CN114148350A

CN114148350A - Control method and device for unmanned equipment

Info

Publication number: CN114148350A
Application number: CN202111571611.8A
Authority: CN
Inventors: 廉渊升; 刘羽霄; 白钰; 马杰; 任冬淳; 夏华夏
Original assignee: Beijing Sankuai Online Technology Co Ltd
Current assignee: Beijing Sankuai Online Technology Co Ltd
Priority date: 2021-12-21
Filing date: 2021-12-21
Publication date: 2022-03-08
Anticipated expiration: 2041-12-21
Also published as: CN114148350B

Abstract

The specification discloses a control method and a control device for unmanned equipment. First, current first driving information of the unmanned equipment is obtained, and current second driving information of the obstacle is obtained. Next, for each obstacle, the speed information of the obstacle in the future is predicted as the predicted speed based on the second travel information corresponding to the obstacle. And then, determining the predicted speed of the obstacle according to the first running information, the second running information corresponding to the obstacle and the predicted speed, and taking the safe distance between the unmanned device and the obstacle when the unmanned device performs simultaneous braking according to the first running information as the safe distance corresponding to the obstacle. And finally, controlling the unmanned equipment according to the safety distance corresponding to each obstacle. The method can predict the speed information which can be reached by the barrier to the greatest extent in the future so as to determine the safe distance between the unmanned equipment and the barrier, thereby improving the safety of the unmanned equipment in the driving process.

Description

Control method and device for unmanned equipment

Technical Field

The present disclosure relates to the field of unmanned driving, and in particular, to a method and an apparatus for controlling an unmanned aerial vehicle.

Background

At present, unmanned equipment can encounter a plurality of obstacles on roads with complex traffic conditions, and the unmanned equipment usually predicts the future driving track of the obstacles according to the historical driving data of the obstacles. The unmanned equipment can avoid collision with the obstacle according to the future driving track of the obstacle and the driving track of the unmanned equipment. However, in practical applications, when the obstacle has a sudden speed change or a steering angle swing, the predicted future driving track of the obstacle may deviate, and there is a possibility of collision with other surrounding obstacles, so that the safety of the unmanned device during driving is reduced.

Therefore, how to improve the safety of the unmanned equipment in the driving process is an urgent problem to be solved.

Disclosure of Invention

The present specification provides a method and an apparatus for controlling an unmanned aerial vehicle, which partially solve the above problems in the prior art.

The technical scheme adopted by the specification is as follows:

the present specification provides a control method for an unmanned aerial vehicle, which is applied to the field of unmanned driving, and comprises the following steps:

acquiring current driving information of the unmanned equipment as first driving information, and acquiring current driving information of obstacles around the unmanned equipment as second driving information;

for each obstacle, determining a maximum steering angle corresponding to the obstacle according to second running information corresponding to the obstacle, predicting a steering angle of the obstacle in the future as a predicted steering angle according to the maximum steering angle corresponding to the obstacle and a preset steering angle probability distribution, and predicting speed information of the obstacle in the future as a predicted speed according to the predicted steering angle corresponding to the obstacle and the second running information corresponding to the obstacle;

determining a safe distance between the unmanned device and the obstacle at the predicted speed and under the condition that the unmanned device performs simultaneous braking at the first running information according to the first running information, the second running information corresponding to the obstacle and the predicted speed, wherein the safe distance is used as the safe distance corresponding to the obstacle;

and controlling the unmanned equipment according to the safety distance corresponding to each obstacle.

Optionally, determining a maximum steering angle corresponding to the obstacle according to the second driving information corresponding to the obstacle specifically includes:

aiming at each obstacle, determining the shortest braking distance corresponding to the obstacle according to the second driving information corresponding to the obstacle;

and determining the maximum steering angle corresponding to the obstacle according to the shortest braking distance corresponding to the obstacle and the preset minimum turning radius.

Optionally, controlling the unmanned aerial vehicle according to a safety distance corresponding to each obstacle specifically includes:

determining a risk score function corresponding to the obstacle according to the safety distance corresponding to the obstacle;

determining the distance between the unmanned equipment and the obstacle according to the first driving information and second driving information corresponding to the obstacle;

inputting the distance into the risk score function, determining a final risk score for the obstacle at the distance from the unmanned device;

and controlling the unmanned equipment according to the final risk score corresponding to each obstacle.

Optionally, the safety distance corresponding to the obstacle includes: the maximum safe distance corresponding to the obstacle and the minimum safe distance corresponding to the obstacle;

determining a risk score function corresponding to the obstacle according to the safety distance corresponding to the obstacle, specifically comprising:

and determining a risk score function corresponding to the obstacle according to the maximum safe distance corresponding to the obstacle and the minimum safe distance corresponding to the obstacle.

Optionally, the maximum safe distance comprises: maximum lateral safety distance and maximum longitudinal safety distance, the minimum safety distance comprising: a minimum lateral safety distance and a minimum longitudinal safety distance, the distances including a lateral distance and a longitudinal distance, the risk scoring function for the obstacle comprising: a transverse risk score function corresponding to the obstacle and a longitudinal risk score function corresponding to the obstacle;

inputting the distance into the risk score function, and determining a final risk score of the obstacle at the distance from the unmanned device, specifically including:

inputting the lateral distance into the lateral risk score function, determining a risk score of the obstacle at the lateral distance from the unmanned device as a lateral risk score corresponding to the obstacle, and inputting the longitudinal distance into the longitudinal risk score function, determining a risk score of the obstacle at the longitudinal distance from the unmanned device as a longitudinal risk score corresponding to the obstacle;

and determining the final risk score according to the transverse risk score corresponding to the obstacle and the longitudinal risk score corresponding to the obstacle.

Optionally, determining the final risk score according to the lateral risk score corresponding to the obstacle and the longitudinal risk score corresponding to the obstacle, specifically including:

and taking the larger risk score of the transverse risk score corresponding to the obstacle and the longitudinal risk score corresponding to the obstacle as the final risk score.

Optionally, controlling the unmanned device according to the final risk score corresponding to each obstacle, specifically including:

determining an adjustment direction to which a final risk score corresponding to each obstacle belongs as an adjustment direction of the unmanned equipment to the obstacle for each obstacle;

and controlling the unmanned equipment according to the adjustment direction of the unmanned equipment for each obstacle.

determining, based on the first travel information, the second travel information corresponding to the obstacle, and the predicted speed, a safe distance between the unmanned aerial vehicle and the obstacle when the unmanned aerial vehicle performs simultaneous braking with the first travel information at the predicted speed, as the safe distance corresponding to the obstacle, specifically including:

determining the maximum safe distance between the unmanned equipment and the obstacle when the unmanned equipment runs at the predicted speed and brakes at the maximum braking acceleration when the unmanned equipment runs at the first running information and the second running information corresponding to the obstacle and the predicted speed; and a minimum safe distance between the unmanned device and the obstacle when braking is performed at the minimum braking acceleration at the same time.

Optionally, determining, according to the first travel information, the second travel information corresponding to the obstacle, and the predicted speed, a maximum safe distance between the unmanned aerial vehicle and the obstacle when the unmanned aerial vehicle travels at the predicted speed and the unmanned aerial vehicle is braked at the maximum braking acceleration while traveling at the first travel information specifically includes:

according to the first running information, determining that the unmanned equipment takes the current position as an initial position, runs at a preset maximum acceleration within a preset maximum reaction time, and takes the position where the unmanned equipment is positioned after braking at the preset maximum braking acceleration after the maximum reaction time as a maximum post-braking position corresponding to the unmanned equipment;

according to the second running information corresponding to the obstacle and the predicted speed, determining that the position of the obstacle at present is used as an initial position, the predicted speed is used as an initial speed, the obstacle runs at a preset maximum acceleration within a preset maximum reaction time, and the position where the obstacle is located after braking at the preset maximum braking acceleration after the maximum reaction time is used as a maximum post-braking position corresponding to the obstacle;

and determining the maximum safe distance corresponding to the obstacle according to the maximum post-braking position corresponding to the unmanned equipment and the maximum post-braking position corresponding to the obstacle.

Optionally, determining, according to the first travel information, the second travel information corresponding to the obstacle, and the predicted speed, a minimum safe distance between the unmanned aerial vehicle and the obstacle when the unmanned aerial vehicle travels at the predicted speed and the unmanned aerial vehicle is braked at the minimum braking acceleration while traveling at the first travel information, specifically includes:

according to the first running information, determining that the unmanned equipment takes the current position as an initial position, runs at a preset minimum acceleration within a preset minimum reaction time, and takes the position where the unmanned equipment is located after braking at a preset minimum braking acceleration after the minimum reaction time as a corresponding minimum post-braking position of the unmanned equipment;

according to the second running information corresponding to the obstacle and the predicted speed, determining that the position of the obstacle at present is used as an initial position, the predicted speed is used as an initial speed, the obstacle runs at a preset minimum acceleration within a preset minimum reaction time, and the position where the obstacle is located after braking at the preset minimum braking acceleration after the minimum reaction time is used as a minimum post-braking position corresponding to the obstacle;

and determining the minimum safe distance corresponding to the obstacle according to the minimum post-braking position corresponding to the unmanned equipment and the minimum post-braking position corresponding to the obstacle.

The present specification provides a control apparatus for an unmanned aerial vehicle, the apparatus being applied to the field of unmanned driving, including:

the system comprises an acquisition module, a display module and a control module, wherein the acquisition module is used for acquiring current driving information of the unmanned equipment as first driving information and acquiring current driving information of obstacles around the unmanned equipment as second driving information;

a prediction module, configured to determine, for each obstacle, a maximum steering angle corresponding to the obstacle according to second traveling information corresponding to the obstacle, predict, as a predicted steering angle, a steering angle of the obstacle in the future according to the maximum steering angle corresponding to the obstacle and a preset steering angle probability distribution, and predict, as a predicted speed, speed information of the obstacle in the future according to the predicted steering angle corresponding to the obstacle and the second traveling information corresponding to the obstacle;

a determining module, configured to determine, according to the first driving information, second driving information corresponding to the obstacle, and the predicted speed, a safe distance between the unmanned device and the obstacle when the unmanned device performs simultaneous braking with the first driving information at the predicted speed as a safe distance corresponding to the obstacle;

and the control module is used for controlling the unmanned equipment according to the safety distance corresponding to each obstacle.

The present specification provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the above-described control method of an unmanned aerial device.

The present specification provides 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 above-mentioned method of controlling an unmanned device when executing the program.

The technical scheme adopted by the specification can achieve the following beneficial effects:

in the control method of the unmanned aerial vehicle provided in the present specification. First, current travel information of the unmanned aerial vehicle is acquired as first travel information, and current travel information of obstacles around the unmanned aerial vehicle is acquired as second travel information. Then, for each obstacle, a maximum steering angle corresponding to the obstacle is determined based on second travel information corresponding to the obstacle, a future steering angle of the obstacle is predicted as a predicted steering angle based on the maximum steering angle corresponding to the obstacle and a preset steering angle probability distribution, and future speed information of the obstacle is predicted as a predicted speed based on the predicted steering angle corresponding to the obstacle and the second travel information corresponding to the obstacle. And then, determining the predicted speed of the obstacle according to the first running information, the second running information corresponding to the obstacle and the predicted speed, and taking the safe distance between the unmanned device and the obstacle when the unmanned device performs simultaneous braking according to the first running information as the safe distance corresponding to the obstacle. And finally, controlling the unmanned equipment according to the safety distance corresponding to each obstacle.

As can be seen from the above method, the method can predict the speed information that the obstacle is most likely to reach in the future when the obstacle has a sudden speed change or a steering angle swing, as the predicted speed, and determine the safe distance between the unmanned device and the obstacle in the case where the unmanned device performs simultaneous braking with the first travel information, so as to avoid collision between the unmanned device and other surrounding obstacles, thereby improving the safety of the unmanned device during travel.

Drawings

The accompanying drawings, which are included to provide a further understanding of the specification and are incorporated in and constitute a part of this specification, illustrate embodiments of the specification and together with the description serve to explain the specification and not to limit the specification in a non-limiting sense. In the drawings:

fig. 1 is a schematic flow chart of a control method of an unmanned aerial vehicle in the present specification;

FIG. 2 is a schematic diagram illustrating a method for determining a steering angle according to an embodiment of the present disclosure;

fig. 3 is a schematic diagram of a control device of an unmanned aerial vehicle provided herein;

fig. 4 is a schematic diagram of an electronic device corresponding to fig. 1 provided in the present specification.

Detailed Description

In order to make the objects, technical solutions and advantages of the present disclosure more clear, the technical solutions of the present disclosure will be clearly and completely described below with reference to the specific embodiments of the present disclosure and the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present disclosure, and not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present specification without any creative effort belong to the protection scope of the present specification.

The technical solutions provided by the embodiments of the present description are described in detail below with reference to the accompanying drawings.

Fig. 1 is a schematic flow chart of a control method of an unmanned aerial vehicle in this specification, including the following steps:

s100: the method includes acquiring current driving information of the unmanned device as first driving information, and acquiring current driving information of obstacles around the unmanned device as second driving information.

The main body of the unmanned aerial vehicle control method according to the present specification may be an unmanned aerial vehicle, or an electronic device such as a server mounted on the unmanned aerial vehicle, and for convenience of description, the unmanned aerial vehicle control method provided in the present specification will be described below with reference to only the unmanned aerial vehicle as the main body of the unmanned aerial vehicle.

In the embodiment of the present specification, the unmanned aerial vehicle may acquire current travel information of itself as the first travel information, and acquire current travel information of obstacles around itself as the second travel information. The first travel information mentioned here may refer to position data of the unmanned device, attitude data of the unmanned device, speed data of the unmanned device, and the like. The second travel information mentioned here may refer to position data of obstacles around the unmanned device, attitude data of obstacles around the unmanned device, speed data of obstacles around the unmanned device, and the like. The travel information may be determined from sensing data acquired by sensors provided on the unmanned device, such as a camera, a laser radar, an inertial measurement unit, a global positioning system, and the like. The sensed data mentioned herein includes: the system comprises image data acquired by a camera, point cloud data acquired by a laser radar, attitude data acquired by an inertial measurement unit, positioning data acquired by a global positioning system and the like.

In this specification, the unmanned device to which the control method of the unmanned device provided in this specification is applied may be used to execute a delivery task in a delivery field, for example, a business scenario of delivery such as express delivery, logistics, and takeout using the unmanned device.

S102: and for each obstacle, determining a maximum steering angle corresponding to the obstacle according to the second running information corresponding to the obstacle, predicting a steering angle of the obstacle in the future as a predicted steering angle according to the maximum steering angle corresponding to the obstacle and a preset steering angle probability distribution, and predicting speed information of the obstacle in the future as a predicted speed according to the predicted steering angle corresponding to the obstacle and the second running information corresponding to the obstacle.

In practical applications, the unmanned device usually determines its own driving track according to the future driving track of the obstacle. However, sudden speed changes or steering angle oscillations may occur during driving of the obstacle, resulting in deviations of the predicted future driving trajectory of the obstacle. Based on the information, the unmanned device can predict the maximum possible speed information of the obstacle in a future period of time, and accordingly, the safe distance between the unmanned device and the obstacle is determined, and collision between the unmanned device and the obstacle is avoided.

In the embodiment of the present specification, the unmanned aerial vehicle may determine, for each obstacle, a maximum steering angle corresponding to the obstacle from the second travel information corresponding to the obstacle, predict a steering angle of the obstacle in the future from the maximum steering angle corresponding to the obstacle and a preset steering angle probability distribution as a predicted steering angle, and predict speed information of the obstacle in the future from the predicted steering angle corresponding to the obstacle and the second travel information corresponding to the obstacle as a predicted speed.

Specifically, the unmanned device may determine, for each obstacle, the shortest braking distance corresponding to the obstacle according to the second driving information corresponding to the obstacle. The shortest braking distance mentioned here may be a braking distance at which the obstacle brakes at the maximum braking acceleration after the set reaction time.

Secondly, the unmanned device can determine the maximum steering angle corresponding to the obstacle according to the shortest braking distance corresponding to the obstacle and the preset minimum turning radius. The minimum turning radius mentioned here may be a minimum turning radius specified artificially and unified for each obstacle, or may be determined by measuring the distance from the front axle center to the rear axle center of each obstacle by a sensor on the unmanned device. As shown in particular in fig. 2.

Fig. 2 is a schematic diagram of a method for determining a steering angle according to an embodiment of the present disclosure.

In fig. 2, the unmanned device may establish a coordinate system with itself as the origin of coordinates. And determining whether the direction of the obstacle deviates or not based on the coordinate system, wherein the steering angle is 0 when the direction of the obstacle does not deviate, is positive when the obstacle deviates anticlockwise, and is negative when the obstacle deviates clockwise. Since the minimum turning radius and the minimum braking distance of the obstacle are known, the drone can determine the range that the obstacle is likely to reach in the future, i.e., the sector area in fig. 2. The boundary of the sector is the outermost boundary that the obstacle may reach. The specific formula is as follows:

in the above formula, φ₀May be used to characterize the maximum steering angle that an obstacle may reach. D may be used to characterize the shortest braking distance. R_minCan be used to characterize the minimum turn radius.

It should be noted that the unmanned device may also determine the maximum steering angle corresponding to the obstacle through other methods. For example, the unmanned aerial vehicle may acquire historical travel information of each obstacle, determine a maximum steering angle reached by each obstacle when steering at different travel speeds during travel of the obstacle according to the historical travel information of each obstacle, and set an average value of the maximum steering angles reached by each obstacle when steering at different travel speeds during travel of the obstacle as the maximum steering angle corresponding to the obstacle at the different travel speeds. Furthermore, for each obstacle, the unmanned device may determine a maximum steering angle corresponding to the obstacle according to the driving speed corresponding to the obstacle.

Further, the unmanned device may predict a steering angle of the obstacle in the future as a predicted steering angle based on a maximum steering angle corresponding to the obstacle and a preset steering angle probability distribution. Since the steering angle of the obstacle may swing by an unknown angle, different steering angles correspond to different driving probabilities. Therefore, the steering angle probability distribution mentioned here may mean that the probability of the obstacle traveling in the original direction is 1, and the probability of the maximum steering angle being reached by deviating from the original direction is 0. The specific formula is as follows:

as can be seen from the above formula, different driving probabilities corresponding to different steering angles are expressed in the form of a piecewise function. The driving probability takes the maximum value of 1 when the steering angle is 0 and takes the maximum value of +/-phi when the steering angle is₀The minimum value is taken as 0, and the rest part is linearly changed according to the formula.

Finally, the unmanned device may predict, as the predicted speed, speed information of the obstacle in the future based on the predicted steering angle corresponding to the obstacle and the second travel information corresponding to the obstacle. The predicted speed mentioned here includes: lateral predicted speed, longitudinal predicted speed. The specific formula is as follows:

in the above formula, v_lon(φ) may be used to characterize the longitudinal prediction speed. v. of_lat(φ) may be used to characterize the lateral prediction speed. Further, the above formula is derived to determine the range of the predicted steering angle as [ - φ ]₀,φ₀]Then solving the equation by Newton method to obtain the numerical solution

(0 if there is no solution in the given range). Finally, the numerical value is solved

And substituting the predicted speed into the formula to determine the transverse predicted speed and the longitudinal predicted speed.

Since the attitude data and the speed data in the second travel information for each obstacle are not the same, the lateral predicted speed and the longitudinal predicted speed for each obstacle are also different.

S104: and determining a safe distance between the unmanned device and the obstacle at the predicted speed and under the condition that the unmanned device performs simultaneous braking at the first running information according to the first running information, the second running information corresponding to the obstacle and the predicted speed, wherein the safe distance is used as the safe distance corresponding to the obstacle.

S106: and controlling the unmanned equipment according to the safety distance corresponding to each obstacle.

In the embodiment of the present specification, the unmanned aerial vehicle may determine, as the safe distance corresponding to the obstacle, the safe distance between the unmanned aerial vehicle and the obstacle when the unmanned aerial vehicle performs simultaneous braking with the first travel information at the predicted speed based on the first travel information, the second travel information corresponding to the obstacle, and the predicted speed. And controlling the unmanned equipment according to the safety distance corresponding to each obstacle.

In practical applications, the unmanned aerial vehicle may encounter various emergency situations during driving, for example, sudden deceleration of an obstacle in front of the unmanned aerial vehicle, sudden lane change of the obstacle at the side of the unmanned aerial vehicle, sudden runaway and braking of the unmanned aerial vehicle itself, and the like. If the distance between the unmanned equipment and the obstacle is short, collision between the unmanned equipment and the obstacle may be caused. Based on the method, the unmanned equipment can determine the final risk score corresponding to each obstacle according to the distance between the unmanned equipment and the obstacle, and control the unmanned equipment according to the final risk score corresponding to each obstacle.

In this embodiment, the unmanned device may determine a risk score function corresponding to the obstacle according to the safety distance corresponding to the obstacle. Next, the unmanned device may determine a distance between the unmanned device and the obstacle according to the first travel information and the second travel information corresponding to the obstacle. The drone may then enter the distance into a risk score function, determining a final risk score for the obstacle at the distance from the drone. And finally, controlling the unmanned equipment according to the final risk score corresponding to each obstacle.

Specifically, the safety distance corresponding to the obstacle includes: the maximum safe distance corresponding to the obstacle and the minimum safe distance corresponding to the obstacle. The unmanned device can determine a risk score function corresponding to the obstacle according to the maximum safe distance corresponding to the obstacle and the minimum safe distance corresponding to the obstacle. The specific formula is as follows:

in the above formula, y may be used to characterize the corresponding risk score for the obstacle. x may be used to characterize the distance between the drone and the obstacle. When the current distance between the unmanned equipment and the obstacle is the maximum safe distance, the risk score corresponding to the obstacle is 0. When the current distance between the unmanned equipment and the obstacle is the minimum safe distance, the risk score corresponding to the obstacle is 1. And inputting the maximum safe distance corresponding to the obstacle and the minimum safe distance corresponding to the obstacle into a risk scoring function, and determining parameters of a, b and c in the risk scoring function so as to determine the risk scoring function corresponding to the obstacle which can be applied to actual driving.

It should be noted that, since different obstacles have different maximum safety distances and minimum safety distances, different risk scoring functions are provided based on different obstacles.

In this embodiment, the unmanned device may determine, according to historical driving data of a vehicle driven by a driver, a reaction time range in which the driver takes braking measures when encountering an emergency (for example, a front vehicle suddenly brakes suddenly or a side vehicle suddenly changes lanes) during driving, an acceleration range at a current time, and a braking acceleration range in which the driver takes braking measures, so as to obtain a maximum reaction time, a minimum reaction time, a maximum braking acceleration, a minimum braking acceleration, and a maximum acceleration and a minimum acceleration during driving when the unmanned device and an obstacle encounter an emergency.

Among the obstacles around the unmanned aerial vehicle are various obstacle types, such as pedestrians, vehicles, and the like. The reaction time, maximum acceleration, minimum acceleration, maximum braking acceleration and minimum braking acceleration of different obstacle types differ. The reaction time, maximum acceleration, minimum acceleration, maximum braking acceleration and minimum braking acceleration for the vehicle and the reaction time, maximum acceleration, minimum acceleration, maximum braking acceleration and minimum braking acceleration for the unmanned device may be the same for the obstacle type.

In practical applications, in order to ensure that no collision occurs between the unmanned device and the obstacle, a safety distance to be maintained between the unmanned device and the obstacle in an extreme case can be considered. If in the extreme case, the safety distance is kept between the unmanned equipment and the obstacle, so that the unmanned equipment and the obstacle cannot collide with each other, the unmanned equipment and the obstacle keep the safety distance in the actual driving process, and the collision cannot occur. Since the driving habits of different drivers are different, the safety distance between the unmanned aerial device and the obstacle is also different. Based on this, the unmanned device can determine the maximum safe distance for the obstacle when the unmanned device and the obstacle travel in the most aggressive driving manner, and determine the minimum safe distance for the obstacle when the unmanned device and the obstacle travel in the most conservative driving manner. The extreme case mentioned here may be a case where the unmanned aerial vehicle brakes simultaneously with the obstacle while the unmanned aerial vehicle travels opposite to the obstacle.

In the embodiment of the present specification, the safety distance corresponding to the obstacle includes: the maximum safe distance corresponding to the obstacle and the minimum safe distance corresponding to the obstacle. The unmanned aerial vehicle can determine that the obstacle runs at the predicted speed according to the first running information, the second running information corresponding to the obstacle and the predicted speed, and the maximum safe distance between the unmanned aerial vehicle and the obstacle can be obtained when the unmanned aerial vehicle runs at the first running information and is braked at the maximum braking acceleration. And a minimum safe distance between the unmanned equipment and the obstacle when braking is performed at the minimum braking acceleration at the same time.

Specifically, if it is determined that the unmanned aerial vehicle and the obstacle are driven in the most aggressive driving manner, the unmanned aerial vehicle may determine, according to the first driving information, that the unmanned aerial vehicle is driven at a preset maximum acceleration within a preset maximum response time with a current position as an initial position, and determine, as a maximum post-braking position corresponding to the unmanned aerial vehicle, a position where the unmanned aerial vehicle is braked at the preset maximum braking acceleration after the maximum response time.

That is, when the unmanned device observes an emergency (sudden stop or sudden lane change) of an obstacle, the unmanned device travels at the maximum acceleration within the maximum reaction time, and the distance traveled by the unmanned device before braking is performed and the speed after the unmanned device travels at the maximum acceleration within the maximum reaction time are determined. And then, determining the braking distance after the unmanned equipment brakes at the maximum braking acceleration, and further determining the maximum post-braking position corresponding to the unmanned equipment according to the distance traveled by the unmanned equipment before braking and the braking distance after the unmanned equipment brakes at the maximum braking acceleration.

Secondly, the unmanned equipment can determine that the obstacle is located at the current position as the initial position according to the second driving information and the predicted speed corresponding to the obstacle, the unmanned equipment drives at the preset maximum acceleration within the preset maximum reaction time by taking the predicted speed as the initial speed, and the unmanned equipment is located at the position after braking at the preset maximum braking acceleration after the maximum reaction time as the maximum post-braking position corresponding to the obstacle.

That is, when the obstacle observes an unexpected situation (sudden braking or sudden lane change) of the unmanned aerial vehicle, the obstacle travels at the maximum acceleration within the maximum reaction time with the predicted speed as the initial speed, and the distance traveled by the obstacle before braking is performed and the speed traveled by the obstacle at the maximum acceleration within the maximum reaction time are determined. And then, determining the braking distance of the obstacle after braking at the maximum braking acceleration, and further determining the maximum post-braking position corresponding to the obstacle according to the distance traveled by the obstacle before braking and the braking distance of the obstacle after braking at the maximum braking acceleration.

Finally, the unmanned equipment can determine the maximum safe distance corresponding to the obstacle according to the maximum post-braking position corresponding to the unmanned equipment and the maximum post-braking position corresponding to the obstacle. That is, the unmanned device may determine a moving distance of the unmanned device according to the initial position and the maximum post-braking position corresponding to the unmanned device, and determine a moving distance of the obstacle according to the initial position and the maximum post-braking position corresponding to the obstacle. Therefore, the unmanned equipment can determine the maximum safe distance corresponding to the obstacle according to the moving distance of the unmanned equipment and the moving distance of the obstacle.

Similarly, if it is determined that the unmanned aerial vehicle and the obstacle travel in the most conservative driving manner, the unmanned aerial vehicle may determine, according to the first travel information, that the unmanned aerial vehicle travels with the current position as the initial position and with the preset minimum acceleration within the preset minimum reaction time, and determine, as the minimum post-braking position corresponding to the unmanned aerial vehicle, the position where the unmanned aerial vehicle is located after being braked with the preset minimum braking acceleration after the minimum reaction time.

That is, when the unmanned device observes an emergency (sudden stop or sudden lane change) of an obstacle, the unmanned device travels with a minimum acceleration within a minimum reaction time, determines a distance traveled by the unmanned device before braking is performed, and a speed after the unmanned device travels with the minimum acceleration within the minimum reaction time. And then, determining the braking distance after the unmanned equipment brakes at the minimum braking acceleration, and further determining the minimum post-braking position corresponding to the unmanned equipment according to the distance traveled by the unmanned equipment before braking and the braking distance after the unmanned equipment brakes at the minimum braking acceleration.

Secondly, the unmanned equipment can determine that the obstacle is located at the current position as the initial position according to the second driving information and the predicted speed corresponding to the obstacle, the unmanned equipment drives at the preset minimum acceleration within the preset minimum reaction time by taking the predicted speed as the initial speed, and the unmanned equipment is located at the position after braking at the preset minimum braking acceleration after the minimum reaction time as the minimum braking position corresponding to the obstacle.

That is, when the obstacle observes an unexpected situation (sudden braking or sudden lane change) of the unmanned aerial vehicle, the obstacle travels with minimum acceleration within the minimum reaction time with the predicted speed as an initial speed, and the distance traveled by the obstacle before braking is performed and the speed traveled by the obstacle with minimum acceleration within the minimum reaction time are determined. And then, determining the braking distance of the obstacle after braking at the minimum braking acceleration, and further determining the minimum post-braking position corresponding to the obstacle according to the distance traveled by the obstacle before braking and the braking distance of the obstacle after braking at the minimum braking acceleration.

Finally, the unmanned equipment can determine the minimum safe distance corresponding to the obstacle according to the minimum post-braking position corresponding to the unmanned equipment and the minimum post-braking position corresponding to the obstacle. That is, the unmanned device may determine a moving distance of the unmanned device according to the initial position and the minimum post-braking position corresponding to the unmanned device, and determine a moving distance of the obstacle according to the initial position and the minimum post-braking position corresponding to the obstacle. Therefore, the unmanned equipment can determine the minimum safety distance corresponding to the obstacle according to the moving distance of the unmanned equipment and the moving distance of the obstacle.

In practical applications, there are a variety of positional relationships between the drone and surrounding obstacles. For example, the obstacle is located forward of the location of the drone and the obstacle is located rearward of the location of the drone. The unmanned aerial vehicle has a plurality of driving direction relations with surrounding obstacles. For example, the unmanned aerial vehicle travels in the same direction as the obstacle and the unmanned aerial vehicle travels opposite to the obstacle. Since the same safety distance corresponds to different positional relationships between the drone and the obstacle. Therefore, the distance between the unmanned device and the obstacle cannot be well controlled only by determining the risk score according to the safety distance between the unmanned device and the obstacle. Based on the safety distance, the unmanned equipment can divide the safety distance into a transverse safety distance and a longitudinal safety distance, and accordingly transverse risk scores and longitudinal risk scores corresponding to the obstacles are determined. Furthermore, the transverse distance and the longitudinal distance between the unmanned equipment and the obstacle are well controlled, and collision between the unmanned equipment and the obstacle is avoided.

In the embodiments of the present specification, the maximum safe distance includes: maximum lateral safety distance and maximum longitudinal safety distance, the minimum safety distance comprising: a minimum lateral safety distance and a minimum longitudinal safety distance, the distances including a lateral distance and a longitudinal distance, the risk scoring function corresponding to the obstacle including: a lateral risk score function corresponding to the obstacle and a longitudinal risk score function corresponding to the obstacle. The predicted speed includes: lateral predicted speed and longitudinal predicted speed.

In the embodiment of the present specification, the maximum longitudinal safety distance and the minimum longitudinal safety distance may be determined differently according to the positional relationship between the unmanned aerial vehicle and the surrounding obstacle and the relationship between the driving directions. The position relation and the driving direction relation between the unmanned equipment and surrounding obstacles do not influence the maximum transverse safe distance and the minimum transverse safe distance.

Specifically, the unmanned device may determine a maximum lateral safety distance between itself and the obstacle. The unmanned device may determine a lateral travel distance of the obstacle within a maximum reaction time based on the lateral predicted speed, the maximum lateral acceleration of the obstacle, and the maximum reaction time.

Second, the drone may determine a maximum lateral braking distance for the obstacle based on the lateral predicted speed, the maximum lateral acceleration and the maximum lateral braking acceleration for the obstacle, and the maximum reaction time.

The drone may then determine, from the lateral velocity, its maximum lateral acceleration, and the maximum reaction time, its lateral travel distance within the maximum reaction time.

Then, the unmanned equipment can determine the maximum lateral braking distance corresponding to the unmanned equipment according to the lateral speed, the maximum lateral acceleration and the maximum lateral braking acceleration of the unmanned equipment, and the maximum reaction time.

Finally, the unmanned device can determine the maximum transverse safe distance between the unmanned device and the obstacle according to the transverse driving distance of the obstacle in the maximum reaction time, the maximum transverse braking distance of the obstacle, the transverse driving distance of the unmanned device in the maximum reaction time, and the maximum transverse braking distance corresponding to the unmanned device. The specific formula is as follows:

in the above formula, d_lat,maxThe method can be used for indicating the maximum transverse safe distance between the obstacle and the unmanned equipment when the obstacle runs in the direction opposite to the transverse direction of the unmanned equipment at the side of the position of the unmanned equipment. μ may be used to represent the lateral perturbation distance. Rho_maxMay be used to indicate the maximum reaction time. | v_1,lat| may be used to represent the lateral predicted velocity for the obstacle.

Can be used to indicate the maximum lateral acceleration of the obstacle

At the reaction time ρ during running_maxAbsolute value of transverse velocity of time, i.e.

Therefore, the temperature of the molten metal is controlled,

can be used for characterizing the barrier at the reaction time rho_maxInner lateral travel distance.

Likewise, | v_2,lat| may be used to represent the corresponding lateral velocity of the drone.

Can be used to indicate maximum lateral acceleration of the drone

Therefore, the temperature of the molten metal is controlled,

can be used for characterizing the reaction time rho of the unmanned equipment_maxInner lateral travel distance.

Further, in the above-mentioned case,

may be used to represent the lateral maximum braking acceleration.

Can be used for characterizing the barrier passing reaction time rho_maxMaximum lateral braking distance at the completion of braking after internal acceleration.

Can be used for characterizing the reaction time p of the unmanned equipment_maxMaximum lateral braking distance at the completion of braking after internal acceleration.

Since the obstacle and the unmanned aerial vehicle travel in the lateral direction in opposition to each other, it is necessary to add an absolute value sign to the lateral predicted speed corresponding to the obstacle and the lateral speed corresponding to the unmanned aerial vehicle.

Specifically, the unmanned device may determine a minimum lateral safety distance between itself and the obstacle. The drone may determine a lateral travel distance of the obstacle within the minimum reaction time based on the lateral predicted speed, the minimum lateral acceleration of the obstacle, and the minimum reaction time.

Second, the drone may determine a minimum lateral braking distance for the obstacle based on the lateral predicted speed, the minimum lateral acceleration and the minimum lateral braking acceleration for the obstacle, and the minimum reaction time.

The drone may then determine a lateral travel distance of itself within the minimum reaction time based on the lateral velocity, the minimum lateral acceleration of itself, and the minimum reaction time.

Then, the unmanned device can determine the corresponding minimum lateral braking distance according to the lateral speed, the minimum lateral acceleration and the minimum lateral braking acceleration of the unmanned device, and the minimum reaction time.

Finally, the unmanned device can determine the minimum lateral safety distance between the unmanned device and the obstacle according to the lateral driving distance of the obstacle in the minimum reaction time, the minimum lateral braking distance of the obstacle, the lateral driving distance of the unmanned device in the minimum reaction time, and the corresponding minimum lateral braking distance of the unmanned device. The specific formula is as follows:

in the above formula, d_lat,minThe method can be used for indicating the minimum transverse safe distance between the obstacle and the unmanned equipment when the obstacle runs in the direction opposite to the transverse direction of the unmanned equipment at the side of the position of the unmanned equipment. Rho_minMay be used to represent a minimum reaction time. | v_1,lat| may be used to represent the lateral predicted velocity for the obstacle.

Can be used to indicate that the obstacle has minimal lateral acceleration

At the reaction time ρ during running_minAbsolute value of transverse velocity of time, i.e.

Therefore, the temperature of the molten metal is controlled,

can be used for characterizing the barrier at the reaction time rho_minInner lateral travel distance.

Can be used to indicate that the unmanned device is at minimum lateral acceleration

Therefore, the temperature of the molten metal is controlled,

can be used for characterizing the reaction time rho of the unmanned equipment_minInner lateral travel distance.

Further, in the above-mentioned case,

may be used to characterize the lateral minimum braking acceleration.

Can be used for characterizing the barrier passing reaction time rho_minAfter internal acceleration, a minimum lateral braking distance at the completion of braking.

Can be used for characterizing the reaction time p of the unmanned equipment_minAfter internal acceleration, a minimum lateral braking distance at the completion of braking.

Since the unmanned aerial vehicle is located in front of the obstacle, and when the unmanned aerial vehicle travels in the same direction as the obstacle, the unmanned aerial vehicle cannot observe the traveling situation of the obstacle behind, there is no reaction time when the unmanned aerial vehicle takes an emergency braking measure against the obstacle, and therefore, there is no need to calculate the longitudinal traveling distance of the unmanned aerial vehicle within the reaction time.

In the embodiment of the present specification, if the unmanned aerial vehicle travels in the same direction as the obstacle, the location of the unmanned aerial vehicle is located in front of the location of the obstacle. The drone may determine a maximum longitudinal safe distance between itself and the obstacle. The drone may determine a longitudinal travel distance of the obstacle within a maximum reaction time based on the longitudinal predicted speed, the maximum longitudinal acceleration of the obstacle, and the maximum reaction time.

Secondly, the unmanned equipment can determine the maximum longitudinal braking distance corresponding to the obstacle according to the longitudinal predicted speed, the maximum longitudinal acceleration and the maximum longitudinal braking acceleration of the obstacle and the maximum reaction time.

Then, the unmanned equipment can determine the maximum longitudinal braking distance corresponding to the unmanned equipment according to the longitudinal speed and the maximum longitudinal braking acceleration of the unmanned equipment;

finally, the unmanned device can determine the maximum longitudinal safe distance between the unmanned device and the obstacle according to the longitudinal driving distance of the obstacle in the maximum reaction time, the maximum longitudinal braking distance corresponding to the obstacle, and the maximum longitudinal braking distance corresponding to the unmanned device. The specific formula is as follows:

in the above formula, d_lon,maxThe method can be used for indicating the maximum longitudinal safety distance between the obstacle and the unmanned equipment when the unmanned equipment is located in front of the obstacle and the unmanned equipment and the obstacle run in the same direction. v. of_1,lonMay be used to represent the corresponding longitudinal predicted speed of the obstacle. v. of_2,lonMay be used to represent the corresponding longitudinal velocity of the drone.

Can be used to represent the maximum longitudinal acceleration,

may be used to represent the longitudinal maximum braking acceleration.

Can be used for characterizing the barrier at the reaction time rho_maxInner longitudinal travel distance.

Can be used for characterizing the barrier passing reaction time rho_maxMaximum longitudinal braking distance at the completion of braking after internal acceleration.

May be used to characterize the maximum longitudinal braking distance of the drone at the completion of braking.

Similarly, if the unmanned aerial vehicle travels in the same direction as the obstacle, the position of the unmanned aerial vehicle is located in front of the position of the obstacle. The drone may determine a minimum longitudinal safe distance between itself and the obstacle. The drone may determine a longitudinal travel distance of the obstacle within the minimum reaction time based on the longitudinal predicted speed, the minimum longitudinal acceleration of the obstacle, and the minimum reaction time.

Secondly, the unmanned device can determine the minimum longitudinal braking distance corresponding to the obstacle according to the longitudinal predicted speed, the minimum longitudinal acceleration and the minimum longitudinal braking acceleration of the obstacle and the minimum reaction time.

Then, the unmanned equipment can determine the corresponding minimum longitudinal braking distance according to the longitudinal speed and the minimum longitudinal braking acceleration of the unmanned equipment;

finally, the unmanned device can determine the minimum longitudinal safety distance between the unmanned device and the obstacle according to the longitudinal driving distance of the obstacle in the minimum response time, the minimum longitudinal braking distance corresponding to the obstacle, and the minimum longitudinal braking distance corresponding to the unmanned device. The specific formula is as follows:

in the above formula, d_lon,minCan be used to indicate noneThe position of the human equipment is located in front of the position of the obstacle, and when the unmanned equipment and the obstacle travel in the same direction, the obstacle and the unmanned equipment have the minimum longitudinal safety distance. v. of_1,lonMay be used to represent the corresponding longitudinal predicted speed of the obstacle. v. of_2,lonMay be used to represent the corresponding longitudinal velocity of the drone.

May be used to represent the minimum longitudinal acceleration,

may be used to represent a longitudinal minimum braking acceleration.

Can be used for characterizing the barrier at the reaction time rho_minInner longitudinal travel distance.

Can be used for characterizing the barrier passing reaction time rho_minMinimum longitudinal braking distance at the completion of braking after internal acceleration.

May be used to characterize the minimum longitudinal braking distance of the drone at the completion of braking.

Since the position of the unmanned aerial vehicle is behind the position of the obstacle, and the obstacle cannot observe the driving situation of the unmanned aerial vehicle when the unmanned aerial vehicle drives in the same direction as the obstacle, the reaction time when the obstacle takes emergency braking measures against the unmanned aerial vehicle does not exist, and the longitudinal driving distance of the obstacle in the reaction time does not need to be calculated.

In the embodiment of the present specification, if the unmanned aerial vehicle travels in the same direction as the obstacle, the location of the unmanned aerial vehicle is located behind the location of the obstacle. The drone may determine a maximum longitudinal safe distance between itself and the obstacle. The unmanned equipment can determine the maximum longitudinal braking distance corresponding to the obstacle according to the longitudinal predicted speed and the maximum longitudinal braking acceleration of the obstacle.

Secondly, the unmanned device can determine the longitudinal driving distance of the unmanned device in the maximum reaction time according to the longitudinal speed, the maximum longitudinal acceleration of the unmanned device and the maximum reaction time.

Then, the unmanned equipment can determine the corresponding maximum longitudinal braking distance according to the longitudinal speed, the maximum longitudinal acceleration and the maximum longitudinal braking acceleration of the unmanned equipment, and the maximum reaction time;

finally, the unmanned device can determine the maximum longitudinal safe distance between the unmanned device and the obstacle according to the maximum longitudinal braking distance corresponding to the obstacle, the longitudinal driving distance of the unmanned device in the maximum reaction time, and the maximum longitudinal braking distance corresponding to the unmanned device. The specific formula is as follows:

in the above formula, d_lon,maxThe method can be used for indicating that the position of the unmanned equipment is behind the position of the obstacle, and the maximum longitudinal safety distance between the obstacle and the unmanned equipment is obtained when the unmanned equipment and the obstacle run in the same direction. v. of_1,lonMay be used to represent the corresponding longitudinal predicted speed of the obstacle. v. of_2,lonMay be used to represent the corresponding longitudinal velocity of the drone.

Can be used to represent the maximum longitudinal acceleration,

may be used to represent the longitudinal maximum braking acceleration.

Can be used for characterizing the reaction time rho of the unmanned equipment_maxInner longitudinal travel distance.

Can be used for characterizing the reaction time p of the unmanned equipment_maxMaximum longitudinal braking distance at the completion of braking after internal acceleration.

Can be used to characterize the maximum longitudinal braking distance of the obstacle when braking is completed.

If the unmanned equipment and the obstacle run in the same direction, and the position of the unmanned equipment is located behind the position of the obstacle. The drone may determine a minimum longitudinal safe distance between itself and the obstacle. The unmanned equipment can determine the minimum longitudinal braking distance corresponding to the obstacle according to the longitudinal predicted speed and the minimum longitudinal braking acceleration of the obstacle.

Secondly, the unmanned device can determine the longitudinal driving distance of the unmanned device within the minimum reaction time according to the longitudinal speed, the minimum longitudinal acceleration of the unmanned device and the minimum reaction time.

Then, the unmanned equipment can determine the corresponding minimum longitudinal braking distance according to the longitudinal speed, the minimum longitudinal acceleration and the minimum longitudinal braking acceleration of the unmanned equipment, and the minimum response time;

finally, the unmanned device can determine the minimum longitudinal safe distance between the unmanned device and the obstacle according to the minimum longitudinal braking distance corresponding to the obstacle, the longitudinal driving distance of the unmanned device within the minimum reaction time, and the minimum longitudinal braking distance corresponding to the unmanned device. The specific formula is as follows:

in the above formula, d_lon,minThe method can be used for indicating the position of the unmanned equipment behind the position of the obstacle, and the minimum longitudinal safety distance between the obstacle and the unmanned equipment when the unmanned equipment and the obstacle run in the same direction. v. of_1,lonCan be used to represent the obstacle pairThe speed should be predicted longitudinally. v. of_2,lonMay be used to represent the corresponding longitudinal velocity of the drone.

May be used to represent the minimum longitudinal acceleration,

may be used to represent a longitudinal minimum braking acceleration.

Can be used for characterizing the reaction time rho of the unmanned equipment_minInner longitudinal travel distance.

Can be used for characterizing the reaction time p of the unmanned equipment_minMinimum longitudinal braking distance at the completion of braking after internal acceleration.

Can be used to characterize the minimum longitudinal braking distance of the obstacle when braking is completed.

In this embodiment, if the drone is traveling in opposition to the obstacle, the drone may determine the maximum longitudinal safe distance between itself and the obstacle. The drone may determine a longitudinal travel distance of the obstacle within a maximum reaction time based on the longitudinal predicted speed, the maximum longitudinal acceleration of the obstacle, and the maximum reaction time.

Second, the drone may determine a maximum longitudinal braking distance of the obstacle based on the longitudinal predicted speed, the maximum longitudinal acceleration and the maximum longitudinal braking acceleration of the obstacle, and the maximum reaction time.

The drone may then determine a longitudinal travel distance of itself within the maximum reaction time based on the longitudinal speed, the maximum longitudinal acceleration of itself, and the maximum reaction time.

Then, the unmanned equipment can determine the maximum longitudinal braking distance corresponding to the unmanned equipment according to the longitudinal speed, the maximum longitudinal acceleration and the maximum longitudinal braking acceleration of the unmanned equipment and the maximum reaction time.

Finally, the unmanned device can determine the maximum longitudinal safety distance between the unmanned device and the obstacle according to the longitudinal driving distance of the obstacle in the maximum reaction time, the maximum longitudinal braking distance of the obstacle, the longitudinal driving distance of the unmanned device in the maximum reaction time, and the maximum longitudinal braking distance corresponding to the unmanned device. The specific formula is as follows:

in the above formula, d_lat,maxCan be used for representing the maximum longitudinal safe distance between the obstacle and the unmanned equipment when the obstacle runs in front of the position of the unmanned equipment and opposite to the longitudinal direction of the unmanned equipment. Rho_maxMay be used to indicate the maximum reaction time. | v_1,lon| may be used to represent the longitudinal predicted speed for the obstacle.

Can be used to indicate maximum longitudinal acceleration of the obstacle

At the reaction time ρ during running_maxAbsolute value of longitudinal speed of time, i.e.

Therefore, the temperature of the molten metal is controlled,

Likewise, v_2,lonMay be used to represent the corresponding longitudinal velocity of the drone.

Can be used to indicate maximum longitudinal acceleration of the drone

Therefore, the temperature of the molten metal is controlled,

Further, in the above-mentioned case,

may be used to represent the longitudinal maximum braking acceleration.

Since a coordinate system is established with the unmanned aerial vehicle as the origin of coordinates and the obstacle and the unmanned aerial vehicle travel in opposition to each other in the longitudinal direction, it is necessary to add an absolute value sign to the longitudinal predicted speed corresponding to the obstacle.

In this illustrative embodiment, if the drone is traveling in opposition to the obstacle, the drone may determine a minimum longitudinal safe distance between itself and the obstacle. The drone may determine a longitudinal travel distance of the obstacle within the minimum reaction time based on the longitudinal predicted speed, the minimum longitudinal acceleration of the obstacle, and the minimum reaction time.

Second, the drone may determine a minimum longitudinal braking distance for the obstacle based on the longitudinal predicted speed, the minimum longitudinal acceleration and the minimum longitudinal braking acceleration for the obstacle, and the minimum reaction time.

The drone may then determine a longitudinal travel distance of itself within the minimum reaction time based on the longitudinal speed, the minimum longitudinal acceleration of itself, and the minimum reaction time.

Then, the unmanned equipment can determine the corresponding minimum longitudinal braking distance according to the longitudinal speed, the minimum longitudinal acceleration and the minimum longitudinal braking acceleration of the unmanned equipment and the minimum reaction time.

Finally, the unmanned device can determine the minimum longitudinal safe distance between the unmanned device and the obstacle according to the longitudinal driving distance of the obstacle in the minimum reaction time, the minimum longitudinal braking distance of the obstacle, the longitudinal driving distance of the unmanned device in the minimum reaction time, and the minimum longitudinal braking distance corresponding to the unmanned device. The specific formula is as follows:

in the above formula, d_lat,minCan be used for representing the minimum longitudinal safe distance between the obstacle and the unmanned equipment when the obstacle runs in front of the position of the unmanned equipment and opposite to the longitudinal direction of the unmanned equipment. Rho_minMay be used to represent a minimum reaction time. | v_1,lon| may be used to represent the longitudinal predicted speed for the obstacle.

Can be used to indicate the minimum longitudinal acceleration of an obstacle

At the reaction time ρ during running_minAbsolute value of longitudinal speed of time, i.e.

Therefore, the temperature of the molten metal is controlled,

Can be used to represent the minimum longitudinal acceleration of the unmanned device

Therefore, the temperature of the molten metal is controlled,

Further, in the above-mentioned case,

may be used to represent a longitudinal minimum braking acceleration.

In this specification embodiment, the unmanned device may determine a lateral risk score function corresponding to the obstacle according to the maximum lateral safety distance corresponding to the obstacle and the minimum lateral safety distance corresponding to the obstacle. Similarly, the unmanned device may determine a longitudinal risk score function corresponding to the obstacle according to the maximum longitudinal safe distance corresponding to the obstacle and the minimum longitudinal safe distance corresponding to the obstacle.

Further, the drone may input a lateral distance into a lateral risk score function, determine a risk score for the obstacle at the lateral distance from the drone as a lateral risk score corresponding to the obstacle, and input a longitudinal distance into a longitudinal risk score function, determine a risk score for the obstacle at the longitudinal distance from the drone as a longitudinal risk score corresponding to the obstacle. And determining a final risk score according to the transverse risk score corresponding to the obstacle and the longitudinal risk score corresponding to the obstacle.

Specifically, the unmanned device may use the greater risk score of the lateral risk score corresponding to the obstacle and the longitudinal risk score corresponding to the obstacle as the final risk score. That is, if the lateral risk score is greater than the longitudinal risk score, the lateral risk score is used as the final risk score, and if the lateral risk score is not greater than the longitudinal risk score, the longitudinal risk score is used as the final risk score.

In practical applications, a plurality of obstacles may exist around the unmanned device, so that the unmanned device can integrate the final risk score of each obstacle to control the unmanned device.

In this specification embodiment, for each obstacle, the unmanned device may determine, as an adjustment direction of the unmanned device for the obstacle, an adjustment direction to which a final risk score corresponding to the obstacle belongs. And controlling the unmanned equipment according to the adjustment direction of the unmanned equipment for each obstacle.

In the process, the method can predict the speed information which is most possible to be reached by the obstacle in the future when the obstacle has sudden speed change or steering angle swing, the speed information is used as the predicted speed, the obstacle is determined to be at the predicted speed, and the transverse safety distance and the longitudinal safety distance between the unmanned equipment and the obstacle are determined under the condition that the unmanned equipment performs simultaneous braking with the first running information, so that the transverse risk score and the longitudinal risk score corresponding to the obstacle are determined, and the unmanned equipment is controlled. Therefore, collision between the unmanned equipment and other surrounding obstacles is avoided, and safety of the unmanned equipment in the driving process is improved.

Based on the same idea, the present specification further provides a corresponding control apparatus for an unmanned aerial vehicle, as shown in fig. 3.

Fig. 3 is a schematic diagram of a control device of an unmanned aerial vehicle provided in the present specification, where the control device is applied to the field of unmanned driving, and the control device includes:

an obtaining module 300, configured to obtain current driving information of an unmanned aerial vehicle as first driving information, and obtain current driving information of obstacles around the unmanned aerial vehicle as second driving information;

a prediction module 302, configured to determine, for each obstacle, a maximum steering angle corresponding to the obstacle according to the second driving information corresponding to the obstacle, predict, as a predicted steering angle, a steering angle of the obstacle in the future according to the maximum steering angle corresponding to the obstacle and a preset steering angle probability distribution, and predict, as a predicted speed, speed information of the obstacle in the future according to the predicted steering angle corresponding to the obstacle and the second driving information corresponding to the obstacle;

a determining module 304, configured to determine, according to the first driving information, the second driving information corresponding to the obstacle, and the predicted speed, a safe distance between the unmanned device and the obstacle when the unmanned device performs simultaneous braking with the first driving information at the predicted speed as a safe distance corresponding to the obstacle;

and the control module 306 is configured to control the unmanned device according to the safety distance corresponding to each obstacle.

Optionally, the predicting module 302 is specifically configured to, for each obstacle, determine a shortest braking distance corresponding to the obstacle according to the second driving information corresponding to the obstacle, and determine a maximum steering angle corresponding to the obstacle according to the shortest braking distance corresponding to the obstacle and a preset minimum turning radius.

Optionally, the control module 306 is specifically configured to determine a risk score function corresponding to the obstacle according to a safe distance corresponding to the obstacle, determine a distance between the unmanned aerial vehicle and the obstacle according to the first driving information and the second driving information corresponding to the obstacle, input the distance into the risk score function, determine a final risk score of the obstacle at the distance from the unmanned aerial vehicle, and control the unmanned aerial vehicle according to the final risk score corresponding to each obstacle.

the control module 306 is specifically configured to determine a risk score function corresponding to the obstacle according to the maximum safe distance corresponding to the obstacle and the minimum safe distance corresponding to the obstacle.

the control module 306 is specifically configured to input the lateral distance into the lateral risk score function, determine a risk score of the obstacle at the lateral distance from the unmanned device as a lateral risk score corresponding to the obstacle, input the longitudinal distance into the longitudinal risk score function, determine a risk score of the obstacle at the longitudinal distance from the unmanned device as a longitudinal risk score corresponding to the obstacle, and determine the final risk score according to the lateral risk score corresponding to the obstacle and the longitudinal risk score corresponding to the obstacle.

Optionally, the control module 306 is specifically configured to use the larger risk score of the lateral risk score corresponding to the obstacle and the longitudinal risk score corresponding to the obstacle as the final risk score.

Optionally, the control module 306 is specifically configured to, for each obstacle, determine an adjustment direction to which a final risk score corresponding to the obstacle belongs, as an adjustment direction of the unmanned device for the obstacle, and control the unmanned device according to the adjustment direction of the unmanned device for each obstacle.

the determining module 304 is specifically configured to determine, according to the first driving information, the second driving information corresponding to the obstacle, and the predicted speed, a maximum safe distance between the unmanned aerial vehicle and the obstacle when the unmanned aerial vehicle drives at the predicted speed and the unmanned aerial vehicle brakes at the maximum braking acceleration while driving at the first driving information; and a minimum safe distance between the unmanned device and the obstacle when braking is performed at the minimum braking acceleration at the same time.

Optionally, the determining module 304 is specifically configured to determine, according to the first driving information, that the unmanned aerial vehicle travels with the current position as an initial position and with a preset maximum acceleration within a preset maximum reaction time, and the position where the unmanned aerial vehicle is located after being braked with the preset maximum braking acceleration after the maximum reaction time is used as a maximum post-braking position corresponding to the unmanned aerial vehicle, determine, according to the second driving information corresponding to the obstacle and the predicted speed, that the obstacle travels with the current position as the initial position and with the predicted speed as an initial speed and with the preset maximum acceleration within a preset maximum reaction time, and the position where the obstacle is located after being braked with the preset maximum braking acceleration after the maximum reaction time is used as a maximum post-braking position corresponding to the obstacle, and determining the maximum safe distance corresponding to the obstacle according to the maximum post-braking position corresponding to the unmanned equipment and the maximum post-braking position corresponding to the obstacle.

Optionally, the determining module 304 is specifically configured to determine, according to the first traveling information, that the unmanned aerial vehicle travels with the current position as an initial position, with a preset minimum acceleration within a preset minimum reaction time, and after the minimum reaction time, with a preset minimum braking acceleration, the position where the unmanned aerial vehicle is located after braking is used as a minimum post-braking position corresponding to the unmanned aerial vehicle, determine, according to the second traveling information corresponding to the obstacle and the predicted speed, that the obstacle travels with the current position as the initial position, with the predicted speed as an initial speed, with the preset minimum acceleration within a preset minimum reaction time, and after the minimum reaction time, with the preset minimum braking acceleration, the position where the obstacle is located after braking is used as a minimum post-braking position corresponding to the obstacle, and determining the minimum safe distance corresponding to the obstacle according to the minimum post-braking position corresponding to the unmanned equipment and the minimum post-braking position corresponding to the obstacle.

The present specification also provides a computer-readable storage medium storing a computer program, which is operable to execute a control method of an unmanned aerial device provided in fig. 1 described above.

This specification also provides a schematic block diagram of an electronic device corresponding to that of figure 1, shown in figure 4. As shown in fig. 4, at the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include hardware required for other services. The processor reads a corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to implement the control method of the unmanned aerial vehicle described in fig. 1. Of course, besides the software implementation, the present specification does not exclude other implementations, such as logic devices or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may be hardware or logic devices.

In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functions of the various elements may be implemented in the same one or more software and/or hardware implementations of the present description.

As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The description has been presented with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the description. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

This description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

The above description is only an example of the present specification, and is not intended to limit the present specification. Various modifications and alterations to this description will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification should be included in the scope of the claims of the present specification.

Claims

1. A control method of unmanned equipment is applied to the field of unmanned driving, and comprises the following steps:

2. The method of claim 1, wherein determining the maximum steering angle corresponding to the obstacle according to the second driving information corresponding to the obstacle comprises:

3. The method of claim 1, wherein controlling the drone according to the safe distance for each obstacle includes:

4. The method of claim 3, wherein the safety distance for the obstacle comprises: the maximum safe distance corresponding to the obstacle and the minimum safe distance corresponding to the obstacle;

5. The method of claim 4, wherein the maximum safe distance comprises: maximum lateral safety distance and maximum longitudinal safety distance, the minimum safety distance comprising: a minimum lateral safety distance and a minimum longitudinal safety distance, the distances including a lateral distance and a longitudinal distance, the risk scoring function for the obstacle comprising: a transverse risk score function corresponding to the obstacle and a longitudinal risk score function corresponding to the obstacle;

6. The method of claim 5, wherein determining the final risk score based on the lateral risk score corresponding to the obstacle and the longitudinal risk score corresponding to the obstacle comprises:

7. The method of claim 6, wherein controlling the drone according to the final risk score for each obstacle comprises:

8. The method of claim 1, wherein the safety distance for the obstacle comprises: the maximum safe distance corresponding to the obstacle and the minimum safe distance corresponding to the obstacle;

9. The method according to claim 8, wherein determining a maximum safe distance between the unmanned aerial vehicle and the obstacle when the unmanned aerial vehicle travels at the predicted speed and the unmanned aerial vehicle is braked at a maximum braking acceleration while traveling at the first travel information based on the first travel information, the second travel information corresponding to the obstacle, and the predicted speed, specifically comprises:

10. The method according to claim 8, wherein determining that the obstacle is traveling at the predicted speed based on the first travel information, the second travel information corresponding to the obstacle, and the predicted speed, and determining a minimum safe distance between the unmanned aerial vehicle and the obstacle when the unmanned aerial vehicle is traveling at the first travel information while braking at a minimum braking acceleration, specifically comprises:

11. A control device for an unmanned aerial vehicle, the device being applied to the field of unmanned driving, comprising:

12. A computer-readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method of any of the preceding claims 1 to 10.

13. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1 to 10 when executing the program.