阅读说明：本技术 驾驶辅助模式切换方法、装置、设备及存储介质 (Driving assistance mode switching method, device, equipment and storage medium ) 是由 罗文� 翟克宁 金旅 朱智斌 石胜明 莫忠婷 林苏华 于 2021-08-04 设计创作，主要内容包括：本发明公开了一种驾驶辅助模式切换方法、装置、设备及存储介质。该方法包括：通过车辆传感器获取油门踏板开度、油门踏板开度变化率、制动踏板开度、制动踏板开度变化率、方向盘转角开度以及方向盘转角开度变化率；通过预设自适应粒子群算法对传感器数据进行分析,确定驾驶员驾驶意图；基于周边感知信息确定当前车辆行驶状态；根据驾驶员驾驶意图以及当前车辆行驶状态确定对应的切换的目标驾驶辅助模式。通过上述方式,根据预设自适应粒子群算法分析得到的驾驶员意图以及车辆行驶状态确定切换的模式,使得驾驶辅助模式切换更智能,避免驾驶员主观意识对辅助模式切换的影响,提高了驾驶安全性,解决了当前驾驶辅助模式切换不及时的问题。(The invention discloses a driving assistance mode switching method, a driving assistance mode switching device, driving assistance equipment and a storage medium. The method comprises the following steps: 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; 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 peripheral perception information; and determining a corresponding switched target driving assistance mode according to the driving intention of the driver and the current vehicle running state. By the mode, the switching mode is determined according to the driver intention and the vehicle running state obtained by analyzing the preset self-adaptive particle swarm algorithm, so that the driving assistance mode is switched more intelligently, the influence of the subjective consciousness of the driver on the switching of the assistance mode is avoided, the driving safety is improved, and the problem that the current driving assistance mode is not switched timely is solved.)

1. A driving assistance mode switching method characterized by comprising:

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;

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 peripheral perception information;

and determining a corresponding target driving assistance mode according to the driving intention of the driver and the current vehicle running state, and switching the current mode to the target driving assistance mode.

2. The driving assist mode switching method according to claim 1, wherein the determining the driver's driving intention 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 by a preset adaptive particle swarm algorithm 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 adaptive particle swarm algorithm to determine an initial driving intention;

acquiring periodic sensing information of an accelerator pedal, periodic sensing information of a brake pedal and periodic sensing information of a steering wheel through a vehicle sensor;

determining a periodic driving intention according to the accelerator pedal period sensing information, the brake pedal period sensing information and the steering wheel period sensing information;

determining a driver driving intention according to the initial driving intention and the periodic driving intention.

3. The driving assist mode switching method according to claim 2, wherein the determining an initial driving intention 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 by a preset adaptive particle swarm algorithm 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 to a preset driver state model;

and optimizing the preset driver state model through a preset self-adaptive particle swarm algorithm, and outputting an initial driving intention.

4. The driving assistance mode switching method according to claim 3, wherein the optimizing the preset driver state model by a preset adaptive particle swarm optimization, 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 an optimized particle speed;

and determining an optimal solution through the preset driver state model according to the optimized particle position and the optimized particle speed to obtain an initial driving intention.

5. The driving assist mode switching method according to claim 2, wherein the accelerator pedal cycle sensing information is a control number of an accelerator pedal in a preset acquisition period, the brake pedal cycle sensing information is a control number of a brake pedal in the preset acquisition period, and the steering wheel cycle sensing information is a control number of a steering wheel in the preset acquisition period;

the determining the periodic driving intention according to the accelerator pedal period sensing information, the brake pedal period sensing information and the steering wheel period sensing information comprises the following steps:

determining a preset threshold value according to the preset acquisition period;

comparing the control times of the accelerator pedal, the brake pedal and the steering wheel with the preset threshold value to obtain a comparison result;

and determining the periodic driving intention according to the comparison result.

6. The driving assistance mode switching method according to claim 5, wherein the determining of the periodic driving intention according to the comparison result includes:

when the control times of the accelerator pedal, the control times of the brake pedal and the control times of the steering wheel are all larger than or equal to the preset threshold value, determining that the periodic driving intention is emergent driving;

when the control times of the accelerator pedal, the brake pedal and the steering wheel are all smaller than the preset threshold value, determining that the periodic driving intention is mild driving;

and when the control times of the accelerator pedal and the control times of the brake pedal are greater than or equal to the preset threshold value and the control times of the steering wheel are less than the preset threshold value, determining that the periodic driving intention is normal driving.

7. The driving assistance mode switching method according to claim 2, wherein the determining a driver driving intention from the initial driving intention and the periodic driving intention includes:

when the periodic driving intention is urgent driving, determining that the driving intention of the driver is urgent driving;

when the initial driving intention is urgent driving or normal driving and the periodic driving intention is normal driving or moderate driving, determining that the driving intention of the driver is normal driving;

determining that the driver's driving intention is the moderate driving when the initial driving intention is the moderate driving and the periodic driving intention is the moderate driving.

8. A driving assistance mode switching device characterized by comprising:

the acquisition module is used for acquiring the opening degree of an accelerator pedal, the opening degree change rate of the accelerator pedal, the opening degree of a brake pedal, the opening degree change rate of the brake pedal, the corner opening degree of a steering wheel and the corner opening change rate of the steering wheel through a 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 the driver;

the determining module is further used for determining the current vehicle running state based on the peripheral perception information;

and the switching module is used for determining a corresponding target driving assistance mode according to the driving intention of the driver and the current vehicle running state and switching the current mode to the target driving assistance mode.

9. 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 being configured to implement the driving assistance mode switching method according to any one of claims 1 to 7.

10. A storage medium, characterized in that the storage medium has stored thereon a driving assistance mode switching program that, when executed by a processor, implements the driving assistance mode switching method according to any one of claims 1 to 7.

Technical Field

The present invention relates to the field of driving assistance technologies, and in particular, to a driving assistance mode switching method, apparatus, device, and storage medium.

Background

When driving the vehicle, the current driver manually selects the driving assistance mode according to the driving experience, and if the front is dangerous or the sight of the driver is shielded, the driver has judgment deviation, so that the adjustment is not timely, and the driving safety is difficult to guarantee.

The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.

Disclosure of Invention

The invention mainly aims to provide a driving assistance mode switching method, a driving assistance mode switching device, driving assistance mode switching equipment and a 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 ensure.

To achieve the above object, the present invention provides a driving assistance mode switching method, including 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;

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 peripheral perception information;

and determining a corresponding target driving assistance mode according to the driving intention of the driver and the current vehicle running state, and switching the current mode to the target driving assistance 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 adaptive particle swarm algorithm to determine an initial driving intention;

acquiring periodic sensing information of an accelerator pedal, periodic sensing information of a brake pedal and periodic sensing information of a steering wheel through a vehicle sensor;

determining a periodic driving intention according to the accelerator pedal period sensing information, the brake pedal period sensing information and the steering wheel period sensing information;

determining a driver driving intention according to the initial driving intention and the periodic driving intention.

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 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 to a preset driver state model;

and optimizing the preset driver state model through a preset self-adaptive particle swarm algorithm, and outputting an initial driving intention.

Optionally, the optimizing the preset driver state model through a preset adaptive particle swarm algorithm, and 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 an optimized particle speed;

and determining an optimal solution through the preset driver state model according to the optimized particle position and the optimized particle speed to obtain an initial driving intention.

Optionally, 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 determining the periodic driving intention according to the accelerator pedal period sensing information, the brake pedal period sensing information and the steering wheel period sensing information comprises the following steps:

determining a preset threshold value according to the preset acquisition period;

comparing the control times of the accelerator pedal, the brake pedal and 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 periodic driving intention according to the comparison result includes:

when the control times of the accelerator pedal, the control times of the brake pedal and the control times of the steering wheel are all larger than or equal to the preset threshold value, determining that the periodic driving intention is emergent driving;

when the control times of the accelerator pedal, the brake pedal and the steering wheel are all smaller than the preset threshold value, determining that the periodic driving intention is mild driving;

and when the control times of the accelerator pedal and the control times of the brake pedal are greater than or equal to the preset threshold value and the control times of the steering wheel are less than the preset threshold value, determining that the periodic driving intention is normal driving.

Optionally, the determining a driver driving intent from the initial driving intent and the periodic driving intent comprises:

when the periodic driving intention is urgent driving, determining that the driving intention of the driver is urgent driving;

when the initial driving intention is urgent driving or normal driving and the periodic driving intention is normal driving or moderate driving, determining that the driving intention of the driver is normal driving;

determining that the driver's driving intention is the moderate driving when the initial driving intention is the moderate driving and the periodic driving intention is the moderate driving.

Further, in order to achieve the above object, the present invention also proposes a driving assistance mode switching device including:

the acquisition module is used for acquiring the opening degree of an accelerator pedal, the opening degree change rate of the accelerator pedal, the opening degree of a brake pedal, the opening degree change rate of the brake pedal, the corner opening degree of a steering wheel and the corner opening change rate of the steering wheel through a 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 the driver;

the determining module is further used for determining the current vehicle running state based on the peripheral perception information;

and the switching module is used for determining a corresponding target driving assistance mode according to the driving intention of the driver and the current vehicle running state and switching the current mode to the target driving assistance mode.

Further, 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.

Further, in order to achieve the above object, the present invention also proposes a storage medium having stored thereon a driving assistance mode switching program that, when executed by a processor, implements the driving assistance mode switching method as described above.

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 opening degree of an accelerator pedal, the opening degree change rate of the accelerator pedal, the opening degree of a brake pedal, the opening degree change rate of the brake pedal, the corner opening degree of a steering wheel and the corner opening change rate of the steering wheel by a preset adaptive particle swarm algorithm to determine the driving intention of a driver; determining a current vehicle driving state based on the peripheral perception information; and determining a corresponding target driving assistance mode according to the driving intention of the driver and the current vehicle running state, and switching the current mode to the target driving assistance mode. By the mode, the intention of the driver is analyzed according to the preset self-adaptive particle swarm algorithm, and the driving assistance mode is determined according to the driving state of the vehicle, so that the driving assistance mode is switched more intelligently, the influence of the subjective consciousness of the driver on the switching of the assistance mode is avoided, the influence on the analysis result of the intention of the driver due to misoperation or invalid operation of the driver is effectively avoided, the condition of switching the driving assistance mode is influenced, the driving safety is improved, and the problem that the current driving assistance mode is not switched timely is solved.

Drawings

Fig. 1 is a schematic structural diagram of a driving assistance mode switching apparatus in a hardware operating environment according to an embodiment of the present invention;

FIG. 2 is a flowchart illustrating a first exemplary embodiment of a driving assistance mode switching method according to 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 configuration of the driving assistance mode switching apparatus according to the first embodiment of the present invention.

The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.

Detailed Description

It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

Referring to fig. 1, fig. 1 is a schematic structural 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 (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also 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 Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.

Those skilled in the art will appreciate that the configuration shown in fig. 1 does not constitute a limitation of the driving assistance mode switching apparatus, and may include more or less components than those shown, or some components in combination, or a different arrangement of components.

As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a network communication module, a user interface module, and a driving assistance mode switching program.

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 device of the present invention may be provided in the driving assistance mode switching device that calls 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 flowchart illustrating a first embodiment of the driving assistance mode switching method according to the present invention.

In this embodiment, the driving assistance mode switching method includes the steps of:

step S10: the method comprises the steps of obtaining the opening degree of an accelerator pedal, the opening degree change rate of the accelerator pedal, the opening degree of a brake pedal, the opening degree change rate of the brake pedal, the opening degree of a steering wheel angle and the opening degree change rate of the steering wheel angle through a vehicle sensor.

It can be understood that the execution subject of this embodiment is a driving assistance mode switching device, and the driving assistance mode switching device may be a vehicle control unit, a controller connected to a vehicle control end, or other devices, and may also be a domain controller, or other devices having the same or similar functions, and this embodiment takes the domain controller as an example for description. The domain controller is connected with a sensor which is arranged on a vehicle and 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 corner, and analyzes and determines the opening degree change rate of the accelerator pedal, the opening degree of the brake pedal and the opening degree of the steering wheel corner 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 corner in a preset collection period.

Step S20: analyzing the opening degree of the accelerator pedal, the opening degree change rate of the accelerator pedal, the opening degree of the brake pedal, the opening degree change rate of the brake pedal, the corner opening degree of the steering wheel and the corner opening change rate of the steering wheel through a preset adaptive particle swarm algorithm, and determining the driving intention of a driver.

It should be noted that, in this embodiment, a preset driver state model which is constructed in advance is input according to an accelerator pedal opening degree, an accelerator pedal opening degree change rate, a brake pedal opening degree change rate, a steering wheel angle opening degree and a steering wheel angle opening degree change rate, the model is analyzed and optimized through a preset adaptive particle swarm algorithm, and a driver driving intention is determined.

Step S30: the current vehicle running state is determined based on the peripheral perception information.

It should be understood that the current vehicle driving state criteria whether the current vehicle is in a dangerous scene, including, a dangerous state and a normal driving state. The peripheral perception information includes: peripheral vehicle information and lane line information. The domain controller is connected with the cameras installed around the vehicle, collects image information around through the cameras, analyzes the image information, determines lane line information under the current environment based on a lane line recognition technology, determines surrounding vehicles under the current environment based on a vehicle recognition technology, and is optional, determines the running state of the current vehicle based on surrounding sensing information, and specifically is that: sensing the surrounding environment through a vehicle body sensor, determining the vehicle deviation 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 deviation degree and the current vehicle speed; determining the 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 vehicle running state according to the matching result.

It should be noted that the vehicle offset degree refers to a ratio of a distance between a vehicle and a road centerline to a road width, and a specific process may be that a mahalanobis chain is used to encode the vehicle offset degree d and a current vehicle speed v to obtain a random array, when a vehicle is in a driving process and a time interval is small, a state of the vehicle at a next time is related to a state at a current time, a discrete adaptive markov chain model (S, P) is used, S is a non-empty state set composed of all states of the mahalanobis chain, and P is a mahalanobis chain state transition probability matrix. Obtaining state transition probability through conditional probability definition, dividing the vehicle deviation degree and the vehicle speed into a plurality of segments to form different states, and forming a state transition matrix P by the different state transitions, assuming [ d ]₀，v₀]Corresponding to the state s of the vehicle at the present moment₀Current time U₀＝s₀Generating a random array r₁，r₂，r₃… } the random array characterizes the possible driving conditions of the vehicle, for example, r₁Indicates the left turn r₂Denotes straight going, r₃Indicating a right turn, in the current vehicle state X (0) ═ s₀The conditional distribution probability of X (1) is p under the conditions that have occurred_0j＝P{X{1}＝s_j|X(0)＝s_iJ is 1, 2, 3, …, n, and the k-th matrix is taken from the state transition matrix P₀All elements are listed; taking a random number r₁Meaning that the vehicle is assumed to be ready to turn left, if any, for a certain k₁Satisfy the requirement ofThat is, the state of the vehicle at the next moment can be determined as s₁I.e. U₁＝s₁I.e. assuming that the state of the left turn of the vehicle satisfies k₁Considering the condition of vehicle state transition at the moment, and considering that the vehicle is ready for left turning at the next moment; by analogy, the prediction model of the random motion of the vehicle has U_n＝{s₁，s₂… }. To pairU_nAnd decoding the state code, obtaining the current Vehicle state by back checking the Vehicle deviation state and the Vehicle speed information corresponding to the code, and judging whether the current Vehicle state belongs to a typical dangerous scene, namely judging whether the Vehicle driving Vehicle at the Vehicle end belongs to a dangerous state Danger or a normal driving state ratio.

Step S40: and determining a corresponding target driving assistance mode according to the driving intention of the driver and the current vehicle running state, and switching the current mode to the target driving assistance mode.

It is understood that the driving intention of the driver includes urgent driving, moderate driving, and normal driving, and optionally, the step S40 includes: when the driving intention of the driver is emergency driving or the vehicle driving state is a dangerous state, determining that a corresponding target driving assistance mode is an emergency assistance mode, and switching a current mode to the emergency assistance mode; when the driver's driving intention is normal driving and the vehicle running state is a normal running state, determining that a corresponding target driving assistance mode is a cautious assistance mode, and switching a current mode to the cautious assistance mode; when the driver's driving intention is a slow travel and the vehicle travel state is a normal travel state, determining that the corresponding target driving assistance mode is a normal assistance mode, and switching the current mode to the normal assistance mode.

It should be noted that, when acquiring the driving intention of the driver and the current vehicle driving state, the domain controller determines the corresponding target driving assistance mode according to equation (1):

wherein, the driving assistance mode (das) is e [1,3], 1 represents an Imminence emergency assistance mode, 2 represents a Cautious assistance mode, and 3 represents a Normal assistance mode; the driving intention of the driver belongs to [1,3], 1 represents urgent 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 understood that when the driving assistance mode is the Imminence emergency assistance mode, which indicates that the driver or the vehicle is currently in a dangerous environment, the assistance manner of the vehicle in the Imminence emergency assistance mode is as follows: the driving assistance such as automatic emergency brake, safety belt, safety air bag and the like adjusts the automatic adjusting function setting such as automatic emergency brake sensitivity, braking distance and the like to the highest, and the safety belt and the safety air bag are triggered at any time to prevent vehicle collision and protect the safety of a driver to the maximum extent. When the driving assistance mode is the Cautious assistance mode, the driving state of a 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 mild, the environment of the vehicle is safe, and the driving assistance is in a Normal state.

In the embodiment, the opening degree of an accelerator pedal, the opening degree change rate of the accelerator pedal, the opening degree of a brake pedal, the opening degree change rate of the brake pedal, the corner opening degree of a steering wheel and the corner opening degree change rate of the steering wheel are obtained through a vehicle sensor; analyzing the opening degree of an accelerator pedal, the opening degree change rate of the accelerator pedal, the opening degree of a brake pedal, the opening degree change rate of the brake pedal, the corner opening degree of a steering wheel and the corner opening change rate of the steering wheel by a preset adaptive particle swarm algorithm to determine the driving intention of a driver; determining a current vehicle driving state based on the peripheral perception information; and determining a corresponding target driving assistance mode according to the driving intention of the driver and the current vehicle running state, and switching the current mode to the target driving assistance mode. By the mode, the intention of the driver is analyzed according to the preset self-adaptive particle swarm algorithm, and the driving assistance mode is determined according to the driving state of the vehicle, so that the driving assistance mode is switched more intelligently, the influence of the subjective consciousness of the driver on the switching of the assistance mode is avoided, the influence on the analysis result of the intention of the driver due to misoperation or invalid operation of the driver is effectively avoided, the condition of switching the driving assistance mode is influenced, the driving safety is improved, and the problem that the current driving assistance mode is not switched timely 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 first embodiment described above, the step S20 of the driving assistance mode switching method of the present embodiment includes:

step S201: 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, and determining an 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 to a preset driver state model; and optimizing the preset driver state model through a preset self-adaptive particle swarm algorithm, and outputting an initial driving intention.

It is understood that the preset driver state model is determined by equation (2):

wherein x is₁∈(α，dα/dt)，x₂∈(θ，dθ/dt)，x₃∈(δ，dδ/dt)，f(x₁，x₂，x₃)∈[1,3]The numerical control system is characterized in that 1 represents emergency driving, 2 represents normal driving, 3 represents slow driving, alpha is accelerator pedal opening, d alpha/dt is accelerator pedal opening change rate, theta is brake pedal opening, d theta/dt is brake pedal opening change rate, delta is steering wheel angle opening, and d delta/dt is steering wheel angle opening change rate.

It should be noted that, optimization is performed through a preset adaptive particle swarm algorithm according to a preset driver state model to obtain an optimal value, and an initial driving intention close to the actual optimal value, namely, emergency driving, normal driving or mild driving is output according to an input accelerator pedal, a brake pedal, a steering wheel steering opening and a corresponding change rate.

Specifically, the optimizing the preset driver state model through a preset adaptive particle swarm algorithm to output 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 an optimized particle speed; and determining an optimal solution through the preset driver state model according to the optimized particle position and the optimized particle speed to obtain an initial driving intention.

It is understood that the preset adaptive particle swarm algorithm is characterized by formula (3) and 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 is 1, 2, 3, i.e. the position of the ith particle in the training set is X_iThe optimal position that the ith particle experiences is Pbest_i＝(p_i1，p_i2，…,p_iL) And i is 1, 2 and 3, namely the current individual optimal position, each position of the particle represents a potential solution of the requirement, and the position of the particle is input into an objective function to obtain the fitness value of the ith particle, so as to judge the quality degree of the particle. The optimal position searched by the whole particle swarm is Gbest_g＝p_ig) I is 1, 2, 3, i.e. the current global optimal position, g denotes the index of the optimal particle position. ω represents the weight of the inertia,for the historical optimal solution searched for the ith particle to the tth generation,for the global optimum position searched so far for the whole population,respectively representing the current position and flight speed of the ith particle, c₁，c₂Denotes a non-negative constant, r₁，r₂Is [0, 1 ]]A random number in between. In this embodiment, the iterative evolution frequency of the algorithm is set to 1000 times, and an acceleration factor c is preset₁＝1.4，c₂The preset inertial weight ω is 0.8, 1.5.

It should be noted that the preset error requirement is the minimum error requirement, and is set by the developer in advance according to the actual situation, and for each particle, the fitness value of each particle is associated with the experienced current individual optimal position Pbest_iAnd if the fitness value is better, the position of the particle is used as the new current individual optimal position. For each particle, its fitness value is compared to the global best experienced position Gbest_gAnd if the fitness value 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 output by the representation is inconsistent with the actual driving intention, the speed and the position of the particle are optimized according to the formula (3) and the formula (4), and the new particle is compared with the current individual optimal position and the global optimal position until the global optimal position meets the minimum error requirement.

Step S202: and acquiring periodic sensing information of an accelerator pedal, periodic sensing information of a brake pedal and periodic sensing information of a steering wheel through a vehicle sensor.

The periodic sensing information is sensing information of an accelerator pedal, a brake pedal and a steering wheel, which is acquired by a vehicle sensor in a preset acquisition period, for example, the control times, the maximum opening degree, the variation amount, the variation rate, the 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 acquires the accelerator pedal control frequency, the brake pedal control frequency and the steering wheel control frequency within the first 10 seconds of the current time through the vehicle sensor.

Step S203: and determining the periodic driving intention according to the periodic sensing information of the accelerator pedal, the periodic sensing information of the brake pedal and the periodic sensing information of the steering wheel.

It is understood that the periodic driving intentions include urgent driving, moderate driving and normal driving, and in a specific implementation, the periodic driving intentions may be determined by searching a preset table according to the control times, and may also be determined by comparing the control times 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 value according to the preset acquisition period; comparing the control times of the accelerator pedal, the brake pedal and 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 periodic sensing information is the number of times of controlling the accelerator pedal, the brake pedal, and the steering wheel by the driver in a period of time, and the preset threshold corresponds to the preset acquisition period one to one, in a specific implementation, the preset acquisition period is set to be within the first 10 seconds of the current time, and the preset threshold is set to be 3 times, and the preset acquisition period may 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 within a mild 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 previous 20 seconds of the current moment, determining the corresponding preset threshold value to be 5 times.

Specifically, the determining the periodic driving intention according to the comparison result includes: when the control times of the accelerator pedal, the control times of the brake pedal and the control times of the steering wheel are all larger than or equal to the preset threshold value, determining that the periodic driving intention is emergent driving; when the control times of the accelerator pedal, the brake pedal and the steering wheel are all smaller than the preset threshold value, determining that the periodic driving intention is mild driving; and when the control times of the accelerator pedal and the control times of the brake pedal are greater than or equal to the preset threshold value and the control times of the steering wheel are less than the preset threshold value, determining that the periodic driving intention is normal driving.

It can be understood that, taking the preset acquisition period as the previous 10 seconds of the current time, the preset threshold is 3 times for explanation, and the periodic driving intention is determined by the formula (5):

wherein the periodic driving intention T epsilon [1,3]]1 for emergency driving, 2 for normal driving, 3 for mild driving, F_αFor the number of accelerator pedal controls, F_θFor the number of brake pedal controls, F_δThe number of times of steering wheel control is set; 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 of the driver are more than or equal to 3 times and the control times of the steering wheel is less than 3 times, the periodic driving intention is considered as normal driving; and if the control times of the accelerator pedal, the brake pedal and the steering wheel by the driver are more than or equal to 3 times, the periodic driving intention is considered to be urgent driving.

Step S204: determining a driver driving intention according to the initial driving intention and the periodic driving intention.

Specifically, the step S204 includes: when the periodic driving intention is urgent driving, determining that the driving intention of the driver is urgent driving; when the initial driving intention is urgent driving or normal driving and the periodic driving intention is normal driving or moderate driving, determining that the driving intention of the driver is normal driving; determining that the driver's driving intention is the moderate driving when the initial driving intention is the moderate driving and the periodic driving intention is the moderate driving.

It should be noted that, the driving intention intent of the driver is determined by formula (6) in combination with the optimal solution of the preset driver state model and the periodic driving intention:

in a specific implementation, the periodic driving intention T is taken as a main part, and when the periodic driving intention T is 1, the driver driving intention duration is determined as emergency driving; when the periodic driving intention T is more than or equal to 2 and the initial driving intention f (x)₁，x₂，x₃)<3, determining the driving intention intent of the driver as normal driving; when the periodic driving intention T is 3 and the initial driving intention f (x)₁，x₂，x₃) When the value is 3, the driver's driving intention duration is determined as the moderate driving.

In the embodiment, the opening degree of an accelerator pedal, the opening degree change rate of the accelerator pedal, the opening degree of a brake pedal, the opening degree change rate of the brake pedal, the corner opening degree of a steering wheel and the corner opening change rate of the steering wheel are analyzed through a preset adaptive particle swarm algorithm, and an initial driving intention is determined; acquiring periodic sensing information of an accelerator pedal, periodic sensing information of a brake pedal and periodic sensing information of a steering wheel through a vehicle sensor; determining a periodic driving intention according to the periodic sensing information of the accelerator pedal, the periodic sensing information of the brake pedal and the periodic sensing information of the steering wheel; determining a driver driving intention according to the initial driving intention and the periodic driving intention; and determining a corresponding target driving assistance mode according to the driving intention of the driver and the current vehicle running state, and switching the current mode to the target driving assistance mode. By the mode, the problem that the driving intention analysis result is influenced due to misoperation or invalid operation of the driver is effectively solved by comprehensively analyzing the current driving operation state and the operation frequency of the driver 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 assistance and the safety of the vehicle are improved.

Furthermore, an embodiment of the present invention also proposes a storage medium having a driving assistance mode switching program stored thereon, which when executed by a processor implements the driving assistance mode switching method as described above.

Since the storage medium adopts all technical solutions of all the embodiments, at least all the beneficial effects brought by the technical solutions of the embodiments are achieved, and no further description is given here.

Referring to fig. 4, fig. 4 is a block diagram showing the configuration of the driving assistance mode switching apparatus according to the first embodiment of the present invention.

As shown in fig. 4, a driving assistance mode switching apparatus according to an 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 through a vehicle sensor.

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, so as to determine a driving intention of a driver.

The determining module 20 is further configured to determine a current vehicle driving state based on the peripheral perception information.

And a switching module 30, configured to determine a corresponding target driving assistance mode according to the driving intention of the driver and the current vehicle driving state, and switch the current mode to the target driving assistance mode.

It should be understood that the above is only an example, and the technical solution of the present invention is not limited in any way, and in a specific application, a person skilled in the art may set the technical solution as needed, and the present invention is not limited thereto.

In the embodiment, the opening degree of an accelerator pedal, the opening degree change rate of the accelerator pedal, the opening degree of a brake pedal, the opening degree change rate of the brake pedal, the corner opening degree of a steering wheel and the corner opening degree change rate of the steering wheel are obtained through a vehicle sensor; analyzing the opening degree of an accelerator pedal, the opening degree change rate of the accelerator pedal, the opening degree of a brake pedal, the opening degree change rate of the brake pedal, the corner opening degree of a steering wheel and the corner opening change rate of the steering wheel by a preset adaptive particle swarm algorithm to determine the driving intention of a driver; determining a current vehicle driving state based on the peripheral perception information; and determining a corresponding target driving assistance mode according to the driving intention of the driver and the current vehicle running state, and switching the current mode to the target driving assistance mode. By the mode, the intention of the driver is analyzed according to the preset self-adaptive particle swarm algorithm, and the driving assistance mode is determined according to the driving state of the vehicle, so that the driving assistance mode is switched more intelligently, the influence of the subjective consciousness of the driver on the switching of the assistance mode is avoided, the influence on the analysis result of the intention of the driver due to misoperation or invalid operation of the driver is effectively avoided, the condition of switching the driving assistance mode is influenced, the driving safety is improved, and the problem that the current driving assistance mode is not switched timely is solved.

It should be noted that the above-described work flows are only exemplary, and do not limit the scope of the present invention, and in practical applications, a person skilled in the art may select some or all of them to achieve the purpose of the solution of the embodiment according to actual needs, and the present invention is not limited herein.

In addition, the technical details that are not elaborated in the embodiment may refer to the driving assistance mode switching method provided by any embodiment of the present invention, and are not described herein again.

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 change rate through a preset adaptive particle swarm algorithm, determine an initial driving intention, obtain accelerator pedal period sensing information, brake pedal period sensing information, and steering wheel period sensing information through a vehicle sensor, determine a period driving intention according to the accelerator pedal period sensing information, the brake pedal period sensing information, and the steering wheel period sensing information, and determine a driver driving intention according to the initial driving intention and the period 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 optimization, and output an initial driving intention.

In an embodiment, the determining module 20 is further configured to obtain a preset acceleration factor, a preset inertial 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, obtaining an optimized particle position and particle speed when the global optimal position meets a preset error requirement, and determining an optimal solution through the preset driver state model according to the optimized particle position and particle speed to obtain an initial driving intention.

In one embodiment, 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 determining module 20 is further configured to determine a preset threshold according to the preset collecting 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 to obtain a comparison result, and determine a 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 an emergency driving when the number of times of controlling the accelerator pedal, the number of times of controlling the brake pedal, and the number of times of controlling the steering wheel are all greater than or equal to the preset threshold, determine that the periodic driving intention is a moderate driving when the number of times of controlling the accelerator pedal, the number of times of controlling the brake pedal, and the number of times of controlling the steering wheel are all less than the preset threshold, and determine that the periodic driving intention is a normal driving when the number of times of controlling the accelerator pedal, the number of times of controlling the brake pedal, and the number of times of controlling the steering wheel are all less than the preset threshold.

In an embodiment, the determining module 20 is further configured to determine that the driver driving intention is the urgent driving when the periodic driving intention is the urgent driving, determine that the driver driving intention is the normal driving when the initial driving intention is the urgent driving or the normal driving and the periodic driving intention is the normal driving or the moderate driving, and determine that the driver driving intention is the moderate driving when the initial driving intention is the moderate driving and the periodic driving intention is the moderate driving.

Further, it is to 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 an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.

The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.

Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g. Read Only Memory (ROM)/RAM, magnetic disk, optical disk), and includes several instructions for enabling a terminal device (e.g. a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.

The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.