CN113581209B - Driving assistance mode switching method, device, equipment and storage medium - Google Patents
Driving assistance mode switching method, device, equipment and storage medium Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
- B60W60/005—Handover processes
- B60W60/0051—Handover processes from occupants to vehicle
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
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- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
- B60W2050/0019—Control system elements or transfer functions
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2540/00—Input parameters relating to occupants
- B60W2540/10—Accelerator pedal position
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2540/00—Input parameters relating to occupants
- B60W2540/12—Brake pedal position
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2540/00—Input parameters relating to occupants
- B60W2540/18—Steering angle
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Abstract
The invention discloses a driving assistance mode switching method, a driving assistance mode switching device, driving assistance mode switching equipment and a storage medium. The method comprises the following steps: acquiring an accelerator pedal opening, an accelerator pedal opening change rate, a brake pedal opening change rate, a steering wheel angle opening and a steering wheel angle opening change rate through a vehicle sensor; analyzing the sensor data through a preset adaptive particle swarm algorithm to determine the driving intention of a driver; determining a current vehicle driving state based on the surrounding sensing information; and determining a corresponding switched target driving auxiliary mode according to the driving intention of the driver and the current running state of the vehicle. By the method, the switching mode is determined according to the intention of the driver and the running state of the vehicle, which are obtained through analysis of the preset adaptive particle swarm algorithm, so that the driving auxiliary mode is switched more intelligently, the influence of subjective consciousness of the driver on the auxiliary mode switching is avoided, the driving safety is improved, and the problem that the current driving auxiliary mode is not switched timely is solved.
Description
Technical Field
The present invention relates to the field of driving assistance technologies, and in particular, to a driving assistance mode switching method, device, apparatus, and storage medium.
Background
When a driver drives a vehicle, a driving auxiliary mode is manually selected according to driving experience, and if the danger exists in front or the sight of the driver is blocked, the driver has judgment deviation, so that the adjustment is not timely, and the driving safety is difficult to ensure.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
The invention mainly aims to provide a driving assistance mode switching method, device, equipment and storage medium, and aims to solve the technical problems that the current driving assistance mode is not switched timely and the driving safety is difficult to guarantee.
To achieve the above object, the present invention provides a driving assistance mode switching method including the steps of:
acquiring an accelerator pedal opening, an accelerator pedal opening change rate, a brake pedal opening change rate, a steering wheel angle opening and a steering wheel angle opening change rate through a vehicle sensor;
analyzing the accelerator pedal opening, the accelerator pedal opening change rate, the brake pedal opening change rate, the steering wheel angle opening and the steering wheel angle opening change rate through a preset adaptive particle swarm algorithm to determine the driving intention of a driver;
Determining a current vehicle driving state based on the surrounding sensing information;
and determining a corresponding target driving auxiliary mode according to the driving intention of the driver and the current vehicle running state, and switching the current mode to the target driving auxiliary mode.
Optionally, the analyzing, by a preset adaptive particle swarm algorithm, the accelerator pedal opening change rate, the brake pedal opening change rate, the steering wheel angle opening and the steering wheel angle opening change rate, to determine the driving intention of the driver includes:
analyzing the accelerator pedal opening, the accelerator pedal opening change rate, the brake pedal opening change rate, the steering wheel angle opening and the steering wheel angle opening change rate through a preset self-adaptive particle swarm algorithm to determine an initial driving intention;
acquiring accelerator pedal cycle sensing information, brake pedal cycle sensing information and steering wheel cycle sensing information through a vehicle sensor;
determining periodic driving intention according to the accelerator pedal periodic sensing information, the brake pedal periodic sensing information and the steering wheel periodic sensing information;
And determining the driving intention of the driver according to the initial driving intention and the periodic driving intention.
Optionally, the analyzing the accelerator pedal opening, the accelerator pedal opening change rate, the brake pedal opening change rate, the steering wheel angle opening and the steering wheel angle opening change rate by a preset adaptive particle swarm algorithm, to determine an initial driving intention includes:
inputting the accelerator pedal opening, the accelerator pedal opening change rate, the brake pedal opening change rate, the steering wheel angle opening and the steering wheel angle opening change rate into a preset driver state model;
and optimizing the preset driver state model through a preset self-adaptive particle swarm algorithm, and outputting the initial driving intention.
Optionally, the optimizing the preset driver state model by a preset adaptive particle swarm algorithm outputs an initial driving intention, including:
acquiring a preset acceleration factor, a preset inertia weight and a preset error requirement;
determining a preset particle set according to the preset driver state model;
Determining a global optimal position based on the fitness value corresponding to each particle in the preset particle set;
when the global optimal position meets the preset error requirement, obtaining an optimized particle position and particle speed;
and determining an optimal solution according to the optimized particle position and particle speed through the preset driver state model to obtain an initial driving intention.
Optionally, the accelerator pedal period sensing information is the control times of the accelerator pedal in a preset collection period, the brake pedal period sensing information is the control times of the brake pedal in the preset collection period, and the steering wheel period sensing information is the control times of the steering wheel in the preset collection period;
the determining the periodic driving intention according to the accelerator pedal periodic sensing information, the brake pedal periodic sensing information and the steering wheel periodic sensing information includes:
determining a preset threshold according to the preset acquisition period;
comparing the control times of the accelerator pedal, the control times of the brake pedal and the control times of the steering wheel with the preset threshold value to obtain a comparison result;
and determining the periodic driving intention according to the comparison result.
Optionally, the determining the periodic driving intention according to the comparison result includes:
determining that the periodic driving intention is emergency driving when the control times of the accelerator pedal, the brake pedal and the steering wheel are all greater than or equal to the preset threshold value;
determining that the periodic driving intention is to alleviate driving when the control times of the accelerator pedal, the brake pedal and the steering wheel are smaller than the preset threshold value;
and when the control times of the accelerator pedal and the control times of the brake pedal are larger than or equal to the preset threshold value and the control times of the steering wheel are smaller than the preset threshold value, determining that the periodic driving intention is normal driving.
Optionally, the determining the driver driving intention according to the initial driving intention and the periodic driving intention includes:
determining that the driver's driving intention is emergency driving when the periodic driving intention is emergency driving;
determining that the driver's driving intention is normal driving when the initial driving intention is emergency driving or normal driving and the periodic driving intention is normal driving or moderate driving;
When the initial driving intention is the moderate driving and the periodic driving intention is the moderate driving, the driver driving intention is determined to be the moderate driving.
In addition, in order to achieve the above object, the present invention also proposes a driving assistance mode switching apparatus including:
the acquisition module is used for acquiring the accelerator pedal opening, the accelerator pedal opening change rate, the brake pedal opening change rate, the steering wheel angle opening and the steering wheel angle opening change rate through the vehicle sensor;
the determining module is used for analyzing the accelerator pedal opening, the accelerator pedal opening change rate, the brake pedal opening change rate, the steering wheel angle opening and the steering wheel angle opening change rate through a preset adaptive particle swarm algorithm to determine the driving intention of a driver;
the determining module is further used for determining the current vehicle running state based on the surrounding perception information;
and the switching module is used for determining a corresponding target driving auxiliary mode according to the driving intention of the driver and the current vehicle running state and switching the current mode to the target driving auxiliary mode.
In addition, to achieve the above object, the present invention also proposes a driving assistance mode switching apparatus including: a memory, a processor, and a driving assistance mode switching program stored on the memory and executable on the processor, the driving assistance mode switching program configured to implement the driving assistance mode switching method as described above.
In addition, in order to achieve the above object, the present invention also proposes a storage medium having stored thereon a driving assistance mode switching program which, when executed by a processor, implements the driving assistance mode switching method as described above.
The method comprises the steps of obtaining an accelerator pedal opening, an accelerator pedal opening change rate, a brake pedal opening change rate, a steering wheel angle opening and a steering wheel angle opening change rate through a vehicle sensor; analyzing the accelerator pedal opening, the accelerator pedal opening change rate, the brake pedal opening change rate, the steering wheel angle opening and the steering wheel angle opening change rate through a preset self-adaptive particle swarm algorithm, and determining the driving intention of a driver; determining a current vehicle driving state based on the surrounding sensing information; and determining a corresponding target driving auxiliary mode according to the driving intention of the driver and the current vehicle running state, and switching the current mode to the target driving auxiliary mode. By the method, the driver intention obtained through analysis according to the preset adaptive particle swarm algorithm and the driving auxiliary mode switched according to the vehicle running state are determined, so that the driving auxiliary mode is switched more intelligently, the influence of subjective consciousness of the driver on the auxiliary mode switching is avoided, the influence on the analysis result of the driver intention due to misoperation or invalid operation of the driver is effectively avoided, the situation of the driving auxiliary mode switching is influenced, the driving safety is improved, and the problem that the current driving auxiliary mode is not timely switched is solved.
Drawings
Fig. 1 is a schematic structural diagram of a driving assistance mode switching apparatus of a hardware operation environment according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a driving assistance mode switching method according to a first embodiment of the present invention;
FIG. 3 is a flowchart illustrating a driving assistance mode switching method according to a second embodiment of the present invention;
fig. 4 is a block diagram showing the construction of a first embodiment of the driving assistance mode switching apparatus of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, fig. 1 is a schematic diagram of a driving assistance mode switching device in a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the driving assistance mode switching apparatus may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (Wi-Fi) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
It will be appreciated by those skilled in the art that the structure shown in fig. 1 does not constitute a limitation of the driving assist mode switching device, and may include more or fewer components than shown, or may combine certain components, or may have a different arrangement of components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and a driving assistance mode switching program may be included in the memory 1005 as one type of storage medium.
In the driving assistance mode switching apparatus shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the driving assistance mode switching apparatus of the present invention may be provided in the driving assistance mode switching apparatus, which invokes the driving assistance mode switching program stored in the memory 1005 through the processor 1001 and executes the driving assistance mode switching method provided by the embodiment of the present invention.
An embodiment of the present invention provides a driving assistance mode switching method, and referring to fig. 2, fig. 2 is a schematic flow chart of a first embodiment of the driving assistance mode switching method of the present invention.
In this embodiment, the driving assistance mode switching method includes the steps of:
step S10: the method comprises the steps of acquiring accelerator pedal opening, accelerator pedal opening change rate, brake pedal opening change rate, steering wheel angle opening and steering wheel angle opening change rate through a vehicle sensor.
It may be understood that the execution body of the embodiment is a driving assistance mode switching device, and the driving assistance mode switching device may be a vehicle controller, a controller connected to a vehicle control end, or other devices with the same or similar functions, and the embodiment is described by taking the domain controller as an example. The domain controller is connected with sensors which are arranged on the vehicle and are used for collecting data of an accelerator pedal, a brake pedal and a steering wheel, receives data of the opening degree of the accelerator pedal, the opening degree of the brake pedal and the opening degree of the steering wheel, and analyzes and determines the change rate of the opening degree of the accelerator pedal, the change rate of the opening degree of the brake pedal and the change rate of the opening degree of the steering wheel according to the data of the opening degree of the accelerator pedal, the opening degree of the brake pedal and the opening degree of the steering wheel in a preset collection period.
Step S20: and analyzing the accelerator pedal opening, the accelerator pedal opening change rate, the brake pedal opening change rate, the steering wheel angle opening and the steering wheel angle opening change rate through a preset self-adaptive particle swarm algorithm, and determining the driving intention of a driver.
In this embodiment, according to the accelerator pedal opening, the accelerator pedal opening change rate, the brake pedal opening change rate, the steering wheel angle opening and the steering wheel angle opening change rate are input into a pre-built preset driver state model, the model is analyzed and optimized through a preset adaptive particle swarm algorithm, and the driving intention of the driver is determined.
Step S30: the current vehicle driving state is determined based on the surrounding sensing information.
It should be appreciated that the current vehicle travel state criteria whether the current vehicle is in a dangerous scenario, including a dangerous state and a normal travel state. The surrounding awareness information includes: surrounding vehicle information and lane line information. The domain controller is connected with cameras arranged around the vehicle, acquires surrounding image information through the cameras, analyzes the image information, determines lane line information in the current environment based on a lane line recognition technology, determines surrounding vehicles in the current environment based on a vehicle recognition technology, and optionally determines the current vehicle running state based on surrounding perception information, and specifically comprises the following steps: sensing the surrounding environment through a vehicle body sensor, determining the vehicle offset degree, and acquiring the current vehicle speed; determining a state transition probability matrix corresponding to the Markov chain; determining a preset vehicle random motion prediction model according to the state transition probability matrix, the vehicle offset degree and the current vehicle speed; determining a current vehicle state according to the preset vehicle random motion prediction model; matching the current vehicle state with a preset typical dangerous scene to obtain a matching result; and determining the running state of the vehicle according to the matching result.
Note that, vehicleThe offset degree refers to the ratio of the distance between the vehicle and the road center line to the road width, and the specific process may be that a mahalanobis chain is adopted to encode the offset degree d of the vehicle and the current vehicle speed v to obtain a random array, when the vehicle is in the driving process and the time interval is smaller, the state of the next moment of the vehicle is related to the state of the current moment, a discrete self-adaptive Markov chain model (S, P) is adopted, S is a non-empty state set formed by all states of the mahalanobis chain, and P is a mahalanobis chain state transition probability matrix. The state transition probability is obtained through conditional probability definition, the vehicle deviation degree and the vehicle speed are divided into a plurality of segments to form different states, the different state transitions form a state transition matrix P, and the assumption of [ d ] 0 ，v 0 ]Corresponding to the state s of the vehicle at the current moment 0 At the current moment U 0 ＝s 0 Generating a random array { r } 1 ，r 2 ，r 3 …, a random array characterizes the state in which a vehicle may be traveling, e.g., r 1 Representing left turn, r 2 Representing straight movement, r 3 Representing right turn, at the current vehicle state X (0) =s 0 Under the condition that the occurrence happens, the conditional distribution probability of X (1) is p 0j ＝P{X{1}＝s j |X(0)＝s i J=1, 2,3, …, n, the kth is taken from the state transition matrix P 0 All elements are lined; taking random number r 1 Representing that the vehicle is assumed to be ready for left turn if there is a right turn for a certain k 1 Satisfy the following requirements
I.e. it can be determined that the next time the vehicle is in the state s 1 U, i.e. U 1 ＝s 1 I.e. assuming that the state of the left turn of the vehicle satisfies k 1 The condition of the state transition of the vehicle at the moment is considered to be that the vehicle is ready for left turn at the next moment; the random motion prediction model of the vehicle obtained by the similar method has U n ＝{s 1 ，s 2 …. To U n The state code is decoded, the current Vehicle state is obtained by reversely checking the Vehicle deviation state and the Vehicle speed information corresponding to the code, and whether the current Vehicle state belongs to a typical dangerous scene or not is judged, so that whether the Vehicle running Vehicle at the Vehicle end belongs to a dangerous state Danger or is judgedConstant driving state ratio.
Step S40: and determining a corresponding target driving auxiliary mode according to the driving intention of the driver and the current vehicle running state, and switching the current mode to the target driving auxiliary mode.
It is to be understood that the driver' S driving intention includes emergency driving, mild driving and normal driving, and optionally, step S40 includes: when the driving intention of the driver is emergency driving or the driving state of the vehicle is dangerous, determining that a corresponding target driving auxiliary mode is an emergency auxiliary mode, and switching a current mode to the emergency auxiliary mode; determining that a corresponding target driving assistance mode is a cautious assistance mode when the driver driving intention is normal driving and the vehicle running state is normal running state, and switching a current mode to the cautious assistance mode; and when the driving intention of the driver is slow driving and the driving state of the vehicle is normal driving state, determining that the corresponding target driving auxiliary mode is normal auxiliary mode, and switching the current mode to the normal auxiliary mode.
It should be noted that, when the domain controller obtains the driving intention of the driver and the current driving state of the vehicle, the domain controller determines the corresponding target driving assistance mode according to formula (1):
wherein, a driving assistance Mode (DAS) E [1,3],1 represents an imminace emergency assistance Mode, 2 represents a Cautious Cautious assistance Mode, and 3 represents a Normal assistance Mode; the driving intention of the driver is epsilon [1,3],1 represents emergency driving, 2 represents moderate driving, and 3 represents normal driving; the current Vehicle running state Vehicle includes a dangerous state Danger and a normal running state ratio.
It should be appreciated that when the driving assistance mode is an imminace emergency assistance mode, the following manner of assistance of the vehicle in the imminace emergency assistance mode is indicative of the current driver or vehicle being in a dangerous environment: the driving assistance such as automatic emergency braking, safety belts, safety airbags and the like can set the automatic adjustment function, such as automatic emergency braking sensitivity, braking distance and the like, to the highest, and the safety belts and the safety airbags can be triggered at any time so as to prevent vehicle collision and maximally protect the safety of a driver. When the driving assistance mode is a Cautious Cautious assistance mode, the driving state of the driver is normal, the environment where the vehicle is located is safe, and the driving assistance is in a Cautious state; when the driving assistance mode is Normal, the driving state of the driver is relaxed, the environment where the vehicle is located is safe, and the driving assistance is in a Normal state.
The method comprises the steps that an accelerator pedal opening, an accelerator pedal opening change rate, a brake pedal opening change rate, a steering wheel angle opening and a steering wheel angle opening change rate are obtained through a vehicle sensor; analyzing the accelerator pedal opening, the accelerator pedal opening change rate, the brake pedal opening change rate, the steering wheel angle opening and the steering wheel angle opening change rate through a preset self-adaptive particle swarm algorithm, and determining the driving intention of a driver; determining a current vehicle driving state based on the surrounding sensing information; and determining a corresponding target driving auxiliary mode according to the driving intention of the driver and the current vehicle running state, and switching the current mode to the target driving auxiliary mode. By the method, the driver intention obtained through analysis according to the preset adaptive particle swarm algorithm and the driving auxiliary mode switched according to the vehicle running state are determined, so that the driving auxiliary mode is switched more intelligently, the influence of subjective consciousness of the driver on the auxiliary mode switching is avoided, the influence on the analysis result of the driver intention due to misoperation or invalid operation of the driver is effectively avoided, the situation of the driving auxiliary mode switching is influenced, the driving safety is improved, and the problem that the current driving auxiliary mode is not timely switched is solved.
Referring to fig. 3, fig. 3 is a flowchart illustrating a driving assistance mode switching method according to a second embodiment of the present invention.
Based on the above-described first embodiment, the step S20 of the driving assistance mode switching method of the present embodiment includes:
step S201: and analyzing the accelerator pedal opening, the accelerator pedal opening change rate, the brake pedal opening change rate, the steering wheel angle opening and the steering wheel angle opening change rate through a preset self-adaptive particle swarm algorithm to determine initial driving intention.
Specifically, the step S201 includes: inputting the accelerator pedal opening, the accelerator pedal opening change rate, the brake pedal opening change rate, the steering wheel angle opening and the steering wheel angle opening change rate into a preset driver state model; and optimizing the preset driver state model through a preset self-adaptive particle swarm algorithm, and outputting the initial driving intention.
It will be appreciated that the predetermined driver state model is determined by equation (2):
wherein x is 1 ∈(α，dα/dt)，x 2 ∈(θ，dθ/dt)，x 3 ∈(δ，dδ/dt)，f(x 1 ，x 2 ，x 3 )∈[1,3]1 represents emergency driving, 2 represents normal driving, 3 represents slow driving, alpha is accelerator pedal opening, dα/dt is accelerator pedal opening change rate, θ is brake pedal opening, dθ/dt is brake pedal opening change rate, δ is steering wheel angle opening, and dδ/dt is steering wheel angle opening change rate.
The optimal value is obtained by optimizing a preset adaptive particle swarm algorithm according to a preset driver state model, and the initial driving intention, which is the actual optimal value, is output as emergency driving, normal driving or moderate driving by the input accelerator pedal, brake pedal, steering opening of the steering wheel and corresponding change rate.
Specifically, the optimizing the preset driver state model by the preset adaptive particle swarm algorithm, outputting an initial driving intention, includes: acquiring a preset acceleration factor, a preset inertia weight and a preset error requirement; determining a preset particle set according to the preset driver state model; determining a global optimal position based on the fitness value corresponding to each particle in the preset particle set; when the global optimal position meets the preset error requirement, obtaining an optimized particle position and particle speed; and determining an optimal solution according to the optimized particle position and particle speed through the preset driver state model to obtain an initial driving intention.
It can be appreciated that the preset adaptive particle swarm algorithm is characterized by the formula (3) and the formula (4):
explaining the optimization process according to the formula (3) and the formula (4), the ith particle in the training set L is expressed as a vector of L, X i ＝(x i1 ，x i2 ，…，x iL ) I=1, 2,3, i.e. the position of the ith particle in the training set is X i The optimal position experienced by the ith particle is Pbest i ＝(p i1 ，p i2 ，…,p iL ) I=1, 2,3, i.e. the current individual optimal position, each position of the particle represents a potential solution of the requirement, and the particle position is input into the objective function to obtain the fitness value of the ith particle, so as to judge the particle quality. The optimal position searched by the whole particle swarm is Gbest g ＝p ig ) I=1, 2,3, i.e. the current global optimum position, g denotes the index of the optimum particle position. ω represents the inertial weight and,
for the i-th particle to t-th generation, starting from the searched historical optimal solution, ++>
To the present for the whole particle swarmGlobal optimum position searched up to +.>
Respectively representing the current position and flying speed of the ith particle, c 1 ，c 2 Represents a constant other than negative, r 1 ，r 2 Is [0,1 ]]Random numbers in between. In this embodiment, the iterative evolutionary frequency of the algorithm is set to 1000 times, and the acceleration factor c is preset 1 ＝1.4，c 2 =1.5, preset inertial weight ω=0.8.
It should be noted that, the preset error requirement is the minimum error requirement, which is set by the developer in advance according to the actual situation, and for each particle, the fitness value and the experienced current individual optimal position Pbest are calculated i And if the fitness value of the particle is better, the position of the particle is taken as the new optimal position of the current individual. For each particle, its fitness value is compared with the best global position Gbest g If the fitness value of the particle is better, the position of the particle is taken as a new global optimal position. If the global optimal position cannot meet the minimum error requirement, the initial driving intention of the output is represented to be inconsistent with the actual, the speed and the position of the particles are optimized according to the formula (3) and the formula (4), and the new particles are compared with the current individual optimal position and the global optimal position until the global optimal position meets the minimum error requirement.
Step S202: the method comprises the steps of acquiring accelerator pedal cycle sensing information, brake pedal cycle sensing information and steering wheel cycle sensing information through a vehicle sensor.
The period sensing information is sensing information of an accelerator pedal, a brake pedal and a steering wheel, which are acquired by a vehicle sensor in a preset acquisition period, for example, control times, a maximum opening value, a variation rate, a variation fluctuation curve and the like. The preset acquisition period can be set to be within the first 10 seconds of the current time, and the domain controller obtains the control times of the accelerator pedal, the control times of the brake pedal and the control times of the steering wheel within the first 10 seconds of the current time through the vehicle sensor.
Step S203: and determining periodic driving intention according to the accelerator pedal periodic sensing information, the brake pedal periodic sensing information and the steering wheel periodic sensing information.
It may be understood that the periodic driving intention includes emergency driving, mild driving, and normal driving, and in a specific implementation, the periodic driving intention may be determined by looking up a preset table according to the control number, and the periodic driving intention may be determined by comparing the control number with a preset value.
Specifically, the accelerator pedal period sensing information is the control times of an accelerator pedal in a preset acquisition period, the brake pedal period sensing information is the control times of a brake pedal in the preset acquisition period, and the steering wheel period sensing information is the control times of a steering wheel in the preset acquisition period;
the step S203 includes: determining a preset threshold according to the preset acquisition period; comparing the control times of the accelerator pedal, the control times of the brake pedal and the control times of the steering wheel with the preset threshold value to obtain a comparison result; and determining the periodic driving intention according to the comparison result.
It should be noted that, in this embodiment, the period sensing information is the number of times that the driver controls the accelerator pedal, the brake pedal and the steering wheel within a period of time, the preset threshold value corresponds to the preset acquisition period one by one, in a specific implementation, the preset acquisition period is set to be within the first 10 seconds of the current time, the preset threshold value is set to be 3 times, and the preset acquisition period can also be selected according to the current vehicle speed, for example, when the current vehicle speed is slow or fast, the preset acquisition period is selected to be within the first 10 seconds of the current time, and when the current vehicle speed is in a gentle vehicle speed range, the preset acquisition period is selected to be within the first 20 seconds of the current time. And when the preset acquisition period is within the first 20 seconds of the current time, determining that the corresponding preset threshold value is 5 times.
Specifically, the determining the periodic driving intention according to the comparison result includes: determining that the periodic driving intention is emergency driving when the control times of the accelerator pedal, the brake pedal and the steering wheel are all greater than or equal to the preset threshold value; determining that the periodic driving intention is to alleviate driving when the control times of the accelerator pedal, the brake pedal and the steering wheel are smaller than the preset threshold value; and when the control times of the accelerator pedal and the control times of the brake pedal are larger than or equal to the preset threshold value and the control times of the steering wheel are smaller than the preset threshold value, determining that the periodic driving intention is normal driving.
It can be understood that, in the first 10 seconds with the preset acquisition period as the current time, the preset threshold is 3 times for explanation, and the period driving intention is determined by the formula (5):
wherein the periodic driving intention T epsilon [1,3 ]]1 represents emergency driving, 2 represents normal driving, 3 represents moderate driving, F α For controlling the number of times of the accelerator pedal, F θ For controlling the number of times of the brake pedal, F δ The number of steering wheel controls; in a preset acquisition period, if the control times of a driver on an accelerator pedal, a brake pedal and a steering wheel are less than 3 times, the periodic driving intention is considered to be mild driving; if the control times of the accelerator pedal and the brake pedal are more than or equal to 3 times and the control times of the steering wheel are less than 3 times, the periodic driving intention is considered to be normal driving; if the number of times of control of the accelerator pedal, the brake pedal and the steering wheel by the driver is 3 or more, the periodic driving intention is considered to be emergency driving.
Step S204: and determining the driving intention of the driver according to the initial driving intention and the periodic driving intention.
Specifically, the step S204 includes: determining that the driver's driving intention is emergency driving when the periodic driving intention is emergency driving; determining that the driver's driving intention is normal driving when the initial driving intention is emergency driving or normal driving and the periodic driving intention is normal driving or moderate driving; when the initial driving intention is the moderate driving and the periodic driving intention is the moderate driving, the driver driving intention is determined to be the moderate driving.
It should be noted that, in combination with the optimal solution of the preset driver state model and the periodic driving intention, the driver driving intention is determined by the formula (6):
in a specific implementation, the periodic driving intention T is taken as the main, and when the periodic driving intention t=1, the driver driving intention is considered as emergency driving; when the periodic driving intention T is equal to or greater than 2 and the initial driving intention f (x 1 ，x 2 ，x 3 )<3, recognizing the driving intention of the driver as normal driving; when the periodic driving intention t=3 and the initial driving intention f (x 1 ，x 2 ，x 3 ) When=3, the driver's driving intention is considered as moderate driving.
According to the embodiment, an accelerator pedal opening change rate, a brake pedal opening change rate, a steering wheel angle opening and a steering wheel angle opening change rate are analyzed through a preset adaptive particle swarm algorithm, and initial driving intention is determined; acquiring accelerator pedal cycle sensing information, brake pedal cycle sensing information and steering wheel cycle sensing information through a vehicle sensor; determining periodic driving intention according to the accelerator pedal periodic sensing information, the brake pedal periodic sensing information and the steering wheel periodic sensing information; determining a driver driving intention according to the initial driving intention and the periodic driving intention; and determining a corresponding target driving auxiliary mode according to the driving intention of the driver and the current vehicle running state, and switching the current mode to the target driving auxiliary mode. By the method, the problem that the analysis result of the driving intention is influenced due to misoperation or invalid operation of the driver is effectively solved by comprehensively analyzing the current driving operation state and the frequency of the driving operation in the period, the problem that the current driving auxiliary mode is not timely switched is solved by combining the driving intention of the driver at the driver end and the driving state of the vehicle at the vehicle end, the current driving auxiliary mode of the vehicle is judged, and the intelligent level of driving auxiliary and the safety of the vehicle are further improved.
In addition, the embodiment of the invention also provides a storage medium, wherein a driving assistance mode switching program is stored on the storage medium, and the driving assistance mode switching program realizes the driving assistance mode switching method when being executed by a processor.
Because the storage medium adopts all the technical schemes of all the embodiments, the storage medium has at least all the beneficial effects brought by the technical schemes of the embodiments, and the description is omitted here.
Referring to fig. 4, fig. 4 is a block diagram showing the structure of a first embodiment of the driving assistance mode switching apparatus of the present invention.
As shown in fig. 4, the driving assistance mode switching apparatus provided by the embodiment of the present invention includes:
the acquisition module 10 is configured to acquire an accelerator pedal opening, an accelerator pedal opening change rate, a brake pedal opening change rate, a steering wheel angle opening, and a steering wheel angle opening change rate by using vehicle sensors.
The determining module 20 is configured to analyze the accelerator pedal opening, the accelerator pedal opening change rate, the brake pedal opening change rate, the steering wheel angle opening and the steering wheel angle opening change rate through a preset adaptive particle swarm algorithm, and determine a driving intention of a driver.
The determining module 20 is further configured to determine a current vehicle driving state based on the surrounding sensing information.
And a switching module 30, configured to determine a corresponding target driving assistance mode according to the driver driving intention and the current vehicle driving state, and switch the current mode to the target driving assistance mode.
It should be understood that the foregoing is illustrative only and is not limiting, and that in specific applications, those skilled in the art may set the invention as desired, and the invention is not limited thereto.
The method comprises the steps that an accelerator pedal opening, an accelerator pedal opening change rate, a brake pedal opening change rate, a steering wheel angle opening and a steering wheel angle opening change rate are obtained through a vehicle sensor; analyzing the accelerator pedal opening, the accelerator pedal opening change rate, the brake pedal opening change rate, the steering wheel angle opening and the steering wheel angle opening change rate through a preset self-adaptive particle swarm algorithm, and determining the driving intention of a driver; determining a current vehicle driving state based on the surrounding sensing information; and determining a corresponding target driving auxiliary mode according to the driving intention of the driver and the current vehicle running state, and switching the current mode to the target driving auxiliary mode. By the method, the driver intention obtained through analysis according to the preset adaptive particle swarm algorithm and the driving auxiliary mode switched according to the vehicle running state are determined, so that the driving auxiliary mode is switched more intelligently, the influence of subjective consciousness of the driver on the auxiliary mode switching is avoided, the influence on the analysis result of the driver intention due to misoperation or invalid operation of the driver is effectively avoided, the situation of the driving auxiliary mode switching is influenced, the driving safety is improved, and the problem that the current driving auxiliary mode is not timely switched is solved.
It should be noted that the above-described working procedure is merely illustrative, and does not limit the scope of the present invention, and in practical application, a person skilled in the art may select part or all of them according to actual needs to achieve the purpose of the embodiment, which is not limited herein.
In addition, technical details not described in detail in the present embodiment may refer to the driving assistance mode switching method provided in any embodiment of the present invention, and are not described herein.
In an embodiment, the determining module 20 is further configured to analyze the accelerator pedal opening, the accelerator pedal opening change rate, the brake pedal opening change rate, the steering wheel angle opening and the steering wheel angle opening change rate through a preset adaptive particle swarm algorithm, determine an initial driving intention, obtain accelerator pedal cycle sensing information, brake pedal cycle sensing information and steering wheel cycle sensing information through a vehicle sensor, determine a cycle driving intention according to the accelerator pedal cycle sensing information, the brake pedal cycle sensing information and the steering wheel cycle sensing information, and determine a driver driving intention according to the initial driving intention and the cycle driving intention.
In an embodiment, the determining module 20 is further configured to input the accelerator pedal opening, the accelerator pedal opening change rate, the brake pedal opening change rate, the steering wheel angle opening and the steering wheel angle opening change rate to a preset driver state model, optimize the preset driver state model through a preset adaptive particle swarm algorithm, and output an initial driving intention.
In an embodiment, the determining module 20 is further configured to obtain a preset acceleration factor, a preset inertia weight, and a preset error requirement;
determining a preset particle set according to the preset driver state model, determining a global optimal position based on fitness values corresponding to particles in the preset particle set, obtaining an optimized particle position and particle speed when the global optimal position meets a preset error requirement, and determining an optimal solution according to the optimized particle position and particle speed through the preset driver state model to obtain an initial driving intention.
In an embodiment, the accelerator pedal period sensing information is the number of times of control of the accelerator pedal in a preset collection period, the brake pedal period sensing information is the number of times of control of the brake pedal in the preset collection period, and the steering wheel period sensing information is the number of times of control of the steering wheel in the preset collection period;
The determining module 20 is further configured to determine a preset threshold according to the preset collection period, compare the control times of the accelerator pedal, the control times of the brake pedal, and the control times of the steering wheel with the preset threshold, obtain a comparison result, and determine the periodic driving intention according to the comparison result.
In an embodiment, the determining module 20 is further configured to determine that the periodic driving intention is emergency driving when the number of times of control of the accelerator pedal, the number of times of control of the brake pedal, and the number of times of control of the steering wheel are all greater than or equal to the preset threshold, determine that the periodic driving intention is mild driving when the number of times of control of the accelerator pedal, the number of times of control of the brake pedal, and the number of times of control of the steering wheel are all less than the preset threshold, and determine that the periodic driving intention is normal driving when the number of times of control of the accelerator pedal, the number of times of control of the brake pedal, and the number of times of control of the steering wheel are all greater than or equal to the preset threshold.
In an embodiment, the determining module 20 is further configured to determine that the driver's driving intention is emergency driving when the periodic driving intention is emergency driving, determine that the driver's driving intention is normal driving when the initial driving intention is emergency driving or normal driving and the periodic driving intention is normal driving or moderate driving, and determine that the driver's driving intention is moderate driving when the initial driving intention is moderate driving and the periodic driving intention is moderate driving.
Furthermore, it should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. Read Only Memory)/RAM, magnetic disk, optical disk) and including several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (8)
1. A driving assistance mode switching method, characterized by comprising:
acquiring an accelerator pedal opening, an accelerator pedal opening change rate, a brake pedal opening change rate, a steering wheel angle opening and a steering wheel angle opening change rate through a vehicle sensor;
analyzing the accelerator pedal opening, the accelerator pedal opening change rate, the brake pedal opening change rate, the steering wheel angle opening and the steering wheel angle opening change rate through a preset adaptive particle swarm algorithm to determine the driving intention of a driver;
determining a current vehicle driving state based on the surrounding sensing information;
determining a corresponding target driving auxiliary mode according to the driving intention of the driver and the current vehicle running state, and switching the current mode to the target driving auxiliary mode;
The method for determining the driving intention of the driver by analyzing the accelerator pedal opening, the accelerator pedal opening change rate, the brake pedal opening change rate, the steering wheel angle opening and the steering wheel angle opening change rate through a preset adaptive particle swarm algorithm comprises the following steps:
analyzing the accelerator pedal opening, the accelerator pedal opening change rate, the brake pedal opening change rate, the steering wheel angle opening and the steering wheel angle opening change rate through a preset self-adaptive particle swarm algorithm to determine an initial driving intention;
acquiring accelerator pedal cycle sensing information, brake pedal cycle sensing information and steering wheel cycle sensing information through a vehicle sensor;
determining periodic driving intention according to the accelerator pedal periodic sensing information, the brake pedal periodic sensing information and the steering wheel periodic sensing information;
determining a driver driving intention from the initial driving intention and the periodic driving intention;
wherein the determining the driver driving intention from the initial driving intention and the periodic driving intention includes:
Determining that the driver's driving intention is emergency driving when the periodic driving intention is emergency driving;
determining that the driver's driving intention is normal driving when the initial driving intention is emergency driving or normal driving and the periodic driving intention is normal driving or moderate driving;
when the initial driving intention is the moderate driving and the periodic driving intention is the moderate driving, the driver driving intention is determined to be the moderate driving.
2. The driving assistance mode switching method according to claim 1, wherein said analyzing the accelerator pedal opening, the accelerator pedal opening change rate, the brake pedal opening change rate, the steering wheel angle opening, and the steering wheel angle opening change rate by a preset adaptive particle swarm algorithm to determine an initial driving intention includes:
inputting the accelerator pedal opening, the accelerator pedal opening change rate, the brake pedal opening change rate, the steering wheel angle opening and the steering wheel angle opening change rate into a preset driver state model;
and optimizing the preset driver state model through a preset self-adaptive particle swarm algorithm, and outputting the initial driving intention.
3. The driving assistance mode switching method according to claim 2, wherein said optimizing said preset driver state model by a preset adaptive particle swarm algorithm, outputting an initial driving intention, comprises:
acquiring a preset acceleration factor, a preset inertia weight and a preset error requirement;
determining a preset particle set according to the preset driver state model;
determining a global optimal position based on the fitness value corresponding to each particle in the preset particle set;
when the global optimal position meets the preset error requirement, obtaining an optimized particle position and particle speed;
and determining an optimal solution according to the optimized particle position and particle speed through the preset driver state model to obtain an initial driving intention.
4. The driving assistance mode switching method according to claim 1, wherein the accelerator pedal cycle sensing information is a number of times of control of an accelerator pedal in a preset acquisition cycle, the brake pedal cycle sensing information is a number of times of control of a brake pedal in the preset acquisition cycle, and the steering wheel cycle sensing information is a number of times of control of a steering wheel in the preset acquisition cycle;
The determining the periodic driving intention according to the accelerator pedal periodic sensing information, the brake pedal periodic sensing information and the steering wheel periodic sensing information includes:
determining a preset threshold according to the preset acquisition period;
comparing the control times of the accelerator pedal, the control times of the brake pedal and the control times of the steering wheel with the preset threshold value to obtain a comparison result;
and determining the periodic driving intention according to the comparison result.
5. The driving assistance mode switching method according to claim 4, characterized in that said determining a periodic driving intention from said comparison result includes:
determining that the periodic driving intention is emergency driving when the control times of the accelerator pedal, the brake pedal and the steering wheel are all greater than or equal to the preset threshold value;
determining that the periodic driving intention is to alleviate driving when the control times of the accelerator pedal, the brake pedal and the steering wheel are smaller than the preset threshold value;
and when the control times of the accelerator pedal and the control times of the brake pedal are larger than or equal to the preset threshold value and the control times of the steering wheel are smaller than the preset threshold value, determining that the periodic driving intention is normal driving.
6. A driving assistance mode switching device, characterized by comprising:
the acquisition module is used for acquiring the accelerator pedal opening, the accelerator pedal opening change rate, the brake pedal opening change rate, the steering wheel angle opening and the steering wheel angle opening change rate through the vehicle sensor;
the determining module is used for analyzing the accelerator pedal opening, the accelerator pedal opening change rate, the brake pedal opening change rate, the steering wheel angle opening and the steering wheel angle opening change rate through a preset adaptive particle swarm algorithm to determine the driving intention of a driver;
the determining module is further used for determining the current vehicle running state based on the surrounding perception information;
the switching module is used for determining a corresponding target driving auxiliary mode according to the driving intention of the driver and the current vehicle running state and switching the current mode to the target driving auxiliary mode;
the determining module is further configured to analyze the accelerator pedal opening, the accelerator pedal opening change rate, the brake pedal opening change rate, the steering wheel angle opening and the steering wheel angle opening change rate through a preset adaptive particle swarm algorithm, and determine an initial driving intention;
Acquiring accelerator pedal cycle sensing information, brake pedal cycle sensing information and steering wheel cycle sensing information through a vehicle sensor;
determining periodic driving intention according to the accelerator pedal periodic sensing information, the brake pedal periodic sensing information and the steering wheel periodic sensing information;
determining a driver driving intention from the initial driving intention and the periodic driving intention;
the determining module is further configured to determine that the driving intention of the driver is emergency driving when the periodic driving intention is emergency driving;
determining that the driver's driving intention is normal driving when the initial driving intention is emergency driving or normal driving and the periodic driving intention is normal driving or moderate driving;
when the initial driving intention is the moderate driving and the periodic driving intention is the moderate driving, the driver driving intention is determined to be the moderate driving.
7. A driving assistance mode switching apparatus, characterized in that the apparatus comprises: a memory, a processor, and a driving assistance mode switching program stored on the memory and executable on the processor, the driving assistance mode switching program configured to implement the driving assistance mode switching method according to any one of claims 1 to 5.
8. A storage medium having stored thereon a driving assistance mode switching program which, when executed by a processor, implements the driving assistance mode switching method according to any one of claims 1 to 5.
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| CN114506327B (en) * | 2022-01-29 | 2023-10-27 | 重庆长安汽车股份有限公司 | Driver intention judging system and method for vehicle drift control |
| CN114537436A (en) * | 2022-03-04 | 2022-05-27 | 阿波罗智能技术(北京)有限公司 | Control method, device and equipment of automatic driving vehicle and automatic driving vehicle |
| CN116653993B (en) * | 2023-03-08 | 2024-04-05 | 广州汽车集团股份有限公司 | Training method of throttle sensitivity control model, sensitivity control method and device |
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