Steering driving assistance system applied to new energy automobile

Abstract

The invention relates to the technical field of driving assistance, in particular to a steering driving assistance system applied to a new energy automobile. The method comprises the following steps: acquiring an external environment parameter set and vehicle real-time information to construct a steering scene model; driver steering decision information is obtained, the driver steering decision information and vehicle real-time information are analyzed based on the steering scene model, safety smooth optimization assistance is conducted on the vehicle steering process, and a smooth steering strategy is determined; based on the smooth steering strategy, according to the steering scene model and the vehicle real-time information, dynamic data tracking is conducted on the vehicle steering process, according to a dynamic data tracking result, the steering scene model and the corresponding smooth steering strategy are dynamically updated, and a dynamic steering strategy is determined; and according to the dynamic steering strategy, dynamic low-sensing type fine adjustment is conducted on the vehicle steering process, and a corresponding steering report is output. The safety and stability of the vehicle steering process are remarkably improved.

Description

Technical Field

[0001] The present application relates to the field of driving assistance technology, and in particular to a steering driving assistance system applied to new energy vehicles. Background Art

[0002] With the rapid development and popularization of new energy vehicles, new energy vehicles are transforming towards intelligence and automation. Steering assistance system, as an important part of intelligent driving technology, can provide a more efficient driving experience and reduce the burden on drivers.

[0003] However, existing steering assistance systems usually rely on preset automatic control algorithms provided by automobile manufacturers, and are regularly updated and maintained through software update services provided by automobile manufacturers. They are unable to properly deal with dynamic problems caused by multi-dimensional influencing factors within the vehicle's environment during actual steering. In addition, existing steering assistance systems have the risk of causing the driver to overreact when dealing with complex situations, and their auxiliary role in the safety of the vehicle's steering process is limited. Summary of the invention

[0004] The present application provides a steering driving assistance system applied to new energy vehicles to solve the above-mentioned technical problems.

[0005] In a first aspect, the present application provides a steering driving assistance system applied to a new energy vehicle, the system comprising:

[0006] Acquire an external environment parameter set and real-time vehicle information, analyze the external environment parameter set and the real-time vehicle information, and construct a steering scenario model;

[0007] Acquire the driver's steering decision information, analyze the driver's steering decision information and the vehicle real-time information based on the steering scenario model, perform safety smooth optimization assistance on the vehicle steering process, and determine a smooth steering strategy;

[0008] Based on the smooth steering strategy, according to the steering scene model and the real-time information of the vehicle, dynamic data tracking is performed on the vehicle steering process, and according to the dynamic data tracking result, the steering scene model and the corresponding smooth steering strategy are dynamically updated to determine the dynamic steering strategy;

[0009] According to the dynamic steering strategy, the vehicle steering process is dynamically and low-sensitivityly fine-tuned, and a corresponding steering report is output.

[0010] Through this solution, a steering scenario model that reflects the driving scenario of the vehicle is constructed according to the external environment parameter set and the real-time information of the vehicle. On this basis, the safety of the vehicle steering process is scientifically evaluated in combination with the driver's steering decision information and the real-time information of the vehicle, and the safety smooth optimization assistance of the vehicle steering process is realized. A smooth steering strategy is derived, and the dynamic data tracking results of the vehicle steering process are analyzed to realize dynamic updating of the steering scenario model and the smooth steering strategy. According to the corresponding dynamic steering strategy, the vehicle steering process is dynamically low-sensitivity fine-tuned, and the corresponding steering report is provided to the driver, so that the steering assistance process can well cope with the dynamic problems caused by the multi-dimensional influencing factors in the environment in which the vehicle is located during the actual steering process. At the same time, through the dynamic low-sensitivity fine-tuning method, the driver's excessive reaction caused by the steering assistance process is avoided, which significantly improves the safety and stability of the steering process.

[0011] Optionally, the external environment parameter set includes a road friction coefficient and peripheral obstacle information, and the vehicle real-time information includes a real-time vehicle speed and a real-time vehicle position. Analyzing the external environment parameter set and the vehicle real-time information to construct a steering scenario model includes:

[0012] According to the peripheral obstacle information, respectively extracting the obstacle length and obstacle width of each peripheral obstacle;

[0013] Analyze the peripheral obstacle information to determine the position center and displacement speed of each peripheral obstacle;

[0014] Based on the real-time vehicle speed and the road friction coefficient, and according to the obstacle length, the obstacle width and the displacement speed of each peripheral obstacle, determine the risk area radius corresponding to each peripheral obstacle;

[0015] Delineating the risk area of ​​each peripheral obstacle according to the position center of each peripheral obstacle and the radius of the risk area;

[0016] The real-time vehicle position and the risk area corresponding to each peripheral obstacle are uniformly spatially mapped and time-series labeled to determine the turning scenario model.

[0017] Through this solution, based on the real-time vehicle speed and road friction coefficient, according to the obstacle length, obstacle width and displacement speed of each peripheral obstacle, the risk area corresponding to each peripheral obstacle is analyzed to be affected by the dual influence of the vehicle state and the obstacle state, and then the radius of the risk area corresponding to each peripheral obstacle is determined. On this basis, combined with the corresponding position center, the risk area of ​​each peripheral obstacle is delineated, and the real-time vehicle position and the risk area corresponding to each peripheral obstacle are uniformly spatially mapped and time-series marked to determine the turning scenario model. By delineating the risk area corresponding to each obstacle, the risks faced by the current vehicle in the turning process can be clarified, and at the same time, a time and space buffer is provided for the steering adjustments that need to be made to the risks, and data support is provided for the subsequent further dynamic analysis of the steering scenario model.

[0018] Optionally, the risk area radius corresponding to each peripheral obstacle is determined based on the real-time vehicle speed and the road friction coefficient according to the obstacle length, the obstacle width and the displacement speed of each peripheral obstacle, specifically as follows:

[0019] ;

[0020] in, For the The radius of the risk area of ​​the peripheral obstacles, is the real-time vehicle speed, is the acceleration due to gravity, is the road friction coefficient, is the preset gain factor, For the the obstacle width of the peripheral obstacles, For the the obstacle length of the peripheral obstacles, is the preset geometric influence coefficient, For the The displacement speed of the peripheral obstacles, is the preset magnification index.

[0021] Through this solution, mathematical analysis is used, based on the real-time vehicle speed and road friction coefficient, according to the obstacle length, obstacle width and displacement speed of each peripheral obstacle, and through mathematical formulas, the influence of the above parameters on the risk area radius is clarified, and then the risk area radius corresponding to each peripheral obstacle is quantified, thereby improving the accuracy and scientificity of the peripheral obstacle risk area, and then improving the targetedness of the subsequent steering control process based on the risk area.

[0022] Optionally, the steering decision information includes a real-time steering angle and a real-time steering speed, and the steering decision information of the driver and the real-time information of the vehicle are analyzed based on the steering scenario model, and safety smoothing optimization assistance is performed on the vehicle steering process to determine a smooth steering strategy, including:

[0023] Based on the steering scenario model, and according to the real-time position of the vehicle, analyzing the real-time relative distance between the current vehicle and the risk area corresponding to each peripheral obstacle;

[0024] The real-time relative distance is compared with the preset safety relative distance, and if the real-time relative distance is less than the preset safety relative distance, the steering angle to be adjusted and the steering speed to be adjusted are determined based on the real-time steering angle and the real-time steering speed according to the real-time relative distance and the preset safety relative distance;

[0025] The smooth steering strategy is determined according to the steering angle to be adjusted and the steering speed to be adjusted.

[0026] Through this solution, based on the steering scenario model and the real-time position of the vehicle, the real-time relative distance between the vehicle and the risk area corresponding to each peripheral obstacle is tracked. When the real-time relative distance is less than the preset safe relative distance, according to the real-time steering angle and the real-time steering speed, it is analyzed that the current vehicle avoids the required adjustment of the steering angle and the steering speed in the corresponding risk area under the dual influence of the current real-time steering angle and the current real-time steering speed, and uses the steering angle and the steering speed as a smooth steering strategy, thereby improving the safety of the vehicle steering process.

[0027] Optionally, based on the real-time steering angle and the real-time steering speed, the steering angle to be adjusted and the steering speed to be adjusted are determined according to the real-time relative distance and the preset safety relative distance, specifically as follows:

[0028] ;

[0029] in, is the steering angle to be adjusted, is the real-time steering angle, is the preset steering sensitivity coefficient, is the preset safe relative distance, For the current vehicle and the real-time relative distance between the risk areas of the peripheral obstacles, is the real-time steering angle, is the maximum design speed of the vehicle, is the steering speed to be adjusted, It is the preset steering speed influence coefficient.

[0030] Through this solution, mathematical analysis is used, based on the real-time steering angle and the real-time steering speed, according to the real-time relative distance and the preset safe relative distance, to scientifically quantify the steering angle and the steering speed to be adjusted to avoid the corresponding risk area under the dual influence of the current real-time steering angle and the current real-time steering speed, so as to improve the accuracy of the steering angle and the steering speed to be adjusted, so as to increase the degree of match between the subsequent steering assistance process according to the smooth steering strategy and the actual situation of the vehicle, thereby improving the steering stability and safety of the vehicle.

[0031] Optionally, the vehicle real-time information further includes a vehicle load index, a vehicle wheelbase, a vehicle accessory length, and a lateral acceleration. Based on the smooth steering strategy, according to the steering scenario model and the vehicle real-time information, dynamic data tracking is performed on the vehicle steering process, and according to the dynamic data tracking result, the steering scenario model and the corresponding smooth steering strategy are dynamically updated to determine the dynamic steering strategy, including:

[0032] According to the steering angle to be adjusted and the steering speed to be adjusted in the smooth steering strategy, the real-time steering angle and the real-time steering speed are adjusted, and in this process, the real-time vehicle information and the steering scenario model are dynamically updated;

[0033] Determining an adjustment influence radius based on the vehicle load index, the vehicle wheelbase, the vehicle accessory length, and the lateral acceleration, according to the steering angle to be adjusted and the steering speed to be adjusted;

[0034] Based on the updated steering scenario model, and according to the adjusted influence radius, determining whether there is the risk area that interferes with the adjusted influence radius;

[0035] If there is a risk area that interferes with the adjusted influence radius, based on the updated real-time steering angle and the real-time steering speed, and according to the updated real-time relative distance and the preset safe relative distance, the smooth steering strategy corresponding to the updated steering scene model is dynamically adjusted to determine the dynamic steering strategy.

[0036] Through this solution, based on the smooth steering strategy, the vehicle steering state is updated, and the vehicle real-time information and steering scenario model are dynamically updated. At the same time, based on the vehicle load index, vehicle wheelbase, vehicle accessory length and lateral acceleration, according to the steering angle to be adjusted and the steering speed to be adjusted, the adjustment influence radius is determined. Based on the adjustment influence radius, the updated steering scenario model is analyzed. When there is a risk area that interferes with the adjustment influence radius, the steering angle to be adjusted and the steering speed to be adjusted are updated according to a series of updated parameters to determine the dynamic steering strategy. The new risks caused by the vehicle steering adjustment are fully considered, and the comprehensiveness and adaptability of the dynamic steering strategy are significantly improved.

[0037] Optionally, the adjustment influence radius is determined based on the vehicle load index, the vehicle wheelbase, the vehicle accessory length and the lateral acceleration, according to the steering angle to be adjusted and the steering speed to be adjusted, specifically as the following formula:

[0038] ;

[0039] in, To adjust the influence radius, is the vehicle load index, is the steering angle to be adjusted, is the steering speed to be adjusted, is the preset speed influence coefficient, is the vehicle wheelbase, is the length of the vehicle accessory, is the lateral acceleration, is the preset acceleration influence coefficient, is the road surface friction coefficient.

[0040] Through this solution, mathematical analysis is used to scientifically quantify the adjustment influence radius of the vehicle during the steering adjustment process based on the vehicle load index, vehicle wheelbase, vehicle accessory length and lateral acceleration, according to the steering angle to be adjusted and the steering speed to be adjusted, thereby making the analysis and judgment of new risks generated by the vehicle during the steering adjustment process more accurate.

[0041] Optionally, the dynamic low-sensitivity fine-tuning of the steering process according to the dynamic steering strategy includes:

[0042] Based on a preset time point division rule, the dynamic steering strategy is analyzed, the steering angle to be adjusted and the steering speed to be adjusted corresponding to each steering time point are determined, and the time point interval is extracted;

[0043] Based on the steering angle to be adjusted and the steering speed to be adjusted corresponding to each steering time point, and according to a preset minimization objective function, an adjustment fluctuation optimization model is constructed;

[0044] According to the adjustment fluctuation optimization model, the fluctuation amplitude of the steering angle to be adjusted and the steering speed to be adjusted corresponding to each steering time point is optimized;

[0045] According to the steering angle to be adjusted and the steering speed to be adjusted after the fluctuation amplitude optimization is completed, the dynamic steering strategy is optimized, and the real-time steering angle and the real-time steering speed are dynamically adjusted accordingly.

[0046] Through this scheme, the dynamic steering strategy is divided and analyzed in time series, and the steering angle to be adjusted, the steering speed to be adjusted and the time point interval corresponding to each steering time point are extracted. On this basis, combined with the preset minimization objective function, an adjustment fluctuation optimization model for optimizing the steering angle and steering speed is constructed. On this basis, the steering angle to be adjusted and the steering speed to be adjusted after the optimization are obtained, so as to update the dynamic steering strategy, and further dynamically adjust the real-time steering angle and real-time steering speed, so that the fluctuation amplitude of the steering angle and steering speed is reduced while meeting the safety requirements, thereby effectively avoiding the steering assistance process causing the driver to overreact.

[0047] Optionally, based on the steering angle to be adjusted and the steering speed to be adjusted corresponding to each steering time point, an adjustment fluctuation optimization model is constructed according to a preset minimization objective function, which is specifically the following formula:

[0048] ;

[0049] in, For said adjusted volatility optimization model, For the preset minimization objective function, is the first Lagrange multiplier, is the preset speed fluctuation threshold, Turning point The steering speed to be adjusted under is the time point interval, is the second Lagrange multiplier, is the preset angle fluctuation threshold, Turning point The steering angle to be adjusted below.

[0050] Through this solution, mathematical analysis is used to construct an adjustment fluctuation optimization model based on the steering angle to be adjusted and the steering speed to be adjusted corresponding to each steering time point according to a preset minimization objective function. This model is used as the basis of a model for optimizing the steering angle to be adjusted and the steering speed to be adjusted, and then the steering angle to be adjusted and the steering speed to be adjusted corresponding to different steering time points are obtained when the driver feels significantly, thereby effectively avoiding excessive reaction of the driver.

[0051] Optionally, the system further includes:

[0052] After the vehicle completes the turn according to the optimized dynamic steering strategy, extracting the turn timestamp and the turn road information;

[0053] Acquire historical turning information of the vehicle, match the turning road information with the historical turning information of the vehicle, determine the occurrence frequency of the turning road information, and thereby determine whether the road corresponding to the current turning road information is a frequently used road section;

[0054] If the road corresponding to the current turning road information is a frequently used section, the dynamic steering strategy is temporarily persisted based on the turning timestamp and the occurrence frequency, and the dynamic steering strategy after the temporary persistence is used as the frequently used section iteration data.

[0055] Through this solution, based on the turning timestamp and the frequency of occurrence, the dynamic steering strategies corresponding to the vehicle's frequently used sections are temporarily persisted, and the dynamic steering strategies after the temporary persistence processing are used as the iteration data of the frequently used sections. When subsequent vehicles pass through the same turning road and generate turning behaviors, they can use the corresponding dynamic steering strategy data that has been temporarily persisted as the basis for iteration, so as to improve the response efficiency of the steering assistance process. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0057] Figure 1 A schematic diagram of an application scenario provided for an embodiment of the present application;

[0058] Figure 2 A flowchart of a steering driving assistance system applied to a new energy vehicle is provided in accordance with an embodiment of the present application. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0060] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0061] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0062] However, existing steering assistance systems usually rely on preset automatic control algorithms provided by automobile manufacturers, and are regularly updated and maintained through software update services provided by automobile manufacturers. They are unable to properly deal with dynamic problems caused by multi-dimensional influencing factors within the vehicle's environment during actual steering. In addition, existing steering assistance systems have the risk of causing the driver to overreact when dealing with complex situations, and their auxiliary role in the safety of the vehicle's steering process is limited.

[0063] Based on this, the present application provides a steering driving assistance system for new energy vehicles. Through this solution, a steering scene model reflecting the driving scene of the vehicle is constructed according to the external environment parameter set and the real-time information of the vehicle, and on this basis, the safety of the vehicle steering process is scientifically evaluated in combination with the driver's steering decision information and the real-time information of the vehicle, and the safety smooth optimization assistance of the vehicle steering process is realized, and the smooth steering strategy is obtained. By analyzing the dynamic data tracking results of the vehicle steering process, the steering scene model and the smooth steering strategy are dynamically updated, and according to the corresponding dynamic steering strategy, the vehicle steering process is dynamically low-sensitivity fine-tuned, and then the corresponding steering report is provided to the driver, so that the steering assistance process can well cope with the dynamic problems caused by the multi-dimensional influencing factors in the environment in which the vehicle is located during the actual steering process of the vehicle. At the same time, through the dynamic low-sensitivity fine-tuning method, the steering assistance process is avoided to cause the driver's overreaction, which significantly improves the safety and stability of the vehicle steering process.

[0064] Figure 1A schematic diagram of an application scenario provided by this application. During vehicle driving, the method provided by this application is applied to enable the steering assistance process to effectively cope with dynamic problems caused by multi-dimensional influencing factors in the vehicle's environment during actual steering, while avoiding excessive driver reaction during the steering assistance process through dynamic low-sensitivity fine-tuning.

[0065] Specifically, the method of the present application is applied to any server, which can be integrated with the vehicle system. The server communicates with the built-in environmental parameter sensor and vehicle parameter sensor of the vehicle respectively. The server obtains and analyzes the external environmental parameter set provided by the environmental parameter sensor and the real-time vehicle information provided by the vehicle parameter sensor, and constructs a steering scenario model that reflects the driving scenario of the vehicle. On this basis, combined with the driver's steering decision information and vehicle real-time information provided by the vehicle parameter sensor, a scientific assessment is made on the safety of the vehicle steering process, and smooth optimization assistance for the safety of the vehicle steering process is achieved, and a smooth steering strategy is derived. By analyzing the dynamic data tracking results of the vehicle steering process, the steering scenario model and the smooth steering strategy are dynamically updated, and according to the corresponding dynamic steering strategy, the vehicle steering process is dynamically low-sensitivity fine-tuned, and then the corresponding steering report is provided to the driver, so that the steering assistance process can well cope with the dynamic problems caused by the multi-dimensional influencing factors in the environment in which the vehicle is located during the actual steering process of the vehicle. At the same time, through the dynamic low-sensitivity fine-tuning method, the steering assistance process is avoided to cause excessive reaction of the driver, which significantly improves the safety and stability of the vehicle steering process.

[0066] For specific implementation methods, please refer to the following embodiments.

[0067] Figure 2 This is a flowchart of a steering driving assistance system for new energy vehicles provided in an embodiment of the present application. The method of this embodiment can be applied to the server in the above scenario. Figure 2 As shown, the method includes:

[0068] S201, obtaining an external environment parameter set and real-time vehicle information, analyzing the external environment parameter set and real-time vehicle information, and constructing a steering scenario model.

[0069] The external environment parameter set may be a set of parameters of the environment in which the vehicle is currently traveling, and may include road condition information, obstacle information, etc. The external environment parameter set may be obtained through a vehicle's built-in environmental parameter sensor, such as a lidar.

[0070] The vehicle real-time information may be internal real-time parameter information during the current vehicle driving process, such as real-time vehicle speed, real-time vehicle position, etc. The vehicle real-time information may be obtained through vehicle parameter sensors built into the vehicle.

[0071] The turning scenario model may be a model used to characterize the current driving scenario of the vehicle.

[0072] Specifically, in the process of assisting the vehicle in steering, since the specific situations faced by different vehicles vary greatly in the actual steering process, before assisting the vehicle in steering, it is necessary to clarify the current driving scenario of the vehicle in order to enhance the steering assistance process's ability to recognize and respond to the external environment, and to improve the adaptability of the steering assistance process. Through mathematical analysis, based on the external environment parameter set and the vehicle's real-time information, mathematical language is used to describe the vehicle's environment to construct a steering scenario model, providing a scientific data framework for subsequent steering process analysis and the generation of steering strategies.

[0073] S202, obtaining the driver's steering decision information, analyzing the driver's steering decision information and vehicle real-time information based on the steering scenario model, performing safety smoothing optimization assistance on the vehicle steering process, and determining a smooth steering strategy.

[0074] The driver's steering decision information may be behavioral information of the vehicle driver on the steering process, and may include real-time steering angle and real-time steering speed, etc. The driver's steering decision information may be obtained through different types of vehicle parameter sensors.

[0075] The smooth steering strategy may be a steering parameter smooth adjustment strategy for vehicle steering safety.

[0076] Specifically, during the vehicle steering process, the driver's decision is the main one, and the steering assistance system is the auxiliary one. On the basis of the steering scenario, according to the driver's steering decision information reflecting the driver's operation and the vehicle's real-time information reflecting the current state of the vehicle, mathematical analysis is used to analyze the possible safety issues of the steering decision made by the driver in the current vehicle state in the vehicle's environment, and the parameters that need to be adjusted in the vehicle steering process are quantified based on the safety issues, so as to achieve safety smooth optimization assistance for the vehicle steering process and derive the corresponding smooth steering strategy.

[0077] S203, based on the smooth steering strategy, according to the steering scene model and the real-time information of the vehicle, the vehicle steering process is dynamically tracked, and according to the dynamic data tracking results, the steering scene model and the corresponding smooth steering strategy are dynamically updated to determine the dynamic steering strategy.

[0078] The dynamic data tracking result may be a data set obtained by integrating the state parameters of the vehicle during the steering process with the corresponding time nodes as markers.

[0079] The dynamic steering strategy may be an adjustment strategy used to adapt to dynamic changes during the vehicle steering process to further improve the vehicle steering safety and stability.

[0080] Specifically, since the vehicle is not in a static scene during the steering process, and the steering behavior of the vehicle itself may also cause new risks in a dynamically changing scenario, in the process of the vehicle adjusting the steering state according to the smooth steering strategy, it is necessary to dynamically track the changes in the vehicle state parameters during the vehicle steering process based on the steering scenario model and the vehicle's real-time information, update the corresponding steering scenario model, and through further mathematical analysis methods, analyze the new risks that may be caused by the vehicle in the process of turning according to the smooth steering strategy under the updated steering scenario model based on the dynamic data tracking results, and quantify the vehicle steering parameters that need to be adjusted for the new risks, so as to dynamically update the smooth steering strategy and determine the dynamic steering strategy used to deal with new risks in the steering process.

[0081] S204: According to the dynamic steering strategy, dynamically adjust the vehicle steering process in a low-sensitivity manner and output a corresponding steering report.

[0082] Dynamic low-sensitivity fine-tuning can be a process of dynamically adjusting the vehicle's steering state with the goal of reducing the driver's perception of steering assist adjustments.

[0083] The steering report may be report information obtained by integrating changes in various parameters during the vehicle steering process and used to characterize changes in the vehicle steering state.

[0084] Specifically, since the vehicle's driving state during the steering process needs to be adjusted during the assist process, if the driver clearly feels the change in the vehicle's state during the steering assist process, it is easy for the driver to misunderstand that the "vehicle is out of control", which in turn causes the driver to overreact to the current steering assist process. In this case, the driver is likely to make sudden and aggressive driving decisions that are inconsistent with the actual vehicle's driving state, which in turn leads to a sharp increase in safety risks. In order to avoid the driver's overreaction, in the process of adjusting the vehicle's driving state during the steering process according to the dynamic steering strategy, mathematical analysis is used to optimize the fluctuation range of the vehicle's driving state during the steering process, reduce the driver's perception of the steering assist process, and achieve dynamic low-sensitivity fine-tuning of the vehicle's driving state. In this process, changes in various driving parameters are collected, and various parameters are integrated and output through data visualization technology, so that the driver can grasp the vehicle's steering state.

[0085] Through this solution, a steering scenario model that reflects the driving scenario of the vehicle is constructed according to the external environment parameter set and the real-time information of the vehicle. On this basis, the safety of the vehicle steering process is scientifically evaluated in combination with the driver's steering decision information and the real-time information of the vehicle, and the safety smooth optimization assistance of the vehicle steering process is realized. A smooth steering strategy is derived, and the dynamic data tracking results of the vehicle steering process are analyzed to realize dynamic updating of the steering scenario model and the smooth steering strategy. According to the corresponding dynamic steering strategy, the vehicle steering process is dynamically low-sensitivity fine-tuned, and the corresponding steering report is provided to the driver, so that the steering assistance process can well cope with the dynamic problems caused by the multi-dimensional influencing factors in the environment in which the vehicle is located during the actual steering process. At the same time, through the dynamic low-sensitivity fine-tuning method, the driver's excessive reaction caused by the steering assistance process is avoided, which significantly improves the safety and stability of the steering process.

[0086] In some embodiments, based on the peripheral obstacle information, the obstacle length and obstacle width of each peripheral obstacle are extracted respectively; the peripheral obstacle information is analyzed to determine the position center and displacement speed of each peripheral obstacle; based on the real-time vehicle speed and the road friction coefficient, the risk area radius corresponding to each peripheral obstacle is determined according to the obstacle length, obstacle width and displacement speed of each peripheral obstacle; based on the position center and risk area radius of each peripheral obstacle, the risk area of ​​each peripheral obstacle is delineated; the real-time vehicle position and the risk area corresponding to each peripheral obstacle are uniformly spatially mapped and time-series marked to determine the steering scenario model.

[0087] The external environment parameter set includes road friction coefficient and peripheral obstacle information.

[0088] The road friction coefficient may be a value used to characterize the strength of friction between a vehicle tire and the road surface.

[0089] The peripheral obstacle information may be information about various obstacles within the monitoring range of the current vehicle, with the current vehicle as the center and the maximum monitoring radius of its built-in environmental sensor as the benchmark.

[0090] The position center may be the position coordinate information of the obstacle center point within the current vehicle monitoring range.

[0091] The displacement speed may be a moving speed corresponding to the obstacle, and the displacement speed may be obtained by monitoring the time series change of the obstacle position.

[0092] The risk area radius may be used to characterize the radius of an area centered on the obstacle position, where there is a safety risk between the current vehicle and the obstacle after the vehicle enters.

[0093] The risk area may be an area centered at the obstacle location, where there is a safety risk between the current vehicle and the obstacle if the vehicle enters.

[0094] Specifically, during vehicle driving, the vehicle's built-in environmental sensors can monitor and collect obstacle information around the vehicle within a certain range with the vehicle position as the center. However, the impact range of different obstacles on the current vehicle varies. The reasons for this difference mainly come from two aspects. On the one hand, the current driving state of the vehicle is mainly affected by the vehicle's real-time speed and the road friction coefficient. The faster the vehicle's real-time speed, the greater the body displacement amplitude caused by the vehicle's small steering adjustment compared to the low speed situation, and the greater the risk of interference with obstacles. At the same time, the smaller the road friction coefficient, the greater the displacement amplitude caused by the vehicle's road slippage during the steering process. The greater the impact of the obstacle, the greater the risk of interference between the vehicle and the obstacle; on the other hand, the state of the obstacle itself is mainly affected by the volume and displacement speed of the obstacle. The larger the volume of the obstacle and the faster the displacement speed, the larger the range of its interference, and the larger the dangerous area it forms; in order to quantify the impact from the above two aspects, the concept of risk area is introduced. The risk area mainly refers to an area formed with the obstacle as the center. When the current vehicle contacts this area, there is a risk of safety accidents caused by the above two aspects. By defining the risk area corresponding to each obstacle, the risks faced by the current vehicle during the steering process can be clarified, and at the same time, time and space buffers are provided for steering adjustments that need to be made based on risks.

[0095] In order to achieve quantitative analysis of the above risk areas, mathematical analysis is used to describe the range changes of the risk areas corresponding to obstacles under the dual influence of vehicle state and obstacle state based on the real-time vehicle speed and road friction coefficient, according to the obstacle length, obstacle width and displacement speed of each peripheral obstacle, so as to quantify the radius of the risk area, and take the center of the obstacle as the origin, divide the risk area corresponding to each obstacle according to the corresponding risk area radius, and then use the monitoring range of the vehicle's built-in environmental monitoring sensor as the benchmark to unify the spatial coordinates of the real-time vehicle position and the risk area corresponding to each obstacle within the monitoring range, and according to the monitoring time points of the real-time vehicle position and the risk area, respectively, time-series mark the real-time vehicle position and the risk area, so as to construct a steering scenario model, which provides data support for the subsequent further dynamic analysis of the steering scenario model.

[0096] Through this solution, based on the real-time vehicle speed and road friction coefficient, according to the obstacle length, obstacle width and displacement speed of each peripheral obstacle, the risk area corresponding to each peripheral obstacle is analyzed to be affected by the dual influence of the vehicle state and the obstacle state, and then the radius of the risk area corresponding to each peripheral obstacle is determined. On this basis, combined with the corresponding position center, the risk area of ​​each peripheral obstacle is delineated, and the real-time vehicle position and the risk area corresponding to each peripheral obstacle are uniformly spatially mapped and time-series marked to determine the turning scenario model. By delineating the risk area corresponding to each obstacle, the risks faced by the current vehicle in the turning process can be clarified, and at the same time, a time and space buffer is provided for the steering adjustments that need to be made to the risks, and data support is provided for the subsequent further dynamic analysis of the steering scenario model.

[0097] In some embodiments, based on the real-time vehicle speed and the road friction coefficient, the risk area radius corresponding to each peripheral obstacle is determined according to the obstacle length, obstacle width and displacement speed of each peripheral obstacle, specifically, the following formula (1):

[0098] (1)

[0099] in, For the The risk area radius of the peripheral obstacles, is the real-time vehicle speed, is the acceleration due to gravity, is the road friction coefficient, is the preset gain factor, For the The obstacle width of the peripheral obstacles, For the The obstacle length of the peripheral obstacles, is the preset geometric influence coefficient, For the The displacement speed of the external obstacles, is the preset magnification index.

[0100] The preset gain coefficient may be a value used to characterize the degree of influence of the vehicle state on the radius of the risk area, and the preset gain coefficient may be obtained by fitting through a vehicle simulation experiment.

[0101] The preset geometric influence coefficient may be a numerical value used to characterize the degree of influence of the geometric features of an obstacle on the radius of the risk area. The preset geometric influence coefficient may be obtained by fitting through a vehicle simulation experiment.

[0102] The preset amplification index may be a value used to characterize the degree of influence of the obstacle displacement speed on the radius of the risk area, and the preset amplification index may be obtained by fitting through a vehicle simulation experiment.

[0103] Specifically, through formula (1) Describe the influence of the torque characteristics of the current vehicle under the friction conditions reflected by the corresponding road friction system on the radius of the peripheral obstacle risk area; Characterize the influence of obstacle geometric features on the radius of the risk area. Selecting the maximum width or length can ensure that the influence of the obtained geometric features covers the interference caused by complex shapes. It describes the nonlinear effect of obstacle displacement speed on the risk area radius, emphasizing that when the obstacle moves quickly, the corresponding risk area radius increases rapidly, while for static obstacles, the corresponding risk area radius will be significantly reduced.

[0104] Through this solution, mathematical analysis is used, based on the real-time vehicle speed and road friction coefficient, according to the obstacle length, obstacle width and displacement speed of each peripheral obstacle, and through mathematical formulas, the influence of the above parameters on the risk area radius is clarified, and then the risk area radius corresponding to each peripheral obstacle is quantified, thereby improving the accuracy and scientificity of the peripheral obstacle risk area, and then improving the targetedness of the subsequent steering control process based on the risk area.

[0105] In some embodiments, based on the steering scenario model, according to the real-time position of the vehicle, the real-time relative distance between the current vehicle and the risk area corresponding to each peripheral obstacle is analyzed; the real-time relative distance is compared with the preset safety relative distance; if the real-time relative distance is less than the preset safety relative distance, then based on the real-time steering angle and the real-time steering speed, according to the real-time relative distance and the preset safety relative distance, the steering angle to be adjusted and the steering speed to be adjusted are determined; according to the steering angle to be adjusted and the steering speed to be adjusted, a smooth steering strategy is determined.

[0106] The steering decision information includes real-time steering angle and real-time steering speed.

[0107] The real-time steering angle may be the current steering angle of the vehicle after being affected by the driver's steering decision.

[0108] The real-time steering speed may be the steering speed of the current vehicle after being affected by the driver's steering decision.

[0109] The real-time relative distance can be the straight-line distance between the center point of the vehicle and the edge of the risk area of ​​different obstacles during the turning process.

[0110] The preset safety relative distance may be a safety distance between the center point of the vehicle and the edge of the obstacle risk area, and the preset safety relative distance may be obtained by fitting based on experimental data.

[0111] The steering angle to be adjusted may be the angle at which the vehicle needs to be turned in order to avoid the corresponding obstacle risk area.

[0112] The steering speed to be adjusted may be a steering speed required for the vehicle to avoid a corresponding obstacle risk area.

[0113] Specifically, after clarifying the risk area corresponding to each peripheral obstacle, the real-time position of the vehicle is tracked based on the steering scenario model, and the real-time relative distance between the vehicle and each peripheral obstacle is calculated through the coordinate point distance calculation formula. When the real-time relative distance is less than the preset safe relative distance, it means that the current vehicle is entering or has exited the risk area during the steering process and needs to be adjusted in time. At this time, mathematical analysis is used to analyze that the current vehicle, under the dual influence of the current real-time steering angle and the current real-time steering speed, avoids the required adjustment of the steering angle and the steering speed to be adjusted in the corresponding risk area, and uses the steering angle and the steering speed to be adjusted as a smooth steering strategy.

[0114] Through this solution, based on the steering scenario model and the real-time position of the vehicle, the real-time relative distance between the vehicle and the risk area corresponding to each peripheral obstacle is tracked. When the real-time relative distance is less than the preset safe relative distance, according to the real-time steering angle and the real-time steering speed, it is analyzed that the current vehicle avoids the required adjustment of the steering angle and the steering speed in the corresponding risk area under the dual influence of the current real-time steering angle and the current real-time steering speed, and uses the steering angle and the steering speed as a smooth steering strategy, thereby improving the safety of the vehicle steering process.

[0115] In some embodiments, based on the real-time steering angle and the real-time steering speed, the steering angle to be adjusted and the steering speed to be adjusted are determined according to the real-time relative distance and the preset safety relative distance, specifically, the following formula (2):

[0116] (2)

[0117] in, is the steering angle to be adjusted, is the real-time steering angle, is the preset steering sensitivity coefficient, To preset a safe relative distance, For the current vehicle and The real-time relative distance between the risk areas of the peripheral obstacles, is the real-time steering angle, is the maximum design speed of the vehicle, is the steering speed to be adjusted, It is the preset steering speed influence coefficient.

[0118] The preset steering sensitivity coefficient may be a value used to adjust the effect of the relative distance difference on the steering angle.

[0119] The preset steering speed influence coefficient may be a value used to adjust the influence of the relative distance ratio on the steering speed.

[0120] Specifically, through formula (2) Reflects the difference between the real-time relative distance and the preset safety relative distance. On this basis, the preset steering sensitivity coefficient is introduced to adjust the impact of the difference on the steering angle, and the speed factor is introduced at the same time. To adjust the steering sensitivity, the speed factor ensures that the steering response is weakened when the vehicle approaches the maximum speed, while at low speeds, the steering angle response sensitivity is increased; at the same time, Describes the ratio relationship between the real-time relative distance and the preset safety relative distance, reflecting the deceleration adjustment effect on the vehicle steering speed when the real-time relative distance is less than the preset safety relative distance. On this basis, the preset steering speed influence coefficient is introduced to adjust the influence of the ratio relationship on the steering speed, and the speed factor is introduced at the same time To ensure that when the vehicle approaches the maximum speed, the steering speed is adjusted less, so that the vehicle's movement stability at high speeds is increased.

[0121] Through this solution, mathematical analysis is used, based on the real-time steering angle and the real-time steering speed, according to the real-time relative distance and the preset safe relative distance, to scientifically quantify the steering angle and the steering speed to be adjusted to avoid the corresponding risk area under the dual influence of the current real-time steering angle and the current real-time steering speed, so as to improve the accuracy of the steering angle and the steering speed to be adjusted, so as to increase the degree of match between the subsequent steering assistance process according to the smooth steering strategy and the actual situation of the vehicle, thereby improving the steering stability and safety of the vehicle.

[0122] In some embodiments, the real-time steering angle and the real-time steering speed are adjusted according to the steering angle to be adjusted and the steering speed to be adjusted in the smooth steering strategy, and in this process, the vehicle real-time information and the steering scenario model are dynamically updated to update the steering scenario model; based on the vehicle load index, the vehicle wheelbase, the vehicle accessory length and the lateral acceleration, the adjustment influence radius is determined according to the steering angle to be adjusted and the steering speed to be adjusted; based on the updated steering scenario model, according to the adjustment influence radius, it is determined whether there is a risk area that interferes with the adjustment influence radius; if there is a risk area that interferes with the adjustment influence radius, based on the updated real-time steering angle and the real-time steering speed, according to the updated real-time relative distance and the preset safety relative distance, the smooth steering strategy corresponding to the updated steering scenario model is dynamically adjusted to determine the dynamic steering strategy.

[0123] Real-time vehicle information also includes vehicle load index, vehicle wheelbase, vehicle attachment length and lateral acceleration.

[0124] The vehicle load index may be a numerical value used to characterize the current real-time load intensity of the vehicle.

[0125] The vehicle wheelbase may be the distance between the front axle and the rear axle of the current vehicle.

[0126] The vehicle accessory length can be the total length of default or custom added vehicle accessories that contribute to the vehicle's body length beyond the wheelbase.

[0127] The lateral acceleration can be the acceleration of the center of the vehicle relative to its turning direction when the vehicle is turning.

[0128] The adjustment influence radius may be a vehicle deviation influence radius generated by an adjustment process when the vehicle steering state is adjusted.

[0129] Specifically, after the smooth steering strategy is derived, the real-time steering angle and real-time steering speed of the vehicle are adjusted according to the steering angle to be adjusted and the steering speed to be adjusted in the smooth steering strategy through the steering control device and the vehicle speed control device built into the vehicle. In this process, based on the updated real-time vehicle speed and road friction coefficient, according to the updated obstacle length, obstacle width and displacement speed of each peripheral obstacle, the risk area radius of the peripheral obstacle is dynamically updated through the formula (1) in the above embodiment, and the steering scene model is updated thereby. At the same time, since the vehicle may cause new risk area interference during the steering adjustment according to the smooth steering strategy, it is necessary to analyze the interference range caused during the vehicle steering adjustment process. The interference range caused during the vehicle steering adjustment process is mainly affected by the vehicle load index, the vehicle wheelbase, the vehicle accessory length and the lateral acceleration. The smaller the vehicle load index, the greater the risk area interference. The greater the centrifugal force of the vehicle during the steering process, the higher the risk of deviation. The vehicle wheelbase and the length of the vehicle accessories jointly determine the overall length of the vehicle body. The longer the overall length of the vehicle body, the larger the interference range during the steering adjustment process. The lateral acceleration directly determines the deviation speed during the vehicle steering process. Through mathematical analysis, based on the vehicle load index, the vehicle wheelbase, the length of the vehicle accessories and the lateral acceleration, according to the steering angle to be adjusted and the steering speed to be adjusted, the adjustment influence radius of the corresponding interference area during the current vehicle steering adjustment process is quantified. Then, combined with the updated steering scenario model, it is determined whether there is a risk area that interferes with the adjustment influence radius. If so, it means that a new risk area interference phenomenon will be caused during the current vehicle steering adjustment process. At this time, according to the above series of updated parameters, the steering angle to be adjusted and the steering speed to be adjusted are updated by formula (2) in the above embodiment to determine the dynamic steering strategy.

[0130] Through this solution, based on the smooth steering strategy, the vehicle steering state is updated, and the vehicle real-time information and steering scenario model are dynamically updated. At the same time, based on the vehicle load index, vehicle wheelbase, vehicle accessory length and lateral acceleration, according to the steering angle to be adjusted and the steering speed to be adjusted, the adjustment influence radius is determined. Based on the adjustment influence radius, the updated steering scenario model is analyzed. When there is a risk area that interferes with the adjustment influence radius, the steering angle to be adjusted and the steering speed to be adjusted are updated according to a series of updated parameters to determine the dynamic steering strategy. The new risks caused by the vehicle steering adjustment are fully considered, and the comprehensiveness and adaptability of the dynamic steering strategy are significantly improved.

[0131] In some embodiments, based on the vehicle load index, the vehicle wheelbase, the vehicle accessory length and the lateral acceleration, the adjustment influence radius is determined according to the steering angle to be adjusted and the steering speed to be adjusted, specifically the following formula (3):

[0132] (3)

[0133] in, To adjust the influence radius, is the vehicle load index, is the steering angle to be adjusted, is the steering speed to be adjusted, is the preset speed influence coefficient, is the vehicle wheelbase, is the length of vehicle accessories, is the lateral acceleration, is the preset acceleration influence coefficient, is the road friction coefficient.

[0134] The preset speed influence coefficient may be a value used to characterize the degree of influence of the vehicle steering speed on the adjustment influence radius, and the preset speed influence coefficient may be obtained by fitting vehicle simulation test data.

[0135] The preset acceleration influence coefficient may be a value used to characterize the influence degree of the lateral acceleration on the adjustment influence radius, and the preset acceleration influence coefficient may be obtained by fitting vehicle simulation test data.

[0136] Specifically, during the steering process, the vehicle will generate its own steering slip, resulting in a change in the steering state. Considering the load and steering conditions of the vehicle, the steering slip phenomenon is expressed as follows using the tangent function relationship of the trigonometric function: , where the numerator reflects the impact of the vehicle's current load state and steering angle on the steering radius, and the denominator comprehensively considers the impact of the vehicle's geometric characteristics, load, and friction. When the vehicle's steering speed changes, its corresponding impact radius will be affected. By introducing the speed factor To adjust this influence, and finally quantify the adjustment influence radius of the vehicle during the steering adjustment process.

[0137] Through this solution, mathematical analysis is used to scientifically quantify the adjustment influence radius of the vehicle during the steering adjustment process based on the vehicle load index, vehicle wheelbase, vehicle accessory length and lateral acceleration, according to the steering angle to be adjusted and the steering speed to be adjusted, thereby making the analysis and judgment of new risks generated by the vehicle during the steering adjustment process more accurate.

[0138] In some embodiments, based on a preset time point division rule, the dynamic steering strategy is analyzed, the steering angle to be adjusted and the steering speed to be adjusted corresponding to each steering time point are determined, and the time point interval is extracted; based on the steering angle to be adjusted and the steering speed to be adjusted corresponding to each steering time point, an adjustment fluctuation optimization model is constructed according to a preset minimization objective function; according to the adjustment fluctuation optimization model, the fluctuation amplitude of the steering angle to be adjusted and the steering speed to be adjusted corresponding to each steering time point is optimized; according to the steering angle to be adjusted and the steering speed to be adjusted after the fluctuation amplitude optimization is completed, the dynamic steering strategy is optimized, and the real-time steering angle and the real-time steering speed are dynamically adjusted accordingly.

[0139] The preset time point division rule may be a preset rule for dividing the turning time points for the vehicle turning period, and the preset time point division rule may be set according to specific data granularity requirements.

[0140] The time point interval may be the interval length between every two adjacent turning time points.

[0141] The preset minimization objective function may be a preset function for minimizing the fluctuation amplitude of the steering angle and the steering speed during the steering process.

[0142] The adjustment fluctuation optimization model may be a mathematical model for obtaining the optimal solution of the steering angle and the steering speed at different time points.

[0143] Specifically, in order to avoid the driver's overreaction during the steering assistance process, it is necessary to meet the steering demand while making the fluctuation amplitude of the steering adjustment process as small as possible within the continuous adjustment period, so that the driver will not obviously feel the intervention of the steering assistance process, thereby avoiding overreaction. To achieve this goal, it is necessary to reduce the fluctuation amplitude of the steering angle and steering speed while meeting the safety requirements. Through the preset time point division rules, a series of steering angles and steering speeds to be adjusted in the dynamic steering strategy are divided into time series, and the corresponding time point intervals are extracted. Through mathematical analysis, based on the preset minimum objective function, an adjustment fluctuation optimization model for optimizing the steering angle and steering speed is constructed, and on this basis, the steering angle and steering speed to be adjusted after the optimization are obtained, so as to update the dynamic steering strategy, and further dynamically adjust the real-time steering angle and real-time steering speed.

[0144] Through this scheme, the dynamic steering strategy is divided and analyzed in time series, and the steering angle to be adjusted, the steering speed to be adjusted and the time point interval corresponding to each steering time point are extracted. On this basis, combined with the preset minimization objective function, an adjustment fluctuation optimization model for optimizing the steering angle and steering speed is constructed. On this basis, the steering angle to be adjusted and the steering speed to be adjusted after the optimization are obtained, so as to update the dynamic steering strategy, and further dynamically adjust the real-time steering angle and real-time steering speed, so that the fluctuation amplitude of the steering angle and steering speed is reduced while meeting the safety requirements, thereby effectively avoiding the steering assistance process causing the driver to overreact.

[0145] In some embodiments, based on the steering angle to be adjusted and the steering speed to be adjusted corresponding to each steering time point, an adjustment fluctuation optimization model is constructed according to a preset minimization objective function, specifically the following formula (4):

[0146] (4)

[0147] in, For said adjusted volatility optimization model, For the preset minimization objective function, is the first Lagrange multiplier, is the preset speed fluctuation threshold, Turning point The steering speed to be adjusted under is the time point interval, is the second Lagrange multiplier, is the preset angle fluctuation threshold, Turning point The steering angle to be adjusted below.

[0148] The preset speed fluctuation threshold may be a maximum steering speed fluctuation value allowed without causing significant sensation to the driver, and the preset speed fluctuation threshold may be obtained by fitting experimental data.

[0149] The preset angle fluctuation threshold may be a maximum steering angle fluctuation value allowed without causing significant sensation to the driver, and the preset angle fluctuation threshold may be obtained by fitting experimental data.

[0150] Lagrange multipliers can be a mathematical tool for dealing with constrained optimization problems. Lagrange multipliers can be obtained by

[0151] Specifically, the objective function is optimized by using the preset minimization objective function in formula (4), and the preset minimization objective function can adopt the following function (5):

[0152] ;

[0153] in, is the total number of turning time points; The weight parameter is adjusted for the preset speed, which is used to adjust the importance of the steering speed in the objective function and can be obtained by fitting the simulation experimental data; is the target steering speed, indicating the desired steering speed; The weight parameter is adjusted for the preset speed, which is used to adjust the importance of the steering angle in the objective function, and can be obtained by fitting the simulation experimental data; is the target steering angle, indicating the desired steering angle;

[0154] Furthermore, through formula (4) The constraints on the steering speed are clarified and integrated into the preset minimization objective function by introducing the first Lagrange multiplier. The constraints on the steering angle are clarified, and the above constraints are integrated into the preset minimization objective function by introducing the second Lagrange multiplier. The adjustment fluctuation optimization model is solved by numerical methods (such as gradient descent method, dynamic programming, etc.) to obtain the optimized steering angle and steering speed to be adjusted.

[0155] Through this solution, mathematical analysis is used to construct an adjustment fluctuation optimization model based on the steering angle to be adjusted and the steering speed to be adjusted corresponding to each steering time point according to a preset minimization objective function. This model is used as the basis of a model for optimizing the steering angle to be adjusted and the steering speed to be adjusted, and then the steering angle to be adjusted and the steering speed to be adjusted corresponding to different steering time points are obtained when the driver feels significantly, thereby effectively avoiding excessive reaction of the driver.

[0156] In some embodiments, after the vehicle completes the turn according to the optimized dynamic steering strategy, the turning timestamp and turning road information are extracted; the vehicle's historical turning information is obtained, the turning road information is matched with the vehicle's historical turning information, and the frequency of occurrence of the turning road information is determined to determine whether the road corresponding to the current turning road information is a commonly used section; if the road corresponding to the current turning road information is a commonly used section, the dynamic steering strategy is temporarily persisted based on the turning timestamp and the frequency of occurrence, and the dynamic steering strategy after the temporary persistence processing is completed is used as the commonly used section iteration data.

[0157] The turning timestamp may be timestamp information including the start time and the end time of the vehicle turning.

[0158] The turning road information may be the road information corresponding to the current turning behavior completed by the vehicle.

[0159] The vehicle's historical turning information may be the vehicle's corresponding turning road information within a certain historical period, and the vehicle's historical turning information may be obtained through a vehicle parameter sensor.

[0160] The frequently used road section may be a road section that the current vehicle often passes through and has a turning demand.

[0161] Specifically, some sections of the vehicle's driving sections usually have periodic characteristics, such as the driver's daily commuting sections. These sections frequently appear in the vehicle's historical driving sections, and the vehicle's steering requirements in these sections show highly similar characteristics. The vehicle parameter sensor extracts the corresponding turning timestamp and turning road information after the turning is completed, and matches the vehicle's historical turning information. According to the frequency of occurrence of the turning road, it is determined whether the road corresponding to the current turning road information is a commonly used section. The judgment process can be achieved by setting a frequency threshold. If the road corresponding to the current turning road information is a commonly used section, a temporary storage time is set for the current dynamic steering strategy according to the frequency of occurrence. The higher the frequency of occurrence, the longer the temporary storage time. The modification time of the dynamic steering strategy corresponding to the current turning road information is updated based on the turning timestamp, so as to realize the persistence processing of the dynamic steering strategy, and the dynamic steering strategy after the temporary persistence processing is completed is used as the iterative data of the commonly used section. When subsequent vehicles pass through the same turning road and generate steering behavior, they can use the corresponding dynamic steering strategy bit data of the temporary persistence processing as the iteration basis to improve the response efficiency of the steering assistance process.

[0162] Through this solution, based on the turning timestamp and the frequency of occurrence, the dynamic steering strategies corresponding to the vehicle's frequently used sections are temporarily persisted, and the dynamic steering strategies after the temporary persistence processing are used as the iteration data of the frequently used sections. When subsequent vehicles pass through the same turning road and generate turning behaviors, they can use the corresponding dynamic steering strategy data that has been temporarily persisted as the basis for iteration, so as to improve the response efficiency of the steering assistance process.

Claims

1. A steering driving assistance system applied to new energy vehicles, characterized in that: include: Acquire an external environment parameter set and real-time vehicle information, analyze the external environment parameter set and the real-time vehicle information, and construct a steering scenario model; Acquire the driver's steering decision information, analyze the driver's steering decision information and the vehicle real-time information based on the steering scenario model, perform safety smooth optimization assistance on the vehicle steering process, and determine a smooth steering strategy; Based on the smooth steering strategy, according to the steering scene model and the real-time information of the vehicle, dynamic data tracking is performed on the vehicle steering process, and according to the dynamic data tracking result, the steering scene model and the corresponding smooth steering strategy are dynamically updated to determine the dynamic steering strategy; According to the dynamic steering strategy, the vehicle steering process is dynamically and low-sensitivityly fine-tuned, and a corresponding steering report is output.

2. The system according to claim 1, characterized in that The external environment parameter set includes road friction coefficient and peripheral obstacle information, and the vehicle real-time information includes real-time vehicle speed and real-time vehicle position. The external environment parameter set and the vehicle real-time information are analyzed to construct a steering scenario model, including: According to the peripheral obstacle information, respectively extracting the obstacle length and obstacle width of each peripheral obstacle; Analyze the peripheral obstacle information to determine the position center and displacement speed of each peripheral obstacle; Based on the real-time vehicle speed and the road friction coefficient, and according to the obstacle length, the obstacle width and the displacement speed of each peripheral obstacle, determine the risk area radius corresponding to each peripheral obstacle; Delineating the risk area of ​​each peripheral obstacle according to the position center of each peripheral obstacle and the radius of the risk area; The real-time vehicle position and the risk area corresponding to each peripheral obstacle are uniformly spatially mapped and time-series labeled to determine the turning scenario model.

3. The system according to claim 2, characterized in that The risk area radius corresponding to each peripheral obstacle is determined based on the real-time vehicle speed and the road friction coefficient according to the obstacle length, the obstacle width and the displacement speed of each peripheral obstacle, specifically as follows: ； in, For the The radius of the risk area of ​​the peripheral obstacles, is the real-time vehicle speed, is the acceleration due to gravity, is the road friction coefficient, is the preset gain factor, For the the obstacle width of the peripheral obstacles, For the the obstacle length of the peripheral obstacles, is the preset geometric influence coefficient, For the The displacement speed of the peripheral obstacles, is the preset magnification index.

4. The system according to claim 2, characterized in that The steering decision information includes a real-time steering angle and a real-time steering speed. Based on the steering scenario model, the driver's steering decision information and the vehicle's real-time information are analyzed to assist in safety smooth optimization of the vehicle steering process and determine a smooth steering strategy, including: Based on the steering scenario model, and according to the real-time position of the vehicle, analyzing the real-time relative distance between the current vehicle and the risk area corresponding to each peripheral obstacle; The real-time relative distance is compared with the preset safety relative distance, and if the real-time relative distance is less than the preset safety relative distance, the steering angle to be adjusted and the steering speed to be adjusted are determined based on the real-time steering angle and the real-time steering speed according to the real-time relative distance and the preset safety relative distance; The smooth steering strategy is determined according to the steering angle to be adjusted and the steering speed to be adjusted.

5. The system according to claim 4, characterized in that The determining of the steering angle to be adjusted and the steering speed to be adjusted based on the real-time steering angle and the real-time steering speed according to the real-time relative distance and the preset safety relative distance is specifically the following formula: ； in, is the steering angle to be adjusted, is the real-time steering angle, is the preset steering sensitivity coefficient, is the preset safe relative distance, For the current vehicle and the real-time relative distance between the risk areas of the peripheral obstacles, is the real-time steering angle, is the maximum design speed of the vehicle, is the steering speed to be adjusted, It is the preset steering speed influence coefficient.

6. The system according to claim 5, characterized in that The vehicle real-time information also includes a vehicle load index, a vehicle wheelbase, a vehicle accessory length, and a lateral acceleration. Based on the smooth steering strategy, according to the steering scenario model and the vehicle real-time information, dynamic data tracking is performed on the vehicle steering process, and according to the dynamic data tracking result, the steering scenario model and the corresponding smooth steering strategy are dynamically updated to determine the dynamic steering strategy, including: According to the steering angle to be adjusted and the steering speed to be adjusted in the smooth steering strategy, the real-time steering angle and the real-time steering speed are adjusted, and in this process, the real-time vehicle information and the steering scenario model are dynamically updated; Determining an adjustment influence radius based on the vehicle load index, the vehicle wheelbase, the vehicle accessory length, and the lateral acceleration, according to the steering angle to be adjusted and the steering speed to be adjusted; Based on the updated steering scenario model, and according to the adjusted influence radius, determining whether there is the risk area that interferes with the adjusted influence radius; If there is a risk area that interferes with the adjusted influence radius, based on the updated real-time steering angle and the real-time steering speed, and according to the updated real-time relative distance and the preset safe relative distance, the smooth steering strategy corresponding to the updated steering scene model is dynamically adjusted to determine the dynamic steering strategy.

7. The system according to claim 6, characterized in that The adjustment influence radius is determined based on the vehicle load index, the vehicle wheelbase, the vehicle accessory length and the lateral acceleration, according to the steering angle to be adjusted and the steering speed to be adjusted, specifically as follows: ； in, To adjust the influence radius, is the vehicle load index, is the steering angle to be adjusted, is the steering speed to be adjusted, is the preset speed influence coefficient, is the vehicle wheelbase, is the length of the vehicle accessory, is the lateral acceleration, is the preset acceleration influence coefficient, is the friction coefficient of the road surface.

8. The system according to claim 7, characterized in that According to the dynamic steering strategy, the steering process is dynamically adjusted in a low-sensitivity manner, including: Based on a preset time point division rule, the dynamic steering strategy is analyzed, the steering angle to be adjusted and the steering speed to be adjusted corresponding to each steering time point are determined, and the time point interval is extracted; Based on the steering angle to be adjusted and the steering speed to be adjusted corresponding to each steering time point, and according to a preset minimization objective function, an adjustment fluctuation optimization model is constructed; According to the adjustment fluctuation optimization model, the fluctuation amplitude of the steering angle to be adjusted and the steering speed to be adjusted corresponding to each steering time point is optimized; According to the steering angle to be adjusted and the steering speed to be adjusted after the fluctuation amplitude optimization is completed, the dynamic steering strategy is optimized, and the real-time steering angle and the real-time steering speed are dynamically adjusted accordingly.

9. The system according to claim 8, characterized in that Based on the steering angle to be adjusted and the steering speed to be adjusted corresponding to each steering time point, an adjustment fluctuation optimization model is constructed according to a preset minimization objective function, which is specifically the following formula: ； in, For said adjusted volatility optimization model, is the preset minimization objective function, is the first Lagrange multiplier, is the preset speed fluctuation threshold, Turning point The steering speed to be adjusted under is the time point interval, is the second Lagrange multiplier, is the preset angle fluctuation threshold, Turning point The steering angle to be adjusted below.

10. The system according to claim 9, characterized in that The system further comprises: After the vehicle completes the turn according to the optimized dynamic steering strategy, extracting the turn timestamp and the turn road information; Acquire historical turning information of the vehicle, match the turning road information with the historical turning information of the vehicle, determine the occurrence frequency of the turning road information, and thereby determine whether the road corresponding to the current turning road information is a frequently used road section; If the road corresponding to the current turning road information is a frequently used section, the dynamic steering strategy is temporarily persisted based on the turning timestamp and the occurrence frequency, and the dynamic steering strategy after the temporary persistence is used as the frequently used section iteration data.

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