marked as a non-optimal radar; if there is a non-optimal radar on the vehicle, it indicates that the vehicle's radar monitoring operation reliability is poor and the driving safety risks are high, and a reliability anomaly signal is generated.

[0021] Furthermore, if there is no non-optimal radar on the vehicle, several detection periods are set during the vehicle's operation during the retrospective period. If a millimeter-wave radar fails during a corresponding detection period, indicating that there is a safety hazard in the driving assistance control during the corresponding detection period, the corresponding detection period will be marked as an exploration period. The number of exploration periods in the retrospective period is obtained and the ratio thereof is calculated with the total number of detection periods to obtain an exploration detection value. The exploration detection value is numerically compared with a preset exploration detection threshold. If the exploration detection value exceeds the preset exploration detection threshold, it indicates that the reliability of the vehicle's radar monitoring operation is poor, which is not conducive to ensuring safe driving of the vehicle, and a reliability abnormality signal is generated; If the adventure detection value does not exceed the preset adventure detection threshold, the radar monitoring evaluation values ​​of all millimeter-wave radars on the vehicle are averaged to obtain the radar quality evaluation value, and the radar fault decision values ​​of all millimeter-wave radars on the vehicle are averaged to obtain the radar abnormality evaluation value; The reliability characteristic coefficient is calculated by weighted summing the exploration detection value, radar quality evaluation value, and radar abnormal evaluation value. That is, the exploration detection value, radar quality evaluation value, and radar abnormal evaluation value are assigned corresponding preset weight coefficients, and the exploration detection value, radar quality evaluation value, and radar abnormal evaluation value are multiplied by the corresponding preset weight coefficients, and the sum of the three sets of product results is marked as the reliability characteristic coefficient. Moreover, the larger the value of the reliability characteristic coefficient, the worse the overall reliability of the vehicle's radar monitoring operation. The reliability characteristic coefficient is numerically compared with the preset reliability characteristic coefficient threshold. If the reliability characteristic coefficient exceeds the preset reliability characteristic coefficient threshold, it indicates that the overall reliability of the vehicle's radar monitoring operation is poor, which is not conducive to ensuring safe driving of the vehicle, and a reliability abnormality signal is generated; if the reliability characteristic coefficient does not exceed the preset reliability characteristic coefficient threshold, it indicates that the overall reliability of the vehicle's radar monitoring operation is good, and a reliability qualified signal is generated.

[0022] Example 3: Figure 2 As shown, the difference between this embodiment and the first and second embodiments is that the operation reliability evaluation module is communicatively connected to the radar monitoring and evaluation module, and the radar monitoring and evaluation module performs a detection, evaluation and analysis on the quality status of all millimeter-wave radars on the vehicle one by one, and obtains the radar monitoring and evaluation value of the corresponding millimeter-wave radar accordingly; The radar monitoring and follow-up evaluation values ​​of the corresponding millimeter-wave radars are sent to the auxiliary control safety assessment module. This not only enables reasonable analysis and accurate judgment of the quality status of each millimeter-wave radar on the vehicle, but also provides data support for the analysis process of the operation reliability assessment module, ensuring the accuracy of its analysis results. The specific analysis process of the radar monitoring and follow-up evaluation module is as follows: The total operating time of the corresponding millimeter-wave radar in the historical stage is obtained and marked as the radar operating value. The temperature of the corresponding millimeter-wave radar and the humidity of the environment in which it is located are also collected. If the temperature of the corresponding millimeter-wave radar or the humidity of the environment in which it is located exceeds the corresponding preset threshold, it is determined that the corresponding millimeter-wave radar is in a high temperature and high humidity state. The total time that the corresponding millimeter-wave radar is in the high temperature and high humidity state in the historical stage is obtained and marked as the high temperature and high humidity condition value; The vibration amplitude and vibration frequency of the corresponding millimeter-wave radar are collected. If the vibration amplitude or vibration frequency of the corresponding millimeter-wave radar exceeds the corresponding preset threshold, the corresponding millimeter-wave radar is judged to be in an abnormal vibration state, and the total duration of the corresponding millimeter-wave radar in the abnormal vibration state in the historical stage is obtained and marked as the mechanical abnormal vibration condition value; and the number of occurrences in which the corresponding millimeter-wave radar is in a high temperature and high humidity state or in an abnormal vibration state for a single time in the historical stage exceeds the corresponding preset duration threshold is obtained and marked as a dangerous frequency; The radar monitoring follow-up evaluation value of the corresponding millimeter-wave radar is obtained by performing weighted summation on the radar operation value, high temperature and humidity condition value, mechanical abnormal vibration condition value and dangerous frequency; that is, the radar operation value, high temperature and humidity condition value, mechanical abnormal vibration condition value and dangerous frequency are respectively assigned corresponding preset weight coefficients, and the radar operation value, high temperature and humidity condition value, mechanical abnormal vibration condition value and dangerous frequency are respectively multiplied by the corresponding preset weight coefficients, and the sum of the four groups of multiplication results is marked as the radar monitoring follow-up evaluation value; and, the larger the value of the radar monitoring follow-up evaluation value, the worse the quality status of the corresponding millimeter-wave radar is overall.

[0023] Example 4: Figure 3 As shown, the difference between this embodiment and the first, second and third embodiments is that the intelligent driving assistance control method based on millimeter wave radar proposed in the present invention includes the following steps: Step 1: Use multiple vehicle-mounted millimeter-wave radar sensors to detect targets; Step 2: Perform multimodal data fusion based on millimeter-wave radar detection information and vehicle-mounted camera monitoring data and output environmental perception results; Step 3: Analyze the environmental perception results to formulate corresponding auxiliary control strategies; Step 4: Execute corresponding operations based on the auxiliary control decision; Step 5: Determine the safety of the auxiliary control and enable the vehicle-mounted display warning module to display and issue a warning when an auxiliary control risk signal is generated.

[0024] The working principle of the present invention is as follows: when in use, the millimeter-wave radar detection module uses multiple vehicle-mounted millimeter-wave radar sensors to perform environmental detection, the data fusion output module performs multimodal data fusion on the radar detection information and the monitoring data of the vehicle-mounted camera and generates an environmental perception result, the driving decision generation module analyzes the environmental perception result and adopts a deep learning algorithm to formulate a corresponding control strategy, the driving assistance control module performs corresponding operations based on the auxiliary control decision, significantly improving driving safety and the vehicle's intelligence and automation level, and analyzes through the auxiliary control safety evaluation module to judge the auxiliary control safety, and when an auxiliary control risk signal is generated, reminds the user to stop driving and take corresponding investigation and handling measures, and when an auxiliary control safety signal is generated, the operation reliability evaluation module analyzes the coordination status of all millimeter-wave radars on the vehicle to judge the operation reliability, and when a reliability abnormality signal is generated, reminds the user to drive cautiously and to inspect and repair the vehicle's millimeter-wave radar in time, further improving subsequent driving safety and reducing the difficulty of vehicle supervision.

[0025] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The determination of the threshold in the technical solution is based on the data mean obtained under training of a large number of data dimensions. The preferred embodiment does not describe all the details in detail, nor does it limit the invention to only a specific implementation method. Obviously, many modifications and changes can be made based on the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent driving assistance control system based on millimeter-wave radar, characterized in that: It includes millimeter-wave radar detection module, data fusion output module, driving decision generation module, driving assistance control module, auxiliary control safety assessment module and vehicle-mounted display warning module; The millimeter-wave radar detection module uses multiple vehicle-mounted millimeter-wave radar sensors to detect targets, and the data fusion output module fuses the millimeter-wave radar detection information and the monitoring data from the vehicle-mounted camera into multimodal data to generate environmental perception results. The driving decision generation module uses deep learning algorithms to analyze environmental perception results to identify different types of obstacles and assess collision risks, and then formulates corresponding auxiliary control strategies. The driving assistance control module then executes corresponding actions based on the auxiliary control decisions. The auxiliary control safety assessment module determines the auxiliary control safety through analysis, generates an auxiliary control safety signal or an auxiliary control risk signal accordingly, and sends the auxiliary control safety signal or auxiliary control risk signal to the vehicle display warning module; The auxiliary control safety assessment module is communicatively connected to the operation reliability assessment module. The auxiliary control safety assessment module sends an auxiliary control safety signal to the operation reliability assessment module. Upon receiving the auxiliary control safety signal, the operation reliability assessment module analyzes the coordination status of all millimeter-wave radars on the vehicle to determine the operation reliability, and sends a reliability qualification signal or a reliability abnormality signal to the vehicle-mounted display warning module. The specific analysis process of the operation reliability assessment module is as follows: Obtain the radar monitoring and evaluation values ​​of all millimeter-wave radars. If the radar monitoring and evaluation value exceeds the preset radar monitoring and evaluation threshold, the corresponding millimeter-wave radar will be marked as a non-optimal radar; If the radar monitoring review value does not exceed the preset radar monitoring review threshold, the radar fault analysis values ​​of all types of faults occurring in the corresponding millimeter-wave radar during the retrospective period are summed up to obtain the radar fault decision value; If the radar fault decision value exceeds the preset radar fault decision threshold, the corresponding millimeter-wave radar will be marked as a non-optimal radar; if a non-optimal radar exists on the vehicle, a reliability anomaly signal will be generated; If there is no non-superior radar on the vehicle, the adventure detection value will be compared with the preset adventure detection threshold. If the adventure detection value exceeds the preset adventure detection threshold, a reliability abnormality signal is generated; if the adventure detection value does not exceed the preset adventure detection threshold, the reliability characteristic coefficient is obtained by weighted summing the adventure detection value, radar quality evaluation value and radar abnormality value; if the reliability characteristic coefficient exceeds the preset reliability characteristic coefficient threshold, a reliability abnormality signal is generated; otherwise, a reliability qualified signal is generated.

2. The intelligent driving assistance control system based on millimeter wave radar according to claim 1 is characterized in that: The millimeter-wave radar detection module includes forward radar and corner radar. The forward radar is installed in the vehicle logo or front grille to detect obstacles in front of the vehicle; the corner radar is installed at the four corners of the car to monitor the blind spots on the side and rear of the vehicle.

3. The intelligent driving assistance control system based on millimeter wave radar according to claim 1, characterized in that: The specific analysis process of the auxiliary control safety assessment module includes: The number of dangerous time durations per unit time is obtained and the ratio thereof to the number of delay time durations is calculated to obtain the dangerous time proportion value. If the dangerous time proportion value exceeds the preset dangerous time proportion threshold, an auxiliary control risk signal is generated; If the dangerous time occupation value does not exceed the preset dangerous time occupation threshold, the auxiliary control effectiveness measurement value is obtained by weighted summing up the dangerous time occupation value, the time excess performance value and the time excess amplitude value. If the auxiliary control effectiveness measurement value exceeds the preset auxiliary control effectiveness measurement threshold, an auxiliary control risk signal is generated; otherwise, an auxiliary control safety signal is generated.

4. The intelligent driving assistance control system based on millimeter wave radar according to claim 1, characterized in that: The operation reliability assessment module is communicatively connected to the radar monitoring and evaluation module. The radar monitoring and evaluation module detects, evaluates and analyzes the quality status of all millimeter-wave radars on the vehicle one by one, and sends the radar monitoring and evaluation values ​​of the corresponding millimeter-wave radars to the auxiliary control safety assessment module.

5. The intelligent driving assistance control system based on millimeter wave radar according to claim 4 is characterized in that: The specific analysis process of the radar monitoring and review module is as follows: Obtain the total operating time of the corresponding millimeter-wave radar in the historical stage and mark it as the radar operating value, obtain the total time the corresponding millimeter-wave radar is in a high temperature and high humidity state in the historical stage and mark it as the high temperature and high humidity condition value, and obtain the total time the corresponding millimeter-wave radar is in an abnormal vibration state in the historical stage and mark it as the mechanical abnormal vibration condition value; And obtain the number of occurrences in which the corresponding millimeter-wave radar is in a high temperature and high humidity state for a single time or in an abnormal vibration state for a single time in the historical stage exceeds the corresponding preset duration threshold and mark it as a dangerous frequency; obtain the radar monitoring follow-up value of the corresponding millimeter-wave radar by weighted summation of the radar operation value, high temperature and high humidity condition value, mechanical abnormal vibration condition value and dangerous frequency.

6. An intelligent driving assistance control method based on millimeter wave radar, characterized in that: The following steps are involved: Step 1: Use multiple vehicle-mounted millimeter-wave radar sensors to detect targets; Step 2: Output the environmental perception results; Step 3: Develop corresponding auxiliary control strategies; Step 4: Execute corresponding operations based on the auxiliary control decision; Step 5: Determine the safety of auxiliary control.

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