CN114670876A

CN114670876A - Method and device for turning around unmanned vehicle in lane and electronic equipment

Info

Publication number: CN114670876A
Application number: CN202210497601.2A
Authority: CN
Inventors: 张弛
Original assignee: Neolix Technologies Co Ltd
Current assignee: Neolix Technologies Co Ltd
Priority date: 2022-05-09
Filing date: 2022-05-09
Publication date: 2022-06-28

Abstract

The disclosure provides a method and a device for turning around in a lane of an unmanned vehicle and electronic equipment. The method is applied to an automatic driving vehicle or an unmanned vehicle, and comprises the following steps: acquiring running state information and current road section information of the unmanned vehicle, and judging whether to trigger turn-around operation in a lane by using turn-around decision conditions; when the operation of turning around in the triggered lane is judged, whether the unmanned vehicle meets the road environment constraint is judged, the road boundary constraint is checked based on the position of the unmanned vehicle in a road coordinate system, the initial scene is judged, the next pose state is calculated based on the displacement zone bit and the current pose state of the unmanned vehicle, whether the vehicle central point corresponding to the next pose is taken as a path key point is judged, all path key points are calculated, and the path formed by the path key points is taken as the driving track of the unmanned vehicle for turning around in the lane. The method and the device ensure the success rate of the turning operation in the driveway of the unmanned vehicle, and improve the turning efficiency in the driveway and the application range of the unmanned vehicle.

Description

Method and device for turning around unmanned vehicle in lane and electronic equipment

Technical Field

The disclosure relates to the technical field of unmanned driving, in particular to a method and a device for realizing turning inside a lane of an unmanned vehicle and electronic equipment.

Background

The unmanned vehicle is a comprehensive system integrating functions of environmental perception, planning decision, multi-level auxiliary driving and the like, and is also called as an automatic driving vehicle and an unmanned vehicle. In the driving process of the unmanned vehicle, the unmanned vehicle needs to turn around to drive in some scenes, for example, the unmanned vehicle is particularly common in the use scenes of a parking lot, so that the unmanned vehicle needs to have the capability of turning around in a narrow road, and the capability is also called turning around in a lane or turning around in a narrow road.

At present, when an existing unmanned vehicle turns around in a narrow road, certain prior information is needed, such as preset turning paths or pose information when turning is completed, and the current pose, the middle pose and the target pose of the vehicle are determined in real time, so that the vehicle runs along a running path corresponding to the poses. In addition, when the existing unmanned vehicle carries out turn-around path planning, how to plan a reasonable turn-around running track under specific constraint is not considered, and judgment on the current road running environment is lacked, the system directly enters the operation of turn-around in the lane instead of deciding whether the current scene can carry out the function of turn-around in the lane or not and then carrying out the function of turn-around in the lane, so that the turn-around in the lane is easy to fail and then falls into a dead zone, and the effect of turn-around in the lane is poor.

Disclosure of Invention

In view of this, the embodiment of the present disclosure provides a method, an apparatus, and an electronic device for an unmanned vehicle to implement in-lane turning, so as to solve the problems in the prior art that in-lane turning is inefficient, a decision on in-lane turning operation is lacked, turning in a lane is prone to failure, and a turning effect in a lane is relatively poor.

In a first aspect of the embodiments of the present disclosure, a method for implementing turning inside a lane by an unmanned vehicle is provided, including: acquiring running state information and current road section information of the unmanned vehicle in the automatic driving process of the unmanned vehicle, and judging whether to trigger turn-around operation in a lane by using turn-around decision conditions based on the running state information and the current road section information; when the operation of turning around in the lane is judged to be triggered, current road environment information is determined based on the current road section information, and whether the unmanned vehicle meets the road environment constraint corresponding to the turning around in the lane is judged according to the current road environment information; when the unmanned vehicle meets the road environment constraint, converting the current position of the unmanned vehicle into a road coordinate system, checking the road boundary constraint based on the position of the unmanned vehicle in the road coordinate system, judging an initial scene when the unmanned vehicle meets the road boundary constraint, determining a displacement marker bit according to the judgment result of the initial scene, and calculating the next pose state of the unmanned vehicle based on the displacement marker bit and the current pose state of the unmanned vehicle; determining the position of an angular point of the unmanned vehicle according to the state of the next position, carrying out safety detection on the position of the unmanned vehicle based on the position of the angular point, judging that the central point of the vehicle corresponding to the next position is taken as a path key point or the central point of the vehicle corresponding to the previous position is taken as a path key point according to the safety detection result, calculating all the path key points, and taking the path formed by all the path key points as the driving track of the unmanned vehicle for realizing turning inside the lane.

In a second aspect of the embodiments of the present disclosure, a device for enabling an unmanned vehicle to turn around in a lane is provided, including: the acquisition module is configured to acquire driving state information and current road section information of the unmanned vehicle in the automatic driving process of the unmanned vehicle, and judge whether to trigger turn-around operation in the lane by using turn-around decision conditions based on the driving state information and the current road section information; the judging module is configured to determine current road environment information based on the current road section information when judging that the operation of turning around in the lane is triggered, and judge whether the unmanned vehicle meets the road environment constraint corresponding to the turning around in the lane according to the current road environment information; the calculation module is configured to convert the current position of the unmanned vehicle into a road coordinate system when the unmanned vehicle meets the road environment constraint, check the road boundary constraint based on the position of the unmanned vehicle in the road coordinate system, judge the initial scene when the unmanned vehicle meets the road boundary constraint, determine a displacement flag bit according to the judgment result of the initial scene, and calculate the next pose state of the unmanned vehicle based on the displacement flag bit and the current pose state of the unmanned vehicle; and the planning module is configured to determine the corner position of the unmanned vehicle according to the state of the next position, perform safety detection on the position of the unmanned vehicle based on the corner position, judge that the vehicle center point corresponding to the next position is taken as a path key point or the vehicle center point corresponding to the previous position is taken as a path key point according to the safety detection result, calculate all the path key points, and take the path formed by all the path key points as the driving track of the unmanned vehicle for realizing turning inside the lane.

In a third aspect of the embodiments of the present disclosure, an electronic device is provided, which includes a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the method when executing the program.

The embodiment of the present disclosure adopts at least one technical scheme that can achieve the following beneficial effects:

the method comprises the steps that driving state information and current road section information of an unmanned vehicle are obtained in the automatic driving process of the unmanned vehicle, and whether turning operation in a lane is triggered or not is judged by using turning decision conditions on the basis of the driving state information and the current road section information; when the operation of turning around in the lane is judged to be triggered, current road environment information is determined based on the current road section information, and whether the unmanned vehicle meets the road environment constraint corresponding to the turning around in the lane is judged according to the current road environment information; when the unmanned vehicle meets the road environment constraint, converting the current position of the unmanned vehicle into a road coordinate system, checking the road boundary constraint based on the position of the unmanned vehicle in the road coordinate system, judging an initial scene when the unmanned vehicle meets the road boundary constraint, determining a displacement marker bit according to the judgment result of the initial scene, and calculating the next pose state of the unmanned vehicle based on the displacement marker bit and the current pose state of the unmanned vehicle; determining the position of an angular point of the unmanned vehicle according to the state of the next position, carrying out safety detection on the position of the unmanned vehicle based on the position of the angular point, judging that the central point of the vehicle corresponding to the next position is taken as a path key point or the central point of the vehicle corresponding to the previous position is taken as a path key point according to the safety detection result, calculating all the path key points, and taking the path formed by all the path key points as the driving track of the unmanned vehicle for realizing turning inside the lane. The method and the device can realize that the turning operation is executed after decision is made, and various constraints are fully considered when the turning path is planned, so that the success rate of the turning operation in the driveway of the unmanned vehicle is ensured, and the turning efficiency in the driveway and the application scene and range of the unmanned vehicle are improved.

Drawings

To more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without inventive efforts.

FIG. 1 is a schematic diagram of an autopilot system provided by an embodiment of the present disclosure;

fig. 2 is a schematic flow chart of a method for implementing inside-lane U-turn by an unmanned vehicle according to an embodiment of the present disclosure;

fig. 3 is a schematic structural diagram of an in-lane turn decision maker provided in an embodiment of the present disclosure;

fig. 4 is a schematic diagram illustrating an algorithm flow of a Replan state in an underlying state machine according to an embodiment of the present disclosure;

fig. 5 is a schematic diagram of an initial scene in a Replan state according to an embodiment of the present disclosure;

fig. 6 is a schematic flow chart of an in-lane turn decision process provided by an embodiment of the present disclosure;

fig. 7 is a schematic structural diagram of an apparatus for implementing inside lane turn around by an unmanned vehicle according to an embodiment of the present disclosure;

fig. 8 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the disclosed embodiments. However, it will be apparent to one skilled in the art that the present disclosure may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present disclosure with unnecessary detail.

As described above, an unmanned vehicle, also called an autonomous vehicle, an unmanned vehicle, or a wheeled mobile robot, is an integrated and intelligent new-generation technical product that integrates multiple elements such as environment sensing, path planning, state recognition, and vehicle control. With the rapid development of unmanned technologies, the application scenarios and ranges of autonomous vehicles are gradually expanding, including unmanned delivery vehicles, unmanned retail vehicles, unmanned cleaning vehicles, unmanned patrol vehicles, etc. In the road driving process, the unmanned vehicle needs to turn around to drive in certain scenes, such as the situation that the front road is blocked and cannot pass, the front road is a broken road and the like, and the situation that the unmanned vehicle turns around to drive is particularly common in parking lot scenes; therefore, the unmanned vehicle needs to have the capability of turning around in a narrow road, which is also called turning around in a lane or turning around in a narrow road.

It should be noted that the turning around driving of the unmanned vehicle is divided into two different situations, namely turning around outside the lane and turning around inside the lane, and the difference of the two situations is that the turning around outside the lane means that the unmanned vehicle often needs to turn around across the lane in an open road, and the unmanned vehicle enters into a reverse lane to drive after turning around, so the space for turning around across the lane is wider; the passable space for turning around in the lane is usually narrow, so that the planning control requirement on the unmanned vehicle is higher when the unmanned vehicle turns around in the lane. It is particularly emphasized that the turning around outside the lane and the turning around inside the lane in the embodiment of the present disclosure may also be referred to as "turning around outside the lane" and "turning around inside the lane", respectively, and the "turning around" and "turning around" have the same meaning in the embodiment of the present disclosure, and are all used to refer to the direction in which the unmanned vehicle turns in the opposite direction on the driving road.

The following describes in detail a method for implementing vehicle turning around and problems in the prior art with reference to two technical solutions in the prior art, and specifically may include the following:

prior art solution one (publication No. CN112660147A), which discloses a method, apparatus, device, and storage medium for controlling vehicle turn, the method iteratively performs the following at least once, according to a current pose of a vehicle and a target pose when the vehicle has finished turning, until it is determined that the proximity of the current pose and the target pose of the vehicle exceeds a threshold: determining an intermediate pose to be reached by the vehicle based on at least the current pose and the target pose of the vehicle; causing the vehicle to travel along a travel path from the current location to an intermediate location; causing the vehicle to stop in response to determining that a condition for switching the travel path is satisfied; and updating the current pose of the vehicle by using the pose when the vehicle stops.

In the second prior art (publication number: CN111891137A), an automatic driving narrow road turning method, system and vehicle are disclosed, the method first has a preset forward turning path, mainly judges whether the current forward tracking path is feasible, if so, the vehicle goes forward along the forward tracking path to realize turning; otherwise, stopping the vehicle and switching the driving direction when the vehicle advances to the preset position along the forward tracking path; and generating a backward tracking path in real time according to the current position of the vehicle and the reversing reference path, reversing the vehicle along the backward tracking path, and generating a forward tracking path in real time according to the position of the vehicle.

Based on the content disclosed by the first prior art scheme, the design theory of the technical scheme is only suitable for turning around on a road section with a wider lane, and because the pose adjustment of turning around needs to be carried out three times theoretically, if the road section is too narrow, the turning around fails midway and then falls into a dead zone, and remote manual intervention is needed; secondly, the technical scheme needs to know the pose (namely the target pose) of the vehicle after the turning is finished in advance, which puts higher requirements on a decision module, and how to accurately calculate the pose after the turning is finished is not mentioned in the technical scheme.

Based on the content disclosed in the second prior art scheme, the second prior art scheme needs to obtain a preset turning path, and adjusts the driving direction of the vehicle through the forward tracking path and the backward tracking path, so as to adjust the posture of the vehicle.

Therefore, in both the first and second prior art schemes, when the vehicle turns around, preset prior information is required, such as a preset turning path or pose information when turning is completed; in addition, the prior art only considers the problem from the perspective of automatic driving planning, and does not combine with vehicle control (namely, driving is executed according to a planned path, and some deviation often exists during execution), and the final turning process fails due to the error of vehicle control following after the turning track is generated at a high probability; in addition, the prior art solutions do not take into account the constraints of road width and road boundaries.

In view of the problems in the prior art solutions, it is needed to provide a method for an unmanned vehicle to achieve turn around in a lane, and the method provided by the embodiments of the present disclosure considers at least the following technical problems:

how to judge that the current road section can not continue to run in the running process of the automatic driving vehicle, and making a decision of turning around in the lane, and how to reduce the probability of making an error decision;

the decision of how to perform turn-around in the lane by the automatic driving system is to plan the running track of the turn-around process under the consideration of road boundary constraint and vehicle control error constraint;

and the automatic driving system decides the decision of turning around in the exit lane and judges how to deal with the current scene after judging that the automatic driving system cannot deal with the current scene.

The following describes functional modules of an automatic driving system related in an actual scene according to an embodiment of the present disclosure with reference to the drawings. Fig. 1 is a schematic structural diagram of an automatic driving system provided in an embodiment of the present disclosure. As shown in fig. 1, the automatic driving system mainly includes the following:

the automatic driving system comprises a sensor 101, a perception module 102, a positioning module 103, a high-precision map module 104, a decision planning module 105, a control module 106, an unmanned vehicle 107 and a remote control center 108; the sensor 101 comprises data acquisition equipment such as a laser radar and a camera which are installed on the unmanned vehicle, the sensing module 102 analyzes the road environment, obstacles, driving scenes and the like around the vehicle according to the data acquired by the sensor 101, and the high-precision map module 104 stores high-precision map information about the road; the decision planning module 105 comprises a decision module and a motion planning module, the decision planning module 105 is used for making a decision on the running state of the unmanned vehicle and planning a running reference line, and the function of turning around in the lane is realized by the decision planning module 105 and then is specifically executed by the control module 106; the unmanned vehicle 107 contains vehicle infrastructure systems such as a vehicle chassis, drives, and the like.

Further, in the embodiment of the present disclosure, the decision module in the decision planning module 105 is mainly used for making a decision of the current driving state and a corresponding driving reference line, such as a cruise function, a lane change function, a reverse driving function, a lane crossing turning function, and the like; the decision module comprises two submodules of decision and behavior planning, and the function of turning around in the lane is mainly completed by the two modules of decision and behavior planning. The decision-making module is mainly used for judging whether the turning around in the lane is required at present; the behavior decision module is used for judging whether the current scene has the condition of turning around in the lane or not, planning the driving guide line (namely the driving reference line) of the turning around in the lane, updating the driving guide line in real time in the turning around process and controlling the steering lamp of the vehicle in the turning around process.

Further, the motion planning module in the decision planning module 105 plans a smooth driving track meeting various vehicle dynamics constraints in real time based on the driving reference line by considering the obstacles, the vehicle constraints and the road environment constraints, and then is executed by the control module 106.

The embodiment of the disclosure focuses on the function of turning around in a lane in a decision module, and specifically introduces how to make a decision of turning around in the lane, how to plan a driving reference line of turning around in the lane, and how to maintain the relationship between the turning around state in the lane and other decision states. The technical solution of the present disclosure is described in detail below with reference to the accompanying drawings and specific embodiments.

Fig. 2 is a schematic flow chart of a method for implementing in-lane turn around by an unmanned vehicle according to an embodiment of the present disclosure. The method of fig. 2 for an unmanned vehicle to effect an in-lane turn may be performed by a decision-making planning module in an autonomous driving system. As shown in fig. 2, the method for implementing turning inside a lane by an unmanned vehicle may specifically include:

s201, in the automatic driving process of the unmanned vehicle, obtaining the driving state information and the current road section information of the unmanned vehicle, and judging whether to trigger the operation of turning around in a lane by using a turning around decision condition based on the driving state information and the current road section information;

s202, when the operation of turning around in the lane is judged to be triggered, current road environment information is determined based on the current road section information, and whether the unmanned vehicle meets the road environment constraint corresponding to the turning around in the lane is judged according to the current road environment information;

s203, when the unmanned vehicle meets the road environment constraint, converting the current position of the unmanned vehicle into a road coordinate system, checking the road boundary constraint based on the position of the unmanned vehicle in the road coordinate system, judging an initial scene when the unmanned vehicle meets the road boundary constraint, determining a displacement marker bit according to the judgment result of the initial scene, and calculating the next pose state of the unmanned vehicle based on the displacement marker bit and the current pose state of the unmanned vehicle;

s204, determining the corner position of the unmanned vehicle according to the state of the next position, performing safety detection on the position of the unmanned vehicle based on the corner position, judging to take the vehicle center point corresponding to the next position as a path key point or take the vehicle center point corresponding to the previous position as the path key point according to the safety detection result, calculating all the path key points, and taking the path formed by all the path key points as the driving track of the unmanned vehicle for realizing turn around in the lane.

Specifically, the unmanned vehicle in-lane turning function of the embodiment of the present disclosure includes a decision module (i.e., a decision maker module) and a behavior decision module (i.e., a reference line module), where the decision maker module is configured to determine whether a current scene can trigger the in-lane turning function, the reference line module is configured to specifically plan a driving track of the in-lane turning, and then the motion planning module and the control module execute the specific in-lane turning function.

Further, the initial scene in the embodiment of the present disclosure refers to a scene corresponding to an initial posture, a road direction, and the like of the unmanned vehicle when performing a turn-around operation in the lane, different initial scenes may correspond to different initial postures and vehicle traveling directions, each initial scene corresponds to a respective displacement flag bit, the displacement flag bits may be considered as vehicle traveling directions corresponding to each scene, such as a left turn, a right turn, an advance, a retreat, and the like, the initial scene is a scene image obtained by converting the posture and the road environment of the unmanned vehicle from a cartesian coordinate system to a Frenet coordinate system and simplifying the converted scene image.

According to the technical scheme provided by the embodiment of the disclosure, the driving state information and the current road section information of the unmanned vehicle are acquired in the automatic driving process of the unmanned vehicle, and the turn-around decision condition is utilized to judge whether to trigger the turn-around operation in the lane; when the operation of triggering turn-around in the lane is judged, judging whether the unmanned vehicle meets the road environment constraint corresponding to turn-around in the lane or not according to the current road environment information; when the road environment constraint is met, converting the current position of the unmanned vehicle into a road coordinate system, checking the road boundary constraint, determining a displacement zone bit according to a judgment result of an initial scene when the road environment constraint is met, and calculating the next pose state of the unmanned vehicle based on the displacement zone bit and the current pose state of the unmanned vehicle; and determining the corner position of the unmanned vehicle according to the next position state, performing safety detection on the position of the unmanned vehicle based on the corner position, updating the path key points according to the safety detection result, calculating all the path key points, and taking the path formed by all the path key points as the driving track of the unmanned vehicle for realizing turning inside the lane. The method and the device can realize that the turning operation is executed after decision is made, and various constraints are fully considered when the turning path is planned, so that the success rate of the turning operation in the driveway of the unmanned vehicle is ensured, and the turning efficiency in the driveway and the application scene and range of the unmanned vehicle are improved.

In some embodiments, the determining whether to trigger the operation of turning around in the lane by using the turning around decision condition based on the driving state information and the current road section information comprises: determining current pose information of the unmanned vehicle based on the driving state information, determining lane information of a road where the unmanned vehicle is located currently based on the current pose information and high-precision map information, judging whether the unmanned vehicle is located in a turn-around road section currently according to the lane information and a preset turn-around road section, and triggering the operation of turning around the unmanned vehicle in the lane when the unmanned vehicle is located in the turn-around road section currently and the current road environment meets a turn-around decision condition; or judging the passing state of the road in front of the unmanned vehicle based on the current road section information, judging whether the unmanned vehicle can replan the route to the destination after turning around when the passing state of the road in front of the unmanned vehicle is not passable and the parking time of the unmanned vehicle exceeds the preset time, and triggering the operation of turning around the unmanned vehicle in the lane when judging that the unmanned vehicle can replan the route to the destination after turning around and the rear of the unmanned vehicle has a turning space.

Specifically, the embodiment of the disclosure provides two ways of triggering the operation of turning around the unmanned vehicle in the lane, the first way is to set a turning-around road section in advance, and when the vehicle enters the turning-around road section and the environmental constraint meets the triggering condition, the operation of turning around the unmanned vehicle in the lane is executed; the second way is to perform the turn-around operation in the lane if the front road is judged to be unable to pass and a road reaching the end point can be found again after turning around according to the real-time environmental information of the road.

Further, in a first manner of triggering a turn-around operation in a lane, a turn-around region (i.e., a region corresponding to a turn-around road section) is preset, when an unmanned vehicle reaches the turn-around region, the vehicle decelerates and stops, and when a road in front of the vehicle and a road behind the vehicle meet a turn-around condition, the turn-around is actively triggered and executed; in the second way of triggering the turn-around operation in the lane, when the obstacle in front of the unmanned vehicle blocks the road or cannot pass through the road, the parking time of the unmanned vehicle exceeds the preset time, the task re-planning after the turn-around still can plan the task path reaching the terminal, and the turn-around space is arranged behind the unmanned vehicle, the turn-around operation in the lane is triggered.

Furthermore, the embodiment of the disclosure does not need to preset priori knowledge of pose and the like after turning is completed, and by presetting a turning road section, in a conventional map format such as openrive, road section information of a lane/road corresponding to the preset turning road section is set to id, start _ s, end _ s and the like; and taking the preset road section id, start _ s and end _ s as a U-turn road section, and judging whether the U-turn can be executed or not when the vehicle enters the range.

In some embodiments, the determining whether to trigger the operation of turning around in the lane by using the turning around decision condition based on the driving state information and the current road section information comprises: determining the automatic driving state of the unmanned vehicle based on the driving state information, and when the automatic driving state is in a preset state that the U-turn operation cannot be triggered, not triggering the U-turn operation of the unmanned vehicle in the lane; determining a road section where the unmanned vehicle is located currently based on current road section information, and not triggering the operation of turning around the unmanned vehicle in the lane when the road section is a preset road section which can not trigger the turning around operation; and judging whether the unmanned vehicle is in a turning operation state or not based on the current operation state of the unmanned vehicle, and when the unmanned vehicle is in the turning operation state, not triggering the operation of turning the unmanned vehicle in the lane.

Specifically, when determining whether the current vehicle state has the condition of turning around in the lane (i.e. whether the operation of turning around in the lane is triggered), the determination may also be made by: the first method is that the judgment is carried out according to the state of the vehicle, and when the vehicle is in a parking state, a posture adjusting state and the like, the triggering cannot be carried out; the second step is to judge according to the road section information, and when the vehicle is in a high-speed road section, a no-stop area road section and the like, the triggering can not be carried out; and the third step is to judge according to the current operation state of the vehicle, for example, when the unmanned vehicle is in a turn-around state in the lane, the triggering operation is not continued.

In some embodiments, determining whether the unmanned vehicle satisfies a road environment constraint corresponding to a turn around in the lane according to the current road environment information includes: when the operation of turning around the unmanned vehicle in the lane is triggered, the laser radar is used for obtaining current road environment information corresponding to the unmanned vehicle, the road environment constraint when the unmanned vehicle turns around in the lane is judged based on the front road information, the rear road information and the obstacle information in the current road environment information, and the state of the unmanned vehicle is adjusted to the operation state of turning around in the lane according to the judgment result, so that the unmanned vehicle starts the function of turning around in the lane.

Specifically, after the operation of triggering the turn around in the lane is judged, whether the function of the turn around in the lane is started or not is judged based on the upper state machine in the reference line module. In practical application, the reference line module is mainly used for generating a specific turning reference line path, updating the reference line in real time and jumping between states, for example, an internal state jumps from Doing to Finish, and an external state jumps from turning in a lane to a cruise state; two state machines complete the turning function in the lane inside the Behavior module (namely, the Behavior decision module). The state between the decision maker module and the reference line module is maintained by a turn-around decision maker in the lane, and the turn-around decision maker in the lane is used for judging and converting the state of the turn-around function in the lane. The following describes specific contents of the in-lane turn-around decision maker with reference to the accompanying drawings and a specific embodiment, and fig. 3 is a schematic structural diagram of the in-lane turn-around decision maker according to the embodiment of the present disclosure. As shown in fig. 3, the in-lane turn-around decision maker mainly includes the following:

the internal states of the turn-around decision maker in the lane comprise an Init state, a Doing state and a Finish state, and the three states respectively correspond to an initialization state, a turn-around execution state and a turn-around completion state; when in the Init state, executing the decision whether the turning function can be triggered currently; when the unmanned vehicle is in the Doing state, the unmanned vehicle is in the turning state, and the turning reference line module works to plan a specific turning path at the moment; and in the Finish state, the turning operation is currently finished, and the state is converted into the Init state.

Furthermore, the upper state machine comprises three states, namely an INIT state, a DOING state and a FINISH state; the INIT state is used for judging whether the current road environment meets the turning operation in the lane, if the current time does not meet the turning operation, the state is maintained in the INIT state, if the restriction of turning in the lane is met, the position coordinates of the vehicle are converted into a road coordinate system (a friends coordinate system), and then the vehicle enters a DOING state; therefore, the coordinate system conversion is carried out, and the purpose is to project the curve onto a straight road, so that the curve is re-projected after a turning path is planned, and the complexity of turning around on the curve can be reduced. The lower-layer state machine is included in the DOING state and is mainly used for planning a real-time guiding line for turning operation and controlling a steering lamp of a vehicle in the turning process; if the turning-around operation is finally completed, entering a FINISH state, and if the turning-around operation fails in the midway, returning to an INIT state; the FINISH state represents that the turning-around action is completed, if a new turning-around task exists, the state is entered into the INIT state, and a new turn-around operation in the lane is restarted.

In some embodiments, converting the current position of the unmanned vehicle into a road coordinate system and checking for road boundary constraints based on the position of the unmanned vehicle in the road coordinate system comprises: converting the current position and the current road section information corresponding to the unmanned vehicle into a road coordinate system from a Cartesian coordinate system, calculating the road width corresponding to the current road section based on the position of the unmanned vehicle in the road coordinate system, and judging whether the road width meets the width requirement of turn-around operation in a lane so as to check the road boundary constraint; the width requirement is the road width determined based on the length of the unmanned vehicle and the automatic driving longitudinal control precision, and the road coordinate system adopts a Frenet coordinate system.

Specifically, the checking operation of the road boundary constraint is completed in a lower layer state machine, which is a subdivision state of the DOING state in an upper layer state machine, and mainly includes the following six sub-states: the state machine of the lower layer enters from the Relan state and ends in the Finish state or Failed state; after the turn-around operation in the lane is triggered, the upper layer state machine is switched in from the state INIT to the state DOING, and then is switched in from the state Reeplan. The function of the behavior decision module in the embodiment of the disclosure is mainly called an internal algorithm of the Revlan state.

In some embodiments, determining an initial scene, determining a displacement flag according to a determination result of the initial scene, and calculating a next pose state of the unmanned vehicle based on the displacement flag and a current pose state of the unmanned vehicle includes: in a road coordinate system, judging an initial scene of the unmanned vehicle based on current pose information and current road section information of the unmanned vehicle, and acquiring a displacement marker bit corresponding to the initial scene; and according to the fixed curvature, calculating the coordinate position of the next pose state in the road coordinate system under the preset search precision by taking the coordinate corresponding to the current pose state as a starting point and according to the direction in the displacement zone bit, and determining the next pose state of the unmanned vehicle based on the coordinate position.

Specifically, the planning of the turnaround path is implemented in a Replan state algorithm in the behavior decision module, and the following describes in detail an internal algorithm of a Replan state in a lower state machine with reference to the drawings and specific embodiments. Fig. 4 is a schematic flowchart of an algorithm of a Replan state in an underlying state machine according to an embodiment of the present disclosure. As shown in fig. 4, the algorithm of the Replan state mainly includes the following contents:

1) road restraint inspection

The position of the unmanned vehicle and the road information are projected to a Frenet coordinate system from a Cartesian coordinate system, so that the subsequent calculation is facilitated, and the difficulty in processing a curve is reduced; and judging whether the road width meets the width requirement of the turning function in the lane or not by calculating the minimum road width of the road section where the unmanned vehicle is currently located. In practical application, the width requirement of the turning function in the lane is that the length of the vehicle body and the longitudinal control precision of the automatic driving are multiplied by 2; 2 is the minimum value, the larger the value is, the wider the required road is; for example: the length of the unmanned vehicle body is 4.6m, the automatic driving longitudinal control precision is 0.5m, and then the lane inner turning can be carried out only if the road width is at least 5.6 m;

2) initial scene determination

Fig. 5 is a schematic diagram of an initial scenario in a Replan state according to an embodiment of the present disclosure. As shown in fig. 5, there are 16 possible situations in the initial scenario, where an arrow indicates a vehicle driving direction or an initial road direction at the end of the final turn, and a reverse direction of the arrow may indicate the vehicle driving direction at the end of the final turn; each scene corresponds to a zone bit of left turn, right turn, forward and backward; for example, the first initial scenario 1F-L in fig. 5, has flag bits corresponding to forward and left turn; in practical application, the initial scenes are obtained by converting a Cartesian coordinate system into a Frenet coordinate system for simplification, so that the road directions are horizontal;

3) searching given displacement flag bit

When searching for the next posture state, the fixed curvature can be adopted, and the (x ', y ', theta ') corresponding to the next posture state can be obtained through calculation according to the left/right, front/back zone bits and the preset searching precision by using the position coordinates (x, y, theta) corresponding to the current posture;

4) security detection

According to the new pose (namely the state of the next pose), calculating the position coordinates of the four corner points corresponding to the vehicle, and further judging whether the shape of the vehicle is in the road according to the position of the driving road of the vehicle in a Frenet coordinate system;

5) abandoning the last search result and updating the key points

If the vehicle center point of the current pose fails to pass the safety detection, the pose needs to be abandoned; at the moment, recording a vehicle center point corresponding to the previous pose as a key point, and readjusting the left/right and front/back states of the next step according to the key point, for example, taking a flag bit with a posture opposite to the previous pose from the left/right and front/back;

6) endless loop detection

Whether the dead loop is trapped or not can be judged from historical key points and point sets among the key points, for example, if the same key points appear, the dead loop is considered to be trapped; in addition, whether the distance between each section of key points is more than 2 times of the longitudinal control precision of the automatic driving can be judged, and if the distance between the key points does not meet the condition, the dead cycle is considered to be involved;

7) complete by turning around

The step is mainly used for judging whether the vehicle is in the state of the third initial scene or the fourth initial scene in fig. 5;

8) coordinate transformation, key point correction

Converting the planning result in the Frenet coordinate system to the Cartesian coordinate system again;

9) validity check 1

Checking whether the key points and the point sets among the key points are in the road or not, wherein the total displacement of the point sets among the key points is more than 2 times of the longitudinal control precision of the automatic driving;

10) trajectory smoothing

Because the operation is a turning driving track obtained by fixed curvature calculation, if the transverse tracking precision of the vehicle is improved, the curvature of the track needs to be smoothed; for example, a conventional spline curve, a clothoid curve and the like are adopted for track smoothing processing among key points;

11) validity check 2

And after the track is smoothed, whether the key points and the point sets among the key points are in the road is checked again, and the total displacement of the point sets among the key points is more than 2 times of the longitudinal control precision of the automatic driving.

In some embodiments, determining a position of an angular point of the unmanned vehicle according to the state of the next pose, performing security detection on the pose of the unmanned vehicle based on the position of the angular point, and determining, according to a security detection result, that a vehicle center point corresponding to the next pose is taken as a path key point or a vehicle center point corresponding to the previous pose is taken as a path key point, includes: determining the position of an angular point of the unmanned vehicle in the next pose state based on the coordinate position in the road coordinate system corresponding to the next pose state, and judging whether the unmanned vehicle is in a lane or not based on the position of the angular point so as to carry out safety detection on the pose according to a judgment result; and when the unmanned vehicle is judged to be in the lane, the vehicle center point corresponding to the next position is taken as a path key point, when the unmanned vehicle is judged not to be in the lane, the state of the next position is abandoned, the vehicle center point corresponding to the previous position is taken as a path key point, and the path is updated according to the path key point until the turning path is planned.

Specifically, when a new pose (i.e., the next pose retrieved) is detected safely, the positions corresponding to the four corners of the vehicle are calculated according to the position of the new pose in the Frenet coordinate system, and then whether the vehicle shape is in the current lane is judged according to the position information of the road where the unmanned vehicle is currently located. The following briefly describes the functions of other sub-states in the lower state machine, which may specifically include the following:

in the Start state, the system is used for extending the starting point and the end point, and in order to enable the motion planning module to better consider the barrier constraint when planning the path; and taking out a first starting Point in the track group, extending the end Point, judging whether the end Point is the last one, and if so, entering a Final Point state.

And in the Final Point state, judging whether the current section in the path is finished or not, entering a Finish state after the finishing, judging whether the parking time is too long or not when the parking time is not finished, re-entering a Replan state to plan a new path when the parking time is too long, otherwise judging whether the deviation between the current position and the front section is too large, and issuing the current section if the deviation is judged to be not.

And in the Inner Points state, the method is used for judging whether the current section is finished or not, and judging whether the current position and the current section have overlarge deviation according to the judgment that whether the current vehicle pose is in a longitudinal error range or not, and judging according to course deviation, transverse distance error and longitudinal distance error.

In some embodiments, after the path composed of all the path key points is taken as the driving track of the unmanned vehicle for realizing turn around in the lane, the method further comprises the following steps: converting the running track from a road coordinate system to a Cartesian coordinate system, carrying out validity check on the running track in the Cartesian coordinate system, carrying out smoothing treatment on the running track, carrying out boundary collision detection on the running track after the smoothing treatment, and judging whether to turn around in a lane or not according to the result of the boundary collision detection; the validity check comprises checking whether a point set among the key points of the path is in the lane or not and whether the total displacement corresponding to the point set among the key points of the path is larger than the preset automatic driving longitudinal control precision or not.

Specifically, after the turning operation in the lane is finished, the planning module triggers task re-planning to generate the next segment of task and then continues to drive; if the behavior planning module triggers the state of the failed turning around in the turning-around midway, the decision module initiates a task failure to the remote control center to request for taking over. In the same road section, repeated triggering can be carried out only n times in a short time, if the turning decision for n times is not finally successful, the vehicle is automatically driven to carry out protected parking, the turning state is not continuously triggered, and meanwhile, remote taking over is requested from the cloud; avoiding falling into endless loop, and n is a preset value.

The following describes in detail an overall decision process of the in-lane turn-around function with reference to the accompanying drawings and specific embodiments, and fig. 6 is a schematic flow chart of the in-lane turn-around decision process provided by the embodiment of the present disclosure. As shown in fig. 6, the in-lane turn-around decision process mainly includes the following steps:

1) judging whether the current vehicle state has the condition of turning around in the lane, such as: judging according to the vehicle state, and when the vehicle is in a parking state, a posture adjusting state and the like, not triggering; judging that the vehicle is in a high-speed road section, a no-parking area road section and the like and cannot be triggered according to the road section information; the judgment is not continued when the vehicle is in the in-lane U-turn state.

2) Maintaining historical trigger road section and trigger frequency information, and judging whether the information needs to be cleared according to the current position; the method is mainly used for preventing the trigger from falling into the dead cycle on a certain road section; for example, the vehicle is always in a certain road section and cannot turn around successfully, the historical triggering times are recorded through the step, and then cloud intervention of the remote control center can be requested.

3) The method has the advantages that the fact that the U-turn in the lane of the current road section is triggered for more than n times is judged, the maximum number of times of triggering of the same road section is set in the step, and the situation that the vehicle always triggers the U-turn but cannot be regulated to finish all the time to cause the vehicle to fall into the dead cycle is prevented.

4) Judging whether the current road section belongs to a preset turning road section, and calculating current road lane information such as id, s and the like based on high-precision map information according to the current vehicle pose information; and judging whether the current vehicle is in a preset turning road section or not.

5) Issuing a parking instruction, judging whether the vehicle stops or not, wherein the vehicle is in a preset U-turn road section, firstly, issuing the parking instruction to a speed planning submodule in a motion planning module, and controlling the vehicle to stop; if the vehicle stops, the stop instruction is maintained, and the turn-around reference line module in the lane is prepared to be called to calculate whether a turn-around path can be planned or not.

6) Judging whether the front road impassable time is longer than the preset time when the vehicle stops, detecting the reason of impassable front through the preset maximum vehicle parking time, wherein the historical motion planning module comprises the reason of parking, if the vehicle can not move for a long time, for example, the vehicle parks for more than 5 minutes, the front road impassable at the moment is shown, and after the turning function is tried, whether other task paths to the terminal point can be planned again.

7) A reference line module is called to judge whether an effective turn-around track in the lane can be generated or not, a path generation function of the reference line module is called, and the reference line module comprehensively judges whether a reasonable turn-around path can be planned or not according to the constraints of road information of the current road section, the control precision of the vehicle and the like; after an initial track is generated, carrying out boundary collision detection on the track again under a Cartesian coordinate system; if the collision detection is passed, the judgment is that the head can be turned, otherwise, the judgment is that the head cannot be turned.

8) Maintaining a trigger counter, if the validity detection of the previous step passes, counting to be 1, otherwise, counting to be 0; the trigger counter records the success and failure states (success is 1 and failure is 0) within a period of time, and then can calculate the information such as average success probability, kurtosis, skewness and the like.

9) And judging whether the total count number of the trigger counter exceeds a threshold value.

10) Judging whether the probability and the skewness calculated by the counter are both greater than respective thresholds, calculating the triggering probability mean value and the skewness of the triggering counter, and if the probability and the skewness both meet the setting, formally triggering and turning around; meanwhile, historical information of the trigger is maintained, and the vehicle state machine skips to maintain; otherwise, the current round of judgment is finished.

According to the technical scheme provided by the embodiment of the disclosure, the unmanned vehicle is influenced by actual roads or cloud tasks in the driving process, and is often required to turn around on narrow roads, and the road width does not meet the constraint of the turning radius of the unmanned vehicle, so that the unmanned vehicle is required to change lanes for multiple times to adjust the posture to complete turning around. The method comprises the steps of firstly utilizing a decision-making device module to judge whether a current scene can trigger a turn-around function in a lane or not, then utilizing a reference line module to specifically plan a turn-around track in the lane when the current scene can be judged to turn around in the lane, and fully considering conditions such as road boundary constraint, vehicle control error constraint, environmental constraint and the like when a turn-around path is planned, so that an unmanned vehicle can complete turn-around operation in a narrow lane. In addition, the turning function in the lane can enable the unmanned vehicle to be applied to wider scenes, the driving area of the unmanned vehicle is expanded, the operation efficiency is improved, the intervention frequency of a driver or a remote cloud is reduced, the turning can be achieved without the need of bypassing an extra road section, or the turning can be achieved through remote intervention, and the operation efficiency of the unmanned vehicle is improved.

The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods. For details not disclosed in the embodiments of the apparatus of the present disclosure, refer to the embodiments of the method of the present disclosure.

Fig. 7 is a schematic structural diagram of an apparatus for implementing inside lane turn around by an unmanned vehicle according to an embodiment of the present disclosure. As shown in fig. 7, the apparatus for implementing inside-lane turning of the unmanned vehicle includes:

the acquisition module 701 is configured to acquire driving state information and current road section information of the unmanned vehicle in an automatic driving process of the unmanned vehicle, and judge whether to trigger a turn-around operation in a lane by using a turn-around decision condition based on the driving state information and the current road section information;

the judging module 702 is configured to, when it is judged that the operation of turning around in the lane is triggered, determine current road environment information based on the current road section information, and judge whether the unmanned vehicle meets a road environment constraint corresponding to the turning around in the lane according to the current road environment information;

a calculating module 703 configured to convert the current position of the unmanned vehicle into a road coordinate system when the unmanned vehicle satisfies a road environment constraint, check a road boundary constraint based on the position of the unmanned vehicle in the road coordinate system, judge an initial scene when the unmanned vehicle satisfies the road boundary constraint, determine a displacement flag according to a judgment result of the initial scene, and calculate a next pose state of the unmanned vehicle based on the displacement flag and the current pose state of the unmanned vehicle;

and the planning module 704 is configured to determine the corner position of the unmanned vehicle according to the state of the next position, perform safety detection on the position of the unmanned vehicle based on the corner position, determine to use the vehicle center point corresponding to the next position as a route key point or use the vehicle center point corresponding to the previous position as a route key point according to a safety detection result, calculate all the route key points, and use a route formed by all the route key points as a driving track of the unmanned vehicle for turning around in the lane.

In some embodiments, the obtaining module 701 in fig. 7 determines current pose information of the unmanned vehicle based on the driving state information, determines lane information of a road where the unmanned vehicle is currently located based on the current pose information and the high-precision map information, determines whether the unmanned vehicle is currently located in a turn road section according to the lane information and a preset turn road section, and triggers an operation of turning around the unmanned vehicle in the lane when the unmanned vehicle is currently located in the turn road section and the current road environment meets a turn decision condition; or judging the passing state of the road in front of the unmanned vehicle based on the current road section information, judging whether the unmanned vehicle can replan the route to the destination after turning around when the passing state of the road at the current side is not passable and the parking time of the unmanned vehicle exceeds the preset time, and triggering the operation of turning around the unmanned vehicle in the lane when judging that the unmanned vehicle can replan the route to the destination after turning around and the rear of the unmanned vehicle has a turning space.

In some embodiments, the obtaining module 701 of fig. 7 determines an automatic driving state of the unmanned vehicle based on the driving state information, and does not trigger an operation of the unmanned vehicle turning around in the lane when the automatic driving state is in a preset state in which the turning around operation cannot be triggered; determining a road section where the unmanned vehicle is located currently based on the current road section information, and when the road section is a preset road section which can not trigger the turn-around operation, not triggering the turn-around operation of the unmanned vehicle in the lane; and judging whether the unmanned vehicle is in a turning operation state or not based on the current operation state of the unmanned vehicle, and when the unmanned vehicle is in the turning operation state, not triggering the operation of turning the unmanned vehicle in the lane.

In some embodiments, the determining module 702 in fig. 7 obtains current road environment information corresponding to the unmanned vehicle by using a laser radar when triggering an operation of turning around the unmanned vehicle in the lane, determines a road environment constraint when the unmanned vehicle turns around in the lane based on front road information, rear road information, and obstacle information in the current road environment information, and adjusts a state of the unmanned vehicle to an operation state of turning around in the lane according to a determination result, so that the unmanned vehicle starts a function of turning around in the lane.

In some embodiments, the calculation module 703 of fig. 7 converts the current position and the current road section information corresponding to the unmanned vehicle from a cartesian coordinate system into a road coordinate system, calculates the road width corresponding to the current road section based on the position of the unmanned vehicle in the road coordinate system, and determines whether the road width meets the width requirement of the turn-around operation in the lane, so as to check the road boundary constraint; the width requirement is the road width determined based on the length of the unmanned vehicle and the automatic driving longitudinal control precision, and the road coordinate system adopts a Frenet coordinate system.

In some embodiments, the calculation module 703 of fig. 7 determines, in the road coordinate system, an initial scene of the unmanned vehicle based on the current pose information and the current road section information of the unmanned vehicle, and obtains a displacement flag corresponding to the initial scene; and according to the fixed curvature, calculating the coordinate position of the next pose state in the road coordinate system under the preset search precision by taking the coordinate corresponding to the current pose state as a starting point and according to the direction in the displacement zone bit, and determining the next pose state of the unmanned vehicle based on the coordinate position.

In some embodiments, the planning module 704 of fig. 7 determines a corner position of the unmanned vehicle in the next attitude state based on the coordinate position in the road coordinate system corresponding to the next attitude state, and determines whether the unmanned vehicle is in the lane based on the corner position, so as to perform safety detection on the attitude according to the determination result; and when the unmanned vehicle is judged to be in the lane, the vehicle center point corresponding to the next position is taken as a path key point, when the unmanned vehicle is judged not to be in the lane, the state of the next position is abandoned, the vehicle center point corresponding to the previous position is taken as a path key point, and the path is updated according to the path key point until the turning path is planned.

In some embodiments, the planning module 704 of fig. 7 converts the driving trajectory from the road coordinate system to a cartesian coordinate system after taking the path formed by all the path key points as the driving trajectory for the unmanned vehicle to perform turn around in the lane, performs validity check on the driving trajectory in the cartesian coordinate system, smoothes the driving trajectory, performs boundary collision detection on the smoothed driving trajectory, and determines whether to turn around in the lane according to the result of the boundary collision detection; the validity check includes checking whether a point set among the path key points is in the lane and whether the total displacement corresponding to the point set among the path key points is greater than the preset automatic driving longitudinal control precision.

It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.

Fig. 8 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure. As shown in fig. 8, the electronic apparatus 8 of this embodiment includes: a processor 801, a memory 802, and a computer program 803 stored in the memory 802 and operable on the processor 801. The steps in the various method embodiments described above are implemented when the computer program 803 is executed by the processor 801. Alternatively, the processor 801 implements the functions of the respective modules/units in the above-described respective apparatus embodiments when executing the computer program 803.

Illustratively, the computer program 803 may be divided into one or more modules/units, which are stored in the memory 802 and executed by the processor 801 to accomplish the present disclosure. One or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 803 in the electronic device 8.

The electronic device 8 may be a desktop computer, a notebook, a palm computer, a cloud server, or other electronic devices. The electronic device 8 may include, but is not limited to, a processor 801 and a memory 802. Those skilled in the art will appreciate that fig. 8 is merely an example of an electronic device 8 and does not constitute a limitation of the electronic device 8 and may include more or fewer components than shown, or some components may be combined, or different components, e.g., the electronic device may also include input-output devices, network access devices, buses, etc.

The Processor 801 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The storage 802 may be an internal storage unit of the electronic device 8, for example, a hard disk or a memory of the electronic device 8. The memory 802 may also be an external storage device of the electronic device 8, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the electronic device 8. Further, the memory 802 may also include both internal storage units and external storage devices of the electronic device 8. The memory 802 is used to store computer programs and other programs and data required by the electronic device. The memory 802 may also be used to temporarily store data that has been output or is to be output.

It should be clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional units and modules is only used for illustration, and in practical applications, the above function distribution may be performed by different functional units and modules as needed, that is, the internal structure of the device is divided into different functional units or modules, so as to perform all or part of the above described functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

In the embodiments provided in the present disclosure, it should be understood that the disclosed apparatus/computer device and method may be implemented in other ways. For example, the above-described apparatus/computer device embodiments are merely illustrative, and for example, a module or a unit may be divided into only one logical function, another division may be made in actual implementation, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, the present disclosure may implement all or part of the flow of the method in the above embodiments, and may also be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of the above methods and embodiments. The computer program may comprise computer program code which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain suitable additions or additions that may be required in accordance with legislative and patent practices within the jurisdiction, for example, in some jurisdictions, computer readable media may not include electrical carrier signals or telecommunications signals in accordance with legislative and patent practices.

The above examples are only intended to illustrate the technical solutions of the present disclosure, not to limit them; although the present disclosure has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present disclosure, and are intended to be included within the scope of the present disclosure.

Claims

1. A method for realizing turn around in a lane by an unmanned vehicle is characterized by comprising the following steps:

acquiring running state information and current road section information of the unmanned vehicle in the automatic driving process of the unmanned vehicle, and judging whether to trigger turn-around operation in a lane by using turn-around decision conditions based on the running state information and the current road section information;

when the operation of turning around in the lane is judged to be triggered, current road environment information is determined based on the current road section information, and whether the unmanned vehicle meets the road environment constraint corresponding to the turning around in the lane is judged according to the current road environment information;

when the unmanned vehicle meets the road environment constraint, converting the current position of the unmanned vehicle into a road coordinate system, checking the road boundary constraint based on the position of the unmanned vehicle in the road coordinate system, judging an initial scene when the unmanned vehicle meets the road boundary constraint, determining a displacement marker bit according to the judgment result of the initial scene, and calculating the next pose state of the unmanned vehicle based on the displacement marker bit and the current pose state of the unmanned vehicle;

and determining the corner position of the unmanned vehicle according to the state of the next position, performing safety detection on the position of the unmanned vehicle based on the corner position, judging to take the vehicle center point corresponding to the next position as a path key point or take the vehicle center point corresponding to the previous position as the path key point according to a safety detection result, calculating all the path key points, and taking a path formed by all the path key points as a driving track of the unmanned vehicle for realizing turn-around in the lane.

2. The method according to claim 1, wherein the determining whether to trigger an operation of turning around in a lane by using a turning around decision condition based on the driving state information and the current section information comprises:

determining current pose information of an unmanned vehicle based on the running state information, determining lane information of a road where the unmanned vehicle is located currently based on the current pose information and high-precision map information, judging whether the unmanned vehicle is located in a turn-around road section currently according to the lane information and a preset turn-around road section, and triggering the operation of turning around the unmanned vehicle in the lane when the unmanned vehicle is located in the turn-around road section currently and the current road environment meets the turn-around decision condition; alternatively, the first and second electrodes may be,

judging the passing state of the road in front of the unmanned vehicle based on the current road section information, judging whether the unmanned vehicle can replan the route to the terminal after turning around when the passing state of the road in front is not passable and the parking time of the unmanned vehicle exceeds the preset time, and triggering the operation of turning around the unmanned vehicle in the lane when judging that the unmanned vehicle can replan the route to the terminal after turning around and the rear of the unmanned vehicle has a turning space.

3. The method according to claim 2, wherein the determining whether to trigger an operation of turning around in a lane by using a turn-around decision condition based on the driving state information and the current road section information comprises:

determining an automatic driving state of the unmanned vehicle based on the driving state information, and when the automatic driving state is in a preset state that the U-turn operation cannot be triggered, not triggering the operation of U-turn of the unmanned vehicle in the lane;

determining a road section where the unmanned vehicle is located currently based on the current road section information, and not triggering the operation of turning around the unmanned vehicle in the lane when the road section is a preset road section which can not trigger the turning around operation;

and judging whether the unmanned vehicle is in a turning operation state or not based on the current operation state of the unmanned vehicle, and when the unmanned vehicle is in the turning operation state, not triggering the operation of turning the unmanned vehicle in the lane.

4. The method of claim 1, wherein the determining whether the unmanned vehicle meets a road environment constraint corresponding to a turn around in a lane according to the current road environment information comprises:

when the operation of turning around the unmanned vehicle in the lane is triggered, the laser radar is used for obtaining the current road environment information corresponding to the unmanned vehicle, the road environment constraint when the unmanned vehicle turns around in the lane is judged based on the front road information, the rear road information and the obstacle information in the current road environment information, and the state of the unmanned vehicle is adjusted to the operation state of turning around in the lane according to the judgment result, so that the unmanned vehicle starts the function of turning around in the lane.

5. The method of claim 1, wherein converting the current position of the unmanned vehicle into a road coordinate system and checking road boundary constraints based on the position of the unmanned vehicle in the road coordinate system comprises:

converting the current position corresponding to the unmanned vehicle and the current road section information into a road coordinate system from a Cartesian coordinate system, calculating the road width corresponding to the current road section based on the position of the unmanned vehicle in the road coordinate system, and judging whether the road width meets the width requirement of turn-around operation in a lane so as to check the road boundary constraint;

wherein the width requirement is a road width determined based on a body length of the unmanned vehicle and an autopilot longitudinal control accuracy, and the road coordinate system adopts a Frenet coordinate system.

6. The method of claim 5, wherein the determining an initial scene and determining a displacement flag according to the determination of the initial scene, and calculating a next pose state of the unmanned vehicle based on the displacement flag and a current pose state of the unmanned vehicle comprises:

judging an initial scene of the unmanned vehicle based on the current pose information and the current road section information of the unmanned vehicle in the road coordinate system, and acquiring a displacement marker bit corresponding to the initial scene;

and according to the fixed curvature, calculating the coordinate position of the next pose state in the road coordinate system under the preset search precision by taking the coordinate corresponding to the current pose state as a starting point and according to the direction in the displacement zone bit, and determining the next pose state of the unmanned vehicle based on the coordinate position.

7. The method according to claim 1, wherein the determining the position of the corner point of the unmanned vehicle according to the state of the next pose, performing security detection on the pose of the unmanned vehicle based on the position of the corner point, and determining whether to use the vehicle center point corresponding to the next pose as a route key point or to use the vehicle center point corresponding to the previous pose as a route key point according to a security detection result comprises:

determining the position of an angular point of the unmanned vehicle in the next attitude state based on the coordinate position of the unmanned vehicle in the road coordinate system corresponding to the next attitude state, and judging whether the unmanned vehicle is in a lane or not based on the position of the angular point so as to carry out safety detection on the attitude according to a judgment result;

and when the unmanned vehicle is judged to be in the lane, the vehicle central point corresponding to the next position is taken as a path key point, when the unmanned vehicle is judged not to be in the lane, the state of the next position is abandoned, the vehicle central point corresponding to the previous position is taken as a path key point, and the path is updated according to the path key point until a turn-around path is planned.

8. The method of claim 1, wherein after the path consisting of all the path key points is taken as a driving track of the unmanned vehicle for turning around in the lane, the method further comprises:

converting the running track from the road coordinate system to a Cartesian coordinate system, carrying out validity check on the running track in the Cartesian coordinate system, carrying out smoothing processing on the running track, carrying out boundary collision detection on the smoothed running track, and judging whether to turn around in a lane according to the result of the boundary collision detection;

and checking the validity to check whether the point sets among the key points of the path are in the lane or not and whether the total displacement corresponding to the point sets among the key points of the path is greater than the preset automatic driving longitudinal control precision or not.

9. The utility model provides a device that unmanned car realized turning around in lane which characterized in that includes:

the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is configured to acquire driving state information and current road section information of an unmanned vehicle in the automatic driving process of the unmanned vehicle, and judge whether to trigger turn-around operation in a lane by using turn-around decision conditions based on the driving state information and the current road section information;

the judging module is configured to determine current road environment information based on the current road section information when judging that the operation of turning around in the lane is triggered, and judge whether the unmanned vehicle meets the road environment constraint corresponding to the turning around in the lane according to the current road environment information;

a calculation module configured to convert a current position of the unmanned vehicle into a road coordinate system when the unmanned vehicle satisfies the road environment constraint, check a road boundary constraint based on the position of the unmanned vehicle in the road coordinate system, judge an initial scene when the unmanned vehicle satisfies the road boundary constraint, determine a displacement flag according to a judgment result of the initial scene, and calculate a next pose state of the unmanned vehicle based on the displacement flag and the current pose state of the unmanned vehicle;

and the planning module is configured to determine the corner position of the unmanned vehicle according to the state of the next position, perform safety detection on the position and posture of the unmanned vehicle based on the corner position, judge that the vehicle center point corresponding to the next position is taken as a path key point or the vehicle center point corresponding to the previous position is taken as a path key point according to a safety detection result, calculate all the path key points, and take a path formed by all the path key points as a driving track of the unmanned vehicle for realizing turning inside the lane.

10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of any one of claims 1 to 8 when the program is executed by the processor.