Vehicle control parameter determination method, vehicle control method and vehicle
By extracting angular velocity and hand torque from vehicle historical data, and using the fusion parameter model to calculate lane change and intention criterion, the problem of mistriggering the traditional lane departure prevention system in complex scenarios is solved, driving safety and experience are improved, and driving safety and experience are ensured. The driver's intentions are accurately understood and the vehicle's precise control.
Patent Information
- Application Number
- CN202411544455.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-31
- Publication Date
- 2025-01-07
AI Technical Summary
Traditional lane departure prevention systems are prone to be triggered by mistake in complex scenarios, resulting in safety hazards and poor driving experience, and lack of understanding of driver intentions, leading to human-machine confrontation and reducing driving safety.
By obtaining the angular speed and hand torque of the steering wheel from the historical operation data of the target vehicle, the fusion parameter model is used to calculate the vehicle lane change criterion and driving intention criterion, accurately judge the driver's intention, avoid improper intervention, and achieve lane deviation prevention and Optimization of driving assistance mode.
It significantly reduces the probability of false triggering of the lane departure prevention system, reduces human-machine confrontation, improves driving safety and experience, and ensures accurate understanding of driver intentions and precise control of the vehicle.
Smart Images
Figure CN119261955A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of vehicle automatic control, and in particular to a method for determining vehicle control parameters, a vehicle control method and a vehicle. Background Art
[0002] LDP (Lane Departure Prevention) mainly relies on a camera installed at the front of the vehicle to work. The camera continuously scans the road in front of the vehicle and identifies lane markings, including solid lines, dashed lines, and curbs. The LDP system analyzes the offset between the vehicle's front wheels and the lane markings. When the vehicle begins to deviate from the lane and the turn signal is not turned on, the LDP system intervenes with the steering wheel and guides the vehicle back to the center by controlling the steering wheel.
[0003] In the context of the rapid development of computer technology. Although the traditional LDP system can achieve lane keeping based on fixed rules and sensors. However, in certain specific scenarios, the LDP system will be triggered by mistake and make inappropriate interventions, posing a safety hazard. For example, for drivers with different operating habits, the LDP system intervention will be triggered by mistake when changing lanes, resulting in a poor driving experience and a reduced safety factor. Also, due to different operating habits of drivers, when the driver wants to take over the vehicle, the vehicle will have a human-machine confrontation, resulting in a poor driving experience, further reducing the safety factor. Summary of the invention
[0004] In view of this, the embodiments of the present application provide a method for determining vehicle control parameters, a vehicle control method and a vehicle, which can effectively solve the problems of low safety factor and poor driving experience of existing vehicles.
[0005] In a first aspect, an embodiment of the present application provides a method for determining a vehicle control parameter, comprising:
[0006] Based on the first judgment condition and the second judgment condition, a target data set is obtained from the historical operation data of the target vehicle; the target data set includes the angular velocity of the steering wheel and the hand torque;
[0007] Based on the target data set, a fusion parameter model is used to obtain a vehicle lane change criterion and a driving intention criterion; the vehicle lane change criterion is used to instruct a driving assistance system that controls the target vehicle to implement a lane departure prevention function for the target vehicle; the driving intention criterion is used to instruct the driving assistance system whether to exit a driving assistance mode.
[0008] In some embodiments, the step of acquiring the target data set from the historical operation data of the target vehicle based on the first judgment condition and the second judgment condition includes:
[0009] If it is determined that the historical vehicle speed of the target vehicle is within the activation speed range of the lane departure prevention system, and it is determined that the target vehicle is in a lane changing state in the corresponding historical period, the steering wheel angular velocity and hand torque in the lane changing state are obtained to obtain the target data set.
[0010] In some embodiments, the method of obtaining a vehicle lane change criterion and a driving intention criterion based on the target data set and using a fusion parameter model includes:
[0011] Inputting each of the angular velocities in the target data set and the hand torque at the corresponding sampling moment into the lane change criterion module in the fusion parameter model to obtain an angular velocity weight and a hand torque weight;
[0012] The vehicle lane changing criterion is calculated using the angular velocity weight, the hand torque weight and a lane changing criterion calculation formula.
[0013] In some embodiments, determining that the target vehicle is in a lane-changing state includes:
[0014] Obtain a first coefficient in a lane curve equation corresponding to the target vehicle; the first coefficient is obtained based on feature extraction of a lane image of the target vehicle; determine a lateral change state of the vehicle based on a relationship between the first coefficient and a lane width corresponding to the target vehicle within a preset time period; and determine whether the current target vehicle is in a lane change state based on the lateral change state of the vehicle.
[0015] In some embodiments, the step of acquiring the target data set from the historical operation data of the target vehicle based on the first judgment condition and the second judgment condition includes:
[0016] If it is determined that the historical turning angle speed of the target vehicle is within a preset straight-ahead angle range, and it is determined that the target vehicle is in a non-lane-changing state during a corresponding historical period, the hand torque of the steering wheel in the non-lane-changing state is obtained to obtain the target data set.
[0017] In some embodiments, the method of obtaining a vehicle lane change criterion and a driving intention criterion based on the target data set and using a fusion parameter model includes:
[0018] Each hand torque in the target data set is input into the driving intention criterion module in the fusion parameter model to obtain the driving intention criterion; the driving intention criterion includes a first hand torque limit value; the first hand torque limit value is used to indicate whether the integrated cruise assistance subsystem in the driving assistance system exits the cruise mode in the driving assistance mode.
[0019] In some embodiments, the first coefficient is a lateral offset between the center point of the rear axle of the target vehicle and the lane line;
[0020] The determining the lateral change state of the vehicle according to the magnitude relationship between the first coefficient and the lane width corresponding to the target vehicle within a preset time period includes:
[0021] If within the preset time period, it is determined that the lateral offset gradually decreases to 0 in the first time period and then suddenly changes to the lane width, and gradually decreases from the lane width to half of the lane width in the second time period.
[0022] In some embodiments, the lane change criterion calculation formula is:
[0023] Y=αX 1
[0024] Wherein, Y is the vehicle lane-changing criterion, which is a vector composed of the angular velocity limit and the second hand torque limit, and α is the weight coefficient; X 1 is a vector composed of an angular velocity weight and a hand torque weight; the angular velocity limit and the second hand torque limit are both used to instruct control of the lane departure prevention subsystem in the driving assistance system to implement the lane departure prevention function of the target vehicle.
[0025] In a second aspect, an embodiment of the present application provides a vehicle control method, the method comprising:
[0026] By utilizing the current angular velocity and hand torque of the vehicle steering wheel obtained, a vehicle lane change criterion and a driving intention criterion are obtained based on a method for determining vehicle control parameters provided in the first aspect of the present application, and the vehicle is controlled by a driving assistance system; wherein, controlling the vehicle includes realizing a lane departure prevention function of the vehicle and determining whether to exit a driving assistance mode.
[0027] In a third aspect, an embodiment of the present application provides a vehicle, which adopts a vehicle control method provided in the second aspect of the present application to control the movement of the vehicle itself.
[0028] The embodiments of the present application have the following beneficial effects:
[0029] The present application obtains a target data set from the historical operation data of the target vehicle based on the first judgment condition and the second judgment condition; the target data set includes the angular velocity of the steering wheel and the hand torque; based on the target data set, a fusion parameter model is used to obtain a vehicle lane change criterion and a driving intention criterion; the vehicle lane change criterion is used to instruct the driving assistance system that controls the target vehicle to implement the lane departure prevention function of the target vehicle; the driving intention criterion is used to indicate whether the driving assistance system exits the driving assistance mode. The present application determines the driving intention criterion and the vehicle lane change criterion according to the driver's driving habits, and can accurately determine the driver's intention to receive the vehicle without human-machine confrontation, and accurately determine whether to change lanes, which greatly reduces the probability of making improper interventions, thereby effectively solving the problem of improving driving safety. BRIEF DESCRIPTION OF THE DRAWINGS
[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.
[0031] Figure 1 A schematic diagram of the structure of a driving assistance system according to an embodiment of the present application is shown;
[0032] Figure 2 A flow chart showing a method for determining vehicle control parameters according to an embodiment of the present application;
[0033] Figure 3 A schematic diagram of a vehicle lane changing process in a method for determining vehicle control parameters according to an embodiment of the present application is shown;
[0034] Figure 4 Another flow chart showing a method for determining vehicle control parameters according to an embodiment of the present application;
[0035] Figure 5 A structural schematic diagram of a device for determining vehicle control parameters according to an embodiment of the present application is shown.
[0036] Description of main component symbols:
[0037] 100 - driving assistance system; 110 - lane departure prevention subsystem; 120 - integrated cruise assistance subsystem; 510 - target data set acquisition module; 520 - criterion determination module. DETAILED DESCRIPTION
[0038] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.
[0039] The components of the embodiments of the present application generally described and shown in the drawings herein may be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.
[0040] Hereinafter, the terms "including", "having" and their cognates that can be used in various embodiments of the present application are intended only to indicate specific features, numbers, steps, operations, elements, components or a combination of the foregoing items, and should not be understood as first excluding the existence of one or more other features, numbers, steps, operations, elements, components or a combination of the foregoing items or increasing the possibility of one or more features, numbers, steps, operations, elements, components or a combination of the foregoing items. In addition, the terms "first", "second", "third" and the like are only used to distinguish descriptions and cannot be understood as indicating or implying relative importance.
[0041] Unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meanings as those generally understood by those skilled in the art to which the various embodiments of the present application belong. The terms (such as those defined in generally used dictionaries) will be interpreted as having the same meanings as the contextual meanings in the relevant technical field and will not be interpreted as having idealized meanings or overly formal meanings unless clearly defined in the various embodiments of the present application.
[0042] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.
[0043] In the face of complex scenarios, such as emergencies, complex road conditions or non-standard lane markings, the performance of lane departure prevention systems is often limited. In addition, traditional lane departure prevention systems lack understanding of the driver's intentions and may make inappropriate interventions in certain situations. The probability of false triggering of lane departure prevention systems is high, and the vehicle driving safety factor is low.
[0044] The existing integrated cruise assist subsystem ICA determines whether the driver manually takes over the vehicle based on a fixed hand torque limit, which is around 1.7NM. However, different drivers usually apply different hand torques on the steering wheel. When the driver wants to take over the vehicle, if the hand torque applied is less than the hand torque limit, the vehicle will continue to cruise control, and more force is needed to take over the vehicle, resulting in a human-machine confrontation and a reduced safety factor.
[0045] In summary, in order to reduce the probability of false triggering of the lane departure prevention system and reduce human-machine confrontation, the present application provides a method for determining vehicle control parameters, a vehicle control method and a vehicle, thereby improving the driving safety factor.
[0046] The present application provides a vehicle, and an embodiment of the present application provides a vehicle, wherein the vehicle adopts a vehicle control method provided by the present application to control the movement of the vehicle itself.
[0047] The present application also provides a vehicle control method, the method comprising:
[0048] By utilizing the current angular velocity and hand torque of the vehicle steering wheel obtained, a vehicle lane change criterion and a driving intention criterion are obtained based on a method for determining vehicle control parameters provided in the first aspect of the present application, and the vehicle is controlled by a driving assistance system 100; wherein, controlling the vehicle movement includes realizing a lane departure prevention function of the vehicle, and determining whether to exit a driving assistance mode.
[0049] The vehicle is provided with a driving assistance system 100 (ADAS); the driving assistance system 100 prevents lane departure of the target vehicle based on a vehicle lane change criterion obtained by a method for determining a vehicle control parameter of the present application, and determines whether to exit the driving assistance mode based on a driving intention criterion obtained by a method for determining a vehicle control parameter of the present application.
[0050] Furthermore, if Figure 1 As shown, the driving assistance system 100 includes a lane departure prevention subsystem 110 (abbreviated as LDP system, also known as lane departure prevention system) and an integrated cruise assistance subsystem 120 (abbreviated as ICA system, also known as integrated cruise assistance system). The first hand torque limit determined based on the fusion parameter model is set to the ICA system, and the ICA system compares the current hand torque of the vehicle with the determined first hand torque limit to determine whether to exit the cruise mode in the driving assistance mode. The angular velocity limit and the second hand torque limit determined based on the fusion parameter model (deep neural network) are set to the LDP system, and the LDP system compares the current angular velocity and hand torque of the vehicle with the determined angular velocity limit and the second hand torque limit to achieve the lane departure prevention function of the target vehicle.
[0051] The method for determining the vehicle control parameters is described below in conjunction with some specific embodiments.
[0052] Figure 2 A flow chart of a method for determining a vehicle control parameter according to an embodiment of the present application is shown. Exemplarily, the method for determining a vehicle control parameter includes the following steps:
[0053] S100, based on the first judgment condition and the second judgment condition, obtaining a target data set from historical operation data of the target vehicle; the target data set includes an angular velocity of the steering wheel and a hand torque.
[0054] S200, based on the target data set, using the fusion parameter model, obtain the vehicle lane change criterion and the driving intention criterion; the vehicle lane change criterion is used to instruct the driving assistance system 100 that controls the target vehicle to implement the lane departure prevention function of the target vehicle; the driving intention criterion is used to instruct the driving assistance system 100 whether to exit the driving assistance mode.
[0055] In one embodiment, in step S100, acquiring a target data set from historical operation data of a target vehicle based on the first judgment condition and the second judgment condition includes:
[0056] If it is determined that the historical speed of the target vehicle is within the activation speed range of the lane departure prevention system, and it is determined that the target vehicle is in a lane change state in the corresponding historical period, the steering wheel angular velocity and hand torque in the lane change state are obtained to obtain the target data set. It can be understood that the first judgment condition is to judge whether the historical speed of the target vehicle is within the activation speed range of the lane departure prevention system, and the second judgment condition is whether the target vehicle is in a lane change state in the corresponding historical period. Exemplarily, the activation speed range of the lane departure prevention system is 60kph to 120kph.
[0057] Further, the determining that the target vehicle is in a lane-changing state includes:
[0058] S110, obtaining a first coefficient in the lane curve equation corresponding to the target vehicle; the first coefficient is obtained by feature extraction based on the lane image of the target vehicle; the first coefficient is the lateral offset between the rear axle center point of the target vehicle and the lane. The first system is obtained by feature extraction based on the lane image by the perception module on the vehicle. Among them, the lane curve equation is:
[0059] y=c 0 +c 1 x+c 2 x 2 +c3 x 3
[0060] Where y represents the lateral offset between the center point of the vehicle's rear axle and the lane line when the vehicle's longitudinal coordinate value is x, and c 0 represents the lateral offset between the rear axle center point of the target vehicle and the lane line at the origin, c 0 is the first coefficient, c 1 is the heading angle of the lane line, c 2 is the road curvature, c 3 is the rate of change of road curvature, and x is the longitudinal coordinate value of the vehicle. A coordinate system is established at the center point of the rear axle of the target vehicle, where the line extending in the direction of the rear axle is the y-axis, and the line extending in the direction of the vehicle body toward the front of the vehicle is the x-axis.
[0061] S120, determining a lateral change state of the vehicle according to a size relationship between the first coefficient and the lane width corresponding to the target vehicle within a preset time period; and determining whether the current target vehicle is in a lane change state according to the lateral change state of the vehicle.
[0062] It can be understood that determining the lateral change state of the vehicle according to the magnitude relationship between the first coefficient and the lane width corresponding to the target vehicle within a preset time period includes:
[0063] If within the preset time period, it is determined that the lateral offset gradually decreases to 0 in the first time period and then suddenly changes to the lane width, and gradually decreases from the lane width to half of the lane width in the second time period. Specifically, if Figure 3 As shown, taking the left lane change as an example, when the lateral offset c 0 Gradually decreases to 0, and then the lateral offset c 0 The lane width suddenly changes to W, and then gradually decreases to c 0 from The changing process of is the lane changing state of the target vehicle.
[0064] Furthermore, in step S200, the vehicle lane change criterion and driving intention criterion are obtained based on the target data set and using the fusion parameter model, including:
[0065] Inputting each of the angular velocities in the target data set and the hand torque at the corresponding sampling moment into the lane change criterion module in the fusion parameter model to obtain an angular velocity weight and a hand torque weight;
[0066] The vehicle lane changing criterion is calculated using the angular velocity weight, the hand torque weight and the lane changing criterion calculation formula. Further, the lane changing criterion calculation formula is:
[0067] Y=αX1
[0068] Wherein, Y is the vehicle lane-changing criterion, which is a vector composed of the angular velocity limit and the second hand torque limit, and α is the weight coefficient; X 1 is a vector composed of an angular velocity weight and a hand torque weight; the angular velocity limit and the second hand torque limit are both used to instruct the control of the lane departure prevention subsystem 110 in the driving assistance system 100 to implement the lane departure prevention function of the target vehicle.
[0069] α is the weight coefficient of the steering wheel angular velocity limit and the second hand torque limit of the steering wheel in the vehicle's straight-ahead state and lane-changing state. The larger α is, the more aggressive the deviation prevention function of the LDP system is during the lane-changing process. Therefore, it is affected by the hand torque applied by the driver on the steering wheel under the control of the LDP system and the angular velocity of the steering wheel when the driver actively changes lanes after turning on the turn signal. The value of α is [0.8~1.2].
[0070] Therefore, this application obtains the steering wheel angular velocity limit and the second-hand torque limit based on deep learning, and anthropomorphically implements the lane departure prevention function of the intelligent car. By imitating the decision-making process of human drivers, it achieves precise control of the vehicle under complex road conditions, effectively prevents the occurrence of lane departure events, reduces the probability of false triggering of the lane departure prevention system, and significantly improves driving safety and ride comfort.
[0071] In one embodiment, the step of acquiring the target data set from the historical operation data of the target vehicle based on the first judgment condition and the second judgment condition includes:
[0072] If it is determined that the historical turning angle speed of the target vehicle is within a preset straight-ahead angle range, and it is determined that the target vehicle is in a non-lane-changing state during a corresponding historical period, the hand torque of the steering wheel in the non-lane-changing state is obtained to obtain the target data set.
[0073] It is understandable that the first judgment condition is whether the historical angular velocity of the target vehicle is within the preset straight-ahead angle range. The second judgment condition is whether the target vehicle is in a non-lane-changing state in the corresponding historical period. It is understandable that the non-lane-changing state of the vehicle is the state of the vehicle except the lane-changing state. Among them, the straight-ahead angle range is -45° to 45°. If it is less than -45° or greater than 45°, it is considered to be a large turn or lane-changing scene. The hand torque of the driver when driving on a normal road is collected, and the steering wheel hand torque in the lane-changing state is eliminated.
[0074] Furthermore, based on the target data set, the fusion parameter model is used to obtain the vehicle lane change criterion and the driving intention criterion, including:
[0075] Each hand torque in the target data set is input into the driving intention criterion module in the fusion parameter model to obtain the driving intention criterion; the driving intention criterion includes a first hand torque limit; the first hand torque limit is used to indicate whether the integrated cruise assistance subsystem 120 in the driving assistance system 100 exits the cruise mode in the driving assistance mode.
[0076] The fusion parameter model includes a driving intention criterion module and a lane change criterion module. Both the driving intention criterion module and the lane change criterion module are constructed and trained using a CNN neural network.
[0077] The following is an example of a method for determining a vehicle control parameter in an embodiment of the present application. Figure 4 As shown:
[0078] S310, obtaining a first coefficient of a lane curve equation from a perception module, and obtaining a historical vehicle speed at a corresponding historical moment.
[0079] The lane line image is acquired from the camera on the target vehicle, and the first coefficient (lateral offset) of the lane line curve equation is obtained using the perception module.
[0080] S320, determine whether the historical vehicle speed is greater than 60kph and less than 120kph, and determine whether it is in a lane change state. If the current vehicle speed is greater than 60kph and less than 120kph, and is in a lane change state, execute step S330; if the current vehicle speed is greater than 60kph and less than 120kph, discard, do not process, and discard the data.
[0081] S330, collecting the steering wheel angular velocity and hand torque from the vehicle chassis data in the historical operation data of the target vehicle, and eliminating abnormal data that are too large or too small, to obtain multiple sets of angular velocity and hand torque data.
[0082] The lateral change state of the vehicle is determined according to the magnitude relationship between the lateral offset and the lane width within a preset time period, and whether the current vehicle is in a lane change state is determined according to the lateral change state of the vehicle.
[0083] S340, determine whether the historical corner speed of the target vehicle is between -45° and 45°, and determine whether it is in a lane-changing state. If the historical corner speed of the target vehicle is between -45° and 45°, and it is not in a lane-changing state, execute step S350, otherwise, do not process and discard the data.
[0084] S350, collecting the hand torque of the steering wheel from the vehicle chassis data in the historical operation data of the target vehicle, and eliminating abnormal data that is too large or too small to obtain hand torque data.
[0085] S360: Input multiple groups of angular velocity and hand torque data into the trained lane change criterion module to obtain angular velocity weights and hand torque weights. Substitute the vectors formed by the angular velocity weights and hand torque weights into the lane change criterion calculation formula to obtain the angular velocity limit and the second hand torque limit.
[0086] S370, inputting the hand torque data into a trained driving intention judgment module to obtain a first hand torque limit value.
[0087] S380, inputting the angular velocity limit and the second hand torque limit into the lane departure prevention subsystem 110, and when the vehicle is moving, comparing the angular velocity limit and the second hand torque limit respectively according to the current angular velocity and the current hand torque, so as to prevent the target vehicle from leaving the lane.
[0088] S390, inputting the first-hand torque limit value into the integrated cruise assist subsystem 120 to exit the cruise mode according to the driving intention.
[0089] The corner speed limit, the second-hand torque limit, and the first-hand torque limit of the present application are all determined based on the driver's habits. When the vehicle faces emergencies, complex road conditions, or non-standard lane markings, the lane departure prevention subsystem 110 is not limited in performance and can accurately determine the driver's intention, that is, whether the driver wants to change lanes, without making inappropriate interventions, thereby improving driving safety and driving experience.
[0090] Similarly, the integrated cruise assist subsystem 120 can also accurately determine whether the driver intends to take over the vehicle based on the first-hand torque limit, which will not cause human-machine confrontation and further improve driving safety.
[0091] Figure 5 A schematic diagram of the structure of a vehicle control parameter determination device according to an embodiment of the present application is shown. Exemplarily, the lane departure prevention system control device includes: a target data set acquisition module 510 and a criterion determination module 520 .
[0092] The target data set acquisition module 510 is used to acquire a target data set from the historical operation data of the target vehicle based on the first judgment condition and the second judgment condition; the target data set includes the angular velocity of the steering wheel and the hand torque;
[0093] The criterion determination module 520 is used to obtain a vehicle lane changing criterion and a driving intention criterion based on the target data set and using a fusion parameter model; the vehicle lane changing criterion is used to instruct the driving assistance system 100 that controls the target vehicle to prevent lane departure of the target vehicle; the driving intention criterion is used to instruct the driving assistance system 100 whether to exit the driving assistance mode.
[0094] It can be understood that the device of this embodiment corresponds to the method for determining the vehicle control parameters of the above embodiment, and the options in the above embodiment are also applicable to this embodiment, so they will not be described repeatedly here.
[0095] The present application also provides a terminal device. Exemplarily, the terminal device includes a processor and a memory, wherein the memory stores a computer program, and the processor runs the computer program to enable the terminal device to execute the above-mentioned method for determining vehicle control parameters or the functions of each module in the above-mentioned lane departure prevention system control device.
[0096] Among them, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including a central processing unit (CPU), a graphics processing unit (GPU) and a network processor (NP), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or at least one of other programmable logic devices, discrete gates or transistor logic devices, and discrete hardware components. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc., which can implement or execute the disclosed methods, steps and logic block diagrams in the embodiments of the present application.
[0097] The memory may be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), etc. The memory is used to store a computer program, and the processor may execute the computer program accordingly after receiving an execution instruction.
[0098] The present application also provides a computer-readable storage medium for storing the computer program used in the above-mentioned terminal device. For example, the computer-readable storage medium may include, but is not limited to, various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.
[0099] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and structure diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in an alternative implementation, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the structure diagram and / or the flow diagram, and the combination of boxes in the structure diagram and / or the flow diagram, can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.
[0100] In addition, the functional modules or units in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
[0101] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a smart phone, a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application.
[0102] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.
Claims
1. A method for determining vehicle control parameters, characterized in that: include: Based on the first judgment condition and the second judgment condition, obtaining a target data set from historical operation data of the target vehicle; The target data set includes the angular velocity of the steering wheel and the hand torque; Based on the target data set, a fusion parameter model is used to obtain a vehicle lane-changing criterion and a driving intention criterion; The vehicle lane change criterion is used to instruct the driving assistance system that controls the target vehicle to implement the lane departure prevention function of the target vehicle; the driving intention criterion is used to instruct the driving assistance system whether to exit the driving assistance mode.
2. The method for determining vehicle control parameters according to claim 1, characterized in that: The step of acquiring a target data set from historical operation data of the target vehicle based on the first judgment condition and the second judgment condition includes: If it is determined that the historical vehicle speed of the target vehicle is within the activation speed range of the lane departure prevention system, and it is determined that the target vehicle is in a lane changing state in the corresponding historical period, the steering wheel angular velocity and hand torque in the lane changing state are obtained to obtain the target data set.
3. The method for determining vehicle control parameters according to claim 2, characterized in that: The method of obtaining a vehicle lane change criterion and a driving intention criterion based on the target data set and utilizing a fusion parameter model includes: Inputting each of the angular velocities in the target data set and the hand torque at the corresponding sampling moment into the lane change criterion module in the fusion parameter model to obtain an angular velocity weight and a hand torque weight; The vehicle lane changing criterion is calculated using the angular velocity weight, the hand torque weight and a lane changing criterion calculation formula.
4. The method for determining vehicle control parameters according to claim 2, characterized in that: The determining that the target vehicle is in a lane-changing state includes: Obtain a first coefficient in a lane curve equation corresponding to the target vehicle; the first coefficient is obtained based on feature extraction of a lane image of the target vehicle; determine a lateral change state of the vehicle based on a relationship between the first coefficient and a lane width corresponding to the target vehicle within a preset time period; and determine whether the current target vehicle is in a lane change state based on the lateral change state of the vehicle.
5. The method for determining vehicle control parameters according to claim 2, 3 or 4, characterized in that: The step of acquiring a target data set from historical operation data of the target vehicle based on the first judgment condition and the second judgment condition includes: If it is determined that the historical turning angle speed of the target vehicle is within a preset straight-ahead angle range, and it is determined that the target vehicle is in a non-lane-changing state during a corresponding historical period, the hand torque of the steering wheel in the non-lane-changing state is obtained to obtain the target data set.
6. The method for determining vehicle control parameters according to claim 5, characterized in that: The method of obtaining a vehicle lane change criterion and a driving intention criterion based on the target data set and utilizing a fusion parameter model includes: Each hand torque in the target data set is input into the driving intention criterion module in the fusion parameter model to obtain the driving intention criterion; the driving intention criterion includes a first hand torque limit value; the first hand torque limit value is used to indicate whether the integrated cruise assistance subsystem in the driving assistance system exits the cruise mode in the driving assistance mode.
7. The method for determining vehicle control parameters according to claim 4, characterized in that: The first coefficient is the lateral offset between the rear axle center point of the target vehicle and the lane line; The determining the lateral change state of the vehicle according to the magnitude relationship between the first coefficient and the lane width corresponding to the target vehicle within a preset time period includes: If within the preset time period, it is determined that the lateral offset gradually decreases to 0 in the first time period and then suddenly changes to the lane width, and gradually decreases from the lane width to half of the lane width in the second time period.
8. The method for determining vehicle control parameters according to claim 3, characterized in that: The lane change criterion calculation formula is: Y=αX1 Among them, Y is the vehicle lane changing criterion, the vehicle lane changing criterion is a vector composed of the angular velocity limit and the second hand torque limit, α is the weight coefficient; X1 is a vector composed of the angular velocity weight and the hand torque weight; the angular velocity limit and the second hand torque limit are both used to indicate the control of the lane departure prevention subsystem in the driving assistance system to realize the lane departure prevention function of the target vehicle.
9. A vehicle control method, characterized in that: include: Utilizing the acquired current angular velocity and hand torque of the vehicle steering wheel, a vehicle lane change criterion and a driving intention criterion are obtained based on the vehicle control parameter determination method described in any one of claims 1 to 8, and the vehicle movement is controlled by a driving assistance system; wherein, controlling the vehicle movement includes realizing the lane departure prevention function of the vehicle, and determining whether to exit the driving assistance mode.
10. A vehicle, characterized in that: The vehicle controls the movement of the vehicle using the vehicle control method according to claim 9.