WO2023045791A1 - 车道保持方法、装置、设备、介质及系统 - Google Patents

车道保持方法、装置、设备、介质及系统 Download PDF

Info

Publication number
WO2023045791A1
WO2023045791A1 PCT/CN2022/118342 CN2022118342W WO2023045791A1 WO 2023045791 A1 WO2023045791 A1 WO 2023045791A1 CN 2022118342 W CN2022118342 W CN 2022118342W WO 2023045791 A1 WO2023045791 A1 WO 2023045791A1
Authority
WO
WIPO (PCT)
Prior art keywords
vehicle
target
driving style
neuron
steering wheel
Prior art date
Application number
PCT/CN2022/118342
Other languages
English (en)
French (fr)
Inventor
李伟男
刘斌
吴杭哲
陈博
Original Assignee
中国第一汽车股份有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 中国第一汽车股份有限公司 filed Critical 中国第一汽车股份有限公司
Publication of WO2023045791A1 publication Critical patent/WO2023045791A1/zh

Links

Images

Classifications

    • 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
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/10—Path keeping
    • B60W30/12—Lane keeping
    • 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
    • B60W10/00—Conjoint control of vehicle sub-units of different type or different function
    • B60W10/20—Conjoint control of vehicle sub-units of different type or different function including control of steering systems
    • 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
    • B60W40/09—Driving style or behaviour
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/12—Lateral speed
    • B60W2520/125—Lateral acceleration
    • 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
    • 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
    • B60W2710/00—Output or target parameters relating to a particular sub-units
    • B60W2710/20—Steering systems
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Definitions

  • the embodiments of the present application relate to the technical field of vehicles, for example, to a lane keeping method, device, device, medium and system.
  • the lane keeping control can be understood as keeping the vehicle in the middle of the lane.
  • Embodiments of the present application provide a lane keeping method, device, device, medium, and system, so as to realize lane keeping control based on a user's driving style and improve the driving experience of the user.
  • the embodiment of the present application provides a lane keeping method, the method comprising:
  • the vehicle driving data includes steering wheel angle, vehicle lateral position and vehicle lateral acceleration
  • a target steering wheel angle of the target vehicle is determined based on the driving style category and vehicle state data of the target vehicle, and the steering wheel angle of the target vehicle is adjusted based on the target steering wheel angle.
  • the embodiment of the present application also provides a lane keeping device, the device comprising:
  • the driving data acquisition module is configured to acquire vehicle driving data of the target vehicle, wherein the vehicle driving data includes steering wheel angle, vehicle lateral position and vehicle lateral acceleration;
  • the driving style identification module is configured to determine the driving style category corresponding to the target vehicle based on the vehicle driving data and the pre-trained driving style identification model;
  • a steering wheel adjustment module configured to determine a target steering wheel angle of the target vehicle based on the driving style category and vehicle state data of the target vehicle, and adjust the steering wheel of the target vehicle based on the target steering wheel angle corner.
  • the embodiment of the present application also provides a lane keeping system, the system includes an industrial computer and a steering wheel assembly, wherein,
  • the steering wheel assembly is configured to obtain the steering wheel angle of the target vehicle, and send the steering wheel angle to the industrial computer;
  • the industrial computer is configured to adjust the steering wheel angle of the target vehicle based on the lane keeping method provided in any embodiment of the present application.
  • the embodiment of the present application further provides an electronic device, and the electronic device includes:
  • storage means configured to store at least one program
  • the at least one processor When the at least one program is executed by the at least one processor, the at least one processor is made to implement the lane keeping method provided in any embodiment of the present application.
  • the embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the lane keeping method provided in any embodiment of the present application is implemented.
  • FIG. 1A is a schematic flowchart of a lane keeping method provided in Embodiment 1 of the present application;
  • FIG. 1B is a target trajectory of a vehicle provided in Embodiment 1 of the present application.
  • FIG. 2 is a schematic flowchart of a lane keeping method provided in Embodiment 2 of the present application;
  • FIG. 3 is a schematic flowchart of a lane keeping method provided in Embodiment 3 of the present application.
  • FIG. 4A is a schematic structural diagram of a lane keeping system provided in Embodiment 4 of the present application.
  • FIG. 4B is a schematic diagram of components of a lane keeping system provided in Embodiment 4 of the present application.
  • FIG. 4C is a schematic diagram of a seat and an electric cylinder in a lane keeping system provided in Embodiment 4 of the present application;
  • FIG. 4D is a schematic diagram of the internal connections of the lane keeping system provided in Embodiment 4 of the present application.
  • Fig. 5 is a schematic structural diagram of a lane keeping device provided in Embodiment 5 of the present application.
  • FIG. 6 is a schematic structural diagram of an electronic device provided in Embodiment 6 of the present application.
  • Fig. 1A is a schematic flowchart of a lane keeping method provided in Embodiment 1 of the present application. This embodiment is applicable to the situation where the vehicle is automatically controlled to drive in the center of the lane according to the driving style of the user driving the vehicle when the vehicle is driving.
  • the method can be performed by a lane keeping device, and the device can be realized by hardware and/or software, and the method includes the following steps:
  • the target vehicle may be a driving vehicle.
  • the vehicle driving data of the target vehicle may be acquired.
  • the vehicle driving data of the target vehicle is acquired to further control the target vehicle to keep driving in the middle of the lane.
  • the vehicle driving data includes the steering wheel angle, the lateral position of the vehicle and the lateral acceleration of the vehicle.
  • the steering wheel angle can be the angle value of the steering wheel of the target vehicle;
  • the lateral position of the vehicle can be the lateral position of the target vehicle relative to the centerline of the lane;
  • the lateral acceleration of the vehicle can be the acceleration of the target vehicle in a direction perpendicular to the centerline of the lane .
  • the steering wheel angle may be obtained by the steering wheel sensor
  • the lateral position of the vehicle may be obtained by the lateral position sensor
  • the lateral acceleration of the vehicle may be obtained by the acceleration sensor.
  • the pre-trained driving style identification model may be a model for identifying the driving style category of the vehicle.
  • An input vector can be formed based on the steering wheel angle of the vehicle driving data, the lateral position of the vehicle, and the lateral acceleration of the vehicle, and input to the driving style identification model to obtain the driving style type output by the driving style identification model.
  • the driving style categories include conservative, normal, and aggressive.
  • the category of driving style can be further divided, such as extremely conservative, generally conservative, normal, generally aggressive, and extremely aggressive.
  • the driving style identification model may be a learning vector quantization neural network model, a convolutional neural network model, a decision tree model, a support vector machine model, or a naive Bayesian model.
  • the training process of the driving style recognition model can be: construct a sample set, the sample set includes the sample driving data and the sample category labels corresponding to the sample driving data, input the sample set into the convolutional neural network, The loss function is calculated based on the predicted category label output by the convolutional neural network and the sample category label, and the parameters of the convolutional neural network are reversely adjusted according to the calculation result of the loss function until the convergence condition is met.
  • S130 Determine a target steering wheel angle of the target vehicle based on the driving style category and vehicle state data of the target vehicle, and adjust the steering wheel angle of the target vehicle based on the target steering wheel angle.
  • the vehicle state data may be information representing the current vehicle driving state of the target vehicle.
  • the vehicle state data may be information such as current vehicle speed, lateral coordinates of the current position, front and rear axle distances of the vehicle, and transmission ratio of the vehicle steering system.
  • the driving style category may be taken into consideration, and the target steering wheel angle corresponding to the driving style category may be determined in combination with the vehicle state data, so as to meet the driving requirements of the driving style category.
  • the corresponding target steering wheel angle may be greater than the target steering wheel angle corresponding to the conservative driving style category.
  • the target steering wheel angle of the target vehicle can be determined according to the driving style category, vehicle state data of the target vehicle, and a pre-established mapping table.
  • the mapping table includes various driving style categories and corresponding steering wheel angles under various vehicle state data.
  • a feature vector can also be formed based on the driving style category and vehicle driving state data of the target vehicle, the feature vector can be input into a pre-trained steering angle determination model, and the target steering wheel angle of the target vehicle can be determined based on the output of the steering angle determination model.
  • the vehicle state data includes the current vehicle speed, the centerline of the lane, the lateral coordinates of the current position, the transmission ratio of the vehicle steering system, and the wheelbase of the front and rear axles of the vehicle, based on the driving style category and the target vehicle.
  • State data determine the target steering wheel angle of the target vehicle, satisfy the following formula:
  • ⁇ opt is the target steering wheel angle
  • L is the front and rear axle distance of the vehicle
  • i is the transmission ratio of the steering system of the vehicle
  • C y represents the driving style category
  • v is the current vehicle speed
  • y(t) is the lateral coordinate of the current position
  • T is the preview time
  • f(t) is the centerline of the lane at time t
  • f(t+T) indicates the lateral coordinates of the centerline of the lane at time t+T
  • d is the preview distance
  • the lateral direction is the transverse direction, which represents the Y-axis direction of the coordinate axis in FIG. 1B .
  • C y 1
  • C y 3.
  • the derivation process of the above formula can be exemplarily described in conjunction with a target trajectory of a vehicle shown in FIG. 1B .
  • f(t) in the figure represents the centerline of the lane, that is, the target trajectory of the vehicle
  • y(t) represents the lateral coordinates of the current position of the vehicle
  • T represents the preview time.
  • the relationship between the preview time T and the preview distance d is:
  • the lateral velocity of the vehicle can be obtained by differentiating the lateral coordinate y(t) of the current position of the vehicle.
  • Lateral acceleration based on the vehicle lateral velocity and vehicle lateral acceleration obtained by differentiation, can predict the lateral coordinate y(t+T) of the vehicle position at time t+T:
  • the lateral coordinate y(t+T) of the vehicle at time t+T should be the same as that at time t+T
  • the lateral coordinate f(t+T) of the target trajectory (that is, the centerline of the lane) remains consistent, namely:
  • the calculation enables the vehicle to achieve the optimal target trajectory tracking effect, that is, the optimal lateral acceleration that can bring the vehicle position back to positive for:
  • R is the turning radius of the vehicle
  • v is the current speed of the vehicle
  • ⁇ is the current disc rotation angle of the vehicle
  • L is the front and rear axle wheelbase of the vehicle
  • i is the transmission ratio of the steering system of the vehicle
  • Y X represents the personalized corner coefficient.
  • the calculation formula of Y X can be as follows:
  • the minimum error principle between the predicted lateral coordinates of the vehicle position at time t+T and the target trajectory at time t+T can be used to calculate, under the current user's driving style category,
  • the steering wheel angle required for the vehicle to return to the centerline of the lane realizes the accurate determination of the optimal steering wheel angle considering the user's driving style, meets the differentiated needs of drivers with different driving habits, and greatly improves the user's driving experience.
  • the steering wheel angle of the target vehicle may be adjusted to the target steering wheel angle, so as to control the target vehicle to return to the lane centerline position.
  • the vehicle driving data of the target vehicle is obtained, and the vehicle driving data including the steering wheel angle, the lateral position of the vehicle, and the lateral acceleration of the vehicle are input into the pre-trained driving style recognition model, and the output of the driving style recognition model is obtained.
  • the driving style category corresponding to the target vehicle determines the user's driving style, and then determines the target steering wheel angle of the target vehicle based on the driving style category and the vehicle state data of the target vehicle, and adjusts the steering wheel angle of the target vehicle based on the target steering wheel angle , to control the vehicle to return to the lane centerline position, realize the lane keeping control based on the user's driving style, meet the differentiated needs of drivers with different driving habits, greatly improve the user's driving experience, and improve the vehicle's System suitability and driving safety.
  • Fig. 2 is a schematic flow chart of the lane keeping method provided in Embodiment 2 of the present application.
  • the lane keeping method further includes: acquiring a sample input vector and the sample input The driving style label corresponding to the vector, input the sample input vector to the input layer of the learning vector quantization neural network; calculate the distance between the sample input vector and the multiple neurons in the competition layer of the learning vector quantization neural network, in Determining the first target neuron and the second target neuron closest to the sample input vector among the plurality of neurons in the competition layer; based on the distance between the first target neuron and the sample input vector, The distance between the second target neuron and the sample input vector, the predicted value of driving style corresponding to the first target neuron, the predicted value of driving style corresponding to the second target neuron, and the corresponding value of the sample input vector driving style label, update the weights corresponding to the first target neuron and/or the second target neuron in the learning vector quantization neural network
  • the lane keeping method provided in this embodiment includes the following steps:
  • the sample input vector may be a sample vector composed of pre-collected vehicle driving data (including steering wheel angle, vehicle lateral position, and vehicle lateral acceleration).
  • the driving style label may be a predetermined driving style category corresponding to the sample input vector, such as conservative, normal, and aggressive.
  • the sample input vector and the driving style label corresponding to the sample input vector may constitute a sample data.
  • the learning vector quantization neural network includes an input layer, a competition layer and a linear output layer.
  • the number of neurons in the output layer is the same as the number of output driving style categories, and the number of neurons in the competition layer may be greater than the number of neurons in the output layer.
  • Each neuron in the input layer is connected to multiple neurons in the competition layer; neurons in the competition layer are only connected to the corresponding neurons in the output layer.
  • the weight between each neuron in the input layer and multiple neurons in the competition layer in the learning vector quantization neural network is 1 by default. Therefore, the process of training the learning vector quantization neural network in this embodiment is mainly a process of reversely adjusting the weights between the input layer neurons and the competition layer neurons.
  • the sample input vector can be input to the input layer of the learning vector quantization neural network to calculate the distance between the sample input vector and each neuron in the competition layer.
  • the data in the sample input vector may be respectively input to multiple neurons of the input layer. For example, if the sample input vector is [b1 b2 b3], then b1 can be sent to the first neuron of the input layer, b2 can be sent to the second neuron of the input layer, and b3 can be sent to the input layer's third neuron.
  • S220 Calculate the distance between the sample input vector and multiple neurons in the competition layer of the learning vector quantization neural network, and determine the first target neuron and the second target that are closest to the sample input vector among the multiple neurons of the competition layer Neurons.
  • the distance between it and multiple neurons in the competition layer is calculated.
  • the distance between the sample input vector and each neuron in the competition layer of the learning vector quantization neural network can be calculated according to the following formula:
  • d i represents the distance between the sample input vector and the i-th neuron in the competition layer
  • S represents the number of neurons in the competition layer
  • x represents the sample input vector
  • x (x 1 , x 2 , .. ., x R ) T
  • R represents the number of data in the sample input vector
  • ⁇ ij represents the weight between the j-th input layer neuron and the i-th competition layer neuron.
  • the distances between the sample input vector and multiple neurons in the competition layer can be calculated sequentially, and then based on the calculated multiple distances, the multiple distances can be sorted from small to large, and the sorted The competitive layer neurons corresponding to the first two distances in the list are used as the first target neuron and the second target neuron. That is, the competitive layer neuron with the closest distance to the sample input vector is determined as the first target neuron, and the competitive layer neuron with the second closest distance to the sample input vector is determined as the second target neuron.
  • the driving style prediction value corresponding to the first target neuron may be determined based on the driving style category corresponding to the output layer neuron connected to the first target neuron.
  • the first target neuron is connected to the output layer neuron output_c
  • the driving style category corresponding to the output layer neuron output_c is a conservative class
  • the driving style prediction value corresponding to the second target neuron may also be the driving style category corresponding to the output layer neuron connected to the second target neuron.
  • the driving style prediction value corresponding to the first target neuron is equal to the driving style label, update the weight corresponding to the first target neuron; if the driving style prediction value corresponding to the second target neuron is The value is equal to the driving style label, and the weight corresponding to the second target neuron is updated; if the driving style prediction value corresponding to the first target neuron and the driving style prediction value corresponding to the second target neuron are both equal to the driving style label, then The weights corresponding to the first target neuron and the second target neuron are updated.
  • the weight corresponding to the first target neuron is the weight between the first target neuron and the input layer neuron; the weight corresponding to the second target neuron is the second target neuron The weight between the neuron and the input layer neuron.
  • the driving style prediction value corresponding to the first target neuron, the second target neuron update the weight corresponding to the first target neuron and/or the second target neuron in the learning vector quantization neural network, including:
  • the distance between the first target neuron and the sample input vector, the distance between the second target neuron and the sample input vector, the driving style prediction value corresponding to the first target neuron, and the driving style prediction value corresponding to the second target neuron determine Whether the preset weight update condition is satisfied; in response to satisfying the preset weight update condition, based on the preset learning rate, sample input vector and driving style label, update the first target neuron and the second target neuron in the learning vector quantization neural network The weight corresponding to the element.
  • the preset weight update condition can be that the distance between the first target neuron and the sample input vector, the distance between the second target neuron and the sample input vector meet the preset window width condition, and the driving value corresponding to the first target neuron
  • the predicted value of style and the predicted value of driving style corresponding to the second target neuron are different (that is, the driving style category represented by the output layer neuron connected to the first target neuron is represented by the output layer neuron connected to the second target neuron. different driving style categories).
  • the preset window width condition can be expressed by the following formula:
  • d a is the distance between the first target neuron and the sample input vector
  • d b is the distance between the second target neuron and the sample input vector
  • ⁇ is the window width.
  • ⁇ 0.5.
  • the weights corresponding to the first target neuron and the second target neuron in the learning vector quantization neural network are updated based on the preset learning rate, the sample input vector and the driving style label , which can be:
  • the weight corresponding to the first target neuron can be increased, and the weight corresponding to the second target neuron can be decreased Corresponding weights; for example, use the following formula to correct the weights corresponding to the first target neuron and the weights corresponding to the second target neuron, so as to increase the weight corresponding to the first target neuron and reduce the weight corresponding to the second target neuron
  • the weight corresponding to the element :
  • ⁇ a-new ⁇ a-old + ⁇ (x- ⁇ a-old )
  • ⁇ b-new ⁇ b-old - ⁇ (x- ⁇ b-old )
  • the weight corresponding to the second target neuron can be increased, and the weight corresponding to the first target neuron can be decreased Corresponding weights; for example, use the following formula to correct the weights corresponding to the first target neuron and the weights corresponding to the second target neuron, so as to increase the weight corresponding to the second target neuron and reduce the weight corresponding to the first target neuron
  • the weight corresponding to the element :
  • ⁇ a-new ⁇ a-old - ⁇ (x- ⁇ a-old )
  • ⁇ b-new ⁇ b-old + ⁇ (x- ⁇ b-old )
  • ⁇ a-new is the weight corresponding to the first target neuron after correction
  • ⁇ a-old is the weight corresponding to the first target neuron before correction
  • ⁇ is the preset learning rate
  • x is the sample input vector
  • ⁇ b-new is the weight corresponding to the second target neuron after correction
  • ⁇ b-old is the weight corresponding to the second target neuron before correction.
  • the preset weight update condition is not satisfied, only the weight corresponding to the first target neuron closest to the sample input vector may be updated.
  • the preset weight update condition it may be judged whether the predicted value of the driving style corresponding to the first target neuron closest to the sample input vector is consistent with the preset driving style label, and in the first target.
  • the weight corresponding to the first target neuron can be increased.
  • the weight corresponding to the first target neuron may be reduced.
  • the weight corresponding to the first target neuron may be updated based on the following formula to increase the weight corresponding to the first target neuron:
  • ⁇ a-new ⁇ a-old + ⁇ (x- ⁇ a-old )
  • the weight corresponding to the first target neuron is updated based on the following formula to reduce the weight corresponding to the first target neuron:
  • ⁇ a-new ⁇ a-old - ⁇ (x- ⁇ a-old )
  • the first target in the competition layer by judging whether the preset weight update condition is satisfied, when the preset weight update condition is met, according to the preset learning rate, sample input vector and driving style label, the first target in the competition layer.
  • the weight corresponding to the neuron and the weight corresponding to the second target neuron are adjusted to improve the accuracy of the training process of the learning vector quantization neural network, thereby improving the accuracy of the driving style identification model and improving the predicted driving style.
  • the accuracy of the category ensures the lane keeping control of the vehicle according to the user's driving style.
  • this process can also be repeated to input other samples into the vector and other samples
  • the driving style label corresponding to the input vector is continuously input into the learning vector quantization neural network, so as to continue to adjust the weights of the neurons in the competitive layer in the network until the training cut-off condition is met.
  • the training cutoff condition may be that all neurons in the competition layer are activated.
  • the neuron in the competition layer with the closest distance to the sample input vector will be activated, and the state of the neuron in the competition layer changes to "1", while the competition layer
  • the state of other unactivated neurons in the layer is still "0”
  • the state of the neuron in the linear output layer connected to the activated competitive layer neuron is also "1”
  • the states of other linear output layer neurons are all is "0”
  • the linear output layer neuron y connected to the activated competitive layer neuron (each competitive layer neuron is only connected to one linear output layer neuron) outputs C y .
  • Repeating the process of inputting multiple sample input vectors into the learning vector quantization neural network can continuously activate the neurons in the competition layer until the training is completed, all the neurons in the competition layer are activated, and the weights of the neurons in the competition layer are uniform. has been updated.
  • the learning vector quantization neural network by inputting a plurality of sample input vectors into the learning vector quantization neural network to update the weights corresponding to the neurons of the competing layers in the learning vector quantization neural network, the number of competing layers in the learning vector quantization neural network can be After the weight update of each neuron is completed, the learning vector quantization neural network is determined as the driving style identification model.
  • the vehicle driving data includes the steering wheel angle, the lateral position of the vehicle and the lateral acceleration of the vehicle.
  • S260 Determine the target steering wheel angle of the target vehicle based on the driving style category and the vehicle state data of the target vehicle, and adjust the steering wheel angle of the target vehicle based on the target steering wheel angle.
  • an optional training method for learning vector quantization neural network is also provided, the method can improve the training accuracy of learning vector quantization neural network, and then improve the prediction accuracy of learning vector quantization neural network, the method includes follows the steps below:
  • Step 1 Initialize the weight ⁇ ij between the input layer neuron j and the competition layer neuron i in the learning vector quantization neural network, and assign a value of 1 to both.
  • Step 3 Select two competitive layer neurons with the smallest distance from the sample input vector, for example, including neuron a and neuron b.
  • Step 4 Determine whether neuron a and neuron b satisfy two conditions at the same time (that is, preset weight update conditions).
  • Condition 1 neuron a and neuron b represent different driving style categories, that is, neuron a corresponds to The predicted value is not equal to the predicted value corresponding to neuron b
  • condition 2 the distance d a between neuron a and the sample input vector, and the distance d b between neuron b and the sample input vector satisfy:
  • step 5 If the two conditions are met at the same time, go to step 5; if the two conditions cannot be met at the same time, go to step 6.
  • Step 5 If the driving style category C a corresponding to neuron a is consistent with the driving style label C x corresponding to the sample input vector, the weights of neuron a and neuron b are corrected according to the following formula:
  • ⁇ a-new ⁇ a-old + ⁇ (x- ⁇ a-old )
  • ⁇ b-new ⁇ b-old - ⁇ (x- ⁇ b-old )
  • the weights of neuron a and neuron b are corrected according to the following formula:
  • Step 6 If the driving style category C a corresponding to neuron a is consistent with the driving style label C x corresponding to the sample input vector, adjust the weight of neuron a based on the following formula:
  • ⁇ a-new ⁇ a-old + ⁇ (x- ⁇ a-old )
  • ⁇ a-new ⁇ a-old - ⁇ (x- ⁇ a-old )
  • the first target neuron and the second target neuron closest to the sample input vector in the competition layer are determined.
  • target neuron and according to the distance between the first target neuron and the sample input vector, the distance between the second target neuron and the sample input vector, the driving style prediction value corresponding to the first target neuron, and the driving style corresponding to the second target neuron
  • the style prediction value and the driving style label corresponding to the sample input vector update the weights corresponding to the first target neuron and/or the second target neuron in the learning vector quantization neural network to obtain a driving style identification model and realize the driving style identification
  • Accurate training of the model improves the output accuracy of the driving style identification model and improves the accuracy of the predicted driving style category of the target vehicle, thereby improving the user's driving experience and vehicle safety.
  • Fig. 3 is a schematic flowchart of a lane keeping method provided in Embodiment 3 of the present application.
  • the target vehicle is adjusted based on the target steering wheel angle.
  • the method further includes: obtaining the vehicle lateral position and the lane centerline lateral position corresponding to the plurality of collection positions; based on the plurality of vehicle lateral positions, the plurality of lane centerline lateral positions and the driving style category, It is determined whether the target vehicle returns to the lane centerline.
  • the lane keeping method provided in this embodiment includes the following steps:
  • S310 Acquire vehicle driving data of the target vehicle, and determine a driving style category corresponding to the target vehicle based on the vehicle driving data and a pre-trained driving style identification model.
  • the vehicle driving data includes the steering wheel angle, the lateral position of the vehicle and the lateral acceleration of the vehicle.
  • S320 Determine a target steering wheel angle of the target vehicle based on the driving style category and vehicle state data of the target vehicle, and adjust the steering wheel angle of the target vehicle based on the target steering wheel angle.
  • the position of the vehicle may be judged to analyze whether the vehicle returns to the centerline of the road.
  • the collection position may be a collection position determined at a preset time interval, for example, the preset time interval may be 0.5s. As shown in Table 1, a vehicle lateral position and a lane centerline lateral position corresponding to multiple collection positions are shown.
  • Table 1 The lateral position of the vehicle and the lateral position of the centerline of the lane corresponding to the collection position
  • S340 Determine whether the target vehicle returns to the lane centerline based on the multiple vehicle lateral positions, the multiple lane centerline lateral positions, and the driving style category.
  • the position gap between each vehicle lateral position and its corresponding lane center line lateral position can be determined according to multiple vehicle lateral positions and multiple lane center line lateral positions, based on the variation trend of the position gap and The driving style category, which determines whether the target vehicle returns to the centerline of the lane.
  • determining whether the target vehicle returns to the centerline of the lane may be: if the change trend of the position gap is that the gap gradually decreases, and the speed of the gap reduction is greater than the driving style category. If the corresponding preset moving speed threshold is determined, then it is determined that the target vehicle returns to the centerline of the lane.
  • the driving style category is conservative, considering that conservative drivers usually return to the centerline of the lane at a slower speed, the preset speed threshold corresponding to the conservative category can be set smaller; the driving style category is aggressive, considering Aggressive drivers usually return to the centerline of the lane faster, and the preset speed threshold corresponding to the aggressive category can be set larger.
  • the driving style category is the conservative category
  • the change trend of the position gap is that the gap gradually decreases, and the decreasing speed is 0.6m/s, and the decreasing speed is greater than the preset speed threshold corresponding to the conservative category (such as 0.5m/s m/s), it is determined that the target vehicle returns to the centerline of the lane.
  • the target vehicle based on multiple vehicle lateral positions, multiple lane centerline lateral positions, and driving style categories, it is determined whether the target vehicle returns to the lane centerline, or it may also be: according to the corresponding A polynomial function is constructed for the lateral position of the vehicle, and the polynomial coefficient of the polynomial function is calculated based on the lateral position of the lane centerline corresponding to multiple collection positions; based on the polynomial coefficient and the driving style category, it is determined whether the target vehicle returns to the lane centerline.
  • polynomial functions corresponding to multiple vehicle lateral positions are constructed.
  • the expression of the polynomial function constructed according to a plurality of lateral vehicle positions is as follows:
  • P n (u) is the polynomial function corresponding to the vehicle lateral position u
  • a 0 , a 1 ,..., a n (n ⁇ m) are the polynomial coefficients of the polynomial function
  • u is the vehicle lateral position .
  • the calculation of the polynomial coefficients of the polynomial function based on the lateral position of the lane centerline corresponding to the plurality of collection positions may be: the lane centerline corresponding to the vehicle lateral position according to the calculation result of the polynomial function of the vehicle lateral position For the error value between the lateral positions, the polynomial coefficients in the polynomial function are adjusted until the error value between the calculation result of the polynomial function and the lateral position of the centerline of the lane corresponding to the lateral position of the vehicle is the smallest.
  • calculating the polynomial coefficients of the polynomial function based on the lateral position of the lane centerline corresponding to the plurality of collection positions can also be: based on the transverse position of the lane center line corresponding to the collection position, the polynomial function is fitted by the least squares method, based on the fitted The result determines the polynomial coefficients of the polynomial function.
  • to calculate the polynomial coefficients of the polynomial function is to solve a 0 , a 1 , . . . , a n (n ⁇ m), so that:
  • the above formula is a system of equations satisfied by the coefficients a 0 , a 1 , ..., a n of P n (u).
  • the system of equations has The unique solution a 0 , a 1 ,..., a n , such that Take the minimum value.
  • the fitting function P n (u) between the lateral position u k of the vehicle and the lateral position w k of the centerline of the lane can be obtained.
  • a 2 +a 3 +...+a n ⁇ 0.001 ⁇ C y Make sure the vehicle returns fully to the centerline of the lane.
  • determining whether the target vehicle returns to the lane centerline may be: if the weighted sum of the polynomial coefficients is less than the weighted value corresponding to the driving style category, then determine that the target vehicle returns to the lane centerline.
  • the polynomial function is constructed by the vehicle lateral positions corresponding to the multiple collection positions, and the multiple polynomial coefficients of the polynomial function are calculated according to the lane centerline lateral positions corresponding to the multiple vehicle lateral positions, and then based on the multiple Polynomial coefficients and driving style categories to judge whether the target vehicle returns to the center line of the lane, and realize the accurate judgment of whether the vehicle combined with the driving style returns to the center of the lane. For each driving style driver, it can be judged individually whether to return to the lane center, which improves the safety of the vehicle.
  • multiple vehicle lateral positions and the lane centerline lateral positions corresponding to the multiple vehicle lateral positions are collected, based on the multiple vehicle lateral positions, the multiple vehicle lateral positions corresponding to The horizontal position of the lane centerline and the driving style category can determine whether the target vehicle returns to the lane centerline, and realizes the accurate judgment of whether the vehicle combined with driving style returns to the lane center, and can accurately judge whether the vehicle returns to the lane center under different driving styles , while improving the driving experience of the user, it also improves the driving safety of the vehicle.
  • FIG. 4A is a schematic structural diagram of a lane keeping system provided in Embodiment 4 of the present application.
  • the system includes an industrial computer 410 and a steering wheel assembly 420, wherein the steering wheel assembly 420 is configured to obtain the steering wheel angle of the target vehicle, And send the steering wheel angle to the industrial computer 410; the industrial computer 410 is configured to adjust the steering wheel angle of the target vehicle based on the lane keeping method provided by any embodiment of the present application.
  • Advantech motherboard aimb781 can be used in the industrial computer 410, the graphics card of the industrial computer 410 can be GTX1070, the CPU of the industrial computer 410 can be i7, the industrial computer 410 is also connected with a display, and the industrial computer 410 is equipped with a driving style recognition system , the driving style recognition system is realized based on computer software, such as PanoSim and MATLAB/Simulink, PanoSim is a system proposed to solve many challenges faced by modern smart cars and smart car technology and product development, testing and verification.
  • a vehicle virtual simulation platform which can simulate the response of the vehicle to the driver, road surface and aerodynamic input.
  • Simulink is a visual simulation tool in MATLAB, which can realize the functions of dynamic system modeling, simulation and analysis. Simulink provides a dynamic An integrated environment for system modeling, simulation, and comprehensive analysis.
  • the PanoSim deployed in the industrial computer 410 can be used to collect vehicle driving data (including steering wheel angle, vehicle lateral position, and vehicle lateral acceleration) of the target vehicle, and the driving style recognition system can determine the driving style according to the vehicle driving data collected by PanoSim. Style category and target steering wheel angle, and input the target steering wheel angle into PanoSim, so that PanoSim controls the target vehicle position back to positive.
  • vehicle driving data including steering wheel angle, vehicle lateral position, and vehicle lateral acceleration
  • the driving style recognition system can determine the driving style according to the vehicle driving data collected by PanoSim. Style category and target steering wheel angle, and input the target steering wheel angle into PanoSim, so that PanoSim controls the target vehicle position back to positive.
  • PanoSim can also be used to collect the lateral position of the vehicle and the lateral position of the lane centerline corresponding to multiple collection positions.
  • the driving style recognition system can judge whether the target vehicle returns to the lane centerline based on the vehicle lateral position and the lateral position of the lane centerline collected by PanoSim.
  • this embodiment also provides a lane keeping system, the components included in the system are shown in Figures 4B-4C, Figure 4B shows a schematic diagram of components of a lane keeping system, and Figure 4C shows a lane keeping system Schematic diagram of the middle seat and electric cylinder.
  • the lane keeping system includes a seat 1, a steering wheel assembly 420, a pedal assembly 3, a bracket 4, a screen 5, an industrial computer 410, a connecting plate 7, a bottom plate 8, an electric cylinder 9, and an electric cylinder control System 10 , universal wheel 11 , audio 12 , accelerator pedal 13 , brake pedal 14 , clutch pedal 15 and display 16 .
  • the steering wheel assembly 420 is fixed on the bracket 4, the pedal assembly 3 is assembled under the bracket 4, the screen 5 is arranged in front of the steering wheel assembly 420 and the bracket 4, and the seat 1 is set corresponding to the steering wheel assembly 420,
  • the bottom of the seat 1 is provided with a connecting plate 7 and a bottom plate 8, an electric cylinder 9 is installed between the top surface of the connecting plate 7 and the bottom of the seat 1, and an electric cylinder 9 is installed between the rear side of the connecting plate 7 and the rear end plate of the bottom plate 8.
  • the electric cylinder 9, the aforementioned electric cylinder 9 are all connected with the electric cylinder control system 10 and controlled by the electric cylinder control system 10, the steering wheel assembly 420, the pedal assembly 3, the screen 5 and the electric cylinder control system 10 are connected with the industrial computer 410, the steering wheel assembly 420, the pedal assembly 3, the screen 5, the industrial computer 410 and the electric cylinder control system 10 are all powered by the same power supply, and a universal round 11.
  • the top surface of support 4 is provided with sound 12, and sound 12 is connected with industrial computer 410 by Universal Serial Bus (Universal Serial Bus, USB), and sound 12 is set to simulate the sound in real driving process.
  • the steering wheel assembly 420 and the industrial computer 410 are connected by a Controller Area Network (CAN) line.
  • the steering wheel assembly 420 adopts the SENSO-Wheel steering wheel assembly.
  • the steering wheel assembly 420 provides free The stiffness, damping and torque adjusted by programming can realize the steering driving experience.
  • the steering wheel assembly 420 is integrated with a steering wheel angle sensor, which collects the steering wheel angle signal, and the steering wheel angle signal is sent to the
  • the industrial computer 410 is used for running the vehicle dynamics model inside the industrial computer 410 .
  • the industrial computer 410 can adjust the steering wheel angle of the target vehicle according to the lane keeping method described in any one of the above embodiments. Alternatively, according to the lane keeping method described in any one of the above embodiments, the steering wheel angle of the target vehicle is adjusted, and it is judged whether the target vehicle has completely returned to the centerline of the lane.
  • the pedal assembly 3 and the industrial computer 410 are connected by a USB cable.
  • the pedal assembly 3 adopts the G29 series pedal assembly, and the accelerator pedal 13, the brake pedal 14 and the clutch pedal 15 are arranged in sequence from right to left, and the accelerator pedal 13 and Brake pedals 14 are integrated with their own pedal displacement sensors, and the corresponding pedal travel signals collected by the displacement sensors are sent to the industrial computer 410 for use by the vehicle dynamics model inside the industrial computer 410 .
  • FIG. 4D shows a schematic diagram of the internal connection of the lane keeping system.
  • Screen 5 is a circular screen.
  • the circular screen is projected by three NEC NP4100+ mainstream engineering projectors. Each projector forms a channel to generate a three-channel splicing display effect in a horizontal spanning manner.
  • the three-way Video Graphics Array (Video Graphics Array , VGA) computer signal to provide display content support, NEC NP4100+ mainstream engineering projectors in adjacent channels adopt hardware nonlinear geometric correction technology, and finally achieve a good projection effect on the circular screen.
  • VGA Video Graphics Array
  • the electric cylinder control system 10 includes a proportional-integral-differential (Proportion Integration Differentiation, PID) controller, a digital-analog (Digital/Analog, D/A) card and a servo amplifier, and the industrial computer 410 generates a speed control command and transmits it through a signal line
  • PID controller calculates based on its own PID algorithm, obtains the speed signal and transmits the signal to the D/A card, the D/A card converts the speed signal into a voltage signal and transmits it to the servo controller, and the servo controller Finally control the movement of electric cylinder 9.
  • the electric cylinder 9 assembled between the top surface of the connecting plate 7 and the bottom of the seat 1 is symmetrically arranged in two groups of four, and there are two electric cylinders 9 assembled between the rear side of the connecting plate 7 and the rear end plate of the base plate 8 .
  • the lane keeping system includes an industrial computer and a steering wheel assembly.
  • the industrial computer can determine the driving style category of the target vehicle, determine the target steering wheel angle, and adjust the steering wheel angle, realizing the driving style based on the user.
  • the lane keeping control meets the differentiated needs of drivers with different driving habits, greatly improves the user's driving experience, and improves the system applicability and driving safety of the vehicle.
  • Fig. 5 is a schematic structural diagram of a lane keeping device provided in Embodiment 5 of the present application. This embodiment is applicable to the situation where the vehicle is automatically controlled to drive in the center of the lane according to the driving style of the user driving the vehicle when the vehicle is driving.
  • the device includes: a driving data acquisition module 510 , a driving style identification module 520 and a steering wheel adjustment module 530 .
  • the driving data acquisition module 510 is configured to acquire vehicle driving data of the target vehicle, wherein the vehicle driving data includes steering wheel angle, vehicle lateral position and vehicle lateral acceleration;
  • the driving style identification module 520 is configured to determine the driving style category corresponding to the target vehicle based on the vehicle driving data and the pre-trained driving style identification model;
  • the steering wheel adjustment module 530 is configured to determine the target steering wheel angle of the target vehicle based on the driving style category and the vehicle state data of the target vehicle, and adjust the steering of the target vehicle based on the target steering wheel angle Pan corner.
  • the lane keeping device further includes a network training module, the network training module includes a sample input unit, a distance calculation unit, a weight adjustment unit and a model determination unit, wherein;
  • the sample input unit is configured to obtain a sample input vector and a driving style label corresponding to the sample input vector, and input the sample input vector to an input layer of a learning vector quantization neural network;
  • the distance calculation unit is configured to calculate the distance between the sample input vector and multiple neurons in the competition layer of the learning vector quantization neural network, and determine the distance between the sample input vector and the sample in the multiple neurons of the competition layer.
  • the weight adjustment unit is configured to be based on the distance between the first target neuron and the sample input vector, the distance between the second target neuron and the sample input vector, and the distance corresponding to the first target neuron.
  • the driving style prediction value, the driving style prediction value corresponding to the second target neuron, and the driving style label corresponding to the sample input vector update the first target neuron and/or all The weight corresponding to the second target neuron;
  • the model determination unit is configured to determine the updated learning vector quantization neural network as the driving style identification model.
  • the weight adjustment unit is set to:
  • the driving style prediction values corresponding to the two target neurons determine whether the preset weight updating condition is satisfied; in response to satisfying the preset weight updating condition, based on the preset learning rate, the sample input vector and the driving style label, update the The learning vector quantifies weights corresponding to the first target neuron and the second target neuron in the neural network.
  • the vehicle state data includes the current vehicle speed, the center line of the lane, the lateral coordinates of the current position, the transmission ratio of the vehicle steering system, and the wheelbase of the front and rear axles of the vehicle
  • the steering wheel adjustment module 530 includes a target rotation angle determination unit, the The target steering angle determination unit is configured to determine the target steering wheel angle of the target vehicle based on the driving style category and the vehicle state data of the target vehicle according to the following formula:
  • ⁇ opt is the target steering wheel angle
  • L is the front and rear axle distance of the vehicle
  • i is the transmission ratio of the steering system of the vehicle
  • C y represents the driving style category
  • v is the current vehicle speed
  • y(t) is the lateral coordinate of the current position
  • T is the preview time
  • f(t) is the centerline of the lane at time t
  • f(t+T) indicates the lateral coordinates of the centerline of the lane at time t+T
  • d is the preview distance
  • the lane keeping device further includes a regression judgment module, and the regression judgment module includes a position collection unit and a position judgment unit, wherein;
  • the position acquisition unit is configured to acquire vehicle lateral positions and lane centerline lateral positions corresponding to a plurality of acquisition positions after the steering wheel angle of the target vehicle is adjusted based on the target steering wheel angle;
  • the position judging unit is configured to determine whether the target vehicle returns to the lane centerline based on a plurality of vehicle lateral positions, a plurality of lane centerline lateral positions and the driving style category.
  • the position judging unit is set to:
  • the vehicle driving data of the target vehicle is acquired through the driving data acquisition module, and the vehicle driving data including the steering wheel angle, vehicle lateral position and vehicle lateral acceleration are input into the pre-trained driving style through the driving style identification module
  • the identification model the driving style category corresponding to the target vehicle output by the driving style identification model is obtained, and the user's driving style is determined, and then the steering wheel adjustment module is used to determine the target vehicle's target vehicle according to the driving style category and the vehicle state data of the target vehicle.
  • Steering wheel angle adjust the steering wheel angle of the target vehicle based on the target steering wheel angle to control the vehicle to return to the lane centerline position, realize the lane keeping control based on the user's driving style, and meet the differentiation of drivers with different driving habits It greatly improves the user's driving experience, and improves the system applicability and driving safety of the vehicle.
  • the lane keeping device provided in the embodiment of the present application can execute the lane keeping method provided in any embodiment of the present application, and has corresponding functional modules for executing the method.
  • FIG. 6 is a schematic structural diagram of an electronic device provided in Embodiment 6 of the present application.
  • FIG. 6 shows a block diagram of an exemplary electronic device 12 suitable for implementing embodiments of the present application.
  • the electronic device 12 shown in FIG. 6 is only an example, and should not impose any limitation on the functions and scope of use of the embodiment of the present application.
  • Device 12 is typically an electronic device that undertakes the lane keeping function.
  • electronic device 12 takes the form of a general-purpose computing device.
  • Components of the electronic device 12 may include, but are not limited to, at least one processor or processing unit 16, a memory 28, and a bus 18 connecting the various components including the memory 28 and the processing unit 16.
  • Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus structures.
  • these architectures include but are not limited to Industry Standard Architecture (Industry Standard Architecture, ISA) bus, Micro Channel Architecture (Micro Channel Architecture, MCA) bus, Enhanced ISA bus, Video Electronics Standards Association (Video Electronics Standards Association, VESA) local bus and peripheral component interconnect (Peripheral Component Interconnect, PCI) bus.
  • Electronic device 12 typically includes a variety of computer-readable media. These media can be any available media that can be accessed by electronic device 12 and include both volatile and nonvolatile media, removable and non-removable media.
  • Memory 28 may include computer device-readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32 .
  • the electronic device 12 may further include other removable/non-removable, volatile/nonvolatile computer storage media.
  • storage device 34 may be used to read from and write to non-removable, non-volatile magnetic media (not shown in FIG. 6, commonly referred to as a "hard drive").
  • a disk drive for reading and writing to a removable nonvolatile disk may be provided, as well as a removable nonvolatile disk (such as a Compact Disc- Read Only Memory, CD-ROM), Digital Video Disc (Digital Video Disc-Read Only Memory, DVD-ROM) or other optical media) CD-ROM drive.
  • each drive may be connected to bus 18 via at least one data medium interface.
  • Memory 28 may include at least one program product 40 having a set of program modules 42 configured to perform the functions of various embodiments of the present application.
  • Program product 40 which may be stored, for example, in memory 28.
  • Such program modules 42 include, but are not limited to, at least one application program, other program modules, and program data, each or some combination of which may include the implementation of a network environment .
  • the program modules 42 generally perform the functions and/or methods of the embodiments described herein.
  • the electronic device 12 can also communicate with at least one external device 14 (such as a keyboard, mouse, camera, etc., and a display), and can also communicate with at least one device that enables the user to interact with the electronic device 12, and/or communicate with the electronic device 12 to allow the user to interact with the electronic device 12. 12. Any device capable of communicating with at least one other computing device (eg, network card, modem, etc.). Such communication may occur through input/output (I/O) interface 22 . Moreover, the electronic device 12 can also communicate with at least one network (such as a local area network (Local Area Network, LAN), wide area network, Wide Area Network, WAN) and/or a public network, such as the Internet, through the network adapter 20.
  • LAN Local Area Network
  • WAN Wide Area Network
  • public network such as the Internet
  • network adapter 20 communicates with other modules of electronic device 12 via bus 18 .
  • other hardware and/or software modules may be used in conjunction with electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, disk arrays (Redundant Arrays) of Independent Disks, RAID) devices, tape drives, and data backup storage devices.
  • the processor 16 executes various functional applications and data processing by running the program stored in the memory 28, such as implementing the lane keeping method provided in the above-mentioned embodiments of the present application, including:
  • the vehicle driving data includes steering wheel angle, vehicle lateral position and vehicle lateral acceleration
  • a target steering wheel angle of the target vehicle is determined based on the driving style category and vehicle state data of the target vehicle, and the steering wheel angle of the target vehicle is adjusted based on the target steering wheel angle.
  • processor can also implement the technical solution of the lane keeping method provided in any embodiment of the present application.
  • Embodiment 7 of the present application also provides a computer-readable storage medium on which a computer program is stored.
  • the program is executed by a processor, the steps of the lane keeping method provided in any embodiment of the present application are implemented.
  • the method includes:
  • the vehicle driving data includes steering wheel angle, vehicle lateral position and vehicle lateral acceleration
  • a target steering wheel angle of the target vehicle is determined based on the driving style category and vehicle state data of the target vehicle, and the steering wheel angle of the target vehicle is adjusted based on the target steering wheel angle.
  • the computer storage medium in the embodiments of the present application may use any combination of at least one computer-readable medium.
  • the computer readable medium may be a computer readable signal medium or a computer readable storage medium.
  • a computer readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof.
  • a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
  • a computer readable signal medium may include a data signal carrying computer readable program code in baseband or as part of a carrier wave. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • a computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. .
  • Program code embodied on a computer readable medium may be transmitted by any appropriate medium, including but not limited to wireless, wire, optical cable, radio frequency (Radio Frequency, RF), etc., or any suitable combination of the above.
  • any appropriate medium including but not limited to wireless, wire, optical cable, radio frequency (Radio Frequency, RF), etc., or any suitable combination of the above.
  • Computer program codes for performing the operations of the embodiments of the present application may be written in one or more programming languages or a combination thereof, the programming languages including object-oriented programming languages-such as Java, Smalltalk, C++, including A conventional procedural programming language - such as "C" or a similar programming language.
  • the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
  • the remote computer can be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (such as through an Internet service provider). Internet connection).

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Mechanical Engineering (AREA)
  • Transportation (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Mathematical Physics (AREA)
  • General Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Molecular Biology (AREA)
  • Software Systems (AREA)
  • Computing Systems (AREA)
  • Chemical & Material Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • General Health & Medical Sciences (AREA)
  • Computational Linguistics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Biology (AREA)
  • Traffic Control Systems (AREA)
  • Steering Control In Accordance With Driving Conditions (AREA)

Abstract

一种车道保持方法、装置、设备、介质及系统，包括获取目标车辆的车辆行驶数据，将包括转向盘转角、车辆横向位置以及车辆横向加速度的车辆行驶数据输入至预先训练的驾驶风格辨识模型中，得到驾驶风格辨识模型输出的目标车辆对应的驾驶风格类别，确定出用户的驾驶风格，进而根据驾驶风格类别和目标车辆的车辆状态数据，确定目标车辆的目标转向盘转角，基于该目标转向盘转角调整目标车辆的转向盘转角，以控制车辆回归至车道中心线位置。

Description

车道保持方法、装置、设备、介质及系统
本申请要求在2021年9月23日提交中国专利局、申请号为202111114543.2的中国专利申请的优先权，该申请的全部内容通过引用结合在本申请中。
技术领域
本申请实施例涉及车辆技术领域，例如涉及一种车道保持方法、装置、设备、介质及系统。
背景技术
近年来，随着智能汽车的不断发展，越来越多的研究人员开始投身于车辆的车道保持控制。其中，车道的保持控制可以理解为让车辆保持在车道中间行驶。
然而，相关技术通常采用测量出的车辆相对于车道中间位置的实时横向偏移量，来控制车辆保持在车道中间行驶，其没有考虑到驾驶人的驾驶风格，无法满足不同驾驶风格的差异化驾驶需求。
发明内容
本申请实施例提供了一种车道保持方法、装置、设备、介质及系统，以实现基于用户的驾驶风格的车道保持控制，提高用户的驾驶体验。
第一方面，本申请实施例提供了一种车道保持方法，所述方法包括：
获取目标车辆的车辆行驶数据，其中，所述车辆行驶数据包括转向盘转角、车辆横向位置以及车辆横向加速度；
基于所述车辆行驶数据以及预先训练的驾驶风格辨识模型，确定所述目标车辆对应的驾驶风格类别；
基于所述驾驶风格类别以及所述目标车辆的车辆状态数据，确定所述目标车辆的目标转向盘转角，并基于所述目标转向盘转角调整所述目标车辆的转向 盘转角。
第二方面，本申请实施例还提供了一种车道保持装置，所述装置包括：
行驶数据获取模块，设置为获取目标车辆的车辆行驶数据，其中，所述车辆行驶数据包括转向盘转角、车辆横向位置以及车辆横向加速度；
驾驶风格辨识模块，设置为基于所述车辆行驶数据以及预先训练的驾驶风格辨识模型，确定所述目标车辆对应的驾驶风格类别；
转向盘调整模块，设置为基于所述驾驶风格类别以及所述目标车辆的车辆状态数据，确定所述目标车辆的目标转向盘转角，并基于所述目标转向盘转角调整所述目标车辆的转向盘转角。
第三方面，本申请实施例还提供了一种车道保持系统，所述系统包括工控机以及转向盘总成，其中，
所述转向盘总成，设置为获取目标车辆的转向盘转角，并将所述转向盘转角发送至所述工控机；
所述工控机，设置为基于如本申请任意实施例提供的车道保持方法，调整所述目标车辆的转向盘转角。
第四方面，本申请实施例还提供了一种电子设备，所述电子设备包括：
至少一个处理器；
存储装置，设置为存储至少一个程序，
当所述至少一个程序被所述至少一个处理器执行，使得所述至少一个处理器实现如本申请任意实施例提供的车道保持方法。
第五方面，本申请实施例还提供了一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时实现如本申请任意实施例提供的车道保持方法。
附图说明
图1A为本申请实施例一所提供的一种车道保持方法的流程示意图；
图1B为本申请实施例一所提供的一种车辆的目标轨迹；
图2为本申请实施例二所提供的一种车道保持方法的流程示意图；
图3为本申请实施例三所提供的一种车道保持方法的流程示意图；
图4A为本申请实施例四所提供的一种车道保持系统的结构示意图；
图4B为本申请实施例四所提供的一种车道保持系统的部件示意图；
图4C为本申请实施例四所提供的一种车道保持系统中座椅和电动缸的示意图；
图4D为本申请实施例四所提供的车道保持系统的内部连接示意图；
图5为本申请实施例五所提供的一种车道保持装置的结构示意图；
图6为本申请实施例六所提供的一种电子设备的结构示意图。
具体实施方式
下面结合附图和实施例对本申请作详细说明。
实施例一
图1A为本申请实施例一提供的一种车道保持方法的流程示意图，本实施例可适用于在车辆行驶时，根据驾驶车辆的用户的驾驶风格自动控制车辆在车道中心位置行驶的情况，该方法可以由车道保持装置来执行，该装置可以由硬件和/或软件来实现，该方法包括如下步骤：
S110、获取目标车辆的车辆行驶数据，其中，车辆行驶数据包括转向盘转角、车辆横向位置以及车辆横向加速度。
其中，目标车辆可以是正在行驶的车辆。可选的，本实施例可以在检测出目标车辆偏移车道中心线时，获取目标车辆的车辆行驶数据。示例性的，在检测出目标车辆的车辆中心位置与车道中心线的距离超过预设阈值时，获取目标车辆的车辆行驶数据，以进一步控制目标车辆保持在车道中间行驶。
在本实施例中，车辆行驶数据包括转向盘转角、车辆横向位置以及车辆横向加速度。其中，转向盘转角可以是目标车辆的转向盘的转角值；车辆横向位 置可以是目标车辆相对于车道中心线的横向位置；车辆横向加速度可以是目标车辆在垂直于车道中心线的方向上的加速度。
示例性的，可以通过转向盘传感器获取转向盘转角，通过横向位置传感器获取车辆横向位置，通过加速度传感器获取车辆横向加速度。
S120、基于车辆行驶数据以及预先训练的驾驶风格辨识模型，确定目标车辆对应的驾驶风格类别。
其中，预先训练的驾驶风格辨识模型可以是用于辨识车辆的驾驶风格类别的模型。可以基于车辆行驶数据的转向盘转角、车辆横向位置以及车辆横向加速度构成输入向量，输入至驾驶风格辨识模型，得到驾驶风格辨识模型输出的驾驶风格类型。示例性的，驾驶风格类别包括保守类、正常类、激进类。当然，还可以对驾驶风格类别作进一步划分，如，极保守、一般保守、正常、一般激进、极激进。
可选的，驾驶风格辨识模型可以是学习向量量化神经网络模型、卷积神经网络模型、决策树模型、支持向量机模型或朴素贝叶斯模型等。以卷积神经网络模型为例，驾驶风格辨识模型的训练过程可以是：构建样本集，样本集包括样本行驶数据以及样本行驶数据对应的样本类别标签，将样本集输入至卷积神经网络中，基于卷积神经网络输出的预测类别标签以及样本类别标签计算损失函数，根据损失函数的计算结果对卷积神经网络的参数进行反向调整，直至满足收敛条件。
S130、基于驾驶风格类别以及目标车辆的车辆状态数据，确定目标车辆的目标转向盘转角，并基于目标转向盘转角调整目标车辆的转向盘转角。
其中，车辆状态数据可以是表征目标车辆的当前车辆行驶状态的信息。例如，车辆状态数据可以是当前车速、当前位置侧向坐标、车辆前后轴轴距、车辆转向系传动比等信息。
示例性的，本实施例可以考虑驾驶风格类别，结合车辆状态数据，确定出该驾驶风格类别对应的目标转向盘转角，以满足该驾驶风格类别的驾驶需求。 例如，针对激进型的驾驶风格类别，其对应的目标转向盘转角可以大于保守型的驾驶风格类别对应的目标转向盘转角。
示例性的，本实施例可以根据驾驶风格类别、目标车辆的车辆状态数据以及预先建立的映射表，确定目标车辆的目标转向盘转角。其中，映射表包括多种驾驶风格类别以及多种车辆状态数据下对应的转向盘转角。或者，还可以基于驾驶风格类别、目标车辆的车辆行驶状态数据构成特征向量，将该特征向量输入至预先训练的转角确定模型中，基于转角确定模型的输出确定目标车辆的目标转向盘转角。
又或者，在一种具体的实施方式中，车辆状态数据包括当前车速、车道中心线、当前位置侧向坐标、车辆转向系传动比以及车辆前后轴轴距，基于驾驶风格类别以及目标车辆的车辆状态数据，确定目标车辆的目标转向盘转角，满足如下公式：
Figure PCTCN2022118342-appb-000001
其中，θ opt为目标转向盘转角，L为车辆前后轴轴距，i为车辆转向系传动比，C y表示驾驶风格类别，v为当前车速，y(t)为当前位置侧向坐标，T为预瞄时间，f(t)为t时刻的车道中心线，f(t+T)表示t+T时刻车道中心线的侧向坐标，d为预瞄距离，
Figure PCTCN2022118342-appb-000002
表示车辆侧向速度。需要说明的是，侧向即为横向，表示图1B中坐标轴的Y轴方向。示例性的，驾驶风格类别为保守类时，C y＝1；驾驶风格类别为正常类时，C y＝2；驾驶风格类别为激进类时，C y＝3。
示例性的，可以结合图1B所示的一种车辆的目标轨迹，对上述公式的推导过程进行示例性说明。如图1B所示，图中f(t)表示车道的中心线，即车辆的目标轨迹，y(t)表示车辆的当前位置侧向坐标，T表示预瞄时间。假设驾驶人预瞄模型中的预瞄距离为d，则预瞄时间T与预瞄距离d之间的关系为：
Figure PCTCN2022118342-appb-000003
根据汽车运动学关系，通过对车辆的当前位置侧向坐标y(t)进行微分可以得到车辆侧向速度，通过对车辆的当前位置侧向坐标y(t)进行二阶微分，可以得到 车辆的侧向加速度，基于微分得到的车辆侧向速度以及车辆侧向加速度，可以对t+T时刻车辆位置的侧向坐标y(t+T)进行预测：
Figure PCTCN2022118342-appb-000004
根据最小误差原则，为了能够使车辆实现对目标轨迹的最优跟踪效果，即，使得误差最小，则车辆在t+T时刻位置的侧向坐标y(t+T)应该与在t+T时刻目标轨迹(即车道中心线)的侧向坐标f(t+T)保持一致，即：
f(t+T)＝y(t+T)
示例性的，计算使车辆实现最优的目标轨迹跟踪效果，即，能够让车辆位置回正的最优的侧向加速度
Figure PCTCN2022118342-appb-000005
为：
Figure PCTCN2022118342-appb-000006
计算车辆的侧向加速度与转向盘转角之间的关系如下：
Figure PCTCN2022118342-appb-000007
其中，R表示车辆转向半径，v为当前车速，θ为车辆的当前盘转角，L为车辆前后轴轴距，i为车辆转向系传动比，Y X代表个性化转角系数。示例性的，Y X的计算公式可以如下：
Figure PCTCN2022118342-appb-000008
联立上述公式进行求解，可以计算出在车道保持过程中，车辆跟踪目标轨迹即车道中心线所需的最优转向盘转角θ opt(目标转向盘转角)的表达式：
Figure PCTCN2022118342-appb-000009
在该可选的实施方式中，可以通过预测得到的t+T时刻车辆位置的侧向坐标与t+T时刻的目标轨迹之间的最小误差原则，计算出在当前用户的驾驶风格类别下，车辆回归车道中心线所需的转向盘转角，实现了考虑用户驾驶风格下 最优转向盘转角的准确确定，满足了不同驾驶习惯的驾驶人的差异化需求，极大地提高了用户的驾驶体验。
示例性的，本实施例在确定出目标车辆的目标转向盘转角后，可以将目标车辆的转向盘转角调整至目标转向盘转角，以控制目标车辆回归至车道中心线位置。
本实施例的技术方案，获取目标车辆的车辆行驶数据，将包括转向盘转角、车辆横向位置以及车辆横向加速度的车辆行驶数据输入至预先训练的驾驶风格辨识模型中，得到驾驶风格辨识模型输出的目标车辆对应的驾驶风格类别，确定出用户的驾驶风格，进而根据驾驶风格类别和目标车辆的车辆状态数据，确定目标车辆的目标转向盘转角，基于该目标转向盘转角调整目标车辆的转向盘转角，以控制车辆回归至车道中心线位置，实现了基于用户驾驶风格的车道保持控制，满足了不同驾驶习惯的驾驶人的差异化需求，极大地提高了用户的驾驶体验，并且，提高了车辆的系统适用性和行驶安全性。
实施例二
图2为本申请实施例二提供的车道保持方法的流程示意图，本实施例在上述各实施例的基础上，可选的，所述车道保持方法还包括：获取样本输入向量以及所述样本输入向量对应的驾驶风格标签，将所述样本输入向量输入至学习向量量化神经网络的输入层；计算所述样本输入向量与所述学习向量量化神经网络的竞争层中多个神经元的距离，在所述竞争层的多个神经元中确定出与所述样本输入向量距离最近的第一目标神经元和第二目标神经元；基于所述第一目标神经元与所述样本输入向量的距离、所述第二目标神经元与所述样本输入向量的距离、所述第一目标神经元对应的驾驶风格预测值、所述第二目标神经元对应的驾驶风格预测值以及所述样本输入向量对应的驾驶风格标签，更新所述学习向量量化神经网络中所述第一目标神经元和/或所述第二目标神经元对应的权值；将更新后的学习向量量化神经网络确定为驾驶风格辨识模型。
其中与上述各实施例相同或相应的术语的解释在此不再赘述。参见图2，本实施例提供的车道保持方法包括以下步骤：
S210、获取样本输入向量以及样本输入向量对应的驾驶风格标签，将样本输入向量输入至学习向量量化神经网络的输入层。
其中，样本输入向量可以是预先采集车辆行驶数据(包括转向盘转角、车辆横向位置以及车辆横向加速度)所构成的样本向量。驾驶风格标签可以是样本输入向量对应的预先确定的驾驶风格类别，如：保守类、正常类、激进类。样本输入向量和样本输入向量对应的驾驶风格标签可以构成一个样本数据。
在本实施例中，学习向量量化神经网络包括输入层、竞争层和线性输出层。其中，输出层神经元的个数与输出的驾驶风格类别的个数相同，竞争层神经元的个数可以大于输出层神经元个数。输入层的每一个神经元，均与竞争层的多个神经元相连接；竞争层的神经元，仅与其对应的输出层神经元相连接。
学习向量量化神经网络中输入层的每个神经元与竞争层多个神经元之间的权值均默认为1。因此，本实施例对学习向量量化神经网络进行训练的过程，主要为对输入层神经元与竞争层神经元之间的权值进行反向调整的过程。
在本实施例中，可以将样本输入向量输入至学习向量量化神经网络的输入层，以计算样本输入向量与竞争层中每个神经元的距离。示例性的，可以将样本输入向量中的数据分别输入至输入层的多个神经元。示例性的，样本输入向量为[b1 b2 b3]，则可以将b1对应送入输入层的第一个神经元，将b2对应送入输入层的第二个神经元，将b3对应送入输入层的第三个神经元。
S220、计算样本输入向量与学习向量量化神经网络的竞争层中多个神经元的距离，在竞争层的多个神经元中确定出与样本输入向量距离最近的第一目标神经元和第二目标神经元。
其中，针对每一个样本输入向量，均计算其与竞争层中多个神经元之间的距离。示例性的，可以按照如下公式，计算样本输入向量与学习向量量化神经网络的竞争层中每个神经元的距离：
Figure PCTCN2022118342-appb-000010
式中，d i表示样本输入向量与竞争层中第i个神经元之间的距离，S表示竞争层神经元的个数，x表示样本输入向量，x＝(x 1，x 2，...，x R) T，R表示样本输入向量中数据的个数，ω ij表示第j个输入层神经元与第i个竞争层神经元之间的权值。
在本实施例中，可以基于上述公式，依次计算出样本输入向量与竞争层多个神经元的距离，进而基于计算出的多个距离，将多个距离从小到大进行排序，取排序后的列表中前两个距离对应的竞争层神经元作为第一目标神经元和第二目标神经元。即，将与样本输入向量距离第一近的竞争层神经元确定为第一目标神经元，将与样本输入向量距离第二近的竞争层神经元确定为第二目标神经元。
S230、基于第一目标神经元与样本输入向量的距离、第二目标神经元与样本输入向量的距离、第一目标神经元对应的驾驶风格预测值、第二目标神经元对应的驾驶风格预测值以及样本输入向量对应的驾驶风格标签，更新学习向量量化神经网络中第一目标神经元和/或第二目标神经元对应的权值。
其中，第一目标神经元对应的驾驶风格预测值，可以基于第一目标神经元所连接的输出层神经元对应的驾驶风格类别确定。如，第一目标神经元与输出层神经元output_c连接，输出层神经元output_c对应的驾驶风格类别为保守类，则第一目标神经元对应的驾驶风格预测值C y＝1(C y＝1代表保守类、C y＝2代表正常类、C y＝3代表激进类)。相应的，第二目标神经元对应的驾驶风格预测值也可以是第二目标神经元所连接的输出层神经元对应的驾驶风格类别。
在一种实施方式中，可以是若第一目标神经元对应的驾驶风格预测值等于驾驶风格标签，对第一目标神经元对应的权值进行更新；若第二目标神经元对应的驾驶风格预测值等于驾驶风格标签，对第二目标神经元对应的权值进行更 新；若第一目标神经元对应的驾驶风格预测值、第二目标神经元对应的驾驶风格预测值均等于驾驶风格标签，则对第一目标神经元、第二目标神经元对应的权值进行更新。需要说明的是，在本实施例中，第一目标神经元对应的权值为第一目标神经元与输入层神经元之间的权值；第二目标神经元对应的权值为第二目标神经元与输入层神经元之间的权值。
在另一种实施方式中，基于第一目标神经元与样本输入向量的距离、第二目标神经元与样本输入向量的距离、第一目标神经元对应的驾驶风格预测值、第二目标神经元对应的驾驶风格预测值以及样本输入向量对应的驾驶风格标签，更新学习向量量化神经网络中第一目标神经元和/或第二目标神经元对应的权值，包括：
基于第一目标神经元与样本输入向量的距离、第二目标神经元与样本输入向量的距离、第一目标神经元对应的驾驶风格预测值、第二目标神经元对应的驾驶风格预测值，判断是否满足预设权值更新条件；响应于满足预设权值更新条件，基于预设学习率、样本输入向量以及驾驶风格标签，更新学习向量量化神经网络中第一目标神经元和第二目标神经元对应的权值。
其中，预设权值更新条件可以是第一目标神经元与样本输入向量的距离、第二目标神经元与样本输入向量的距离满足预设窗口宽度条件，且，第一目标神经元对应的驾驶风格预测值以及第二目标神经元对应的驾驶风格预测值不同(即，第一目标神经元连接的输出层神经元所代表的驾驶风格类别与第二目标神经元连接的输出层神经元所代表的驾驶风格类别不同)。
示例性的，预设窗口宽度条件可以用如下公式表示：
Figure PCTCN2022118342-appb-000011
其中，d a为第一目标神经元与样本输入向量的距离，d b为第二目标神经元与样本输入向量的距离，ρ为窗口宽度。可选的，ρ＝0.5。
示例性的，若满足预设权值更新条件，则基于预设学习率、样本输入向量以及驾驶风格标签，更新学习向量量化神经网络中第一目标神经元和第二目标 神经元对应的权值，可以是：
若驾驶风格标签等于第一目标神经元对应的驾驶风格预测值，则可以基于预设学习率、样本输入向量以及驾驶风格标签，增加第一目标神经元对应的权值，降低第二目标神经元对应的权值；例如，采用如下公式对第一目标神经元对应的权值和第二目标神经元对应的权值进行修正，以增加第一目标神经元对应的权值，降低第二目标神经元对应的权值：
ω a-new＝ω a-old+η(x-ω a-old)
ω b-new＝ω b-old-η(x-ω b-old)
若驾驶风格标签等于第二目标神经元对应的驾驶风格预测值，则可以基于预设学习率、样本输入向量以及驾驶风格标签，增加第二目标神经元对应的权值，降低第一目标神经元对应的权值；例如，采用如下公式对第一目标神经元对应的权值和第二目标神经元对应的权值进行修正，以增加第二目标神经元对应的权值，降低第一目标神经元对应的权值：
ω a-new＝ω a-old-η(x-ω a-old)
ω b-new＝ω b-old+η(x-ω b-old)
其中，ω a-new为修正后的第一目标神经元对应的权值，ω a-old为修正前的第一目标神经元对应的权值，η为预设学习率，x为样本输入向量；ω b-new为修正后的第二目标神经元对应的权值，ω b-old为修正前的第二目标神经元对应的权值。
当然，若不满足预设权值更新条件，则可以仅更新与样本输入向量最近的第一目标神经元对应的权值。示例性的，可以在不满足预设权值更新条件时，判断与样本输入向量距离最近的第一目标神经元对应的驾驶风格预测值，是否与预设的驾驶风格标签一致，在第一目标神经元对应的驾驶风格预测值与预设的驾驶风格标签一致的情况下，可以增加第一目标神经元对应的权值，在第一目标神经元对应的驾驶风格预测值与预设的驾驶风格标签不一致的情况下，可以降低第一目标神经元对应的权值。
示例性的，可以基于如下公式更新第一目标神经元对应的权值，以增加第 一目标神经元对应的权值：
ω a-new＝ω a-old+η(x-ω a-old)
基于如下公式更新第一目标神经元对应的权值，以降低第一目标神经元对应的权值：
ω a-new＝ω a-old-η(x-ω a-old)
在该可选的实施方式中，通过判断是否满足预设权重更新条件，在满足预设权重更新条件时，根据预设学习率、样本输入向量以及驾驶风格标签，对竞争层中的第一目标神经元对应的权值和第二目标神经元对应的权值进行调整，提高了学习向量量化神经网络的训练过程的准确性，从而提高了驾驶风格辨识模型的准确性，提高了预测的驾驶风格类别的准确性，保证了根据用户驾驶风格进行车辆的车道保持控制。
需要说明的是，在完成上述过程对学习向量量化神经网络中第一目标神经元和/或第二目标神经元的权值进行更新之后，还可以重复此过程，将其它样本输入向量以及其它样本输入向量对应的驾驶风格标签继续输入至学习向量量化神经网络中，以继续对网络中竞争层神经元的权值进行调整，直至满足训练截止条件。可选的，训练截止条件可以是竞争层中所有的神经元均被激活。
示例性的，在一个样本输入向量被送入学习向量量化神经网络后，会激活竞争层中与样本输入向量距离最接近的神经元，该竞争层神经元的状态变化为“1”，而竞争层中其它未被激活的神经元的状态仍然是“0”，与被激活的竞争层神经元相连接的线性输出层神经元状态也为“1”，而其他线性输出层神经元的状态均为“0”，与被激活的竞争层神经元相连接的线性输出层神经元y(每个竞争层神经元只与一个线性输出层神经元相连接)输出C y。重复将多个样本输入向量输入至学习向量量化神经网络的过程，可以不断地激活竞争层中的神经元，直至训练完成，竞争层中的神经元均被激活，竞争层神经元的权值均已被更新。
S240、将学习向量量化神经网络确定为驾驶风格辨识模型。
在本实施例中，通过将多个样本输入向量输入至学习向量量化神经网络， 以更新学习向量量化神经网络中竞争层神经元对应的权值，可以在学习向量量化神经网络中竞争层的多个神经元的权值更新完成后，将该学习向量量化神经网络确定为驾驶风格辨识模型。
S250、获取目标车辆的车辆行驶数据，基于车辆行驶数据以及预先训练的驾驶风格辨识模型，确定目标车辆对应的驾驶风格类别。
其中，车辆行驶数据包括转向盘转角、车辆横向位置以及车辆横向加速度。
S260、基于驾驶风格类别以及目标车辆的车辆状态数据，确定目标车辆的目标转向盘转角，并基于目标转向盘转角调整目标车辆的转向盘转角。
在本实施例中，还提供一种可选的学习向量量化神经网络的训练方法，该方法可以提高学习向量量化神经网络的训练精度，进而提高学习向量量化神经网络的预测准确性，该方法包括如下步骤：
步骤1、将学习向量量化神经网络中输入层神经元j和竞争层神经元i之间的权值ω ij初始化，均赋值为1。
步骤2、将样本输入向量x＝(x 1，x 2，...，x R) T输入至学习向量量化神经网络的输入层，并基于如下公式计算样本输入向量与每一个竞争层神经元的距离；
Figure PCTCN2022118342-appb-000012
步骤3、选择与样本输入向量距离最小的两个竞争层神经元，如，包括神经元a和神经元b。
步骤4、判断神经元a、神经元b是否同时满足两个条件(即，预设权重更新条件)，条件1：神经元a和神经元b代表不同的驾驶风格类别，即神经元a对应的预测值不等于神经元b对应的预测值，条件2：神经元a与样本输入向量的距离d a，和神经元b与样本输入向量的距离d b满足：
Figure PCTCN2022118342-appb-000013
若同时满足两个条件，则执行步骤5，若无法同时满足两个条件，则执行步骤6。
步骤5、若神经元a对应的驾驶风格类别C a与样本输入向量对应的驾驶风格标签C x一致，则神经元a与神经元b的权值根据下式进行修正：
ω a-new＝ω a-old+η(x-ω a-old)
ω b-new＝ω b-old-η(x-ω b-old)
若神经元b对应的驾驶风格类别C b与输入向量对应的驾驶风格类别C x一致，则神经元a与神经元b的权值根据下式进行修正：
Figure PCTCN2022118342-appb-000014
步骤6、若神经元a对应的驾驶风格类别C a与样本输入向量对应的驾驶风格标签C x一致，则基于下述公式调整神经元a的权值：
ω a-new＝ω a-old+η(x-ω a-old)
若神经元a对应的驾驶风格类别C a与样本输入向量对应的驾驶风格标签C x不一致，则基于下述公式调整神经元a的权值：
ω a-new＝ω a-old-η(x-ω a-old)
需要说明的是，各步骤所涉及到的各公式中的参数，可参见前述对各参数的解释。
本实施例的技术方案，通过计算样本输入向量与学习向量量化神经网络的竞争层中多个神经元的距离，确定出竞争层中与该样本输入向量距离最近的第一目标神经元、第二目标神经元，并根据第一目标神经元与样本输入向量的距离、第二目标神经元与样本输入向量的距离、第一目标神经元对应的驾驶风格预测值、第二目标神经元对应的驾驶风格预测值、样本输入向量对应的驾驶风格标签，更新学习向量量化神经网络中第一目标神经元和/或第二目标神经元对应的权值，以得到驾驶风格辨识模型，实现了驾驶风格辨识模型的准确训练，提高了驾驶风格辨识模型的输出精度，提高了预测的目标车辆的驾驶风格类别的准确性，从而提高了用户的驾驶体验感和车辆安全性。
实施例三
图3为本申请实施例三提供的一种车道保持方法的流程示意图，本实施例在上述各实施例的基础上，可选的，在所述基于所述目标转向盘转角调整所述目标车辆的转向盘转角之后，所述方法还包括：获取多个采集位置对应的车辆横向位置、车道中心线横向位置；基于多个车辆横向位置、多个车道中心线横向位置以及所述驾驶风格类别，确定所述目标车辆是否回归车道中心线。
其中与上述各实施例相同或相应的术语的解释在此不再赘述。参见图3，本实施例提供的车道保持方法包括以下步骤：
S310、获取目标车辆的车辆行驶数据，基于车辆行驶数据以及预先训练的驾驶风格辨识模型，确定目标车辆对应的驾驶风格类别。
其中，车辆行驶数据包括转向盘转角、车辆横向位置以及车辆横向加速度。
S320、基于驾驶风格类别以及目标车辆的车辆状态数据，确定目标车辆的目标转向盘转角，并基于目标转向盘转角调整目标车辆的转向盘转角。
S330、获取多个采集位置对应的车辆横向位置、车道中心线横向位置。
示例性的，本实施例在调整目标车辆的转向盘转角，以控制车辆进行车道保持之后，可以对车辆的位置进行判断，分析车辆是否回归道路中心线。
其中，采集位置可以是预设时间间隔下确定的采集位置，示例性的，预设时间间隔可以是0.5s。如表1所示，展示了一种多个采集位置对应的车辆横向位置、车道中心线横向位置。
表1采集位置对应的车辆横向位置、车道中心线横向位置
Figure PCTCN2022118342-appb-000015
S340、基于多个车辆横向位置、多个车道中心线横向位置以及驾驶风格类别，确定目标车辆是否回归车道中心线。
在一种实施方式中，可以根据多个车辆横向位置、多个车道中心线横向位 置，确定每个车辆横向位置与其对应的车道中心线横向位置之间的位置差距，基于位置差距的变化趋势以及驾驶风格类别，确定目标车辆是否回归车道中心线。
示例性的，基于位置差距的变化趋势以及驾驶风格类别，确定目标车辆是否回归车道中心线，可以是：若位置差距的变化趋势为差距逐渐减小，且差距减小的速度大于驾驶风格类别所对应的预设移速阈值，则确定目标车辆回归车道中心线。如，驾驶风格类别为保守类，考虑到保守类的驾驶员通常回归车道中心线的速度较慢，保守类对应的预设移速阈值可以设置的较小；驾驶风格类别为激进类，考虑到激进类的驾驶员通常回归车道中心线的速度较快，激进类对应的预设移速阈值可以设置的较大。示例性的，驾驶风格类别为保守类，位置差距的变化趋势为差距逐渐减小，且减小的速度为0.6m/s，减小的速度大于保守类对应的预设移速阈值(如0.5m/s)，则确定目标车辆回归车道中心线。
在另一种可选的实施方式中，基于多个车辆横向位置、多个车道中心线横向位置以及驾驶风格类别，确定目标车辆是否回归车道中心线，还可以是：根据多个采集位置对应的车辆横向位置构建多项式函数，基于多个采集位置对应的车道中心线横向位置计算多项式函数的多项式系数；基于多项式系数以及驾驶风格类别，确定目标车辆是否回归车道中心线。
其中，针对多个个车辆横向位置，构建多个车辆横向位置对应的多项式函数。示例性的，根据多个车辆横向位置构建的多项式函数的表达式如下：
P n(u)＝a nu n+…+a 1u+a 0
式中，P n(u)为车辆横向位置u对应的多项式函数，a 0、a 1、......、a n(n＜m)为多项式函数的多项式系数，u为车辆横向位置。
在该可选的实施方式中，基于多个采集位置对应的车道中心线横向位置计算多项式函数的多项式系数，可以是：根据车辆横向位置的多项式函数的计算结果与车辆横向位置对应的车道中心线横向位置之间的误差值，对多项式函数中的多项式系数进行调整，直至多项式函数的计算结果与车辆横向位置对应的 车道中心线横向位置之间的误差值最小。或者，基于多个采集位置对应的车道中心线横向位置计算多项式函数的多项式系数，还可以是：基于采集位置对应的车道中心线横向位置，对多项式函数进行最小二乘法拟合，基于拟合的结果确定多项式函数的多项式系数。
示例性的，计算多项式函数的多项式系数即求解a 0，a 1，...，a n(n＜m)，可以使：
Figure PCTCN2022118342-appb-000016
取δ(a 0，a 1，...，a n)的最小值，如此确定的多项式函数就是数据(u k，w k)，k＝1，2，...，m的最小二乘拟合多项式。
即，
Figure PCTCN2022118342-appb-000017
或上述公式可以写成：
Figure PCTCN2022118342-appb-000018
示例性的，引进记号
Figure PCTCN2022118342-appb-000019
则有，s ja 0+s j+1a 1+...+s j+na n＝u j(j＝1，2，...，n)；
上述公式为P n(u)系数a 0，a 1，...，a n满足的方程组，当车辆横向位置u 1，u 2，...，u n互异时，该方程组有唯一解a 0，a 1，...，a n，使得
Figure PCTCN2022118342-appb-000020
取最小值。此时，便可得到车辆横向位置u k与车道中心线横向位置w k之间的拟合函数P n(u)，当a 2+a 3+…+a n＜0.001×C y时，可以确定车辆完全回归车道中心线。其中，C y代表确定的驾驶风格类别(C y＝1代表保守类、C y＝2代表正常类、C y＝3代表激进类)。
即，基于多项式系数以及驾驶风格类别，确定目标车辆是否回归车道中心线，可以是：若多项式系数的加权和小于驾驶风格类别对应的加权值，则确定目标车辆回归车道中心线。
在该可选的实施方式中，通过多个采集位置对应的车辆横向位置构建多项式函数，并根据多个车辆横向位置对应的车道中心线横向位置计算多项式函数 的多个多项式系数，进而基于多个多项式系数和驾驶风格类别，判断目标车辆是否回归车道中心线，实现了结合驾驶风格的车辆是否回归车道中心的准确判断，针对每个驾驶风格的驾驶人，均可个性化地判断出是否回归车道中心，提高了车辆的安全性。
本实施例的技术方案，在调整目标车辆的转向盘转角之后，采集多个车辆横向位置和多个车辆横向位置对应的车道中心线横向位置，基于多个车辆横向位置、多个车辆横向位置对应的车道中心线横向位置、以及驾驶风格类别，判断目标车辆是否回归车道中心线，实现了结合驾驶风格的车辆是否回归车道中心的准确判断，能够在不同驾驶风格下准确判断出车辆是否回归车道中心，提高了用户驾驶体验的同时，提高了车辆的驾驶安全性。
实施例四
图4A为本申请实施例四提供的一种车道保持系统的结构示意图，该系统包括工控机410以及转向盘总成420，其中，转向盘总成420，设置为获取目标车辆的转向盘转角，并将转向盘转角发送至工控机410；工控机410，设置为基于本申请任一实施例提供的车道保持方法，调整目标车辆的转向盘转角。
示例性的，工控机410中可以采用研华主板aimb781，工控机410的显卡可以是GTX1070，工控机410的CPU可以是i7，工控机410还连接有显示器，工控机410内设置有驾驶风格辨识系统，驾驶风格辨识系统是基于计算机软件实现的，例如涉及的软件有PanoSim和MATLAB/Simulink，PanoSim是为解决现代智能汽车与汽车智能化技术与产品开发、测试与验证面临的诸多挑战而提出的一款汽车虚拟仿真平台，能够仿真车辆对驾驶员、路面及空气动力学输入的响应，Simulink是MATLAB中的一种可视化仿真工具，能够实现动态系统建模、仿真和分析的功能，Simulink提供一个动态系统建模、仿真和综合分析的集成环境。
可选的，工控机410中部署的PanoSim可以用于采集目标车辆的车辆行驶 数据(包括转向盘转角、车辆横向位置、车辆横向加速度)，驾驶风格辨识系统可以根据PanoSim采集的车辆行驶数据确定驾驶风格类别以及目标转向盘转角，并将目标转向盘转角输入至PanoSim，以使PanoSim控制目标车辆位置回正。
PanoSim还可以用于采集多个采集位置对应的车辆横向位置、车道中心线横向位置，驾驶风格辨识系统可以根据PanoSim采集的车辆横向位置、车道中心线横向位置，判断目标车辆是否回归车道中心线。
示例性的，本实施例还提供一种车道保持系统，该系统包含的部件如图4B-4C所示，图4B展示了一种车道保持系统的部件示意图，图4C展示了一种车道保持系统中座椅和电动缸的示意图。结合图4B-4C，该车道保持系统包括座椅1、转向盘总成420、踏板总成3、支架4、屏幕5、工控机410、连接板7、底板8、电动缸9、电动缸控制系统10、万向轮11、音响12、加速踏板13、制动踏板14、离合踏板15以及显示器16。
其中，转向盘总成420固定在支架4上，踏板总成3装配在支架4的下方，屏幕5设在转向盘总成420和支架4的前方，座椅1对应转向盘总成420设置，座椅1的下方设置有连接板7和底板8，连接板7顶面与座椅1底部之间装配有电动缸9，连接板7的后侧与底板8的后端板之间也装配有电动缸9，前述的电动缸9均与电动缸控制系统10相连接并由电动缸控制系统10控制工作，转向盘总成420、踏板总成3、屏幕5和电动缸控制系统10与工控机410相连接，转向盘总成420、踏板总成3、屏幕5、工控机410和电动缸控制系统10均由同一电源提供电力，连接板7的底面与底板8顶面之间装配有万向轮11。
支架4的顶面上设置有音响12，音响12通过通用串行总线(Universal Serial Bus，USB)与工控机410相连接，音响12设置为模拟真实驾驶过程中的声音。转向盘总成420与工控机410之间由控制器局域网络(Controller Area Network，CAN)线相连接，转向盘总成420采用SENSO-Wheel转向盘总成，该转向盘总成420提供自由可编程调节的刚度、阻尼和扭矩，能够实现转向的驾驶感觉体验，转向盘总成420内部集成设置有转向盘转角传感器，转角传感器采集转向 盘转角信号，转向盘转角信号以CAN报文形式发送到工控机410，供工控机410内部的车辆动力学模型运行使用。工控机410可以根据上述任一实施例所述的车道保持方法，调整目标车辆的转向盘转角。或者，根据上述任一实施例所述的车道保持方法，调整所述目标车辆的转向盘转角，并判断目标车辆是否完全回归车道中心线。
踏板总成3与工控机410之间由USB线相连接，踏板总成3采用G29系列踏板组件，从右至左依次布置有加速踏板13、制动踏板14和离合踏板15，加速踏板13和制动踏板14内部都集成设置有各自的踏板位移传感器，位移传感器采集得到对应的踏板行程信号发送至工控机410，供工控机410内部的车辆动力学模型运行使用。上述各部件的连接关系可参见图4D，图4D展示了车道保持系统的内部连接示意图。
屏幕5为环形屏幕，环形屏幕采用三台NEC NP4100+主流工程型投影仪进行投影，每台投影仪组成一个通道，以水平跨越的方式生成三通道拼接显示效果，三路视频图形阵列(Video Graphics Array，VGA)计算机信号提供显示内容支持，相邻通道的NEC NP4100+主流工程型投影仪之间采用硬件非线性几何矫正技术，最终实现了在环形屏幕上的良好投影效果。
电动缸控制系统10中包括有比例-积分-微分(Proportion Integration Differentiation，PID)控制器、数模(Digital/Analog，D/A)卡和伺服放大器，工控机410产生速度控制指令通过信号线传递到PID控制器，PID控制器基于自身的PID算法进行解算，获得速度信号并将信号传递给D/A卡，D/A卡将速度信号转化为电压信号传递给伺服控制器，伺服控制器最终控制电动缸9的运动。连接板7顶面与座椅1底部之间装配的电动缸9对称设置有两组四个，连接板7的后侧与底板8的后端板之间装配的电动缸9设置有两个。连接板7的底面与底板8顶面之间装配的万向轮11设置有四个。
本实施例提供的车道保持系统，包括工控机以及转向盘总成，工控机可以进行目标车辆的驾驶风格类别的确定、目标转向盘转角的确定以及转向盘转角 的调整，实现了基于用户驾驶风格的车道保持控制，满足了不同驾驶习惯的驾驶人的差异化需求，极大地提高了用户的驾驶体验，并且，提高了车辆的系统适用性和行驶安全性。
实施例五
图5为本申请实施例五提供的一种车道保持装置的结构示意图，本实施例可适用于在车辆行驶时，根据驾驶车辆的用户的驾驶风格自动控制车辆在车道中心位置行驶的情况，该装置包括：行驶数据获取模块510、驾驶风格辨识模块520以及转向盘调整模块530。
行驶数据获取模块510，设置为获取目标车辆的车辆行驶数据，其中，所述车辆行驶数据包括转向盘转角、车辆横向位置以及车辆横向加速度；
驾驶风格辨识模块520，设置为基于所述车辆行驶数据以及预先训练的驾驶风格辨识模型，确定所述目标车辆对应的驾驶风格类别；
转向盘调整模块530，设置为基于所述驾驶风格类别以及所述目标车辆的车辆状态数据，确定所述目标车辆的目标转向盘转角，并基于所述目标转向盘转角调整所述目标车辆的转向盘转角。
可选的，所述车道保持装置还包括网络训练模块，所述网络训练模块包括样本输入单元、距离计算单元、权重调整单元和模型确定单元，其中；
所述样本输入单元，设置为获取样本输入向量以及所述样本输入向量对应的驾驶风格标签，将所述样本输入向量输入至学习向量量化神经网络的输入层；
所述距离计算单元，设置为计算所述样本输入向量与所述学习向量量化神经网络的竞争层中多个神经元的距离，在所述竞争层的多个神经元中确定出与所述样本输入向量距离最近的第一目标神经元和第二目标神经元；
所述权重调整单元，设置为基于所述第一目标神经元与所述样本输入向量的距离、所述第二目标神经元与所述样本输入向量的距离、所述第一目标神经元对应的驾驶风格预测值、所述第二目标神经元对应的驾驶风格预测值以及所 述样本输入向量对应的驾驶风格标签，更新所述学习向量量化神经网络中所述第一目标神经元和/或所述第二目标神经元对应的权值；
所述模型确定单元，设置为将更新后的学习向量量化神经网络确定为驾驶风格辨识模型。
可选的，所述权重调整单元设置为：
基于所述第一目标神经元与所述样本输入向量的距离、所述第二目标神经元与所述样本输入向量的距离、所述第一目标神经元对应的驾驶风格预测值、所述第二目标神经元对应的驾驶风格预测值，判断是否满足预设权重更新条件；响应于满足预设权值更新条件，基于预设学习率、所述样本输入向量以及所述驾驶风格标签，更新所述学习向量量化神经网络中所述第一目标神经元和所述第二目标神经元对应的权值。
可选的，所述车辆状态数据包括当前车速、车道中心线、当前位置侧向坐标、车辆转向系传动比以及车辆前后轴轴距，所述转向盘调整模块530包括目标转角确定单元，所述目标转角确定单元，设置为按照如下公式，基于所述驾驶风格类别以及所述目标车辆的车辆状态数据，确定所述目标车辆的目标转向盘转角：
Figure PCTCN2022118342-appb-000021
其中，θ opt为目标转向盘转角，L为车辆前后轴轴距，i为车辆转向系传动比，C y表示驾驶风格类别，v为当前车速，y(t)为当前位置侧向坐标，T为预瞄时间，f(t)为t时刻的车道中心线，f(t+T)表示t+T时刻车道中心线的侧向坐标，d为预瞄距离，
Figure PCTCN2022118342-appb-000022
表示车辆侧向速度。
可选的，所述车道保持装置还包括回归判断模块，所述回归判断模块包括位置采集单元和位置判断单元，其中；
所述位置采集单元，设置为在所述基于所述目标转向盘转角调整所述目标车辆的转向盘转角之后，获取多个采集位置对应的车辆横向位置、车道中心线横向位置；
所述位置判断单元，设置为基于多个车辆横向位置、多个车道中心线横向位置以及所述驾驶风格类别，确定所述目标车辆是否回归车道中心线。
可选的，所述位置判断单元设置为：
根据所述多个采集位置对应的所述车辆横向位置构建多项式函数，基于所述多个采集位置对应的车道中心线横向位置计算所述多项式函数的多项式系数；基于所述多项式系数以及所述驾驶风格类别，确定所述目标车辆是否回归车道中心线。
在本实施例中，通过行驶数据获取模块，获取目标车辆的车辆行驶数据，通过驾驶风格辨识模块，将包括转向盘转角、车辆横向位置以及车辆横向加速度的车辆行驶数据输入至预先训练的驾驶风格辨识模型中，得到驾驶风格辨识模型输出的目标车辆对应的驾驶风格类别，确定出用户的驾驶风格，进而通过转向盘调整模块，根据驾驶风格类别和目标车辆的车辆状态数据，确定目标车辆的目标转向盘转角，基于该目标转向盘转角调整目标车辆的转向盘转角，以控制车辆回归至车道中心线位置，实现了基于用户驾驶风格的车道保持控制，满足了不同驾驶习惯的驾驶人的差异化需求，极大地提高了用户的驾驶体验，并且，提高了车辆的系统适用性和行驶安全性。
本申请实施例所提供的车道保持装置可执行本申请任意实施例所提供的车道保持方法，具备执行方法相应的功能模块。
值得注意的是，上述系统所包括的各个单元和模块只是按照功能逻辑进行划分的，但并不局限于上述的划分，只要能够实现相应的功能即可；另外，各功能单元的具体名称也只是为了便于相互区分，并不用于限制本申请实施例的保护范围。
实施例六
图6是本申请实施例六提供的一种电子设备的结构示意图。图6示出了适于用来实现本申请实施方式的示例性电子设备12的框图。图6显示的电子设备 12仅仅是一个示例，不应对本申请实施例的功能和使用范围带来任何限制。设备12典型的是承担车道保持功能的电子设备。
如图6所示，电子设备12以通用计算设备的形式表现。电子设备12的组件可以包括但不限于：至少一个处理器或者处理单元16，存储器28，连接不同组件(包括存储器28和处理单元16)的总线18。
总线18表示几类总线结构中的一种或多种，包括存储器总线或者存储器控制器，外围总线，图形加速端口，处理器或者使用多种总线结构中的任意总线结构的局域总线。举例来说，这些体系结构包括但不限于工业标准体系结构(Industry Standard Architecture，ISA)总线，微通道体系结构(Micro Channel Architecture，MCA)总线，增强型ISA总线、视频电子标准协会(Video Electronics Standards Association，VESA)局域总线以及外围组件互连(Peripheral Component Interconnect，PCI)总线。
电子设备12典型地包括多种计算机可读介质。这些介质可以是任何能够被电子设备12访问的可用介质，包括易失性和非易失性介质，可移动的和不可移动的介质。
存储器28可以包括易失性存储器形式的计算机装置可读介质，例如随机存取存储器(Random Access Memory，RAM)30和/或高速缓存存储器32。电子设备12可以进一步包括其它可移动/不可移动的、易失性/非易失性计算机存储介质。仅作为举例，存储装置34可以用于读写不可移动的、非易失性磁介质(图6未显示，通常称为“硬盘驱动器”)。尽管图6中未示出，可以提供用于对可移动非易失性磁盘(例如“软盘”)读写的磁盘驱动器，以及对可移动非易失性光盘(例如只读光盘(Compact Disc-Read Only Memory，CD-ROM)、数字视盘(Digital Video Disc-Read Only Memory，DVD-ROM)或者其它光介质)读写的光盘驱动器。在这些情况下，每个驱动器可以通过至少一个数据介质接口与总线18相连。存储器28可以包括至少一个程序产品40，该程序产品40具有一组程序模块42，这些程序模块被配置以执行本申请各实施例的功能。程序产品40， 可以存储在例如存储器28中，这样的程序模块42包括但不限于至少一个应用程序、其它程序模块以及程序数据，这些示例中的每一个或某种组合中可能包括网络环境的实现。程序模块42通常执行本申请所描述的实施例中的功能和/或方法。
电子设备12也可以与至少一个外部设备14(例如键盘、鼠标、摄像头等和显示器)通信，还可与至少一个使得用户能与该电子设备12交互的设备通信，和/或与使得该电子设备12能与至少一个其它计算设备进行通信的任何设备(例如网卡，调制解调器等等)通信。这种通信可以通过输入/输出(I/O)接口22进行。并且，电子设备12还可以通过网络适配器20与至少一个网络(例如局域网(Local Area Network，LAN)，广域网Wide Area Network，WAN)和/或公共网络，例如因特网)通信。如图所示，网络适配器20通过总线18与电子设备12的其它模块通信。应当明白，尽管图中未示出，可以结合电子设备12使用其它硬件和/或软件模块，包括但不限于：微代码、设备驱动器、冗余处理单元、外部磁盘驱动阵列、磁盘阵列(Redundant Arrays of Independent Disks，RAID)装置、磁带驱动器以及数据备份存储装置等。
处理器16通过运行存储在存储器28中的程序，从而执行各种功能应用以及数据处理，例如实现本申请上述实施例所提供的车道保持方法，包括：
获取目标车辆的车辆行驶数据，其中，所述车辆行驶数据包括转向盘转角、车辆横向位置以及车辆横向加速度；
基于所述车辆行驶数据以及预先训练的驾驶风格辨识模型，确定所述目标车辆对应的驾驶风格类别；
基于所述驾驶风格类别以及所述目标车辆的车辆状态数据，确定所述目标车辆的目标转向盘转角，并基于所述目标转向盘转角调整所述目标车辆的转向盘转角。
当然，本领域技术人员可以理解，处理器还可以实现本申请任意实施例所提供的车道保持方法的技术方案。
实施例七
本申请实施例七还提供一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时实现如本申请任意实施例所提供的车道保持方法步骤，该方法包括：
获取目标车辆的车辆行驶数据，其中，所述车辆行驶数据包括转向盘转角、车辆横向位置以及车辆横向加速度；
基于所述车辆行驶数据以及预先训练的驾驶风格辨识模型，确定所述目标车辆对应的驾驶风格类别；
基于所述驾驶风格类别以及所述目标车辆的车辆状态数据，确定所述目标车辆的目标转向盘转角，并基于所述目标转向盘转角调整所述目标车辆的转向盘转角。
本申请实施例的计算机存储介质，可以采用至少一个计算机可读的介质的任意组合。计算机可读介质可以是计算机可读信号介质或者计算机可读存储介质。计算机可读存储介质例如可以是——但不限于——电、磁、光、电磁、红外线、或半导体的系统、装置或器件，或者任意以上的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括：具有至少一个导线的电连接、便携式计算机磁盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器((Erasable Programmable Read-Only Memory，EPROM)或闪存)、光纤、便携式紧凑磁盘只读存储器(CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。在本文件中，计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。
计算机可读的信号介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了计算机可读的程序代码。这种传播的数据信号可以采用多种形式，包括但不限于电磁信号、光信号或上述的任意合适的组合。计算机可读 的信号介质还可以是计算机可读存储介质以外的任何计算机可读介质，该计算机可读介质可以发送、传播或者传输用于由指令执行系统、装置或者器件使用或者与其结合使用的程序。
计算机可读介质上包含的程序代码可以用任何适当的介质传输，包括——但不限于无线、电线、光缆、射频(Radio Frequency，RF)等等，或者上述的任意合适的组合。
可以以一种或多种程序设计语言或其组合来编写用于执行本申请实施例操作的计算机程序代码，所述程序设计语言包括面向对象的程序设计语言-诸如Java、Smalltalk、C++，还包括常规的过程式程序设计语言——诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络——包括局域网(LAN)或广域网(WAN)-连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。

Claims (10)

  1. 一种车道保持方法，包括：
    获取目标车辆的车辆行驶数据，其中，所述车辆行驶数据包括转向盘转角、车辆横向位置以及车辆横向加速度；
    基于所述车辆行驶数据以及预先训练的驾驶风格辨识模型，确定所述目标车辆对应的驾驶风格类别；
    基于所述驾驶风格类别以及所述目标车辆的车辆状态数据，确定所述目标车辆的目标转向盘转角，并基于所述目标转向盘转角调整所述目标车辆的转向盘转角。
  2. 根据权利要求1所述的方法，还包括：
    获取样本输入向量以及所述样本输入向量对应的驾驶风格标签，将所述样本输入向量输入至学习向量量化神经网络的输入层；
    计算所述样本输入向量与所述学习向量量化神经网络的竞争层中的多个神经元的距离，在所述竞争层的多个神经元中确定出与所述样本输入向量距离最近的第一目标神经元和第二目标神经元；
    基于所述第一目标神经元与所述样本输入向量的距离、所述第二目标神经元与所述样本输入向量的距离、所述第一目标神经元对应的驾驶风格预测值、所述第二目标神经元对应的驾驶风格预测值以及所述样本输入向量对应的驾驶风格标签，更新所述学习向量量化神经网络中所述第一目标神经元和所述第二目标神经元中的至少之一对应的权值；
    将更新后的学习向量量化神经网络确定为驾驶风格辨识模型。
  3. 根据权利要求2所述的方法，其中，所述基于所述第一目标神经元与所述样本输入向量的距离、所述第二目标神经元与所述样本输入向量的距离、所述第一目标神经元对应的驾驶风格预测值、所述第二目标神经元对应的驾驶风格预测值以及所述样本输入向量对应的驾驶风格标签，更新所述学习向量量化神经网络中所述第一目标神经元和所述第二目标神经元中的至少之一对应的权值，包括：
    基于所述第一目标神经元与所述样本输入向量的距离、所述第二目标神经元与所述样本输入向量的距离、所述第一目标神经元对应的驾驶风格预测值、所述第二目标神经元对应的驾驶风格预测值，判断是否满足预设权值更新条件；
    响应于满足预设权值更新条件，基于预设学习率、所述样本输入向量以及所述驾驶风格标签，更新所述学习向量量化神经网络中所述第一目标神经元和所述第二目标神经元对应的权值。
  4. 根据权利要求1所述的方法，其中，所述车辆状态数据包括当前车速、车道中心线、当前位置侧向坐标、车辆转向系传动比以及车辆前后轴轴距，所述基于所述驾驶风格类别以及所述目标车辆的车辆状态数据，确定所述目标车辆的目标转向盘转角，满足如下公式：
    Figure PCTCN2022118342-appb-100001
    其中，θ opt为目标转向盘转角，L为车辆前后轴轴距，i为车辆转向系传动比，C y表示驾驶风格类别，v为当前车速，y(t)为当前位置侧向坐标，T为预瞄时间，f(t)为t时刻的车道中心线，f(t+T)表示t+T时刻车道中心线的侧向坐标，d为预瞄距离，
    Figure PCTCN2022118342-appb-100002
    表示车辆侧向速度。
  5. 根据权利要求1所述的方法，在所述基于所述目标转向盘转角调整所述目标车辆的转向盘转角之后，所述方法还包括：
    获取多个采集位置对应的车辆横向位置、车道中心线横向位置；
    基于多个车辆横向位置、多个车道中心线横向位置以及所述驾驶风格类别，确定所述目标车辆是否回归车道中心线。
  6. 根据权利要求5所述的方法，其中，所述基于多个车辆横向位置、多个车道中心线横向位置以及所述驾驶风格类别，确定所述目标车辆是否回归车道中心线，包括：
    根据所述多个采集位置对应的所述车辆横向位置构建多项式函数，基于所述多个采集位置对应的车道中心线横向位置计算所述多项式函数的多项式系数；
    基于所述多项式系数以及所述驾驶风格类别，确定所述目标车辆是否回归 车道中心线。
  7. 一种车道保持装置，包括：
    行驶数据获取模块，设置为获取目标车辆的车辆行驶数据，其中，所述车辆行驶数据包括转向盘转角、车辆横向位置以及车辆横向加速度；
    驾驶风格辨识模块，设置为基于所述车辆行驶数据以及预先训练的驾驶风格辨识模型，确定所述目标车辆对应的驾驶风格类别；
    转向盘调整模块，设置为基于所述驾驶风格类别以及所述目标车辆的车辆状态数据，确定所述目标车辆的目标转向盘转角，并基于所述目标转向盘转角调整所述目标车辆的转向盘转角。
  8. 一种车道保持系统，包括工控机以及转向盘总成，其中，
    所述转向盘总成，设置为获取目标车辆的转向盘转角，并将所述转向盘转角发送至所述工控机；
    所述工控机，设置为基于权利要求1-6中任一所述的车道保持方法，调整所述目标车辆的转向盘转角。
  9. 一种电子设备，包括：
    至少一个处理器；
    存储装置，设置为存储至少一个程序，
    当所述至少一个程序被所述至少一个处理器执行，使得所述至少一个处理器实现如权利要求1-6中任一所述的车道保持方法。
  10. 一种计算机可读存储介质，所述计算机可读存储介质上存储有计算机程序，所述计算机程序被处理器执行时实现如权利要求1-6中任一所述的车道保持方法。
PCT/CN2022/118342 2021-09-23 2022-09-13 车道保持方法、装置、设备、介质及系统 WO2023045791A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202111114543.2A CN113696890B (zh) 2021-09-23 2021-09-23 车道保持方法、装置、设备、介质及系统
CN202111114543.2 2021-09-23

Publications (1)

Publication Number Publication Date
WO2023045791A1 true WO2023045791A1 (zh) 2023-03-30

Family

ID=78661742

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2022/118342 WO2023045791A1 (zh) 2021-09-23 2022-09-13 车道保持方法、装置、设备、介质及系统

Country Status (2)

Country Link
CN (1) CN113696890B (zh)
WO (1) WO2023045791A1 (zh)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113696890B (zh) * 2021-09-23 2023-04-07 中国第一汽车股份有限公司 车道保持方法、装置、设备、介质及系统
FR3142729A1 (fr) * 2022-12-05 2024-06-07 Renault S.A.S. Procédé de pilotage d’un véhicule en virage

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190071079A1 (en) * 2017-09-01 2019-03-07 Honda Motor Co., Ltd. Vehicle control system, vehicle control method, and storage medium
CN110758382A (zh) * 2019-10-21 2020-02-07 南京航空航天大学 一种基于驾驶意图的周围车辆运动状态预测系统及方法
WO2020103347A1 (zh) * 2018-11-19 2020-05-28 江苏大学 一种可变车速下的可拓自适应车道保持控方法
CN112622899A (zh) * 2021-01-18 2021-04-09 中国重汽集团济南动力有限公司 一种基于预瞄面积控制的车辆车道保持方法及系统
CN113696890A (zh) * 2021-09-23 2021-11-26 中国第一汽车股份有限公司 车道保持方法、装置、设备、介质及系统

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009166722A (ja) * 2008-01-17 2009-07-30 Toyota Motor Corp 車両制御装置
CN108072381B (zh) * 2016-11-18 2020-10-27 中国移动通信有限公司研究院 一种路径规划的方法及装置
CN111717189B (zh) * 2019-03-18 2022-03-29 毫末智行科技有限公司 车道保持控制方法、装置及系统
CN111532339B (zh) * 2020-04-20 2021-11-12 合肥工业大学 一种智能车辆个性化侧向辅助驾驶方法及其系统
CN112537303B (zh) * 2020-12-14 2022-02-18 英博超算(南京)科技有限公司 一种智能车辆车道居中保持方法
CN113158349A (zh) * 2021-05-24 2021-07-23 腾讯科技（深圳）有限公司 车辆换道仿真方法、装置、电子设备及存储介质
CN113415276B (zh) * 2021-07-30 2022-10-14 东风商用车有限公司 一种智能驾驶预瞄控制方法、装置和存储介质

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20190071079A1 (en) * 2017-09-01 2019-03-07 Honda Motor Co., Ltd. Vehicle control system, vehicle control method, and storage medium
WO2020103347A1 (zh) * 2018-11-19 2020-05-28 江苏大学 一种可变车速下的可拓自适应车道保持控方法
CN110758382A (zh) * 2019-10-21 2020-02-07 南京航空航天大学 一种基于驾驶意图的周围车辆运动状态预测系统及方法
WO2021077725A1 (zh) * 2019-10-21 2021-04-29 南京航空航天大学 一种基于驾驶意图的周围车辆运动状态预测系统及方法
CN112622899A (zh) * 2021-01-18 2021-04-09 中国重汽集团济南动力有限公司 一种基于预瞄面积控制的车辆车道保持方法及系统
CN113696890A (zh) * 2021-09-23 2021-11-26 中国第一汽车股份有限公司 车道保持方法、装置、设备、介质及系统

Also Published As

Publication number Publication date
CN113696890A (zh) 2021-11-26
CN113696890B (zh) 2023-04-07

Similar Documents

Publication Publication Date Title
WO2023045791A1 (zh) 车道保持方法、装置、设备、介质及系统
JP7000638B2 (ja) 車両制御方法、装置、コンピュータ機器及び記憶媒体
KR102292277B1 (ko) 자율 주행 차량에서 3d cnn 네트워크를 사용하여 솔루션을 추론하는 lidar 위치 추정
KR102335389B1 (ko) 자율 주행 차량의 lidar 위치 추정을 위한 심층 학습 기반 특징 추출
JP7256758B2 (ja) 自動運転車両においてｒｎｎとｌｓｔｍを用いて時間平滑化を行うｌｉｄａｒ測位
WO2021212728A1 (zh) 基于对抗模仿学习的无人驾驶车辆换道决策方法和系统
WO2023024879A1 (zh) 电控后轮转向方法、装置、电子设备及存储介质
WO2022247203A1 (zh) 自动驾驶车辆的控制方法、装置、设备以及存储介质
CN104462716B (zh) 一种基于人‑车‑路模型的脑‑控车辆的脑‑机接口参数和动力学参数设计方法
CN111204348A (zh) 调节车辆行驶参数方法、装置、车辆及存储介质
CN114265411B (zh) 一种解决车辆预测模型性能受限于感知数据性能的方法
CN109991974A (zh) 自动驾驶路径跟随方法、装置及控制设备
CN107697155A (zh) 车轮转向角的控制方法、装置和车轮转向角控制器
CN113822593B (zh) 一种安全态势评估方法、装置、存储介质及电子设备
CN113044037A (zh) 智能汽车的控制方法、装置和控制系统
CN114030485A (zh) 一种考虑附着系数的自动驾驶汽车类人换道决策规划方法
CN114021840A (zh) 换道策略生成方法和装置、计算机存储介质、电子设备
CN116560223A (zh) 一种基于物理信息神经网络的智能车辆动力学模型、ilqr控制算法及轨迹跟踪控制器
WO2024051188A1 (zh) 一种车辆行驶路面坡度预测方法、系统及其车辆
CN113788022A (zh) 车辆控制方法、装置、计算机可读存储介质及电子设备
CN115542899A (zh) 车辆路径跟踪的方法、装置、车辆、电子设备及介质
CN114148349A (zh) 一种基于生成对抗模仿学习的车辆个性化跟驰控制方法
CN110654458B (zh) 车辆方向盘控制方法、装置、计算机设备及车辆
CN113753063B (zh) 一种车辆行驶指令确定方法、装置、设备及存储介质
CN113781244B (zh) 车险保费的生成方法及系统、电子设备及存储介质

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22871839

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE