CN113581208A - Driving assistance mode switching method, device, equipment and storage medium - Google Patents
Driving assistance mode switching method, device, equipment and storage medium Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
- B60W60/005—Handover processes
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
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- B60W2050/0028—Mathematical models, e.g. for simulation
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Abstract
The invention discloses a driving assistance mode switching method, a driving assistance mode switching device, driving assistance equipment and a storage medium. The method comprises the following steps: determining a driving intention according to vehicle control information acquired by a vehicle sensor; sensing the surrounding environment through a vehicle body sensor to determine the vehicle offset degree and obtain the current vehicle speed; determining a vehicle running state through a preset vehicle random motion prediction model according to the vehicle deviation degree and the current vehicle speed; and determining a corresponding target driving assistance mode according to the driving intention and the vehicle running state, and switching the current mode to the target driving assistance mode. By the mode, the driving state of the vehicle is determined according to the deviation degree of the vehicle and the current speed of the vehicle, and the switching mode is determined according to the intention of the driver and the driving state of the vehicle, so that the switching of the driving assistance mode is more intelligent, the influence of the subjective consciousness of the driver on the switching of the assistance mode is avoided, the accuracy of judging the driving assistance mode is improved, and the driving safety is improved.
Description
Technical Field
The present invention relates to the field of driving assistance technologies, and in particular, to a driving assistance mode switching method, apparatus, device, and storage medium.
Background
When driving the vehicle, the current driver manually selects the driving assistance mode according to the driving experience, and if the front is dangerous or the sight of the driver is shielded, the driver has judgment deviation, so that the adjustment is not timely, and the driving safety is difficult to guarantee.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a driving assistance mode switching method, a driving assistance mode switching device, driving assistance mode switching equipment and a storage medium, and aims to solve the technical problems that the driving assistance mode is not judged accurately and the driving safety is difficult to guarantee.
To achieve the above object, the present invention provides a driving assistance mode switching method, including the steps of:
acquiring vehicle control information through a vehicle sensor, and determining a driving intention according to the vehicle control information;
sensing the surrounding environment through a vehicle body sensor, determining the vehicle deviation degree, and acquiring the current vehicle speed;
determining a vehicle running state through a preset vehicle random motion prediction model according to the vehicle deviation degree and the current vehicle speed;
and determining a corresponding target driving assistance mode according to the driving intention and the vehicle running state, and switching the current mode to the target driving assistance mode.
Optionally, the determining a vehicle driving state according to the vehicle offset degree and the current vehicle speed through a preset vehicle random motion prediction model includes:
determining a state transition probability matrix corresponding to the Markov chain;
determining a preset vehicle random motion prediction model according to the state transition probability matrix, the vehicle deviation degree and the current vehicle speed;
determining the current vehicle state according to the preset vehicle random motion prediction model;
matching the current vehicle state with a preset typical dangerous scene to obtain a matching result;
and determining the vehicle running state according to the matching result.
Optionally, the determining a preset vehicle random motion prediction model according to the state transition probability matrix, the vehicle offset degree, and the current vehicle speed includes:
coding the vehicle offset degree and the current vehicle speed according to the Markov chain, and randomly forming a plurality of state groups;
determining a first state group corresponding to a first moment according to the state transition probability matrix and the state groups, and determining a first state meeting a preset requirement according to the first state group;
determining a second state group corresponding to a second moment according to the first state group and the state transition probability matrix, and determining a second state meeting a preset requirement according to the second state group;
determining a prediction state meeting the preset requirement within the preset prediction time by analogy;
and determining a preset vehicle random motion prediction model according to the target state, the first state, the second state and the prediction state.
Optionally, the determining the current vehicle state according to the preset vehicle random motion prediction model includes:
decoding according to the state code corresponding to the preset vehicle random motion prediction model, and determining the predicted vehicle deviation degree and the predicted vehicle speed;
and determining the current vehicle state according to the predicted vehicle deviation degree and the predicted vehicle speed.
Optionally, before the current vehicle state is matched with a preset typical dangerous scene to obtain a matching result, the method further includes:
acquiring driving lane number information, road mouth shape information, lane number information in the same direction, time period information, scene area information and traffic flow information in the driving process of a vehicle;
forming a sample set according to a plurality of groups of the driving lane number information, the road mouth shape information, the equidirectional lane number information, the time period information, the scene area information and the traffic flow information;
and performing cluster analysis according to the sample set to generate a preset typical dangerous scene.
Optionally, the performing cluster analysis according to the sample set to generate a preset typical dangerous scene includes:
determining sample distances among the samples according to the sample set;
determining typical characteristics corresponding to each scene according to the sample distance;
and generating a preset typical dangerous scene according to the typical characteristics.
Optionally, the determining a corresponding target driving assistance mode according to the driving intention and the vehicle driving state, and switching the current mode to the target driving assistance mode includes:
when the driving intention is an emergency or the vehicle running state is a dangerous state, determining that a corresponding target driving assistance mode is an emergency assistance mode, and switching a current mode to the emergency assistance mode;
when the driving intention is normal and the vehicle running state is a safe state, determining that a corresponding target driving assistance mode is a cautious assistance mode, and switching a current mode to the cautious assistance mode;
and when the driving intention is slow and the vehicle running state is a safe state, determining that the corresponding target driving assistance mode is a normal assistance mode, and switching the current mode to the normal assistance mode.
Further, in order to achieve the above object, the present invention also proposes a driving assistance mode switching device including:
the acquisition module is used for acquiring vehicle control information through a vehicle sensor and determining a driving intention according to the vehicle control information;
the determining module is used for sensing the surrounding environment through a vehicle body sensor, determining the vehicle deviation degree and acquiring the current vehicle speed;
the determining module is further used for determining a vehicle running state through a preset vehicle random motion prediction model according to the vehicle deviation degree and the current vehicle speed;
and the switching module is used for determining a corresponding target driving assistance mode according to the driving intention and the vehicle running state and switching the current mode to the target driving assistance mode.
Further, to achieve the above object, the present invention also proposes a driving assistance mode switching apparatus including: a memory, a processor, and a driving assistance mode switching program stored on the memory and executable on the processor, the driving assistance mode switching program configured to implement the driving assistance mode switching method as described above.
Further, in order to achieve the above object, the present invention also proposes a storage medium having stored thereon a driving assistance mode switching program that, when executed by a processor, implements the driving assistance mode switching method as described above.
According to the invention, vehicle control information is obtained through a vehicle sensor, and a driving intention is determined according to the vehicle control information; sensing the surrounding environment through a vehicle body sensor, determining the vehicle deviation degree, and acquiring the current vehicle speed; determining a vehicle running state through a preset vehicle random motion prediction model according to the vehicle deviation degree and the current vehicle speed; and determining a corresponding target driving assistance mode according to the driving intention and the vehicle running state, and switching the current mode to the target driving assistance mode. By the mode, the driving intention is determined according to the vehicle control information, the vehicle running state is determined according to the vehicle deviation degree and the current vehicle speed, the switching mode is selected according to the driver intention and the vehicle running state, the intelligence of driving auxiliary mode switching is improved, the mode needing to be switched is determined according to objective perception data around the vehicle, the influence of the subjective consciousness of the driver on the auxiliary mode switching is avoided, the driving safety is improved, and the problem that the driving auxiliary mode is inaccurate in judgment is solved.
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Fig. 1 is a schematic structural diagram of a driving assistance mode switching apparatus in a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a first exemplary embodiment of a driving assistance mode switching method according to the present invention;
FIG. 3 is a flowchart illustrating a driving assistance mode switching method according to a second embodiment of the present invention;
fig. 4 is a block diagram showing the configuration of the driving assistance mode switching apparatus according to the first embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a driving assistance mode switching device in a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the driving assistance mode switching apparatus may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (Wi-Fi) interface). The Memory 1005 may be a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in fig. 1 does not constitute a limitation of the driving assistance mode switching apparatus, and may include more or less components than those shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a network communication module, a user interface module, and a driving assistance mode switching program.
In the driving assistance mode switching apparatus shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the driving assistance mode switching device of the present invention may be provided in the driving assistance mode switching device that calls the driving assistance mode switching program stored in the memory 1005 through the processor 1001 and executes the driving assistance mode switching method provided by the embodiment of the present invention.
An embodiment of the present invention provides a driving assistance mode switching method, and referring to fig. 2, fig. 2 is a flowchart illustrating a first embodiment of the driving assistance mode switching method according to the present invention.
In this embodiment, the driving assistance mode switching method includes the steps of:
step S10: vehicle control information is acquired through a vehicle sensor, and driving intentions are determined according to the vehicle control information.
It can be understood that the execution subject of this embodiment is a driving assistance mode switching device, and the driving assistance mode switching device may be a vehicle control unit, a controller connected to a vehicle control end, or other devices, and may also be a domain controller, or other devices having the same or similar functions, and this embodiment takes the domain controller as an example for description. The domain controller is connected with a sensor which is arranged on the vehicle and used for collecting vehicle control information, receives the vehicle control information sent by the sensor, and determines the driving intention according to the vehicle control information.
It should be noted that the vehicle control information includes accelerator pedal opening, accelerator pedal opening change rate, brake pedal opening change rate, steering wheel angle opening change rate, accelerator pedal cycle control times, brake pedal cycle control times and steering wheel cycle control times, the accelerator pedal opening change rate, the brake pedal opening change rate, the steering wheel angle opening and the steering wheel angle opening change rate are input to a pre-constructed pre-set driver state model, the model is analyzed and optimized by a pre-set adaptive particle swarm optimization to determine an initial driving intention, a cycle driving intention is determined according to the accelerator pedal cycle control times, the brake pedal cycle control times and the steering wheel cycle control times, and the analysis is performed by combining the initial driving intention and the initial driving intention, the driving intention is obtained.
It is understood that the pre-constructed preset driver state model is determined by equation (1):
wherein x is1∈(α，dα/dt)，x2∈(θ，dθ/dt)，x3∈(δ，dδ/dt)，f(x1，x2，x3)∈[1，3]1 represents emergency driving, 2 represents normal driving, 3 represents slow driving, alpha is accelerator pedal opening, d alpha/dt is accelerator pedal opening change rate, theta is brake pedal opening, and d theta/dt is brake pedal opening change rateThe change rate δ is the steering wheel angle opening, and d δ/dt is the steering wheel angle opening change rate.
It should be noted that, optimization is performed through a preset adaptive particle swarm algorithm according to a preset driver state model to obtain an optimal value, and an initial driving intention close to the actual optimal value, namely, emergency driving, normal driving or mild driving is output according to an input accelerator pedal, a brake pedal, a steering wheel steering opening and a corresponding change rate.
It is understood that the preset adaptive particle swarm algorithm is characterized by formula (2) and formula (3):
explaining the optimization process by combining the formula (2) and the formula (3), the ith particle in the training set L is expressed as a vector of L, Xi＝(xi1，xi2，...，xiL) I is 1, 2, 3, i.e. the position of the ith particle in the training set is XiThe optimal position that the ith particle experiences is Pbesti＝(pi1，pi2，...，piL) And i is 1, 2 and 3, namely the current individual optimal position, each position of the particle represents a potential solution of the requirement, and the position of the particle is input into an objective function to obtain the fitness value of the ith particle, so as to judge the quality degree of the particle. The optimal position searched by the whole particle swarm is Gbestg＝(pjg) I is 1, 2, 3, i.e. the current global optimal position, g denotes the index of the optimal particle position. ω represents the weight of the inertia,
for the historical optimal solution searched for the ith particle to the tth generation,
the global optimum position searched so far for the whole particle swarm.
Respectively representing the current position and flight speed of the ith particle, c1，c2Denotes a non-negative constant, r1，r2Is [0, 1]]A random number in between. In this embodiment, the iterative evolution frequency of the algorithm is set to 1000 times, and an acceleration factor c is preset1＝1.4，c2The preset inertial weight ω is 0.8, 1.5. In the optimization process, for each particle, the fitness value of the particle is compared with the fitness value of the experienced current individual optimal position Pbesti, and if the fitness value is better, the position of the particle is used as a new current individual optimal position. For each particle, its fitness value is compared to the global best experienced position GbestgAnd if the fitness value is better, the position of the particle is taken as a new global optimal position. If the global optimal position cannot meet the minimum error requirement, the initial driving intention output by the representation is inconsistent with the actual driving intention, the speed and the position of the particle are optimized according to the formula (2) and the formula (3), and the new particle is compared with the current individual optimal position and the global optimal position until the global optimal position meets the minimum error requirement.
The process of determining the periodic driving intention according to the number of accelerator pedal period controls, the number of brake pedal period controls, and the number of steering wheel period controls is as follows: explaining by taking the acquisition period as the previous 10 seconds of the current time, the periodic driving intention is determined by formula (4):
wherein the periodic driving intention T epsilon [1, 3]]1 for emergency driving, 2 for normal driving, 3 for mild driving, FαFor the number of accelerator pedal controls, FθFor the number of brake pedal controls, FδThe number of times of steering wheel control is set; within a preset acquisition periodIf the control times of the driver on the accelerator pedal, the brake pedal and the steering wheel are less than 3 times, the periodic driving intention is considered to be moderate driving; if the control times of the accelerator pedal and the brake pedal of the driver are more than or equal to 3 times and the control times of the steering wheel is less than 3 times, the periodic driving intention is considered as normal driving; and if the control times of the accelerator pedal, the brake pedal and the steering wheel by the driver are more than or equal to 3 times, the periodic driving intention is considered to be urgent driving.
In a specific implementation, the driving intention intent entry is determined by formula (5) in combination with the initial driving intention and the initial driving intention:
in a specific implementation, the periodic driving intention T is taken as a main part, and when the periodic driving intention T is 1, the driving intention duration is determined as emergency driving; when the periodic driving intention T is more than or equal to 2 and the initial driving intention f (x)1，x2，x3) If the driving intention duration is less than 3, the driving intention duration is determined as normal driving; when the periodic driving intention T is 3 and the initial driving intention f (x)1，x2，x3) When the driving intention duration is 3, the driving intention duration is determined as the moderate driving.
Step S20: the peripheral environment is sensed through the vehicle body sensors, the vehicle deviation degree is determined, and the current vehicle speed is obtained.
The vehicle offset degree is the ratio of the distance between the vehicle and the road center line to the road width, in the specific implementation, the lane line position is identified by the aid of a lane line identification technology through cameras installed on the periphery of the vehicle, the distance between the vehicle and the lane line is determined according to internal and external parameters and installation height of the cameras, the distance between the vehicle and the road center line is further determined, the road width is a fixed numerical value, and the vehicle offset degree can be obtained according to the pre-stored road width when the distance between the vehicle and the road center line is obtained. The current speed is the running speed of the vehicle at the current moment and is acquired through a speed sensor arranged on the vehicle.
Step S30: and determining the running state of the vehicle through a preset vehicle random motion prediction model according to the vehicle deviation degree and the current vehicle speed.
It should be noted that a random array is constructed according to the vehicle offset degree and the current vehicle speed and used for representing the current state, a state transition matrix is constructed based on a Markov chain model, the state corresponding to the next moment is predicted according to the state transition matrix and the current state, by analogy, the states corresponding to a plurality of moments are determined, a preset vehicle random motion prediction model is generated according to the states corresponding to the plurality of moments, and the vehicle running state is determined according to the preset vehicle random motion prediction model.
Step S40: and determining a corresponding target driving assistance mode according to the driving intention and the vehicle running state, and switching the current mode to the target driving assistance mode.
Specifically, the step S40 includes: when the driving intention is an emergency or the vehicle running state is a dangerous state, determining that a corresponding target driving assistance mode is an emergency assistance mode, and switching a current mode to the emergency assistance mode; when the driving intention is normal and the vehicle running state is a safe state, determining that a corresponding target driving assistance mode is a cautious assistance mode, and switching a current mode to the cautious assistance mode; and when the driving intention is slow and the vehicle running state is a safe state, determining that the corresponding target driving assistance mode is a normal assistance mode, and switching the current mode to the normal assistance mode.
When the driving intention and the vehicle driving state are acquired, the domain controller determines the corresponding target driving assistance mode according to equation (6):
wherein, the driving assistance mode (das) is e [1, 3], 1 represents an Imminence emergency assistance mode, 2 represents a Cautious assistance mode, and 3 represents a Normal assistance mode; the driving intention duration is formed by [1, 3], 1 represents urgent driving, 2 represents moderate driving, and 3 represents normal driving; the Vehicle running state Vehicle includes a dangerous state Danger and a normal running state ratio.
It should be understood that when the driving assistance mode is the Imminence emergency assistance mode, which indicates that the driver or the vehicle is currently in a dangerous environment, the assistance manner of the vehicle in the Imminence emergency assistance mode is as follows: the driving assistance such as automatic emergency brake, safety belt, safety air bag and the like adjusts the automatic adjusting function setting such as automatic emergency brake sensitivity, braking distance and the like to the highest, and the safety belt and the safety air bag are triggered at any time to prevent vehicle collision and protect the safety of a driver to the maximum extent. When the driving assistance mode is the Cautious assistance mode, the driving state of a driver is normal, the environment where the vehicle is located is safe, and the driving assistance is in a Cautious state; when the driving assistance mode is Normal, the driving state of the driver is mild, the environment of the vehicle is safe, and the driving assistance is in a Normal state.
The embodiment acquires vehicle control information through a vehicle sensor and determines the driving intention according to the vehicle control information; sensing the surrounding environment through a vehicle body sensor, determining the vehicle deviation degree, and acquiring the current vehicle speed; determining a vehicle running state through a preset vehicle random motion prediction model according to the vehicle deviation degree and the current vehicle speed; and determining a corresponding target driving assistance mode according to the driving intention and the vehicle running state, and switching the current mode to the target driving assistance mode. By the mode, the driving intention is determined according to the vehicle control information, the vehicle running state is determined according to the vehicle deviation degree and the current vehicle speed, the switching mode is selected according to the driver intention and the vehicle running state, the intelligence of driving auxiliary mode switching is improved, the mode needing to be switched is determined according to objective perception data around the vehicle, the influence of the subjective consciousness of the driver on the auxiliary mode switching is avoided, the driving safety is improved, and the problem that the driving auxiliary mode is inaccurate in judgment is solved.
Referring to fig. 3, fig. 3 is a flowchart illustrating a driving assistance mode switching method according to a second embodiment of the present invention.
Based on the first embodiment described above, the step S30 of the driving assistance mode switching method of the present embodiment includes:
step S301: and determining a state transition probability matrix corresponding to the Markov chain.
It can be understood that the present embodiment employs a discrete adaptive markov chain, and has a markov chain model (S, P), where S is a non-empty state set composed of all states of the markov chain, and P is a markov chain state transition probability matrix. The specific process can be as follows: the state transition probability is determined by the conditional probability definition by equation (7):
pij＝P{X{t+1}＝sj|X(t)＝si} (7)
wherein p isijIs the transition probability of the Markov chain { X (t) } at the t moment and represents the time from the t moment siTransition to t +1 time sjI.e., the probability of the vehicle state at the present moment transitioning to the vehicle state at the next moment. In the training set, the vehicle deviation degree and the vehicle speed are divided into a plurality of segments to form different states, and a state transition probability matrix P is formed according to the different state transitions, and is expressed as formula (8):
wherein N isijIndicating the current vehicle state siThe next time is shifted to sjFrequency of (1, sigma)j NijIndicating the current state siThe frequency of transitions to all states at the next time.
Step S302: and determining a preset vehicle random motion prediction model according to the state transition probability matrix, the vehicle deviation degree and the current vehicle speed.
It should be noted that the preset vehicle random motion prediction model is a state at a plurality of moments determined according to the state transition probability matrix, the vehicle offset degree and the current vehicle speed, and the preset vehicle random motion prediction model is formed according to the state at the plurality of moments.
Specifically, the step S302 includes: coding the vehicle offset degree and the current vehicle speed according to the Markov chain, and randomly forming a plurality of state groups; determining a first state group corresponding to a first moment according to the state transition probability matrix and the state groups, and determining a first state meeting a preset requirement according to the first state group; determining a second state group corresponding to a second moment according to the first state group and the state transition probability matrix, and determining a second state meeting a preset requirement according to the second state group; determining a prediction state meeting the preset requirement within the preset prediction time by analogy; and determining a preset vehicle random motion prediction model according to the target state, the first state, the second state and the prediction state.
It can be appreciated that suppose [ d ]0，v0]Corresponding to the state s of the vehicle at the present moment0Current time U0＝s0Generating a random array, i.e. a number of state sets { r }1，r2，r3,., a random array of states that characterize the likely travel of the vehicle, e.g., r1Indicates the left turn r2Denotes straight going, r3Indicating a right turn, in the current vehicle state X (0) ═ s0Predicting the vehicle state at the next moment under the condition that the vehicle state occurs, wherein the conditional distribution probability of the vehicle state X (1) at the next moment is p0j＝P{X{1}＝sj|X(0)＝siThe state transition probability matrix represents the probability that the vehicle state at the current moment is transferred to a certain state of the vehicle at the next moment, for example, the vehicle at the current moment is in a straight line, and the probability of the vehicle turning left, the probability of the vehicle turning straight, the probability of the vehicle turning right, and the like at the next moment.
It should be noted that, the process of determining to predict the state at the next time according to the state transition probability matrix and the state groups may be: taking the kth from the state transition probability matrix P0All elements are listed; taking a random number r1If there is a k for a certain1Satisfy the requirement of
Namely canDetermining the state of the vehicle at the next moment as s1I.e. U1＝s1。
It will be appreciated that a random number r is assumed1K representing taking the state transition probability matrix assuming the vehicle is ready to turn left1And the line represents that the vehicle state transition probability at the next moment is taken out, and if the left turning state of the vehicle is assumed to satisfy k1And (4) considering the condition of vehicle state transition at the moment, preparing left turn at the next moment of the vehicle, and repeating the steps, and if the condition is not met, taking a random number r2Continue at k1And comparing the vehicle state transition probabilities at the moment. Until the state of the vehicle transition is assumed to satisfy the vehicle state transition probability. By analogy, according to k1Time of day vehicle state and state transition probability matrix pair k2Predicting the vehicle states at all times to obtain a plurality of vehicle states to form a preset vehicle random motion prediction model Un＝{s1，s2，...)。
Step S303: and determining the current vehicle state according to the preset vehicle random motion prediction model.
Specifically, the step S303 includes: decoding according to the state code corresponding to the preset vehicle random motion prediction model, and determining the predicted vehicle deviation degree and the predicted vehicle speed; and determining the current vehicle state according to the predicted vehicle deviation degree and the predicted vehicle speed.
It can be understood that the corresponding vehicle deviation state and the corresponding vehicle speed information are determined through back-check coding according to the preset vehicle random motion prediction model, and the current vehicle state is obtained.
Step S304: and matching the current vehicle state with a preset typical dangerous scene to obtain a matching result.
Further, before the step S304, the method further includes: acquiring driving lane number information, road mouth shape information, lane number information in the same direction, time period information, scene area information and traffic flow information in the driving process of a vehicle; forming a sample set according to a plurality of groups of the driving lane number information, the road mouth shape information, the equidirectional lane number information, the time period information, the scene area information and the traffic flow information; and performing cluster analysis according to the sample set to generate a preset typical dangerous scene.
It should be noted that, when analyzing a preset typical dangerous scene, the method collects features of 7 scene elements, which includes: travel lane number information, intersection shape information, number of lanes in the same direction information, time slot information, scene area information, and traffic flow information. In a specific implementation, the distance between different variables is defined as 1, the distance between the same variables is defined as 0, and the variable codes in the scene elements in the collected sample set are converted into vectors, for example: the variables of the time zone information are day and night, and the corresponding vectors are coded with day [1, 0] and night [0, 1 ]. And performing cluster analysis according to the converted sample set to generate a preset typical dangerous scene.
Specifically, performing cluster analysis according to the sample set to generate a preset typical dangerous scene, including: determining sample distances among the samples according to the sample set; determining typical characteristics corresponding to each scene according to the sample distance; and generating a preset typical dangerous scene according to the typical characteristics.
It can be appreciated that suppose X1，X2，X3，...，XnFor p-ary samples from the sample set, the ith sample is denoted Xi＝(xi1，xi2，xi3，...，xip) (i ═ 1, 2, 3.., n), the distance d between the ith and jth samplesijExpressed by equation (9):
according to the comparison between the proportion of the sample space in each scene element and the proportion of the sample space in the overall training set, the typical feature of each scene element is determined, in the embodiment, 3 preset typical dangerous scenes are determined, namely: a driving lane number (lane No. 1), a lane intersection shape (non-intersection), the number of lanes in the same direction (lane 2), a time period (daytime), a scene area (city), and a traffic flow (vehicle on the right side); and the second method comprises the following steps: a driving lane number (lane No. 3), a lane intersection shape (t-intersection), the number of lanes in the same direction (lane No. 3), a time period (evening), a scene area (high speed), a traffic flow (intruding from both sides); and the third is that: the number of the running lanes (lane 1), the shape of the road junction (non-junction), the number of the lanes in the same direction (lane 1), the time period (daytime), the scene area (suburb) and the traffic flow (vehicles on the left side).
In the specific implementation, when the current vehicle state is matched with the preset typical dangerous scene, discrete information is selected from the preset typical dangerous scene, and the discrete information is matched with the current vehicle state, so that a matching result is obtained.
Step S305: and determining the vehicle running state according to the matching result.
It should be noted that, whether the current Vehicle state belongs to a typical dangerous scene is determined, and a Vehicle driving state Vehicle at the Vehicle end is obtained, which includes a dangerous state Danger and a normal driving state ratio.
In the embodiment, a state transition probability matrix corresponding to a mahalanobis chain is determined; determining a preset vehicle random motion prediction model according to the state transition probability matrix, the vehicle offset degree and the current vehicle speed; determining the current vehicle state according to a preset vehicle random motion prediction model; matching the current vehicle state with a preset typical dangerous scene to obtain a matching result; and determining the vehicle running state according to the matching result. And determining a corresponding target driving assistance mode according to the driving intention and the vehicle running state, and switching the current mode to the target driving assistance mode. Through the mode, the vehicle state is predicted according to the Markov chain state transition probability matrix, the vehicle running state is determined according to the matching result of the vehicle state and the preset typical dangerous scene, the switching mode is selected according to the intention of a driver and the vehicle running state, the intelligence of switching the driving auxiliary mode is improved, the dangerous running state is predicted in advance, the driving safety is improved, and the problem that the judgment of the driving auxiliary mode is inaccurate is solved.
Furthermore, an embodiment of the present invention also proposes a storage medium having a driving assistance mode switching program stored thereon, which when executed by a processor implements the driving assistance mode switching method as described above.
Since the storage medium adopts all technical solutions of all the embodiments, at least all the beneficial effects brought by the technical solutions of the embodiments are achieved, and no further description is given here.
Referring to fig. 4, fig. 4 is a block diagram showing the configuration of the driving assistance mode switching apparatus according to the first embodiment of the present invention.
As shown in fig. 4, a driving assistance mode switching apparatus according to an embodiment of the present invention includes:
the acquisition module 10 is used for acquiring vehicle control information through a vehicle sensor and determining the driving intention according to the vehicle control information.
The determining module 20 is configured to sense the surrounding environment through a vehicle body sensor, determine a vehicle deviation degree, and obtain a current vehicle speed.
The determining module 20 is further configured to determine a vehicle running state according to the vehicle deviation degree and the current vehicle speed through a preset vehicle random motion prediction model.
And a switching module 30, configured to determine a corresponding target driving assistance mode according to the driving intention and the vehicle driving state, and switch a current mode to the target driving assistance mode.
It should be understood that the above is only an example, and the technical solution of the present invention is not limited in any way, and in a specific application, a person skilled in the art may set the technical solution as needed, and the present invention is not limited thereto.
The embodiment acquires vehicle control information through a vehicle sensor and determines the driving intention according to the vehicle control information; sensing the surrounding environment through a vehicle body sensor, determining the vehicle deviation degree, and acquiring the current vehicle speed; determining a vehicle running state through a preset vehicle random motion prediction model according to the vehicle deviation degree and the current vehicle speed; and determining a corresponding target driving assistance mode according to the driving intention and the vehicle running state, and switching the current mode to the target driving assistance mode. By the mode, the driving intention is determined according to the vehicle control information, the vehicle running state is determined according to the vehicle deviation degree and the current vehicle speed, the switching mode is selected according to the driver intention and the vehicle running state, the intelligence of driving auxiliary mode switching is improved, the mode needing to be switched is determined according to objective perception data around the vehicle, the influence of the subjective consciousness of the driver on the auxiliary mode switching is avoided, the driving safety is improved, and the problem that the driving auxiliary mode is inaccurate in judgment is solved.
It should be noted that the above-described work flows are only exemplary, and do not limit the scope of the present invention, and in practical applications, a person skilled in the art may select some or all of them to achieve the purpose of the solution of the embodiment according to actual needs, and the present invention is not limited herein.
In addition, the technical details that are not elaborated in the embodiment may refer to the driving assistance mode switching method provided by any embodiment of the present invention, and are not described herein again.
In an embodiment, the determining module 20 is further configured to determine a state transition probability matrix corresponding to the mahalanobis chain, determine a preset vehicle random motion prediction model according to the state transition probability matrix, the vehicle offset degree, and the current vehicle speed, determine a current vehicle state according to the preset vehicle random motion prediction model, match the current vehicle state with a preset typical dangerous scene to obtain a matching result, and determine a vehicle driving state according to the matching result.
In an embodiment, the determining module 20 is further configured to encode the vehicle deviation degree and the current vehicle speed according to a mahalanobis chain, randomly form a plurality of state groups, determine a first state group corresponding to a first time according to the state transition probability matrix and the plurality of state groups, determine a first state meeting a preset requirement according to the first state group, determine a second state group corresponding to a second time according to the first state group and the state transition probability matrix, determine a second state meeting the preset requirement according to the second state group, and so on, determine a prediction state meeting the preset requirement within a preset prediction time, and determine a preset vehicle random motion prediction model according to the target state, the first state, the second state, and the prediction state.
In an embodiment, the determining module 20 is further configured to decode according to a state code corresponding to the preset vehicle random motion prediction model, determine a predicted vehicle deviation degree and a predicted vehicle speed, and determine a current vehicle state according to the predicted vehicle deviation degree and the predicted vehicle speed.
In one embodiment, the driving assistance mode switching apparatus further includes a scene generation module;
the scene generation module is used for acquiring driving lane number information, road mouth shape information, equidirectional lane number information, time period information, scene area information and traffic flow information in the driving process of a vehicle, forming a sample set according to a plurality of groups of driving lane number information, road mouth shape information, equidirectional lane number information, time period information, scene area information and traffic flow information, performing cluster analysis according to the sample set, and generating a preset typical dangerous scene.
In an embodiment, the scene generation module is further configured to determine a sample distance between each sample according to the sample set, determine a typical feature corresponding to each scene according to the sample distance, and generate a preset typical dangerous scene according to the typical feature.
In an embodiment, the switching module 30 is further configured to determine that the corresponding target driving assistance mode is an emergency assistance mode when the driving intention is emergency or the vehicle driving state is a dangerous state, switch the current mode to the emergency assistance mode, determine that the corresponding target driving assistance mode is a cautious assistance mode when the driving intention is normal and the vehicle driving state is a safe state, switch the current mode to the cautious assistance mode, determine that the corresponding target driving assistance mode is a normal assistance mode when the driving intention is slow and the vehicle driving state is a safe state, and switch the current mode to the normal assistance mode.
Further, it is to be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g. Read Only Memory (ROM)/RAM, magnetic disk, optical disk), and includes several instructions for enabling a terminal device (e.g. a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A driving assistance mode switching method characterized by comprising:
acquiring vehicle control information through a vehicle sensor, and determining a driving intention according to the vehicle control information;
sensing the surrounding environment through a vehicle body sensor, determining the vehicle deviation degree, and acquiring the current vehicle speed;
determining a vehicle running state through a preset vehicle random motion prediction model according to the vehicle deviation degree and the current vehicle speed;
and determining a corresponding target driving assistance mode according to the driving intention and the vehicle running state, and switching the current mode to the target driving assistance mode.
2. The driving-assist mode switching method according to claim 1, wherein the determining a vehicle running state by a preset vehicle random motion prediction model according to the degree of vehicle deviation and the current vehicle speed includes:
determining a state transition probability matrix corresponding to the Markov chain;
determining a preset vehicle random motion prediction model according to the state transition probability matrix, the vehicle deviation degree and the current vehicle speed;
determining the current vehicle state according to the preset vehicle random motion prediction model;
matching the current vehicle state with a preset typical dangerous scene to obtain a matching result;
and determining the vehicle running state according to the matching result.
3. The driving-assist mode switching method according to claim 2, wherein the determining a preset vehicle stochastic motion prediction model based on the state transition probability matrix, the degree of vehicle deviation, and the current vehicle speed includes:
coding the vehicle offset degree and the current vehicle speed according to the Markov chain, and randomly forming a plurality of state groups;
determining a first state group corresponding to a first moment according to the state transition probability matrix and the state groups, and determining a first state meeting a preset requirement according to the first state group;
determining a second state group corresponding to a second moment according to the first state group and the state transition probability matrix, and determining a second state meeting a preset requirement according to the second state group;
determining a prediction state meeting the preset requirement within the preset prediction time by analogy;
and determining a preset vehicle random motion prediction model according to the target state, the first state, the second state and the prediction state.
4. The driving-assist mode switching method according to claim 2, wherein the determining the current vehicle state according to the preset vehicle stochastic motion prediction model includes:
decoding according to the state code corresponding to the preset vehicle random motion prediction model, and determining the predicted vehicle deviation degree and the predicted vehicle speed;
and determining the current vehicle state according to the predicted vehicle deviation degree and the predicted vehicle speed.
5. The driving assist mode switching method according to claim 2, wherein before the matching of the current vehicle state with a preset typical risk scenario results in a matching result, the method further comprises:
acquiring driving lane number information, road mouth shape information, lane number information in the same direction, time period information, scene area information and traffic flow information in the driving process of a vehicle;
forming a sample set according to a plurality of groups of the driving lane number information, the road mouth shape information, the equidirectional lane number information, the time period information, the scene area information and the traffic flow information;
and performing cluster analysis according to the sample set to generate a preset typical dangerous scene.
6. The driving assistance mode switching method according to claim 5, wherein the performing cluster analysis according to the sample set to generate a preset typical risk scenario includes:
determining sample distances among the samples according to the sample set;
determining typical characteristics corresponding to each scene according to the sample distance;
and generating a preset typical dangerous scene according to the typical characteristics.
7. The driving assistance mode switching method according to any one of claims 1 to 6, wherein the determining a corresponding target driving assistance mode according to the driving intention and the vehicle running state, and switching a current mode to the target driving assistance mode includes:
when the driving intention is an emergency or the vehicle running state is a dangerous state, determining that a corresponding target driving assistance mode is an emergency assistance mode, and switching a current mode to the emergency assistance mode;
when the driving intention is normal and the vehicle running state is a safe state, determining that a corresponding target driving assistance mode is a cautious assistance mode, and switching a current mode to the cautious assistance mode;
and when the driving intention is slow and the vehicle running state is a safe state, determining that the corresponding target driving assistance mode is a normal assistance mode, and switching the current mode to the normal assistance mode.
8. A driving assistance mode switching device characterized by comprising:
the acquisition module is used for acquiring vehicle control information through a vehicle sensor and determining a driving intention according to the vehicle control information;
the determining module is used for sensing the surrounding environment through a vehicle body sensor, determining the vehicle deviation degree and acquiring the current vehicle speed;
the determining module is further used for determining a vehicle running state through a preset vehicle random motion prediction model according to the vehicle deviation degree and the current vehicle speed;
and the switching module is used for determining a corresponding target driving assistance mode according to the driving intention and the vehicle running state and switching the current mode to the target driving assistance mode.
9. A driving assistance mode switching apparatus characterized in that the apparatus comprises: a memory, a processor, and a driving assistance mode switching program stored on the memory and executable on the processor, the driving assistance mode switching program being configured to implement the driving assistance mode switching method according to any one of claims 1 to 7.
10. A storage medium, characterized in that the storage medium has stored thereon a driving assistance mode switching program that, when executed by a processor, implements the driving assistance mode switching method according to any one of claims 1 to 7.
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