Collision threshold optimization method and device, equipment and storage medium

Abstract

The invention relates to the technical field of vehicle parameter optimization, and discloses a collision threshold optimization method and device, equipment and a storage medium, and the method comprises the steps: setting a plurality of collision scenes; respectively carrying out collision simulation in different collision scenes to obtain sample data of each collision scene; determining a corresponding collision signal feature value based on the sample data of each collision scene; optimizing a preset reference collision threshold value according to the collision signal characteristic value to obtain an optimized collision threshold value corresponding to each collision scene; when it is detected that the collision signal of the vehicle reaches the optimized collision threshold value, a sentry mode of the vehicle is triggered. The accuracy and reliability of the sentry mode in vehicle safety monitoring can be effectively and remarkably improved, it is ensured that the vehicle can accurately recognize the potential collision risk and respond in time in different environments and scenes, and the driving safety is effectively improved.

Description

Technical Field

[0001] This application relates to the technical field of vehicle parameter optimization, and particularly to a method, device, equipment, and storage medium for optimizing collision thresholds. Background Art

[0002] With the rapid development of intelligent networked vehicle technology, the sentry mode, as an important safety function, has been widely applied in vehicles. The sentry mode integrates multiple sensors (such as cameras, radars, ultrasonic sensors, etc.) to monitor the vehicle's surrounding environment in real time and issues alarms or takes corresponding measures when detecting potential collision risks. However, how to accurately calibrate the collision threshold of the sentry mode to balance the sensitivity and false alarm rate of safety monitoring is an important challenge faced by current technologies. Currently, many safety monitoring systems rely on simple rules or empirical values for setting the collision threshold. For example, a fixed threshold is set according to the vibration amplitude of the environment or image changes. These traditional methods often cannot adapt to complex and variable actual scenarios, resulting in frequent false alarms or missed alarms in the sentry mode, which affects the accuracy and reliability of vehicle safety monitoring.

[0003] Therefore, how to improve the accuracy and reliability of the sentry mode in vehicle safety monitoring is an urgent problem to be solved currently.

[0004] The above content is only used to assist in understanding the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of this application is to provide a method, device, equipment, and storage medium for optimizing collision thresholds, aiming to solve the technical problem that the setting of traditional collision thresholds depends on simple rules or empirical values, resulting in frequent false alarms or missed alarms in the sentry mode, which affects the accuracy and reliability of vehicle safety monitoring.

[0006] To achieve the above purpose, this application proposes a method for optimizing collision thresholds, and the method includes:

[0007] Set multiple collision scenarios;

[0008] Conduct collision simulations respectively under different collision scenarios to obtain sample data for each collision scenario;

[0009] Determine the corresponding collision signal characteristic values based on the sample data of each collision scenario;

[0010] Optimize the preset reference collision threshold according to the collision signal characteristic values to obtain the optimized collision threshold corresponding to each collision scenario;

[0011] When it is detected that the collision signal of the vehicle reaches the optimized collision threshold, trigger the sentry mode of the vehicle.

[0012] In one embodiment, the collision simulations are respectively carried out under different collision scenarios to obtain the sample data of each collision scenario, including:

[0013] Controlling a collision vehicle and a calibration vehicle to perform a collision simulation under different collision scenarios;

[0014] Collecting the acceleration data of the calibration vehicle during the collision simulation;

[0015] Determining the sample data of each collision scenario according to the acceleration data.

[0016] In one embodiment, the controlling a collision vehicle and a calibration vehicle to perform a collision simulation under different collision scenarios includes:

[0017] Obtaining a plurality of collision speeds, a plurality of collision directions and a plurality of collision angles;

[0018] Controlling the collision vehicle to impact the calibration vehicle from each of the collision directions of the calibration vehicle according to each of the collision speeds and each of the collision angles under different collision scenarios.

[0019] In one embodiment, the determining the corresponding collision signal eigenvalue based on the sample data of each collision scenario includes:

[0020] Integrating and filtering the sample data of each collision scenario to obtain the filtered sample data;

[0021] Determining the corresponding collision signal eigenvalue according to the filtered sample data.

[0022] In one embodiment, the determining the corresponding collision signal eigenvalue according to the filtered sample data includes:

[0023] Obtaining the collision timestamp corresponding to each collision scenario;

[0024] Determining the corresponding acceleration change amount according to the collision timestamp and the filtered sample data;

[0025] Performing feature extraction according to the acceleration change amount to obtain the eigenvalue of the corresponding collision signal.

[0026] In one embodiment, the optimizing the preset reference collision threshold according to the collision signal eigenvalue to obtain the optimized collision threshold corresponding to each collision scenario includes:

[0027] Determining the interval in which the acceleration change amount is located according to the collision signal eigenvalue;

[0028] Optimize the preset reference collision threshold according to the interval where the acceleration change amount is located to obtain the optimized collision threshold corresponding to each collision scenario.

[0029] In one embodiment, after optimizing the preset reference collision threshold according to the collision signal eigenvalue to obtain the optimized collision threshold corresponding to each collision scenario, it further includes:

[0030] Conduct a collision test according to the optimized collision threshold to obtain the number of times the sentry mode is triggered;

[0031] Determine the trigger probability based on the number of times the sentry mode is triggered;

[0032] When the trigger probability does not reach the preset probability threshold, adjust the optimized collision threshold and re - execute the step of conducting a collision test according to the optimized collision threshold to obtain the number of times the sentry mode is triggered;

[0033] When the trigger probability reaches the preset probability threshold, execute the step of triggering the sentry mode of the vehicle when it is detected that the collision signal of the vehicle reaches the optimized collision threshold.

[0034] In addition, to achieve the above - mentioned purpose, the present application also proposes a collision threshold optimization device, and the collision threshold optimization device includes:

[0035] A setting module for setting a plurality of collision scenarios;

[0036] A simulation module for respectively conducting collision simulations under different collision scenarios to obtain sample data of each collision scenario;

[0037] A determination module for determining the corresponding collision signal eigenvalue based on the sample data of each collision scenario;

[0038] An optimization module for optimizing the preset reference collision threshold according to the collision signal eigenvalue to obtain the optimized collision threshold corresponding to each collision scenario;

[0039] A trigger module for triggering the sentry mode of the vehicle when it is detected that the collision signal of the vehicle reaches the optimized collision threshold.

[0040] In addition, to achieve the above - mentioned purpose, the present application also proposes a collision threshold optimization device, and the device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the collision threshold optimization method as described above.

[0041] In addition, to achieve the above object, the present application further provides a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium, and when the computer program is executed by a processor, the steps of the collision threshold optimization method described above are implemented.

[0042] In addition, to achieve the above object, the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the collision threshold optimization method described above are implemented.

[0043] The present application provides a collision threshold optimization method. The present application first sets multiple collision scenarios; performs collision simulations separately under different collision scenarios to obtain sample data for each collision scenario; determines corresponding collision signal characteristic values based on the sample data of each collision scenario; optimizes a preset reference collision threshold according to the collision signal characteristic values to obtain optimized collision thresholds corresponding to each collision scenario; when it is detected that the collision signal of the vehicle reaches the optimized collision threshold, the sentinel mode of the vehicle is triggered, which can effectively and significantly improve the accuracy and reliability of the sentinel mode in vehicle safety monitoring, ensure that the vehicle can accurately identify potential collision risks and respond in a timely manner in different environments and scenarios, and effectively improve driving safety.

[0044] In summary, it can be seen that by setting multiple collision scenarios and performing collision simulations separately under different collision scenarios to obtain sample data for each collision scenario, the present application can effectively improve the richness of the sample data. Furthermore, based on the collision signal characteristic values determined from the sample data of each collision scenario, optimizing the preset reference collision threshold can effectively improve the accuracy of collision threshold calibration. Thus, when it is detected that the collision signal of the vehicle reaches the optimized collision threshold, triggering the sentinel mode of the vehicle can effectively improve the accuracy and reliability of the sentinel mode in vehicle safety monitoring, ensure that the vehicle can accurately identify potential collision risks and respond in a timely manner in different environments and scenarios, and effectively improve driving safety. It overcomes the technical defect that the setting of the traditional collision threshold depends on simple rules or empirical values, resulting in frequent false alarms or missed alarms in the sentinel mode, which affects the accuracy and reliability of vehicle safety monitoring, and can effectively and significantly improve the accuracy and reliability of the sentinel mode in vehicle safety monitoring, ensure that the vehicle can accurately identify potential collision risks and respond in a timely manner in different environments and scenarios, and effectively improve driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The drawings herein are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0046] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0047] Figure 1 It is a schematic flowchart provided for the first embodiment of the collision threshold optimization method of the present application;

[0048] Figure 2 It is a schematic flowchart provided for the second embodiment of the collision threshold optimization method of the present application;

[0049] Figure 3 It is a schematic module structure diagram of the collision threshold optimization device in the embodiment of the present application;

[0050] Figure 4 It is a schematic device structure diagram of the hardware operating environment involved in the collision threshold optimization method in the embodiment of the present application.

[0051] The realization of the purpose, functional characteristics and advantages of the present application will be further described in conjunction with the embodiments and with reference to the drawings. Specific Embodiments

[0052] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0053] To better understand the technical solutions of the present application, the following will be described in detail in conjunction with the drawings of the specification and specific embodiments.

[0054] The main solution of the embodiment of the present application is: setting multiple collision scenarios; respectively performing collision simulations under different collision scenarios to obtain sample data of each collision scenario; determining corresponding collision signal characteristic values based on the sample data of each collision scenario; optimizing a preset reference collision threshold according to the collision signal characteristic values to obtain optimized collision thresholds corresponding to each collision scenario; when it is detected that the collision signal of the vehicle reaches the optimized collision threshold, triggering the sentry mode of the vehicle.

[0055] With the rapid development of intelligent connected vehicle technology, the sentry mode, as an important safety feature, is widely applied in vehicles. The sentry mode integrates multiple sensors (such as cameras, radars, ultrasonic sensors, etc.) to monitor the vehicle's surrounding environment in real time and issues alerts or takes corresponding measures when detecting potential collision risks. However, how to accurately calibrate the collision threshold of the sentry mode to balance the sensitivity of safety monitoring and the false alarm rate is an important challenge faced by current technologies. Currently, many safety monitoring systems rely on simple rules or empirical values for setting the collision threshold. For example, a fixed threshold is set according to the vibration amplitude of the environment or image changes. These traditional methods often fail to adapt to complex and changing actual scenarios, resulting in frequent false alarms or missed detections in the sentry mode, which affects the accuracy and reliability of vehicle safety monitoring. Therefore, how to improve the accuracy and reliability of the sentry mode in vehicle safety monitoring is an urgent problem to be solved currently.

[0056] This application can effectively improve the richness of sample data by setting multiple collision scenarios and conducting collision simulations separately under different collision scenarios to obtain sample data for each collision scenario. Furthermore, based on the collision signal characteristic values determined from the sample data of each collision scenario, the preset benchmark collision threshold can be optimized, effectively improving the accuracy of collision threshold calibration. Thus, when it is detected that the collision signal of the vehicle reaches the optimized collision threshold, the sentry mode of the vehicle is triggered, which can effectively improve the accuracy and reliability of the sentry mode in vehicle safety monitoring, ensure that the vehicle can accurately identify potential collision risks and respond in a timely manner in different environments and scenarios, effectively improve driving safety, overcome the technical defect that the setting of the traditional collision threshold depends on simple rules or empirical values, resulting in frequent false alarms or missed detections in the sentry mode, affecting the accuracy and reliability of vehicle safety monitoring, and can effectively and significantly improve the accuracy and reliability of the sentry mode in vehicle safety monitoring, ensure that the vehicle can accurately identify potential collision risks and respond in a timely manner in different environments and scenarios, and effectively improve driving safety.

[0057] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication, and program running functions, such as a tablet computer, personal computer, mobile phone, etc., or an electronic device, collision threshold optimization device, etc. that can implement the above functions. Hereinafter, taking the collision threshold optimization device as an example, this embodiment and the following embodiments will be described.

[0058] Based on this, the embodiments of this application provide a method for optimizing the collision threshold, referring to Figure 1 , Figure 1 is a schematic flowchart of the first embodiment of the method for optimizing the collision threshold of this application.

[0059] In this embodiment, the method for optimizing the collision threshold includes steps S10 to S40:

[0060] Step S10, set multiple collision scenarios.

[0061] It should be noted that the collision scenarios refer to various collision situations that may be encountered during vehicle driving, such as minor collisions, medium collisions, medium-heavy collisions, heavy collisions, etc. Each collision scenario has different collision characteristics and influencing factors. For example, a minor collision may involve a low-speed impact on a stationary object, while a heavy collision may involve a high-speed impact on a moving object. This embodiment does not make specific restrictions on this. By simulating these scenarios, data under different collision conditions can be collected.

[0062] It can be understood that the setting of the collision scenarios can also be comprehensively considered according to factors such as the actual road environment, vehicle type, traffic rules, etc., to ensure that the simulated scenarios cover as many actual possible collision situations as possible. For example, collision scenarios under various road environments such as urban roads, highways, mountain roads, etc., as well as collision scenarios under different speeds and different weather conditions can be set. This embodiment does not make specific restrictions on this.

[0063] Step S20, perform collision simulations respectively under different collision scenarios to obtain sample data for each collision scenario.

[0064] It should be noted that the collision simulation can be implemented through computer simulation technology. Using pre-set physical models and parameters, the dynamic responses of the vehicle under different collision scenarios are simulated. During the simulation process, key parameters such as the vehicle's motion state, collision force, acceleration, etc. can be recorded, so as to obtain rich collision sample data. The collision simulation can not only include collisions between vehicles, but also include collision situations between vehicles and pedestrians, fixed obstacles and other objects. This embodiment does not make specific restrictions on this. This embodiment takes the collision between vehicles as an example for illustration.

[0065] It can be understood that in this embodiment, by the method of controlling variables, the parameters in the collision scenarios (such as speed, distance, angle, etc.) are adjusted to simulate collision events under different situations, so that the sample data should be expanded as much as possible to cover more possible collision situations. During the simulation process, high-precision sensors and high-speed cameras can be used to record the changes of various physical quantities during the collision process to ensure the accuracy and reliability of the sample data.

[0066] Step S30, determine the corresponding collision signal characteristic values based on the sample data of each of the collision scenarios.

[0067] It should be noted that the collision signal eigenvalue refers to the value that can represent the characteristics of a collision event extracted by analyzing key parameters such as the vehicle's motion state, collision force, and acceleration during the collision simulation process. These eigenvalues can reflect the intensity, type, and possible damage degree of the collision. For example, the peak value of the collision force, the collision duration, and the acceleration change of the vehicle after the collision are all important collision signal eigenvalues. By analyzing these eigenvalues, the severity of the collision can be judged more accurately, and the collision threshold can be optimized accordingly.

[0068] In a feasible implementation manner, step S30 may include: integrating and filtering the sample data of each of the collision scenarios to obtain the filtered sample data; determining the corresponding collision signal eigenvalues according to the filtered sample data.

[0069] It should be noted that integration means summarizing the sample data under different collision scenarios for unified analysis and processing. Filtering is to remove the noise and irregular fluctuations in the data to ensure that the obtained collision signal eigenvalues can truly reflect the essential characteristics of the collision event.

[0070] It can be understood that various signal processing techniques, such as adaptive filtering and median filtering, can be used during the integration and filtering processes to improve the accuracy and reliability of the data. Among them, adaptive filtering can use the LMS algorithm to adapt to the signal characteristic changes under different collision scenarios. Median filtering is suitable for removing random noise in the data and maintaining the edge characteristics of the signal. Through these processes, the accuracy and consistency of the collision signal eigenvalues can be ensured, providing reliable data support for the subsequent optimization of the collision threshold.

[0071] In a feasible implementation manner, the determining the corresponding collision signal eigenvalues according to the filtered sample data includes: obtaining the collision timestamps corresponding to each collision scenario; determining the corresponding acceleration change amount according to the collision timestamps and the filtered sample data; performing feature extraction according to the acceleration change amount to obtain the eigenvalues of the corresponding collision signal.

[0072] It should be noted that the collision timestamps corresponding to each collision scenario refer to the timestamps for data acquisition during the collision simulation under different collision scenarios, that is, the time points corresponding to each sample data. After obtaining the collision timestamps, the acceleration change amount can be determined in combination with the filtered sample data. The acceleration change amount is a direct manifestation of the vehicle speed change during the collision, which can reflect the severity of the collision and the magnitude of the force on the vehicle.

[0073] It can be understood that the sample data of each collision scenario collected may include acceleration data. Among them, the acceleration data includes X-axis acceleration data, Y-axis acceleration data, Z-axis acceleration data, etc., and this embodiment does not make specific limitations on this. The acceleration change amount includes X-axis acceleration change amount, Y-axis acceleration change amount, Z-axis acceleration change amount, etc. According to the X, Y, and Z-axis acceleration data collected at every two timestamps, the corresponding acceleration difference is the X-axis acceleration change amount Δgx, the Y-axis acceleration change amount Δgy, and the Z-axis acceleration change amount Δgz, with the unit of m / s2, which needs to be converted to mg. For example, Δgx / 9.8*1000.

[0074] It is worth noting that for feature extraction based on the acceleration change amount, various methods can be used, such as peak detection, energy analysis, spectrum analysis, etc. Peak detection focuses on the maximum value in the acceleration change amount, which can indicate the most intense moment of the collision; energy analysis focuses on the cumulative energy of the acceleration change amount during the entire collision process, which can reflect the overall intensity of the collision; spectrum analysis converts the acceleration change amount to the frequency domain and analyzes the distribution of different frequency components to reveal the dynamic characteristics of the collision. Through these feature extraction methods, a series of collision signal feature values can be obtained, providing a basis for optimizing the collision threshold.

[0075] Step S40, optimize the preset reference collision threshold according to the collision signal feature values to obtain the optimized collision threshold corresponding to each collision scenario.

[0076] It should be noted that the preset reference collision threshold is the initial set value used in the collision detection system to determine whether a collision has occurred. The preset reference collision threshold may be determined based on industry standards or manufacturer's recommended values. By initially setting a reference collision threshold to determine the threshold magnitude and adjustment accuracy (for example: 50mg, accuracy ±10mg), this reference collision threshold should take into account the accuracy of the sensor, the dynamic characteristics of the vehicle, and the characteristics of common collision scenarios.

[0077] It can be understood that the optimized collision threshold aims to improve the accuracy and sensitivity of collision detection and reduce the situations of false alarms and missed detections.

[0078] It is worth noting that for the optimization of the preset reference collision threshold, machine learning algorithms such as support vector machines and neural networks can be used to train the collected sample data, so as to learn the relationship between the collision signal feature values and the collision threshold under different collision scenarios. In this way, the collision threshold can be dynamically adjusted to make it more adaptable to the actual collision situation and reduce the occurrence of false alarms and missed detections. This embodiment does not make specific limitations on this.

[0079] In a feasible implementation manner, step S40 may include: determining the interval where the acceleration change amount is located according to the collision signal eigenvalue; optimizing the preset reference collision threshold according to the interval where the acceleration change amount is located to obtain the optimized collision threshold corresponding to each collision scenario.

[0080] It should be noted that determining the interval where the acceleration change amount is located according to the maximum and minimum values of the acceleration change amount in the collision signal eigenvalue is to classify the collision signal eigenvalue, so as to more accurately adjust the collision threshold. For example, different acceleration change amount intervals can be set, such as a minor collision interval, a medium collision interval, and a severe collision interval, and each interval corresponds to a different threshold adjustment strategy. The minor collision interval may correspond to a lower threshold to avoid misjudging minor collisions as severe collisions; while the severe collision interval requires a higher threshold to ensure that an alarm can be accurately triggered when the vehicle suffers a severe collision.

[0081] It can be understood that after determining the interval where the acceleration change amount is located, the preset reference collision threshold can be adjusted according to the statistical characteristics of the collision signal eigenvalue in this interval, such as the average value, median, or standard deviation. For example, if the average value of the acceleration change amount is within the minor collision interval, the threshold can be appropriately reduced to improve the detection sensitivity to minor collisions; conversely, within the severe collision interval, the threshold is increased to ensure that an alarm can be accurately triggered when the vehicle suffers a severe collision. In this way, it can be ensured that the optimization of the collision threshold is both targeted and adaptable, thereby improving the overall performance of the collision detection system.

[0082] It is worth noting that the optimized collision threshold needs to be tested to verify its accuracy and reliability in various collision situations. The testing process includes but is not limited to conducting actual vehicle collision tests in a controlled environment and virtual collision simulations in a simulated environment. Through these tests, it can be evaluated whether the optimized collision threshold can effectively distinguish collisions of different severities and whether it can reduce false alarms and missed alarms in actual applications. In addition, the testing should also include evaluating the performance of the collision threshold under different vehicle types, different speeds, different collision angles, etc., to ensure that the optimized collision threshold has wide applicability and stability. Finally, through these strict tests, it can be ensured that the collision detection system can provide accurate and reliable collision detection results in actual applications, thereby improving vehicle safety performance and the safety guarantee of drivers.

[0083] In a feasible implementation, after step S40, the method further includes: performing a collision test according to the optimized collision threshold to obtain the number of times the sentinel mode is triggered; determining the triggering probability based on the number of times the sentinel mode is triggered; when the triggering probability does not reach the preset probability threshold, adjusting the optimized collision threshold, and re - executing the step of performing a collision test according to the optimized collision threshold to obtain the number of times the sentinel mode is triggered; when the triggering probability reaches the preset probability threshold, performing the step of triggering the sentinel mode of the vehicle when it is detected that the collision signal of the vehicle reaches the optimized collision threshold.

[0084] It should be noted that the number of times the sentinel mode is triggered refers to the number of times the sentinel mode is triggered during the collision test. The sentinel mode is a vehicle safety function. When the vehicle detects a potential collision risk, that is, when the collision signal reaches the calibrated collision threshold, it will be automatically activated to alert the driver or automatically take measures, such as tightening the seat belt, adjusting the seat position, etc., to prepare for a possible collision. By recording the number of times the sentinel mode is triggered, the sensitivity and reaction speed of the collision detection system can be evaluated.

[0085] It can be understood that based on the number of times the sentinel mode is triggered, the triggering probability can be calculated, that is, under specific conditions, the frequency at which the sentinel mode is triggered. If the triggering probability does not reach the preset probability threshold, it means that the collision detection system may be too sensitive or not sensitive enough, and the optimized collision threshold needs to be adjusted. After adjustment, a collision test needs to be performed again to verify whether the adjusted collision threshold can make the triggering probability reach the preset threshold. When the triggering probability reaches the preset probability threshold, it means that the performance of the collision detection system already meets the design requirements. At this time, the final step can be executed: triggering the sentinel mode of the vehicle when it is detected that the collision signal of the vehicle reaches the optimized collision threshold. In this way, when the vehicle actually encounters a collision situation, it can respond in a timely manner, thereby improving the safety of the vehicle and passengers. Among them, the preset probability threshold can be 95%, or other values, and this embodiment does not make specific limitations on this.

[0086] It is worth noting that by repeating the collision simulation and performing the test steps in data sampling, the number of times the sentinel mode is triggered is verified to ensure its effectiveness and reliability in various complex scenarios. According to the verification results, the optimized collision threshold is adjusted and optimized as necessary (for example, if the triggering probability < 95%, the threshold can be appropriately reduced to improve the recognition sensitivity).

[0087] Step S50: Trigger the sentinel mode of the vehicle when it is detected that the collision signal of the vehicle reaches the optimized collision threshold.

[0088] It should be noted that the collision signal refers to the acceleration change generated when a vehicle collides. The acceleration change is captured by a sensor and converted into an electrical signal, which is then analyzed and processed by the collision detection system.

[0089] It can be understood that when it is detected that the collision signal reaches the optimized collision threshold, it indicates that the vehicle may be at risk of collision. At this time, the system will automatically activate the sentry mode. The activation of the sentry mode can include a series of safety measures, such as automatically tightening the seat belt, adjusting the seat position, activating the emergency call system, etc., to ensure the safety of the vehicle and passengers. In addition, the detailed information of the collision event, such as the collision time, location, intensity, etc., can also be recorded to provide data support for subsequent accident analysis and liability determination. In this way, the vehicle can respond in a timely manner when encountering a potential collision, thereby effectively reducing the harm caused by the accident.

[0090] This embodiment provides a method for optimizing the collision threshold. In this embodiment, multiple collision scenarios are first set; collision simulations are respectively carried out under different collision scenarios to obtain sample data for each collision scenario; the corresponding collision signal characteristic values are determined based on the sample data of each collision scenario; the preset reference collision threshold is optimized according to the collision signal characteristic values to obtain the optimized collision threshold corresponding to each collision scenario; when it is detected that the collision signal of the vehicle reaches the optimized collision threshold, the sentry mode of the vehicle is triggered, which can effectively and significantly improve the accuracy and reliability of the sentry mode in vehicle safety monitoring, ensure that the vehicle can accurately identify potential collision risks and respond in a timely manner in different environments and scenarios, and effectively improve driving safety.

[0091] In summary, in this embodiment, by setting multiple collision scenarios and respectively carrying out collision simulations under different collision scenarios to obtain sample data for each collision scenario, the richness of the sample data can be effectively improved. Furthermore, based on the collision signal characteristic values determined from the sample data of each collision scenario, the preset reference collision threshold is optimized, which can effectively improve the accuracy of collision threshold calibration. Thus, when it is detected that the collision signal of the vehicle reaches the optimized collision threshold, the sentry mode of the vehicle is triggered, which can effectively improve the accuracy and reliability of the sentry mode in vehicle safety monitoring, ensure that the vehicle can accurately identify potential collision risks and respond in a timely manner in different environments and scenarios, and effectively improve driving safety. It overcomes the technical defect that the traditional setting of the collision threshold depends on simple rules or empirical values, resulting in frequent false alarms or missed alarms of the sentry mode, which affects the accuracy and reliability of vehicle safety monitoring, and can effectively and significantly improve the accuracy and reliability of the sentry mode in vehicle safety monitoring, ensure that the vehicle can accurately identify potential collision risks and respond in a timely manner in different environments and scenarios, and effectively improve driving safety.

[0092] Based on the first embodiment of this application, in the second embodiment of this application, the same or similar content as that in the above-mentioned first embodiment can be referred to the above introduction and will not be repeated hereinafter. On this basis, please refer to Figure 2 , step S20 further includes steps S201 - S203:

[0093] Step S201, control the collision vehicle and the calibration vehicle to conduct collision simulations under different collision scenarios.

[0094] It should be noted that before conducting the collision simulation, it is necessary to prepare the vehicle environment, that is, the vehicle needs to meet the software and hardware conditions required for the sentry mode. For example, the calibration vehicle is parked statically, the gyroscope works normally, etc. It is also necessary to clear the IMU (gyroscope) cache data to ensure the accuracy of the test. The configurations of the collision vehicle and the calibration vehicle should be as consistent as possible to ensure the comparability of the simulation results. During the simulation process, the dynamic responses of the vehicle should be recorded, including key parameters such as acceleration and speed changes. These data will be used for subsequent extraction of collision signal characteristic values.

[0095] It can be understood that the collision vehicle refers to the vehicle actually participating in the collision simulation, while the calibration vehicle is the vehicle used to record and analyze the collision data. During the simulation process, the collision vehicle will simulate different collision scenarios, and the calibration vehicle will record the changes of various physical quantities during the collision through its sensors. In this way, rich collision data can be collected, providing a basis for subsequent extraction of collision signal characteristic values and optimization of collision thresholds.

[0096] It is worth noting that a collision test is conducted on the vehicle, and at the same time, the time node when each collision test occurs, that is, the collision timestamp of this scenario, is recorded for analyzing the collision signal characteristic values. By controlling the collision vehicle and the calibration vehicle to conduct collision simulations, various different collision situations can be simulated, thus obtaining rich collision data samples.

[0097] In a feasible implementation manner, step S201 may include: obtaining multiple collision speeds, multiple collision directions, and multiple collision angles; controlling the collision vehicle to impact the calibration vehicle from each of the collision directions of the calibration vehicle according to each of the collision speeds and each of the collision angles under different collision scenarios.

[0098] It should be noted that the collision speed can be speed values such as 15 km / h, 10 km / h, 5 km / h, 3 km / h, etc., the collision direction can be the front of the calibration vehicle, the left front of the calibration vehicle, the right front of the calibration vehicle, the left driver's door of the calibration vehicle, the right passenger door of the calibration vehicle, the left rear door of the calibration vehicle, the right rear door of the calibration vehicle, the rear of the calibration vehicle, the right rear of the calibration vehicle, the left rear of the calibration vehicle, etc., and the collision angle can be 90°, 60°, 30°, 15°, etc. This embodiment does not make specific limitations on this.

[0099] Step S202, collect the acceleration data of the calibration vehicle during the collision simulation process.

[0100] It should be noted that the collision vehicle impacts the calibrated vehicle at speeds of 15 km / h, 10 km / h, 5 km / h, and 3 km / h respectively from 90°, 60°, 30°, and 15° in front of the calibrated vehicle directly, records the acceleration sampling data output by the IMU, and designates it as sample 1, repeating the operation 50 times; the collision vehicle impacts the calibrated vehicle at speeds of 15 km / h, 10 km / h, 5 km / h, and 3 km / h respectively from 90°, 60°, 30°, and 15° in front of the left side of the calibrated vehicle, records the acceleration sampling data output by the IMU, and designates it as sample 2, repeating the operation 50 times; the collision vehicle impacts the calibrated vehicle at speeds of 15 km / h, 10 km / h, 5 km / h, and 3 km / h respectively from 90°, 60°, 30°, and 15° in front of the right side of the calibrated vehicle, records the acceleration sampling data output by the IMU, and designates it as sample 3, repeating the operation 50 times; the collision vehicle impacts the calibrated vehicle at speeds of 15 km / h, 10 km / h, 5 km / h, and 3 km / h respectively from 90°, 60°, 30°, and 15° of the driver's door on the left side of the calibrated vehicle, records the acceleration sampling data output by the IMU, and designates it as sample 4, repeating the operation 50 times; the collision vehicle impacts the calibrated vehicle at speeds of 15 km / h, 10 km / h, 5 km / h, and 3 km / h respectively from 90°, 60°, 30°, and 15° of the co-driver's door on the right side of the calibrated vehicle, records the acceleration sampling data output by the IMU, and designates it as sample 5, repeating the operation 50 times; the collision vehicle impacts the calibrated vehicle at speeds of 15 km / h, 10 km / h, 5 km / h, and 3 km / h respectively from 90°, 60°, 30°, and 15° of the rear left door of the calibrated vehicle, records the acceleration sampling data output by the IMU, and designates it as sample 6, repeating the operation 50 times; the collision vehicle impacts the calibrated vehicle at speeds of 15 km / h, 10 km / h, 5 km / h, and 3 km / h respectively from 90°, 60°, 30°, and 15° of the rear right door of the calibrated vehicle, records the acceleration sampling data output by the IMU, and designates it as sample 7, repeating the operation 50 times; the collision vehicle impacts the calibrated vehicle at speeds of 15 km / h, 10 km / h, 5 km / h, and 3 km / h respectively from 90°, 60°, 30°, and 15° directly behind the calibrated vehicle, records the acceleration sampling data output by the IMU, and designates it as sample 8, repeating the operation 50 times; the collision vehicle impacts the calibrated vehicle at speeds of 15 km / h, 10 km / h, 5 km / h, and 3 km / h respectively from 90°, 60°, 30°, and 15° of the rear right side of the calibrated vehicle, records the acceleration sampling data output by the IMU, and designates it as sample 9, repeating the operation 50 times; the collision vehicle impacts the calibrated vehicle at speeds of 15 km / h, 10 km / h, 5 km / h, and 3 km / h respectively from 90°, 60°, 30°, and 15° of the rear left side of the calibrated vehicle, records the acceleration sampling data output by the IMU, and designates it as sample 10, repeating the operation 50 times; the calibrated vehicle is parked stationary, records the acceleration sampling data output by the IMU, and designates it as sample 11, repeating the operation 50 times.

[0101] Step S203: Determine the sample data of each collision scenario according to the acceleration data.

[0102] It should be noted that after the collision script is executed, the data collected by the IMU (gyroscope) is exported, and the sample data of each collision scenario can be determined according to the acceleration sampling data corresponding to Samples 1 to 11.

[0103] In this embodiment, by controlling the collision of the collision vehicle and the calibration vehicle in different collision scenarios and collecting the acceleration data of the calibration vehicle during the collision simulation, the sample data of each collision scenario is determined, which can effectively improve the richness of the collision data samples and thus improve the accuracy of the collision threshold calibration.

[0104] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the collision threshold optimization method of this application. Based on this technical concept, more forms of simple transformations are within the protection scope of this application.

[0105] This application also provides a collision threshold optimization device. Please refer to Figure 3 , the collision threshold optimization device includes:

[0106] A setting module 10 for setting a plurality of collision scenarios.

[0107] A simulation module 20 for performing collision simulations respectively in different collision scenarios to obtain the sample data of each collision scenario.

[0108] A determination module 30 for determining the corresponding collision signal characteristic values based on the sample data of each collision scenario.

[0109] An optimization module 40 for optimizing the preset reference collision threshold according to the collision signal characteristic values to obtain the optimized collision thresholds corresponding to each collision scenario.

[0110] A trigger module 50 for triggering the sentry mode of the vehicle when it detects that the collision signal of the vehicle reaches the optimized collision threshold.

[0111] This embodiment provides a collision threshold optimization device. This embodiment sets multiple collision scenarios; performs collision simulations separately under different collision scenarios to obtain sample data for each collision scenario; determines corresponding collision signal eigenvalue based on the sample data of each collision scenario; optimizes a preset reference collision threshold according to the collision signal eigenvalue to obtain an optimized collision threshold corresponding to each collision scenario; when it is detected that the collision signal of the vehicle reaches the optimized collision threshold, the sentinel mode of the vehicle is triggered, which can effectively and significantly improve the accuracy and reliability of the sentinel mode in vehicle safety monitoring, ensure that the vehicle can accurately identify potential collision risks and respond in a timely manner in different environments and scenarios, and effectively improve driving safety.

[0112] In summary, in this embodiment, by setting multiple collision scenarios and performing collision simulations separately under different collision scenarios to obtain sample data for each collision scenario, the richness of the sample data can be effectively improved. Furthermore, based on the collision signal eigenvalue determined from the sample data of each collision scenario, the preset reference collision threshold is optimized, which can effectively improve the accuracy of collision threshold calibration. Thus, when it is detected that the collision signal of the vehicle reaches the optimized collision threshold, the sentinel mode of the vehicle is triggered, which can effectively improve the accuracy and reliability of the sentinel mode in vehicle safety monitoring, ensure that the vehicle can accurately identify potential collision risks and respond in a timely manner in different environments and scenarios, and effectively improve driving safety. This overcomes the technical defect that the setting of the traditional collision threshold depends on simple rules or empirical values, resulting in frequent false alarms or missed alarms in the sentinel mode, which affects the accuracy and reliability of vehicle safety monitoring, and can effectively and significantly improve the accuracy and reliability of the sentinel mode in vehicle safety monitoring, ensure that the vehicle can accurately identify potential collision risks and respond in a timely manner in different environments and scenarios, and effectively improve driving safety.

[0113] Optionally, the simulation module 20 is further configured to control a collision vehicle and a calibration vehicle to perform a collision simulation under different collision scenarios; collect the acceleration data of the calibration vehicle during the collision simulation; and determine the sample data of each collision scenario according to the acceleration data.

[0114] Optionally, the simulation module 20 is further configured to obtain multiple collision speeds, multiple collision directions, and multiple collision angles; and control the collision vehicle to impact the calibration vehicle from each of the collision directions of the calibration vehicle according to each of the collision speeds and each of the collision angles under different collision scenarios.

[0115] Optionally, the determination module 30 is further configured to integrate and filter the sample data of each collision scenario to obtain filtered sample data; and determine the corresponding collision signal eigenvalue according to the filtered sample data.

[0116] Optionally, the determining module 30 is further configured to obtain the collision timestamps corresponding to the respective collision scenarios; determine the corresponding acceleration change amount according to the collision timestamps and the filtered sample data; perform feature extraction according to the acceleration change amount to obtain the eigenvalue of the corresponding collision signal.

[0117] Optionally, the optimizing module 40 is further configured to determine the interval in which the acceleration change amount is located according to the eigenvalue of the collision signal; optimize the preset reference collision threshold according to the interval in which the acceleration change amount is located to obtain the optimized collision threshold corresponding to each collision scenario.

[0118] Optionally, the collision threshold optimizing device further includes an adjusting module, configured to perform a collision test according to the optimized collision threshold to obtain the number of times the sentry mode is triggered; determine the triggering probability based on the number of times the sentry mode is triggered; when the triggering probability does not reach the preset probability threshold, adjust the optimized collision threshold and re-perform the step of performing a collision test according to the optimized collision threshold to obtain the number of times the sentry mode is triggered; when the triggering probability reaches the preset probability threshold, perform the step of triggering the sentry mode of the vehicle when it is detected that the collision signal of the vehicle reaches the optimized collision threshold.

[0119] The collision threshold optimizing device provided by the present application adopts the collision threshold optimizing method in the above embodiment, and can solve the technical problem that the setting of the traditional collision threshold depends on simple rules or empirical values, resulting in frequent false alarms or missed alarms in the sentry mode, affecting the accuracy and reliability of vehicle safety monitoring. Compared with the prior art, the beneficial effects of the collision threshold optimizing device provided by the present application are the same as those of the collision threshold optimizing method provided by the above embodiment, and the other technical features in the collision threshold optimizing device are the same as the features disclosed in the above embodiment method, and will not be elaborated here.

[0120] The present application provides a collision threshold optimizing device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the collision threshold optimizing method in the first embodiment above.

[0121] Next, refer to Figure 4, which shows a schematic structural diagram of a collision threshold optimization device suitable for implementing the embodiments of the present application. The collision threshold optimization device in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The shown collision threshold optimization device is merely an example and should not impose any limitations on the functions and usage scope of the embodiments of the present application.

[0122] As Figure 4 shown, the collision threshold optimization device may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which may perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM: Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of the collision threshold optimization device are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems may be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 may allow the collision threshold optimization device to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows a collision threshold optimization device having various systems, it should be understood that it is not required to implement or have all the shown systems. More or fewer systems may be alternatively implemented or had.

[0123] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by a processing device 1001, the above functions defined in the methods of the embodiments disclosed in the present application are executed.

[0124] The collision threshold optimization device provided by the present application adopts the collision threshold optimization method in the above embodiments, and can solve the technical problem that the setting of the traditional collision threshold depends on simple rules or empirical values, resulting in frequent false alarms or missed alarms in the sentry mode, affecting the accuracy and reliability of vehicle safety monitoring. Compared with the prior art, the beneficial effects of the collision threshold optimization device provided by the present application are the same as those of the collision threshold optimization method provided by the above embodiments, and other technical features in the collision threshold optimization device are the same as those disclosed in the method of the previous embodiment, and will not be elaborated here.

[0125] It should be understood that the various parts disclosed in the present application can be implemented by hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0126] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present application, and all should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0127] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the collision threshold optimization method in the above embodiments.

[0128] The computer-readable storage medium provided by this application can be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this embodiment, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or component. The program code contained on the computer-readable storage medium can be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0129] The above computer-readable storage medium can be included in the collision threshold optimization device; it can also exist separately without being assembled into the collision threshold optimization device.

[0130] The above computer-readable storage medium carries one or more programs. When the one or more programs are executed by the collision threshold optimization device, the collision threshold optimization device is caused to: set multiple collision scenarios; perform collision simulations separately under different collision scenarios to obtain sample data for each collision scenario; determine corresponding collision signal characteristic values based on the sample data of each collision scenario; optimize a preset reference collision threshold according to the collision signal characteristic values to obtain optimized collision thresholds corresponding to each collision scenario; and trigger the vehicle's sentry mode when it is detected that the vehicle's collision signal reaches the optimized collision threshold.

[0131] Computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0132] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0133] The modules described in the embodiments of this application can be implemented in software or in hardware. Among them, the name of the module does not constitute a limitation on the unit itself in some cases.

[0134] The readable storage medium provided by this application is a computer-readable storage medium. The computer-readable storage medium stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned collision threshold optimization method, which can solve the technical problem that the setting of traditional collision thresholds depends on simple rules or empirical values, resulting in frequent false alarms or missed alarms in the sentry mode, affecting the accuracy and reliability of vehicle safety monitoring. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by this application are the same as those of the collision threshold optimization method provided by the above embodiment, and will not be elaborated here.

[0135] This application also provides a computer program product, including a computer program, and when the computer program is executed by a processor, it realizes the steps of the collision threshold optimization method as described above.

[0136] The computer program product provided by this application can solve the technical problem that the setting of traditional collision thresholds depends on simple rules or empirical values, resulting in frequent false alarms or missed alarms in the sentry mode, affecting the accuracy and reliability of vehicle safety monitoring. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as those of the collision threshold optimization method provided by the above embodiment, and will not be elaborated here.

[0137] The above are only some embodiments of this application, and thus do not limit the patent scope of this application. Any equivalent structural transformation made by using the content of the specification and drawings of this application under the technical concept of this application, or any direct / indirect application in other related technical fields, is included in the patent protection scope of this application.

Claims

1. A collision threshold optimization method, characterized in that: The method comprises: Set up multiple collision scenarios; Carry out collision simulations in different collision scenarios to obtain sample data for each collision scenario; Determining a corresponding collision signal characteristic value based on sample data of each of the collision scenarios; Optimizing a preset reference collision threshold according to the collision signal characteristic value to obtain an optimized collision threshold corresponding to each collision scenario; When it is detected that the collision signal of the vehicle reaches the optimized collision threshold, the sentry mode of the vehicle is triggered.

2. The method according to claim 1, characterized in that The collision simulation is performed respectively under different collision scenarios to obtain sample data of each collision scenario, including: Control collision vehicles and calibration vehicles to perform collision simulations in different collision scenarios; Collecting acceleration data of the calibrated vehicle during the collision simulation; Sample data of each collision scene is determined according to the acceleration data.

3. The method according to claim 2, characterized in that The controlling the collision vehicle and the calibration vehicle to perform collision simulation under different collision scenarios includes: Obtain multiple collision speeds, multiple collision directions and multiple collision angles; In different collision scenarios, the collision vehicle is controlled to collide with the calibration vehicle from each of the collision directions of the calibration vehicle according to each of the collision speeds and each of the collision angles.

4. The method according to claim 1, characterized in that The determining of the corresponding collision signal characteristic value based on the sample data of each collision scene includes: Integrating and filtering the sample data of each collision scene to obtain filtered sample data; The corresponding collision signal characteristic value is determined according to the filtered sample data.

5. The method according to claim 4, characterized in that The determining the corresponding collision signal characteristic value according to the filtered sample data includes: Get the collision timestamp corresponding to each collision scene; Determine a corresponding acceleration change according to the collision timestamp and the filtered sample data; Feature extraction is performed according to the acceleration variation to obtain a corresponding feature value of the collision signal.

6. The method according to claim 1, characterized in that The step of optimizing the preset reference collision threshold according to the collision signal characteristic value to obtain the optimized collision threshold corresponding to each collision scenario includes: Determining the interval of the acceleration variation according to the collision signal characteristic value; The preset reference collision threshold is optimized according to the interval in which the acceleration variation is located, so as to obtain an optimized collision threshold corresponding to each collision scenario.

7. The method according to claim 1, characterized in that After optimizing the preset reference collision threshold according to the collision signal characteristic value to obtain the optimized collision threshold corresponding to each collision scene, the method further includes: Perform a collision test according to the optimized collision threshold to obtain the number of sentinel mode triggering times; Determining a trigger probability based on the number of sentinel mode triggers; When the trigger probability does not reach the preset probability threshold, adjusting the optimized collision threshold, and re-performing the step of performing a collision test according to the optimized collision threshold to obtain the number of sentinel mode triggering times; When the trigger probability reaches a preset probability threshold, a step of triggering the sentry mode of the vehicle is performed when a collision signal of the vehicle is detected to reach the optimized collision threshold.

8. A collision threshold optimization device, characterized in that: The collision threshold optimization device comprises: Setting module, used to set multiple collision scenarios; A simulation module is used to perform collision simulations in different collision scenarios and obtain sample data for each collision scenario; A determination module, configured to determine a corresponding collision signal characteristic value based on sample data of each of the collision scenarios; An optimization module, used to optimize a preset reference collision threshold according to the collision signal characteristic value to obtain an optimized collision threshold corresponding to each collision scenario; The trigger module is used to trigger the sentry mode of the vehicle when it is detected that the collision signal of the vehicle reaches the optimized collision threshold.

9. A collision threshold optimization device, characterized in that: The collision threshold optimization device comprises: a memory, a processor, and a collision threshold optimization program stored in the memory and executable on the processor, wherein the collision threshold optimization program is configured to implement the collision threshold optimization method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a collision threshold optimization program, and when the collision threshold optimization program is executed by the processor, the collision threshold optimization method according to any one of claims 1 to 7 is implemented.

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