CN108564234B - Intersection no-signal self-organizing traffic control method of intelligent networked automobile - Google Patents
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Abstract
The invention discloses a control method for signalless self-organizing traffic at intersections of intelligent networked automobiles. The intelligent internet automobile has the related technologies of intelligent interconnection and automatic driving, and the traditional signal timing method is not suitable for the travel control of the intelligent internet automobile at the intersection. The specific process is as follows: the intersection environment is expressed by a mathematical language, so that the intersection area is divided, and a basis is provided for a traffic decision; the method based on the Monte Carlo decision tree is adopted to optimize the vehicle passing sequence, so that the time-space resource utilization rate of the intersection is improved; and determining the motion trail of the vehicle according to the decided passing sequence.
Description
Technical Field
The invention belongs to the field of intelligent traffic, relates to the technology related to the Internet of vehicles and automatic driving of automobiles, and provides a strategy for the traffic control of an intelligent Internet-connected automobile at a signalless intersection.
Background
The intelligent internet automobile is a concept which is proposed only in recent years, and the core technology of the intelligent internet automobile is intelligent internet technology and automatic driving technology. The intelligent interconnection technology is an Internet of vehicles environment which realizes vehicle-vehicle communication, vehicle-road communication and road communication based on advanced communication technology so as to form information sharing. The automatic driving technology is mainly concerned by academic circles and industrial circles at home and abroad at present, global well-known enterprises including Google, Tesla, Baidu and the like invest a great deal of funds to research and develop, and related products are frequently made, so that the aim of realizing the full landing application of a real automatic driving automobile in 5 to 10 years in the future is fulfilled. By combining the technologies of intelligent interconnection and automatic driving, the intelligent networked automobile running in the traffic network has the functions of information perception, intelligent decision and execution control, not only can the running safety be ensured, but also the route selection and the running track can be reasonably planned, and the service level of road traffic is improved.
The signal timing control method is a mainstream intersection passing mode at home and abroad at present. The signal timing control method takes a traditional man-made driving automobile as an object, and traffic flow at different entrances of the intersection is guided to pass by arranging signal lamps with different colors, so that the occurrence of traffic sequence disorder and traffic accidents caused by man-made subjectivity is greatly reduced. However, the control method of the signal timing control method is inflexible, different signal time distributions are difficult to adjust in real time along with the traffic change of the intersection, and the method is not suitable for intelligent internet automobiles with intelligent and objective characteristics, so that a signal-free self-organized traffic strategy is required to be adopted for traffic control of the intelligent internet automobiles at the intersection.
At present, many people study the traffic control strategy of an intelligent internet automobile at an intersection, but most of the existing studies are directed at the scenes of degraded intersections with less traffic flow and less traffic directions, the collision is avoided mainly by adjusting the motion parameters of conflict vehicles, and the study is fresh for the motion control model of the vehicle traffic sequence and large-scale traffic flow.
Disclosure of Invention
The invention provides a no-signal self-organizing traffic control method for an intelligent networked automobile in a travel scene at an intersection. The physical characteristics of the intersection environment are expressed through reasonable mathematical language, effective information is transmitted to vehicles in an arrival area, and basic information is provided for formulating a traffic control strategy. Taking into account all vehicles arriving in the area, the traffic order of the vehicles is optimized using a monte carlo decision tree based approach. And finally, calculating the decided vehicle running track, wherein the decided vehicle running track mainly comprises the acceleration, speed and position of the planned vehicle which change along with time.
The technical scheme of the invention comprises the following contents:
(1) intersection environment data representation
The physical characteristics of the intersection comprise the number of entrances of the intersection, the number of lanes of each entrance and the size parameter information of the road. In order to improve the passing efficiency of the intelligent internet automobile intersection, the running speed of the automobile needs to be ensured, and parking and low-speed running are reduced. An advance decision is required before the vehicle reaches the intersection stop line. The intersection area is divided into a decision area, an execution area and a conflict area. The decision area and the execution area are areas before the stop line, the specific length of the areas needs to be determined through simulation experiments, and the conflict area comprises all conflict points of traffic flow in different driving directions. The two-dimensional space-time matrix is adopted to represent the vehicle occupation time sequence of all conflict points encountered by the vehicle passing through the intersection, and the improvement of the occupation density of the two-dimensional space-time matrix is the improvement of the space-time resource utilization rate of the intersection area.
(2) Monte Carlo decision tree based traffic order decision
The passing order of the vehicles restricts the passing efficiency of the intersection, and the reasonable arrangement of the passing order of the vehicles is the key for improving the passing efficiency of the intersection. In fact, the passing order of the vehicles is the permutation and combination of all the vehicles in the decision area, and the combination number increases with the increase of the number of the vehicles in the decision area. The Monte Carlo decision tree is an efficient heuristic search algorithm, and combines the universality of random simulation and the accuracy of tree search. The algorithm idea is as follows: generating a traffic sequence sample by a Monte Carlo method, and designing a reasonable value function (namely traffic delay) to judge the advantages and disadvantages of each traffic sequence; and (3) constructing a decision tree structure, namely a complete representation of all the conditions of the passing order, then updating the weight of the node in the decision tree through the superiority and inferiority of the passing order in each iteration, and determining the probability of the node being selected in priority next time according to the weight (a first selection method or a roulette method can be adopted). In the case of limited sample space of the traffic order, the method of the monte carlo decision tree can always search the optimal traffic order.
(3) Vehicle motion control model
The method of the invention adopts decision-making in advance, so that the vehicle passes acceleration and deceleration between the stop lines at the intersection, the time for the vehicle to reach the stop lines is controlled, and then the vehicle can pass through the intersection area according to the optimal speed. When the vehicle reaches the junction of the decision area and the execution area, the motion trail of the vehicle needs to be determined, the time of the vehicle reaching a stop line is calculated according to the current passing sequence and the time sequence of all conflict points to be passed by the vehicle, and then the vehicle speed is controlled in a constant speed driving-decelerating-parking-accelerating mode, so that the optimal speed is ensured to be reached when the vehicle reaches the stop line. And finally, updating the numerical value of the conflict space-time matrix. Here, the optimum speed refers to a free-stream running speed of the vehicle.
Drawings
FIG. 1 is a flow chart of the method
FIG. 2 is a schematic view of an intersection area
FIG. 3 shows the division of the intersection area
FIG. 4 is a Monte Carlo decision tree flow
FIG. 5 is a schematic diagram of a vehicle motion trajectory
Reference numbers in the figures: 101 is an intersection conflict area; 102 is the traffic direction; and 103 is an intersection conflict point. A decision area 201; 202 is an execution area.
Detailed description of the preferred embodiments
The invention is described in detail below with reference to the drawings and embodiments, with the understanding that the examples are intended to be illustrative of the invention and are not intended to limit the scope of the invention.
FIG. 1 is a flow chart of a method, the method of which is performed in sequence according to the contents of the flow chart. The intersection environment is expressed by a mathematical language, so that the intersection area is divided, and a basis is provided for a traffic decision; the method based on the Monte Carlo decision tree is adopted to optimize the vehicle passing sequence, so that the time-space resource utilization rate of the intersection is improved; and determining the motion trail of the vehicle according to the decided passing sequence.
As shown in fig. 2, the study object of the present invention is a plane intersection environment, the intersection shown in the figure is a typical intersection with four entrances, each of which has three lanes, namely left-turn lane, straight lane and right-turn lane from left to right. For each of the left turn and straight directions, there may be conflicts with other traffic flows when passing through the intersection area, for example, a left turn of the east inlet may conflict with a left turn of the north inlet, a straight turn of the south inlet, a straight turn of the west inlet, and a left turn of the south inlet, and a straight turn of the east inlet may conflict with a straight turn of the south inlet, a left turn of the north inlet, a left turn of the west inlet, and a straight turn of the north inlet. And the right-turn traffic flow of each inlet does not conflict with other traffic flows and can pass freely. There are 8 conflict points in the figure. In order to make full use of space-time resources of intersection regions, a two-dimensional matrix is adopted to describe the occupation situation of all conflict points in an intersection, the first dimension represents conflict point sequences, the second dimension represents time sequences, an element '0' in the matrix represents idle, and a '1' in the matrix represents occupation.
As shown in fig. 3, a road before reaching a stop line at an intersection may be divided into a decision zone and an execution zone. Therefore, the vehicle needs to pass through the intersection area sequentially through the decision area, the execution area and the conflict area. When the vehicle reaches the junction of the decision area and the execution area, the motion trail behind the vehicle needs to be determined, and the vehicles in the decision area of all the entrance lanes are considered. The longer the length of the decision zone, the more vehicles are considered per decision, and the greater the computational complexity of the algorithm. Therefore, there is a trade-off between timeliness and effectiveness of the algorithm calculations, and in general, the length of the decision zone may be set to a fixed value over a period of time. The length of the execution area is changed, and on the basis of the initial length, the length of the decision area is increased along with the increase of the number of vehicles in the decision area, so that the purpose of ensuring that the vehicles have enough driving distance to realize the deceleration of the vehicles to the parking and then accelerate to the optimal speed is achieved.
FIG. 4 is a general operational flow of a Monte Carlo decision tree, after determining the tree structure, first determining an order in turn according to the node weights; in the process of determining the sequence, if a new node exists, the new node is preferentially selected, namely, the expansion is carried out; calculating the value of the cost function according to the selection-expansion logic until reaching the termination condition; and finally, judging according to the value function, and reversely transmitting the node weight value passed by the updating sequence. For the traffic order decision involved in the present invention, the specific decision process is as follows:
(1) acquiring information (including position, speed and acceleration) of vehicles in decision areas of all imported vehicles, and adding the information into a decision set;
(2) constructing a decision tree, wherein the number of nodes at the first layer of the decision tree is equal to the number of vehicles in a decision area, then, each layer is decreased by one node, and the weight values of all the nodes of the decision tree are initialized to be 0;
(3) according to the node weight of the decision tree, the smaller the weight, the larger the selection probability, and a passing order is obtained by adopting a Monte Carlo method;
(4) according to the space-time matrix of the conflict area, sequentially updating the space-time matrix into a temporary space-time matrix according to the selected traffic sequence;
(5) calculating the total delay of all the vehicles in the decision-making area passing through the intersection, wherein the average delay of each vehicle is equal to the difference between the passing time under the condition and the passing time running at the optimal speed, and updating the weight of each decision-making tree node passed by the passing sequence according to the delay;
(6) and (5) repeating the steps (3) to (5) until the iteration is finished, and acquiring the corresponding passing sequence when the total delay in the iteration process is minimum.
Through the process of determining the passing order of the vehicles, the time when the vehicles reach the stop line and pass through each conflict point is obtained, and therefore the movement condition of the vehicles in the execution area can be calculated. Under different intersection scenes, the vehicle has various motion tracks, as shown in fig. 5. A represents that the vehicle firstly runs at the optimal speed at a constant speed, then decelerates to stop, and accelerates to the optimal speed after waiting for stopping; b represents that the vehicle firstly runs at the optimal speed at a constant speed, then decelerates to a low speed, then keeps running at the low speed at the constant speed, and then accelerates to the optimal speed; c represents that the vehicle runs at the optimal speed at a constant speed, then undergoes a deceleration-acceleration process and then reaches the optimal speed; d indicates that the vehicle is constantly traveling at an optimal speed at a constant speed. The motion trail of the vehicle is determined by parameters such as the distance between the vehicle and a stop line, the time for the vehicle to reach the stop line, the optimal speed of the vehicle, the maximum acceleration and the maximum deceleration of the vehicle, and the like, and fig. 4 shows only a few simplified motion trail diagrams. On the premise of ensuring that the vehicle reaches a stop line at a set moment, the intelligent internet automobile can run according to a more complex motion trail by considering factors such as energy consumption, riding comfort and the like, and the motion trail does not influence the passing efficiency of an intersection.
The implementation process of the present invention is described in detail above, but the present invention is not limited to the specific details in the above embodiment, and within the scope of the technical idea of the present invention, the specific details may be changed and replaced, for example, different problems may be studied only by changing the rule of the actor path selection in the simulation kernel, and the simulation system has high universality, and all fall into the protection scope of the present invention.
Claims (3)
1. An intersection no-signal self-organizing traffic control method of an intelligent networked automobile comprises the following steps:
(1) data representation of intersection environment
Dividing the intersection into a decision area, an execution area and a conflict area by combining the physical characteristics of the intersection, including the number of entrances of the intersection, the number of each lane of the entrances and the size parameter information of the road;
(2) monte Carlo decision tree based traffic order decision
Generating a traffic sequence sample by a Monte Carlo method, and designing a reasonable value function, namely traffic delay to judge the superiority and inferiority of each traffic sequence; building a decision tree structure, namely completely representing all conditions of a traffic sequence, then updating the weight of a node in the decision tree through the advantages and disadvantages of the traffic sequence in each iteration, determining the probability of the node being selected preferentially next time according to the weight, and always searching the optimal traffic sequence by using the Monte Carlo decision tree method under the condition that the sample space of the traffic sequence is limited;
(3) vehicle motion control model
The method comprises the steps of determining a passing track of a vehicle when the vehicle reaches a junction of a decision area and an execution area, calculating the time of the vehicle reaching a stop line according to a current passing sequence and a time sequence of all conflict points which the vehicle passes through, and then determining a motion track of the vehicle by controlling the change of the vehicle speed to ensure that the optimal speed is reached when the vehicle reaches the stop line, wherein the optimal speed refers to the free running speed of the vehicle.
2. The intersection no-signal self-organizing traffic control method of the intelligent networked automobile according to claim 1, wherein the decision area and the execution area are areas before a stop line, the collision area comprises all conflict points of traffic flows in different driving directions, and a two-dimensional space-time matrix is adopted to represent the vehicle occupancy of all the conflict points encountered by the vehicle passing through the intersection.
3. The intersection no-signal self-organizing traffic control method of the intelligent networked automobile according to claim 2, wherein a two-dimensional space-time matrix is used, a first dimension represents a collision point sequence, a second dimension represents a time sequence, an element "0" in the matrix represents idle, and an element "1" in the matrix represents occupied.
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| CN114495547B (en) * | 2022-02-22 | 2023-02-24 | 北京航空航天大学 | Signal intersection cooperative passing method for automatically-driven automobile |
| CN115171386B (en) * | 2022-07-07 | 2023-12-12 | 中南大学 | Distributed collaborative driving method based on Monte Carlo tree search |
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