CN113753047B - State machine, state machine switching method and unmanned automobile - Google Patents
State machine, state machine switching method and unmanned automobile 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
- B60W60/001—Planning or execution of driving tasks
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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
- 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
- B60W30/18—Propelling the vehicle
- B60W30/18009—Propelling the vehicle related to particular drive situations
- B60W30/18163—Lane change; Overtaking manoeuvres
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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
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- B60—VEHICLES IN GENERAL
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- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
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Abstract
The embodiment of the invention relates to the technical field of unmanned vehicles, in particular to a state machine, a switching method of the state machine and an unmanned vehicle, wherein when the state machine is in a starting state, whether input information is complete or not and variables related to an initialization task are checked, if yes, the state is switched to a lane returning state, the unmanned vehicle can be ensured to accurately return to a lane, and the unmanned vehicle is a premise of safe operation. When the state machine is in the lane returning state, judging whether the lane returning of the unmanned automobile is successful or not, if so, switching to the line-patrol state, and improving the safety. When the state machine is in a line inspection state, judging whether the lane needs to be switched according to the current driving road condition, and if so, switching to the lane switching state to enable the behavior of the unmanned automobile to accord with the road condition. From the three aspects, the state machine is switched, so that the behavior of the unmanned automobile in the running process can be accurately and stably planned.
Description
Technical Field
The embodiment of the invention relates to the technical field of unmanned aerial vehicles, in particular to a state machine, a switching method of the state machine and an unmanned automobile.
Background
Behavior control of an unmanned vehicle during driving is an important part of the field of automatic driving. The behavior control is based on positioning information, perception information, prediction information, map information and global planning information to determine the real-time behavior which the unmanned vehicle should execute, so that automatic unmanned is realized.
At present, the switching method of the state machine mainly depends on the accuracy of the perception information and the prediction result, and when the perception information and the prediction result are wrong or jump occurs, stable planning is often difficult to make.
Disclosure of Invention
The embodiment of the invention mainly solves the technical problem of providing a state machine, a switching method of the state machine and an unmanned automobile, and can accurately and stably plan the behavior of the unmanned automobile in the driving process.
In order to solve the technical problems, in a first aspect, the embodiment of the invention provides a switching method of a state machine, wherein the state machine is arranged in an unmanned automobile and is used for realizing behavior planning of the unmanned automobile, and the state machine comprises four working states, namely a starting state, a lane returning state, a line patrol state and a lane cutting state; when the system is in a starting state, the state machine checks whether the input information is complete and the variables related to the initialization task, and when the system is in a lane returning state, the state machine sends a lane returning operation instruction to a local planning module of the unmanned vehicle; when the vehicle is in a line patrol state, the state machine sends a running instruction along the current route to a local planning module of the unmanned vehicle; when the vehicle is in a lane cutting state, the state machine sends an instruction for switching from a current lane to a target lane to a local planning module of the unmanned vehicle; the switching method of the state machine comprises the following steps:
When the state machine is in a starting state, checking whether the input information is complete and the variables related to the initialization task, if so, switching to a regression lane state;
when the state machine is in the lane returning state, judging whether the lane returning of the unmanned automobile is successful, if so, switching to the line patrol state;
when the state machine is in the line inspection state, judging whether the lane needs to be switched according to the current driving road condition, and if so, switching to the lane switching state.
In some embodiments, when the state machine is in the line patrol state, determining whether the lane needs to be switched according to the current driving road condition, and if so, switching to the lane switching state includes:
respectively calculating traffic values of all lanes, wherein the traffic values are the number of lanes required to be switched when lane switching is carried out from a lane to a steering lane, and the steering lane is a lane corresponding to the unmanned vehicle when turning in the process of driving to a terminal point;
and judging whether the lane needs to be switched and the target lane needs to be switched according to the traffic values and the static obstacle distance on each lane.
In some embodiments, the step of determining whether to switch lanes and the target lane to be switched according to each traffic value and the static obstacle distance on each lane includes:
If no static obstacle exists in front of the current lane within a first preset distance, determining that lane switching is not needed;
if a static obstacle exists in front of the current lane within the first preset distance, calculating the switching cost of each lane, and determining the lane needing to be switched and the lane with the minimum switching cost as the target lane.
In some embodiments, the step of calculating the switching cost of each lane includes:
calculating the switching cost of a lane i according to the following formula, wherein the lane i is any one of the lanes;
wherein, cost (i) For the switching cost of the lane i, j represents whether there is a static obstacle in front of the lane i within the first preset distance, j=1 when there is a static obstacle in front of the lane i within the first preset distance, j=0 when there is no static obstacle in front of the lane i within the first preset distance, a represents the traffic value of the lane i, d i Representing a static obstacle in front of the lane iDistance of object, b represents whether there is no static obstacle and there is a dynamic obstacle in front of the lane i, b=1 when there is no static obstacle and there is a dynamic obstacle in front of the lane i, otherwise b=0, c represents whether there is a pedestrian in front of the lane i, c=1 when there is a pedestrian in front of the lane i, otherwise c=0, e i Is the distance of the pedestrian in front.
In some embodiments, further comprising:
if the lane-cutting state lasts for a preset time and the direction of the target lane is consistent with the direction indicated by the steering lamp of the unmanned automobile, controlling the unmanned automobile to change the lane to the target lane;
otherwise, switching to the line patrol state.
In some embodiments, further comprising:
and if the deviation between the unmanned automobile and the current lane is greater than a preset deviation threshold value, switching to a lane returning state.
And if the deviation between the unmanned automobile and the current lane is smaller than or equal to the preset deviation threshold value, switching to a line patrol state.
In some embodiments, if the state machine further includes a park state,
the method further comprises:
and if the distance between the unmanned automobile and the driving end point is smaller than or equal to a second preset distance, switching to a parking state.
In some embodiments, if the state machine also includes a checkpointed state,
the method further comprises:
and when the distance between the unmanned automobile and the check point is smaller than or equal to a third preset distance, switching to a check point state, wherein the check point is a position point on the driving path of the unmanned automobile.
In order to solve the technical problems, in a second aspect, the embodiment of the invention provides a state machine, which is arranged in an unmanned automobile and is used for realizing behavior planning of the unmanned automobile, wherein the state machine is at least provided with four working states, namely a starting state, a lane returning state, a line patrol state and a lane cutting state; when the state machine is in a starting state, the state machine checks whether the input information is complete and the variables related to the initialization task, and when the state machine is in a lane returning state, the state machine sends a lane returning operation instruction to a local planning module of the unmanned automobile; when the unmanned vehicle is in a line patrol state, the state machine sends a running instruction along a current route to a local planning module of the unmanned vehicle; when the vehicle is in a lane switching state, the state machine sends an instruction for switching from a current lane to a target lane to a local planning module of the unmanned vehicle; the state machine includes:
the checking unit is used for checking whether the input information is complete or not and the variables related to the initialization task when the state machine is in a starting state, and if so, switching to a regression lane state;
the first judging unit is used for judging whether the driverless automobile returns to the lane successfully or not when the state machine is in the lane returning state, and if so, switching to a line patrol state;
And the second judging unit is used for judging whether the lane needs to be switched according to the current driving road condition when the state machine is in the line inspection state, and switching to the lane switching state if the lane needs to be switched.
In order to solve the above technical problem, in a third aspect, an embodiment of the present invention provides an unmanned automobile, including: at least one processor, and
a memory communicatively coupled to the at least one processor, wherein,
the memory stores a program or instructions executable by the at least one processor, which when executed by the processor implement the state machine switching method according to the first aspect.
The embodiment of the invention has the beneficial effects that: different from the situation of the prior art, the state machine, the switching method of the state machine and the unmanned vehicle provided by the embodiment of the invention are used for checking whether the input information is complete or not and initializing the task related variables when the state machine is in the starting state, and if so, switching to the lane returning state, so that the unmanned vehicle can be ensured to accurately return to the lane, and the unmanned vehicle is a premise of safe operation. When the state machine is in the lane returning state, judging whether the lane returning of the unmanned automobile is successful or not, if so, switching to the line-patrol state, and improving the safety. When the state machine is in a line inspection state, judging whether the lane needs to be switched according to the current driving road condition, and if so, switching to the lane switching state to enable the behavior of the unmanned automobile to accord with the road condition. From the three aspects, the state machine is switched, so that the behavior of the unmanned automobile in the running process can be accurately and stably planned.
Drawings
One or more embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which like references indicate similar elements, and in which the figures of the drawings are not to be taken in a limiting sense, unless otherwise indicated.
Fig. 1 is a driving scene diagram of an unmanned automobile provided in an embodiment of the present application;
fig. 2 is a schematic structural diagram of an electronic device according to an embodiment of the present application;
FIG. 3 is a state transition diagram of a state machine provided by an embodiment of the present application;
fig. 4 is a flowchart of a switching method of a state machine according to an embodiment of the present application.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the present invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications could be made by those skilled in the art without departing from the inventive concept. These are all within the scope of the present invention.
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
It should be noted that, if not conflicting, the various features of the embodiments of the present invention may be combined with each other, which are all within the protection scope of the present application. In addition, while functional block division is performed in a device diagram and logical order is shown in a flowchart, in some cases, the steps shown or described may be performed in a different order than the block division in the device, or in the flowchart. Moreover, the words "first," "second," "third," and the like as used herein do not limit the data and order of execution, but merely distinguish between identical or similar items that have substantially the same function and effect.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and/or" as used in this specification includes any and all combinations of one or more of the associated listed items.
In addition, the technical features of the embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
In automatic driving and assisted driving, the unmanned vehicle can sense the surrounding environment through sensors. Among them, the sensors may include, but are not limited to, millimeter wave radar, laser radar, ultrasonic radar, vision sensor, and the like. The unmanned automobile can detect and classify the environment around the automobile through the sensor, and transmit the information to the control module to form a decision on the future running direction and speed of the automobile, and finally the decision is executed through the actuator to complete the whole auxiliary driving or automatic driving process. It will be appreciated that control of an unmanned vehicle is extremely important when the vehicle is driving on a road.
In one example, a driving scenario for an unmanned car is shown in FIG. 1. In the scenario shown in fig. 1, the unmanned vehicle needs to travel from a starting point to a destination, the unmanned vehicle travels on a road having 4 lanes, specifically, in lane # 2, when there is an obstacle in front of the unmanned vehicle, the unmanned vehicle needs to be controlled to switch lanes to avoid, when the unmanned vehicle needs to turn right but is in a straight lane, the unmanned vehicle needs to be controlled to switch to a right-turn lane, when the unmanned vehicle reaches the destination quickly, the unmanned vehicle needs to be controlled to park, and the like. It will be appreciated that the unmanned vehicle has other behaviors that require control, and that the above examples are not intended to be limiting, and are merely illustrative, and are not intended to limit the driving scenario and control behaviors of the unmanned vehicle.
At present, a method for planning the behavior of an unmanned automobile mainly depends on the accuracy of sensing information and a prediction result, and when the sensing information and the prediction result are wrong or jump occurs, stable control is often difficult to make.
Therefore, in order to ensure that the unmanned vehicle is accurately and stably controlled in the driving process, an embodiment of the application provides a switching method of a state machine, wherein the state machine is arranged in the unmanned vehicle and is used for realizing behavior planning of the unmanned vehicle, and the behavior of the unmanned vehicle in the driving process can be accurately and stably planned.
It will be appreciated that a state machine is a program model running on a processor, which defines a plurality of states and transitions between states. The state machine operates in response to a series of events that, when they meet certain trigger conditions, will cause the state machine to migrate from the current state to the next state. Among the defined states, there is at least one initial state and at least one final state, from which the state machine starts to operate, and when it transitions to the final state, the state machine stops operating. The state machine operates according to a state machine definition (state diagram). Wherein the states are different states of the thing.
The switching method of the state machine may be performed by a chip, a processor or a control device, where the chip, the processor or the control device may be installed in an electronic apparatus, so as to perform the switching method of the state machine provided by the embodiments of the present application through the chip, the processor or the control device. In some embodiments, the chip, processor or control device may also be mounted in an unmanned vehicle, where the electronic device is the unmanned vehicle. It is to be understood that the electronic device may be any device having computing processing capabilities, such as a computer, server or mobile terminal, without any limitation to the specific form of electronic device. When the electronic equipment is not an unmanned automobile, the electronic equipment is in communication connection with the unmanned automobile so as to send a control signal to the unmanned automobile, and control of the unmanned automobile is achieved.
In the following description of the unmanned vehicle, another embodiment of the present application further provides an unmanned vehicle, referring to fig. 2, the unmanned vehicle 10 includes at least one processor 11 and a memory 12 (bus connection, one processor is taken as an example in fig. 2) which are communicatively connected.
The processor 11 is configured to provide computing and control capabilities to control the unmanned vehicle 10 to perform corresponding tasks, for example, to control the unmanned vehicle 10 to perform any of the state machine switching methods provided in the following embodiments.
It is understood that the processor 11 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processing, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
The memory 12 is used as a non-transitory computer readable storage medium for storing non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the switching method of the state machine in the embodiments of the present invention. The processor 11 may implement the method of switching state machines in any of the method embodiments described below by running non-transitory software programs, instructions and modules stored in the memory 12. In particular, the memory 12 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 12 may also include memory located remotely from the processor, which may be connected to the processor via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The following describes in detail a switching method of a state machine provided in an embodiment of the present application. The state machine is arranged in the unmanned automobile and used for realizing behavior planning of the unmanned automobile. The state machine comprises four working states, namely a starting state, a lane returning state, a line inspection state and a lane cutting state; when the state machine is in a starting state, the state machine checks whether the input information is complete and the variables related to the initialization task, and when the state machine is in a lane returning state, the state machine sends a lane returning operation instruction to a local planning module of the unmanned vehicle; when the vehicle is in a line patrol state, the state machine sends a running instruction along the current route to a local planning module of the unmanned vehicle; when the vehicle is in a lane-cutting state, the state machine sends a command for switching from a current lane to a target lane to a local planning module of the unmanned vehicle.
In some embodiments, as can be seen from the state transition diagram shown in fig. 3, the state machine includes a parking state, a check point state, a cancel task state, and the like, in addition to a start state, a return lane state, a patrol state, and a cut lane state.
The states can be switched, for example, in a line patrol state, if an obstacle exists 500 meters in front (an event meets a trigger condition), the lane-cutting state is switched, and after the lane-cutting state is completed, if a large deviation between the unmanned vehicle and the lane exists (the event meets the trigger condition), the lane-cutting state is switched to a lane returning state. It will be appreciated that the switching between the different operating states may occur by event triggering, which is not illustrated here. In this embodiment, it is the driving road condition that triggers the state of the state machine to switch.
Referring to fig. 4, the method includes, but is not limited to, the following steps:
s10: when the state machine is in a starting state, whether the input information is complete or not and the variables related to the initialization task are checked, and if yes, the state machine is switched to a regression lane state.
Wherein the start state is a start state of the state machine. The input information includes positioning information, map information, communication information of each sensor on the unmanned vehicle, and the like. It can be understood that the start state points to the default state when the state machine is started, and after entering the start state of the state machine, firstly, whether the input information is complete or not is checked, whether the subsequent state switching is accurate or not can be effectively ensured, and the method is a premise of accurately planning the behavior of the unmanned automobile.
Then, the task related variables are initialized, specifically, the task related functions need to be called and the variables in the functions need to be initialized. The task related variables are variables required in determining each state of the state machine, and are variables in functions, and may include, for example, variables in functions for regression lanes, variables in functions for patrol lines, and variables in functions for lane cutting.
Before switching to the lane returning state, detecting whether the input information completes the variable related to the initialization task, and ensuring that the unmanned automobile accurately returns to the lane is a premise of safe operation.
S20: when the state machine is in the lane returning state, judging whether the lane returning of the unmanned automobile is successful or not, and if so, switching to the line patrol state.
The regression lane state is used for indicating the regression lane operation, namely, no deviation exists between the unmanned automobile and the current lane. The patrol state is used for indicating patrol operation, namely, enabling the unmanned automobile to run along the current lane.
It can be understood that whether the lane returning operation is successful is a precondition for the line patrol operation of the unmanned vehicle. If the lane returning operation is unsuccessful, the line patrol operation deviates from the lane, and potential safety hazards are caused. Therefore, after the unmanned vehicle is judged to return to the lane successfully, the vehicle is switched to the line patrol state, so that the safety can be improved.
S30: when the state machine is in the line inspection state, judging whether the lane needs to be switched according to the current driving road condition, and if so, switching to the lane switching state.
The driving road condition refers to the external environment of the unmanned automobile in the driving process, such as obstacles, pedestrians, other vehicles, traffic lights, road signs, curves or parking spaces, and the like. The driving road condition can be obtained by sensing sensors such as millimeter wave radar, laser radar, ultrasonic radar, vision sensor and the like.
In this embodiment, the state of the driving road condition trigger state machine is switched. After the driving road condition is obtained, judging whether the lane needs to be switched according to the driving road condition, and if so, switching to a lane switching state to guide the behavior of the unmanned automobile.
For example, in the patrol state, if there is an obstacle 500 m ahead (the event satisfies the trigger condition), the state is switched to the lane-cut state.
In the implementation, when the state machine is in a starting state, whether the input information is complete or not and the variables related to the initialization task are checked, if yes, the state machine is switched to a lane returning state, the unmanned automobile can be ensured to accurately return to a lane, and the unmanned automobile is a premise of safe operation. When the state machine is in the lane returning state, judging whether the lane returning of the unmanned automobile is successful or not, if so, switching to the line patrol state, and improving the safety. When the state machine is in a line inspection state, judging whether the lane needs to be switched according to the current driving road condition, and if so, switching to the lane switching state to enable the behavior of the unmanned automobile to accord with the road condition. From the three aspects, the state machine is switched, so that the behavior of the unmanned automobile in the running process can be accurately and stably planned.
In some embodiments, when the state machine is in the line patrol state, determining whether the lane needs to be switched according to the current driving road condition, and if so, switching to the lane switching state includes:
respectively calculating traffic values of all lanes, wherein the traffic values are the number of lanes required to be switched when lane switching is carried out from a lane to a steering lane, and the steering lane is a lane corresponding to the unmanned vehicle when turning in the process of driving to a terminal point;
and judging whether the lane needs to be switched and the target lane needs to be switched according to the traffic values and the static obstacle distance on each lane.
For example, as shown in fig. 1, the unmanned vehicle travels on a road having 4 lanes, lane 4# on the far right is a right turn lane, lane 1# is a left turn lane, and lanes 2# and 3# are straight lanes. Currently running on lane 2# and for running to the destination (destination), the unmanned vehicle needs to make at least one turn, and the unmanned vehicle should turn right at the next intersection, so lane 4# is the corresponding steering lane when the unmanned vehicle turns in the process of running to the destination. The traffic value of lane 1# is the number of lanes to be switched from lane 1# to lane 4#, that is, the traffic value of lane 1# is 3, the traffic value of lane 2# is the number of lanes to be switched from lane 2# to lane 4#, that is, the traffic value of lane 2# is 2, the traffic value of lane 3# is the number of lanes to be switched from lane 3# to lane 4#, that is, the traffic value of lane 3# is 1, it is understood that lane 4# does not need to be switched, and the traffic value is 0. Therefore, the larger the traffic value is, the larger the number of lanes to be switched is, and the traffic is unfavorable.
If there is a static obstacle in front of the current lane 2# where the unmanned automobile is located, lane changing is needed, if there is no static obstacle in a certain distance in front of the lane 1# and the lane 3# and lane 3# is changed to be a better choice, and lane changing to lane 4# can be fast carried out when the follow-up turning is convenient.
Considering that the traffic value and the obstacle condition on each lane are the main factors constituting the driving road condition, whether the lane needs to be switched and the target lane needs to be switched are judged according to the traffic value of each lane and the static obstacle distance on each lane. The target lane is a lane after the lane change of the unmanned automobile. For example, in the above example, lane 3# is the target lane.
In this embodiment, whether the lane needs to be switched and the target lane needs to be switched are judged according to the traffic value of each lane and the static obstacle distance on each lane, so that the target lane is more reasonable, and a better lane change can be realized.
In some embodiments, the step of determining whether to switch the lane and the target lane to be switched according to the traffic values and the static obstacle distance on each lane specifically includes:
if no static obstacle exists in front of the current lane within a first preset distance, determining that lane switching is not needed;
If a static obstacle exists in front of the current lane within the first preset distance, calculating the switching cost of each lane, and determining the lane needing to be switched and the lane with the minimum switching cost as the target lane.
The first preset distance may be an empirical value set manually, for example, 800 meters, that is, if there is no static obstacle (such as no forbidden sign) in 800 meters in front of the current lane, the target lane is still the current lane, that is, the lane does not need to be changed, and the vehicle can continue to run along the current lane. If a static obstacle exists in the current lane by 800 meters, calculating the switching cost of each lane, and determining the lane with the minimum switching cost as the target lane.
Wherein the switching cost reflects the degree of merit when the lane is taken as the target lane. For example, referring again to fig. 1, if there is no static obstacle in front of lane 2# where the current unmanned vehicle is located, there is no need to switch lanes. If there is a static obstacle in front of lane 2# where the current unmanned car is located, it is necessary to switch lanes. Specifically, if there is no static obstacle in a certain distance in front of the lane 1# and the lane 3# then the lane changing to the lane 3# is a better choice, when the vehicle is convenient for subsequent turning, the lane changing to the lane 4# can be fast, and the lane changing to the lane 1# is a worse choice, because the lane changing from the lane 1# to the lane 4# is also needed when the vehicle is subsequently turned, and the lane changing is not easy. Thus, in this example, the cost of switching lane 3# is less than the cost of switching lane 1#. It can be understood that if there is a static obstacle in front of the current lane 2#, the switching cost of the current lane 2# is the largest, if there is no static obstacle in front of the lane 4# and the traffic value of the lane 4# is larger than that of the lane 3#, the switching cost of the lane 4# is greater than that of the lane 3#, and the switching cost of the lane 3# is the smallest.
In the embodiment, the lane with the minimum switching cost is determined as the target lane, so that the optimal lane change can be realized, and the running of the unmanned automobile is more convenient and reasonable.
In some embodiments, the step of calculating the switching cost of each lane includes:
calculating the switching cost of a lane i according to the following formula, wherein the lane i is any one of the lanes;
wherein, cost (i) For the switching cost of the lane i, j represents whether there is a static obstacle in front of the lane i within the first preset distance, j=1 when there is a static obstacle in front of the lane i within the first preset distance, j=0 when there is no static obstacle in front of the lane i within the first preset distance, a represents the traffic value of the lane i, d i Representing the distance of the static obstacle in front of the lane i, b representing whether the lane i is not in front of the static obstacle and has a dynamic obstacle, b=1 when the lane i is not in front of the static obstacle and has a dynamic obstacle, b=0 otherwise, c representing whether a pedestrian is in front of the lane i, c=1 when a pedestrian is in front of the lane i, c=0 otherwise, e i Is the distance of the pedestrian in front.
It will be appreciated that lane i is any lane in the road, and may also be the current lane of an unmanned car. When there is a static obstacle in the first preset distance in front of the lane i, the switching cost is infinite, and when there is no static obstacle in the first preset distance in front of the lane i, the traffic value, whether there is a dynamic obstacle b in front, and the distance d of the static obstacle in front are considered i Whether or not there is a pedestrian c in frontDistance e of pedestrian ahead i To calculate the handover cost. The consideration factors are comprehensive, so that the switching cost can more accurately reflect the quality degree of the lane i when the lane i is taken as the target lane.
In this embodiment, the switching cost is calculated from a plurality of factors such as the passing value, whether there is a dynamic obstacle in front, the distance of a static obstacle in front, whether there is a pedestrian in front, and the distance of a pedestrian in front, so that the switching cost can more accurately reflect the degree of merit of the lane i when it is taken as the target lane.
In some embodiments, the method for switching a state machine further includes:
if the lane-cutting state lasts for a preset time and the direction of the target lane is consistent with the direction indicated by the steering lamp of the unmanned automobile, controlling the unmanned automobile to change the lane to the target lane; otherwise, switching to the line patrol state.
It will be appreciated that when entering the lane-switching state, the unmanned vehicle is controlled to switch lanes, a turn signal is required before switching lanes, for example, a left turn signal is required when switching lanes to the left, a right turn signal is required when switching lanes to the right, and when switching lanes is completed, the turn signal is turned off, and the lane-switching state is ended.
In order to ensure the safety when changing lanes, if the lane-cutting state continues for a preset time, for example, the lane-cutting state continues for 1s, the lane-cutting state is stable and no state jump occurs, and further, if the direction of the target lane is consistent with the direction indicated by the steering lamp of the unmanned automobile, the unmanned automobile is controlled to change lanes to the target lane. If the lane-cutting state does not last for a preset time, for example, only for 0.1s, and/or the direction of the target lane is inconsistent with the direction indicated by the steering lamp of the unmanned automobile, for example, the direction of the target lane is right and the steering lamp is turned on to the left, the lane-cutting state is switched to the line-patrol state, namely, the lane-cutting state continues to run along the current road, and lane-changing operation is not performed, so that the safety and the stability in lane change are ensured.
In some embodiments, the method further comprises:
and if the deviation between the unmanned automobile and the current lane is greater than a preset deviation threshold value, switching to a lane returning state.
And if the deviation between the unmanned automobile and the current lane is smaller than or equal to the preset deviation threshold value, switching to a line patrol state.
The deviation between the unmanned automobile and the current lane refers to an included angle between the head direction of the unmanned automobile and the current lane. It will be appreciated that if the deviation between the unmanned vehicle and the current lane is greater than the preset deviation threshold, the unmanned vehicle may deviate from the current lane, with the risk of collision with the vehicle in the adjacent lane. Therefore, the correction should be timely performed, i.e. the state of switching to the lane returning state is performed, so as to instruct the unmanned vehicle to return to the current lane, and the deviation between the unmanned vehicle and the current lane is reduced.
And if the deviation between the unmanned automobile and the current lane is smaller than or equal to the preset deviation threshold value, switching to a line patrol state to indicate the unmanned automobile to continue running along the current lane.
It will be appreciated that the preset deviation threshold is an empirical value set by a person skilled in the art, and may be specifically determined according to the actual situation, and may be set to 20 °.
In the embodiment, by monitoring the deviation between the unmanned automobile and the current lane, the return lane state and the line patrol state can be accurately determined, so that the risk of collision of the unmanned automobile is reduced.
In some embodiments, the state machine further includes a park state, where the park state refers to a state when the unmanned vehicle begins to park to completion. That is, when the vehicle is switched to the parking state, the unmanned vehicle is controlled to start parking, and when the parking is completed, the parking state is completed.
The method further comprises the steps of:
and if the distance between the unmanned automobile and the driving end point is smaller than or equal to a second preset distance, switching to a parking state.
In this embodiment, when the distance between the unmanned vehicle and the travel end point is less than or equal to the second preset distance, the parking state is entered to instruct the unmanned vehicle to park at a reduced speed. The second preset distance is an empirical value set by a person skilled in the art, and may be specifically set according to practical situations, for example, the second preset distance may be 20 meters.
It will be appreciated that parking may be in a lane, and possibly in a parking space. If the vehicle stops in the lane, controlling the unmanned vehicle to stop on the stop line, namely controlling the unmanned vehicle to stop in front of the stop line of the given lane; and if the vehicle is parked in the parking space, controlling the unmanned vehicle to perform parking behavior of the parking space, namely controlling the unmanned vehicle to enter the given parking space.
In this embodiment, whether to enter the parking state can be accurately determined according to the distance between the unmanned vehicle and the travel destination.
In some embodiments, the state machine further comprises a checkpointed state.
The method further comprises the steps of:
and when the distance between the unmanned automobile and the check point is smaller than or equal to a third preset distance, switching to a check point state, wherein the check point is a position point on the driving path of the unmanned automobile.
The checkpoint state is a state when the unmanned vehicle passes near the checkpoint, that is, when the unmanned vehicle passes near the checkpoint a, the distance from the checkpoint a does not exceed a third predetermined distance (for example, 5 meters), and the state is switched to the checkpoint state during this time. The check point is a position point on the driving path, and can be set on a dotting map. The position, orientation angle, curvature, and various speed limit values of the check point are recorded on the check point
Thus, when the distance between the unmanned vehicle and the checkpoint is less than or equal to the third preset distance, switching to the checkpoint state. It should be noted that the third preset distance is an empirical value set by a person skilled in the art, and may be specifically set according to the actual situation. In the inspection point state, the unmanned automobile can conduct local driving path planning according to data recorded by inspection points.
In this embodiment, it may be accurately determined whether to enter the checkpointed state based on the distance between the unmanned vehicle and the checkpointed state.
In summary, according to the switching method of the state machine provided by the application, when the state machine is in the starting state, whether the input information is complete or not and the variables related to the initialization task are checked, if yes, the state is switched to the lane returning state, and the unmanned automobile can be ensured to accurately return to the lane, so that the method is a premise of safe operation. When the state machine is in the lane returning state, judging whether the lane returning of the unmanned automobile is successful or not, if so, switching to the line-patrol state, and improving the safety. When the state machine is in a line inspection state, judging whether the lane needs to be switched according to the current driving road condition, and if so, switching to the lane switching state to enable the behavior of the unmanned automobile to accord with the road condition. From the three aspects, the state machine is switched, so that the behavior of the unmanned automobile in the running process can be accurately and stably planned.
The state machine is arranged in the unmanned automobile and is used for realizing behavior planning of the unmanned automobile, and the state machine is at least provided with four working states, namely a starting state, a lane returning state, a line patrol state and a lane cutting state; when the state machine is in a starting state, the state machine checks whether the input information is complete and the variables related to the initialization task, and when the state machine is in a lane returning state, the state machine sends a lane returning operation instruction to a local planning module of the unmanned automobile; when the unmanned vehicle is in a line patrol state, the state machine sends a running instruction along a current route to a local planning module of the unmanned vehicle; when the vehicle is in a lane switching state, the state machine sends an instruction for switching from a current lane to a target lane to a local planning module of the unmanned vehicle; the state machine includes:
the checking unit is used for checking whether the input information is complete or not and the variables related to the initialization task when the state machine is in a starting state, and if so, switching to a regression lane state;
the first judging unit is used for judging whether the driverless automobile returns to the lane successfully or not when the state machine is in the lane returning state, and if so, switching to a line patrol state;
And the second judging unit is used for judging whether the lane needs to be switched according to the current driving road condition when the state machine is in the line inspection state, and switching to the lane switching state if the lane needs to be switched.
In this embodiment, when the state machine is in the start state, the checking unit checks whether the input information is complete and the variables related to the initialization task, if so, the state is switched to the lane returning state, so that the unmanned vehicle can be ensured to accurately return to the lane, and the unmanned vehicle is a precondition of safe operation. And when the state machine is in the lane returning state, the first judging unit judges whether the lane returning of the unmanned automobile is successful or not, and if so, the unmanned automobile is switched to the line-patrol state, so that the safety can be improved. And when the state machine is in a line patrol state, judging whether the lane needs to be switched according to the current driving road condition by the second judging unit, and if so, switching to the lane switching state to enable the behavior of the unmanned automobile to accord with the road condition. By adopting the three units, the state can be switched by the state machine, so that the behavior of the unmanned automobile in the running process can be accurately and stably planned.
It should be noted that the above-described apparatus embodiments are merely illustrative, and the units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
From the above description of embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus a general purpose hardware platform, or may be implemented by hardware. Those skilled in the art will appreciate that all or part of the processes implementing the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and where the program may include processes implementing the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-only Memory (ROM), a random-access Memory (Random Access Memory, RAM), or the like.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; the technical features of the above embodiments or in the different embodiments may also be combined within the idea of the invention, the steps may be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (10)
1. The switching method of the state machine is arranged in the unmanned automobile and is used for realizing behavior planning of the unmanned automobile, and is characterized in that the state machine comprises four working states, namely a starting state, a lane returning state, a line patrol state and a lane cutting state, wherein the lane returning state is used for indicating lane returning operation so that no deviation exists between the unmanned automobile and a current lane; when the state machine is in a starting state, the state machine checks whether the input information is complete and the variables related to the initialization task, and when the state machine is in a lane returning state, the state machine sends a lane returning operation instruction to a local planning module of the unmanned automobile; when the unmanned vehicle is in a line patrol state, the state machine sends a running instruction along a current route to a local planning module of the unmanned vehicle; when the vehicle is in a lane switching state, the state machine sends an instruction for switching from a current lane to a target lane to a local planning module of the unmanned vehicle; the switching method of the state machine comprises the following steps:
when the state machine is in a starting state, checking whether the input information is complete and the variables related to the initialization task, if so, switching to a regression lane state;
When the state machine is in a lane returning state, judging whether the lane returning of the unmanned automobile is successful or not, and if so, switching to a line patrol state;
when the state machine is in a line inspection state, judging whether the lane needs to be switched according to the current driving road condition, and if so, switching to a lane switching state.
2. The method according to claim 1, wherein when the state machine is in the line patrol state, the step of judging whether the lane needs to be switched according to the current driving road condition, and if so, switching to the lane switching state comprises:
respectively calculating traffic values of all lanes, wherein the traffic values are the number of lanes required to be switched when lane switching is carried out from a lane to a steering lane, and the steering lane is a lane corresponding to the unmanned vehicle when turning in the process of driving to a terminal point;
and judging whether the lane needs to be switched and the target lane needs to be switched according to the traffic values and the static obstacle distance on each lane.
3. The method of claim 2, wherein the step of determining whether the lane needs to be switched and the target lane needs to be switched according to each traffic value and the static obstacle distance on each lane comprises:
If no static obstacle exists in front of the current lane within a first preset distance, determining that lane switching is not needed;
if a static obstacle exists in front of the current lane within the first preset distance, calculating the switching cost of each lane, and determining the lane needing to be switched and the lane with the minimum switching cost as the target lane.
4. A method according to claim 3, wherein the step of calculating the switching cost of each lane comprises:
calculating the switching cost of a lane i according to the following formula, wherein the lane i is any one of the lanes;
wherein, cost (i) For the switching cost of the lane i, j represents whether there is a static obstacle in front of the lane i within the first preset distance, j=1 when there is a static obstacle in front of the lane i within the first preset distance, j=0 when there is no static obstacle in front of the lane i within the first preset distance, a represents the traffic value of the lane i, d i Representing the distance of the static obstacle in front of the lane i, b representing whether the lane i is not in front of the static obstacle and has a dynamic obstacle, b=1 when the lane i is not in front of the static obstacle and has a dynamic obstacle, b=0 otherwise, c representing whether a pedestrian is in front of the lane i, c=1 when a pedestrian is in front of the lane i, c=0 otherwise, e i Is the distance of the pedestrian in front.
5. The method according to claim 4, characterized in that the method comprises:
if the lane-cutting state lasts for a preset time and the direction of the target lane is consistent with the direction indicated by the steering lamp of the unmanned automobile, controlling the unmanned automobile to change the lane to the target lane;
otherwise, switching to the line patrol state.
6. The method according to any one of claims 1-5, further comprising:
if the deviation between the unmanned automobile and the current lane is greater than a preset deviation threshold value, switching to a lane returning state;
and if the deviation between the unmanned automobile and the current lane is smaller than or equal to the preset deviation threshold value, switching to a line patrol state.
7. The method of any one of claims 1-5, wherein the state machine further comprises a park state,
the method further comprises:
and if the distance between the unmanned automobile and the driving end point is smaller than or equal to a second preset distance, switching to a parking state.
8. The method of any of claims 1-5, wherein the state machine further comprises a checkpointed state,
The method further comprises:
and when the distance between the unmanned automobile and the check point is smaller than or equal to a third preset distance, switching to a check point state, wherein the check point is a position point on the driving path of the unmanned automobile.
9. The state machine is arranged in the unmanned automobile and is used for realizing behavior planning of the unmanned automobile, and is characterized by being provided with at least four working states, namely a starting state, a lane returning state, a line patrol state and a lane cutting state, wherein the lane returning state is used for indicating lane returning operation so that no deviation exists between the unmanned automobile and a current lane; when the state machine is in a starting state, the state machine checks whether the input information is complete and the variables related to the initialization task, and when the state machine is in a lane returning state, the state machine sends a lane returning operation instruction to a local planning module of the unmanned automobile; when the unmanned vehicle is in a line patrol state, the state machine sends a running instruction along a current route to a local planning module of the unmanned vehicle; when the vehicle is in a lane switching state, the state machine sends an instruction for switching from a current lane to a target lane to a local planning module of the unmanned vehicle; the state machine includes:
The checking unit is used for checking whether the input information is complete or not and the variables related to the initialization task when the state machine is in a starting state, and if so, switching to a regression lane state;
the first judging unit is used for judging whether the driverless automobile returns to the lane successfully or not when the state machine is in the lane returning state, and if so, switching to a line patrol state;
and the second judging unit is used for judging whether the lane needs to be switched according to the current driving road condition when the state machine is in the line inspection state, and switching to the lane switching state if the lane needs to be switched.
10. An unmanned vehicle, the unmanned vehicle comprising: at least one processor, and
a memory communicatively coupled to the at least one processor, wherein,
the memory stores a program or instructions executable by the at least one processor, which when executed by the processor, implements the state machine switching method of any of claims 1-8.
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