CN108986469B - Expressway emergency identification method for unmanned aerial vehicle path planning based on minimum safe steering circle tangent method - Google Patents
Expressway emergency identification method for unmanned aerial vehicle path planning based on minimum safe steering circle tangent method Download PDFInfo
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Abstract
The patent discloses a highway emergency identification method for unmanned aerial vehicle path planning based on a minimum safe steering circle tangent method, which comprises the following steps: the method comprises the following steps: the occurrence of an emergency event is detected. Step two: and (4) primarily determining the accident site. Step three: and (5) primarily preparing unmanned plane path planning. Step four: and planning the path by using a minimum safe steering circle tangent method. Step five: and the unmanned aerial vehicle surveys the accident area. The invention has the advantages of high reaction speed, automation, intellectualization, high reliability and the like.
Description
Technical Field
The invention relates to the field of traffic and the technical field of unmanned aerial vehicles. In particular to a method for rapidly identifying an emergency on a highway by an unmanned aerial vehicle automatically receiving an instruction, generating a track and flying to an accident point.
Background
With the continuous increase of the quantity of motor vehicles kept in China, the mileage of a highway network and the occupation rate of the highway network, the accident rate of the highway is increased, and the accident rate becomes one of important factors which prevent the highway network system from playing a role in high-efficiency transportation. When an emergency happens on a highway, the consequences are often serious, and if traffic police personnel cannot arrive at the site in time for treatment, more serious casualties are easily caused. Dozens of people are died and injured as the highway fog-cloud event that previously occurred in Anhui province. After an accident occurs, the highway is seriously jammed, traffic policemen cannot rapidly arrive at the accident occurrence point and cannot know the concrete conditions of the scene, so that the on-site order is disordered, and the highway traffic is paralyzed for a long time.
When an emergency happens on a highway, the traditional method is that a traffic police department is informed by alarming after an accident is found artificially, the efficiency is low, the waiting time is long, and the traffic police department is in a passive state. Therefore, when an emergency happens on the highway, the accident is not discovered and treated in time, which is a significant cause of traffic comprehensive paralysis and serious disasters. The traditional accident detection and identification technology cannot meet the requirements of rapid, safe and intelligent development of highway networks in China.
With the wide use of sensors and internet technologies, intelligent traffic technologies are rapidly developing and popularizing, and the traffic system is urged to change intelligently, so that powerful technical support is provided for intelligent accident point identification of highways and intelligent rapid accident identification of unmanned aerial vehicles.
The identification of highway accident points and the rapid identification of accident categories are novel subjects in the traffic field. In the existing method, after receiving accident alarm information, a traffic police department manually transmits an instruction to an unmanned aerial vehicle, and the unmanned aerial vehicle helps traffic police personnel to investigate an accident at an accident occurrence point. The existing method has excessive human participation, insufficient intellectualization, long time consumption for accident investigation and analysis and poor practicability; and unmanned aerial vehicle is not by fine application, and the practicality that unmanned aerial vehicle used as new technology means is low excessively.
Traditional path planning algorithms can be divided into two broad categories, namely 'non-evolutionary algorithms' and 'evolutionary algorithms'. The "non-evolutionary algorithm" mainly comprises: a unilateral search method, an artificial potential field method and a Dijkstra algorithm; the "evolutionary algorithm" mainly comprises: genetic algorithm, ant colony algorithm and particle swarm optimization algorithm. The calculation cost of the path planning is generally a function of the size of the map, and the larger the map is, the larger the calculation cost of the path planning becomes. Compared with other path plans, the unmanned aerial vehicle path plan has the characteristics of sparse clearance obstacles, scattered small-volume obstacles and concentrated large-volume obstacles. In unmanned aerial vehicle path planning, because the map is generally large and the sparsity of the map is high, more calculation time is wasted by adopting a traditional path planning algorithm, and the generated path is unstable, so that the obtained path cannot be guaranteed to be the shortest path. The path planning of the unmanned aerial vehicle should use a simple and reliable path generation method which is not influenced by the size of the map.
The invention provides a method for generating the path of an unmanned aerial vehicle by using a minimum safe steering circle tangent method, so that the unmanned aerial vehicle can independently and safely and quickly identify the highway accident. The detector module automatically detects and analyzes the accident point, accident information is sent to the unmanned aerial vehicle, and the unmanned aerial vehicle automatically generates a path through a minimum safe steering circle tangent method after receiving an instruction and goes to the accident point to identify the accident. The method solves the problem of excessive dependence on personnel in the detection and analysis of the highway accidents, and has better accuracy, rapidity and reliability compared with the traditional method.
Disclosure of Invention
The invention aims to solve the problems of long time spent on identifying accident points and accident categories of the existing expressway, serious personnel dependence and poor stability, fully exerts the advantages of high flexibility and low use cost of the unmanned aerial vehicle, and provides an expressway emergency identification method for planning unmanned aerial vehicle paths based on a minimum safe steering circle tangent method.
Firstly, processing detector data of the highway by utilizing a highway traffic wave theory and a sensor detection technology, so as to analyze the traffic state of a highway lane and determine the accident site of an emergency; then, transmitting the accident information to the unmanned aerial vehicle by using a wireless communication technology; finally, generating an optimal flight path of the unmanned aerial vehicle by using a minimum safe steering circle tangent method, so that the unmanned aerial vehicle can quickly reach an accident area to execute a task, and the accurate positioning of an accident point and the identification of an accident category are completed; the method achieves the rapid, accurate and efficient identification of the highway emergency, and provides reliable execution basis for accident handling decision, thereby reducing the influence of the highway emergency on the normal traffic of the highway.
In order to achieve the purpose, the specific technical scheme of the invention is as follows:
the method comprises the following steps: the occurrence of an emergency event is detected. Whether an emergency happens on the expressway can be detected by the duty condition of the detector. The detector is divided into an occupied state and an empty state, and when the vehicle is above the detector, the detector is in the occupied state; otherwise, the state is empty. When the traffic flow on the expressway is normal, the detector has the phenomenon that occupation and vacancy alternately appear. When an accident occurs, traffic jam is often accompanied. After an accident occurs, the vehicle flow speed of the road on the upstream of the accident point is quickly reduced to zero, and the detector on the upstream of the accident point is changed into a continuous occupation state when blockage occurs; the detectors downstream of the accident point will become continuously empty until the accident is cleared. Whether an accident happens or not can be judged according to the state of the detector, and when the detector is occupied or vacant for delta T > T, the traffic accident is determined to happen.
Wherein T is a time threshold value representing the maximum time interval between the occurrence of occupancy and the occurrence of idle alternation of a detector through which traffic on the highway passes in a non-traffic-jam condition; t is related to various parameters such as date, time, and location of the highway, so it can be known that T is set according to a specific highway, and T set by the highway detector between different provinces and cities is different and needs to be obtained according to statistical analysis.
Step two: and (4) primarily determining the accident site. Starting time t when the upstream detector is occupied after the accident is determined1The start time t at which the downstream detector is left idle2And both are subsequently determined. The traffic quantity q (vehicles/h) and the average vehicle speed v (km/h) of the traffic flow can be directly calculated by data calculated by the detector.
The accident site is determined according to the traffic wave theory. And (3) calculating the traffic flow density k (vehicle/km) when the road normally passes:
traffic flow density k during road congestionjAs can be obtained by statistical analysis. Calculating the relative position of the accident point and the detector:
wherein: u. ofwThe wave speed of the traffic waves; Δ t*Is the traffic wave propagation time; Δ q is a change value of the traffic volume; delta k is a change value of the traffic flow density; q. q.s2Is the traffic flow downstream of the traffic density interface; q. q.s1For the flow k of traffic upstream of the density interface2The density of the traffic flow at the downstream of the traffic flow density interface; k is a radical of1Is the traffic flow density upstream of the traffic density interface. Δ L is the distance from the point of the accident to the detector. The latitude and longitude of the detector are known, and when the Delta L is calculated, the coordinates of the accident point are obtained. The detector sends accident information to the unmanned aerial vehicle closest to the accident point, and waits for the unmanned aerial vehicle to fly to the accident point for investigation.
Step three: and (5) primarily preparing unmanned plane path planning. The calculation cost of the route planning is generally a function of the Size of the map, i.e., length, which is the length of the map, and width, which is the width of the map. In unmanned aerial vehicle path planning, because the value of the Size of the map is generally large and the sparsity of the map is high, more calculation time is wasted by adopting a traditional path planning algorithm, and the generated path is unstable, so that the shortest path of the obtained path cannot be ensured. According to the environment characteristic that obstacles on the expressway are sparse, a simple and feasible 'minimum safe steering circle tangent method' generation path is provided, is irrelevant to the size of a map, and is high in applicability to the flying environment with sparse obstacles. Firstly, determining the meaning of each symbol in the method: coordinate X of map X axis, coordinate Y of map Y axis, defined flying height H of unmanned aerial vehicle, and maximum flying speed V of unmanned aerial vehicleMAXMaximum yaw rate (flat steering speed) ω of unmanned aerial vehicleMAXCruise speed V of unmanned aerial vehicleCAnd calculating the minimum turning radius R of the unmanned aerial vehicle
It is assumed that most of the sections of the unmanned aerial vehicle flying are high-altitude flights at the limit flying height H. According to the limited flight speed H of the unmanned aerial vehicle, marking the object with the height higher than H as an obstacle, marking the obstacle in the flight area on a path planning map, and projecting the three-dimensional path planning map to a two-dimensional plane.
Step four: and planning the path by using a minimum safe steering circle tangent method.
First, whether there is an obstacle between the starting point and the target point. If starting point (x)A,yA) And target point (x)B,yB) No barrier exists between the two paths, and the ideal shortest path L is determined according to the Euclidean distance0Length of (2)
The Euclidean distance is L0Namely, the actual optimal path is generated. And otherwise, planning the path of the unmanned aerial vehicle according to a minimum safe steering circle tangent method.
And secondly, obtaining a minimum safe steering circle. When starting point (x)A,yA) And target point (x)B,yB) When there is an obstacle in between, make the circumcircle O of the obstacle outline line closest to point A, the radius is R0Marking the center O (x) of the circumscribed circle on the mapO,yO). Considering the minimum turning radius R of the unmanned aerial vehicle, taking the point O as the circle center and the radius R1＝R0+ R is re-rounded to obtain the minimum safe steering circle O1. Making a circle O by passing through the starting point A and the target point B1To obtain a tangent point C1，D1And C2，D2。
And thirdly, calculating the length of the safe path and reserving the optimal path. By means of a second step, the available safety path is a line segment
Segment of arc
Comparison
And
selecting the small value as the optimal path lAC+lCDAnd (5) reserving.
lCD＝θ·R1(5)
Wherein, theta is a line segment SCDAt the circle O1Central angle of pair (1):
and fourthly, planning the path of the residual area. Judging the target point B and the minimum safe steering circle O1If there is an obstacle between the tangent points D, and if there is no obstacle, the line segment l is selectedDBAs an unmanned aerial vehicle path; otherwise, the unmanned aerial vehicle is repositioned, and the point D is set as a new initial point A; and (5) performing iteration from the first step to the third step to generate a path, and finishing the path planning of the unmanned aerial vehicle.
Step five: and the unmanned aerial vehicle surveys the accident area. And the unmanned aerial vehicle reaches the area where the accident point is located along the generated path, and the accident point is accurately positioned, data is returned and the accident type is identified.
The technical advantages of the invention are as follows:
the invention has the advantages of high reaction speed, automation, intellectualization, high reliability and the like.
(1) The invention provides a road networking accident detection technology by using a detector according to passivity and personnel dependence of the existing technology when an expressway emergency is found, and an area where an accident point is located and the accident occurrence time can be quickly determined after the accident occurs.
(2) The invention changes the working mode of manually sending accident instructions to the unmanned aerial vehicle in the past into the mode that the accident detection device automatically sends the accident instructions to the unmanned aerial vehicle. The instruction transmission step is simplified, and the time can be greatly saved; meanwhile, the method does not depend on personnel, and the accuracy of instruction transmission is improved.
(3) The invention provides a new path generation method-a minimum safe steering circle tangent method which is suitable for an unmanned aerial vehicle for highway accident investigation according to the defects of the existing path generation algorithm; compared with the existing path generation algorithm, the method is simpler, has high stability, and can effectively solve the problem of path generation of the unmanned aerial vehicle for highway accident investigation, so that the unmanned aerial vehicle can identify traffic accidents within the shortest time.
Drawings
FIG. 1 is a schematic view of a traffic accident detection method of the present invention;
FIG. 2 is a schematic diagram of path generation under a simple scenario in accordance with the present invention;
fig. 3 is a schematic diagram of a path planning process according to the present invention.
In FIG. 1, A is a cross section B where the detector is located: cross section C of the detector: cross section P of the detector: accident point uw1: speed of wave of accident traffic wave propagating upstream of accident point
uw2: speed of wave of accident traffic wave propagating downstream of accident point
In fig. 2, 1: unmanned aerial vehicle 2: obstacle 3, minimum safe steering circle 4, circumscribed circle 5: obstacle 6: and 7, accident vehicle: and (3) highway A: starting point B: target point C1、C2: tangent point D of starting point and minimum steering circle1、D2: tangent point of target point and minimum steering circle
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. The technical scheme of the invention in concrete implementation is clearly and completely described below with reference to the accompanying drawings of the invention.
A straight expressway with two one-way lanes as shown in figure 1, no entrance ramp interference and uniform traffic arrival rate is taken as a research object. Suppose thatA set of detector devices is uniformly distributed every 1km of the highway, namely L in figure 1AB＝LBC1km and 2 km; the time threshold T of the detector is 10 s; at 35 minutes in the morning, a traffic accident occurs at point P in FIG. 1, and the accident reason is unknown; the two lanes are blocked at the same time, and the passing vehicles can not pass through.
The method comprises the following steps: the occurrence of an emergency event is detected. Time t of occurrence of accident010:35:00, assuming 10 am: 35:22, the dwell time Δ t of the vehicle on the detector at a is 12s>When T is 10s, the detector judges that an accident occurs.
Step two: and (4) primarily determining the accident site. The detector calculates the detector automatically calculates t in FIG. 1 at A110:35: 10; similarly, the detector at B calculates t210:35: 20. Meanwhile, the traffic volume q of the traffic flow at the accident point before the accident happens can be known according to the data recorded by the detector02100 (vehicle/h), average vehicle speed v070 (km/h); calculating the traffic flow density in normal traffic
(vehicle/km); the traffic condition at the pre-accident point is (0, k)00) — (2100/h, 30/km, 70 km/h). Obviously, assuming that the average vehicle length of the highway is 8m (measured by the detector), considering the distance between the front and rear vehicles, the traffic flow density at the time of road congestion can be estimated as kj100 pieces/km; the traffic condition of the road upstream of the point of the accident becomes (0, k)j0) — (0,100/km, 0); the traffic condition on the road downstream of the point of the accident becomes (0,0, v)0) (0,0,70 km/h); calculating the wave speed of the accident traffic wave propagated to the upstream of the accident point caused by the traffic accident:
a negative value indicates a direction opposite to the direction of traffic flow, which has been explained above as propagating upstream, uw1Should take a positive value, so uw130 km/h; similarly, the wave velocity of the accident traffic wave propagating to the downstream of the accident point is as follows:
calculating the relative position between the accident point and the detector, where L is uw·Δt*. The distance L from the fault point P to the cross section A of the upstream detector in FIG. 11＝uw1×(t1-t0) The distance L from the fault point P to the cross section B of the downstream detector2＝uw2×(t2-t0). The detectors are networked, and the coordinates of the detectors can be directly determined; the distance L between the detectors is thus directly available. Assuming that the detectors are uniformly distributed, L equals 1 km. Since the critical time point is selected in units of time measured as the time the vehicle has entered and exited the detector, the distance traveled by the vehicle is less than the distance from the accident point to the downstream detector. Obviously, there are: l is1+L2Less than or equal to L. For convenience of calculation, take L1+L2L. And (3) preliminarily calculating the position of the accident point by using a simultaneous equation set:
after being dissolved, the product is obtained
Converting the unit into second(s) as time unit, meter (m) as length unit and meter/second (m/s) as speed unit; the data is substituted into the data stream,
i.e. the accident point is 241.7m downstream of the cross-section a, the coordinates of the accident point are then obtained. The detector sends accident information to the unmanned aerial vehicle closest to the accident point, and waits for the unmanned aerial vehicle to fly to the accident point for investigation.
Step three: and (5) primarily preparing unmanned plane path planning. And after receiving the position information of the accident point, the unmanned aerial vehicle initializes the task map. As shown in fig. 2, initializing a mission map includes (1) marking terrain and objects above a defined drone flight height H as obstacles in a three-dimensional path plan map; (2) completing the projection of the map of the three-dimensional path planning to a two-dimensional plane; (3) mark the starting point as A, the target point is the accident pointMarked B, the starting point A (x) is marked on the two-dimensional path planning mapA,yA) Target point B (x)B,yB) And coordinates of the obstacle; (4) calculating the minimum safe steering radius R of the unmanned aerial vehicle,
wherein, ω isMAXIs the maximum yaw speed (flat steering speed), V, of the unmanned aerial vehicleCCruise speed for unmanned aerial vehicle.
Step four: and planning the path by using a minimum safe steering circle tangent method.
1. And judging whether an obstacle exists between the starting point and the target point. According to the starting point A (x) on the mapA,yA) Target point B (x)B,yB) And the coordinates of the obstacle, it can be known A, B whether there is an obstacle between them. A. B, if no obstacle exists between B, the ideal shortest path L is determined according to the Euclidean distance0Length of (2)
And otherwise, planning the path of the unmanned aerial vehicle according to a minimum safe steering circle tangent method. In the scenario of fig. 2, there are obstacles 5 between A, B, so that path planning between A, B is required by the "safe minimum steering circle tangent method".
2. The minimum safe turning circle is obtained. As shown in FIG. 2, a circumscribed circle O having a radius R is generated from the contour line of the obstacle 50Marking the center O (x) of the circumscribed circle on the mapO,yO). Considering the minimum turning radius R of the unmanned aerial vehicle, taking the point O as the circle center and the radius R1＝R0+ R is re-rounded to obtain the minimum safe steering circle O1. Making a circle O by passing through the starting point A and the target point B1To obtain a tangent point C1，D1And C2，D2。
3. And calculating the length of the safe path and reserving the optimal path. From the above step, two safety paths, line segments
Segment of arc
Comparing the lengths of the two paths to select the optimal path, and comparing
And
selecting the small value as the optimal path lAC+lCDAnd (5) reserving.
Solving a system of equations
To obtain
Then
In the context of figure 3, in the context of figure,
so as to change the line segment
Segment of arc
And storing as a path.
4. And planning the path of the residual area. In the scenario of FIG. 3, point D1There is no obstacle between the target points B, so the line segment
To fit the path, the path is
And (5) storing.
Step five: and the unmanned aerial vehicle surveys the accident area. The drone, along the generated path: line segment
Segment of arc
Line segment
The flight reaches the area of the accident site. And step three, the longitude and latitude of the accident point calculated out have certain errors, and the accident point is accurately positioned through a GPS module of the unmanned aerial vehicle. Meanwhile, the unmanned aerial vehicle carries out data return through a 4G network through aerial accident area videos of the camera carried on, and ground workers confirm accident categories according to returned video data.
Claims (1)
1. A highway emergency identification method for unmanned aerial vehicle path planning based on a minimum safe steering circle tangent method is characterized by comprising the following steps:
the method comprises the following steps: detecting the occurrence of an emergency event, determined by the duty cycle of the detector; when the detector is occupied or vacant for delta T > T, the traffic accident is determined to occur;
step two: preliminarily determining the accident site; calculating the relative position of the accident point and the detector:
wherein: u. ofwThe wave speed of the traffic waves; Δ t*Is the traffic wave propagation time; Δ q is a change value of the traffic volume; deltak is a change value of the traffic flow density; q. q.s2Is the traffic flow downstream of the traffic density interface; q. q.s1Is the traffic flow at the upstream of the traffic density interface; k is a radical of2The density of the traffic flow at the downstream of the traffic flow density interface; k is a radical of1The density of the traffic flow at the upstream of the traffic flow density interface;
step three: preliminarily preparing unmanned aerial vehicle path planning; calculating the minimum turning radius of the unmanned aerial vehicle; marking objects with the height higher than H as obstacles according to the limited flight height H of the unmanned aerial vehicle, marking the obstacles in the flight area on a path planning map, and projecting the three-dimensional path planning map to a two-dimensional plane;
step four: and planning the flight path of the unmanned aerial vehicle, wherein the fourth step comprises the following steps: firstly, determining whether an obstacle exists between a flight starting point and a target point of the unmanned aerial vehicle; if starting point (x)A,yA) And target point (x)B,yB) Between them, there is no barrier, then the Euclidean distance
Generating a path which is the actual optimal path; if the obstacle is determined to exist between the flight starting point and the target point of the unmanned aerial vehicle, planning the path of the unmanned aerial vehicle according to a minimum safe steering circle tangent method; the minimum safe steering circle tangent method comprises making a circumscribed circle of the barrier contour line closest to the point A with a radius of R0Marking the center O (x) of the circumscribed circle on the mapO,yO) (ii) a Considering the minimum turning radius R of the unmanned aerial vehicle, taking the point O as the circle center and the radius R1＝R0+ R is re-rounded to obtain the minimum safe steering circle O1Then, a circle O is drawn through the starting point A and the target point B1To obtain a tangent point C1，D1And C2，D2(ii) a The obtained safety path is a line segment
Segment of arc
Comparison
And
selecting the small value as the optimal path lAC+lCDReserving; then, the target point B and the minimum safe steering circle O are judged1If there is an obstacle between the tangent points D, and if there is no obstacle, the line segment l is selectedDBAs an unmanned aerial vehicle path; otherwise, the unmanned aerial vehicle replans the remaining path, and the point D is set as a new initial point A; generating a path according to a minimum safe steering circle tangent method, and finishing unmanned aerial vehicle path planning;
step five: the unmanned aerial vehicle flies to an accident area according to the planned path to carry out investigation; and the unmanned aerial vehicle reaches the area where the accident point is located along the generated path, and carries out accident point positioning, data returning and accident type identification.
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