CN112721929B - Decision-making method for lane changing behavior of automatic driving vehicle based on search technology - Google Patents
Decision-making method for lane changing behavior of automatic driving vehicle based on search technology Download PDFInfo
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- CN112721929B CN112721929B CN202110030986.7A CN202110030986A CN112721929B CN 112721929 B CN112721929 B CN 112721929B CN 202110030986 A CN202110030986 A CN 202110030986A CN 112721929 B CN112721929 B CN 112721929B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
- B60W30/18—Propelling the vehicle
- B60W30/18009—Propelling the vehicle related to particular drive situations
- B60W30/18163—Lane change; Overtaking manoeuvres
Abstract
The invention discloses a decision-making method for lane changing behavior of an automatic driving vehicle based on a search technology, which comprises the following steps: (S1) after predicting the future track of the surrounding vehicles, automatically driving the automobile to decide the driving behavior of the automobile; and (S2) in the behavior decision, making a decision on the lane changing behavior of the automatic driving vehicle through a behavior searching module and a behavior processing module respectively. Through the scheme, the automatic driving system achieves the purpose of efficient automatic driving, and has high practical value and popularization value.
Description
Technical Field
The invention belongs to the technical field of automatic driving, and particularly relates to a method for deciding lane changing behavior of an automatic driving vehicle based on a search technology.
Background
Many studies have proposed many medium model predictions for autonomous driving, which are divided as follows:
predicting lane change based on the scene model: a new highway lane-change assisting and automatic driving control algorithm based on scene model predictive control (scmcp) is proposed. The basic idea is to interpret the uncertainty in the traffic environment by a small number of future scenarios to perform a safe lane change.
Incentive-based decentralized coordinated lane change for autonomous vehicles: an excitation-based decision framework for decentralized and collaborative lane change of an automatic driving vehicle determines corresponding decisions by respectively adopting an excitation-based model and a collision avoidance coordination algorithm.
Predicting lane change based on a cellular automaton model: a classical cellular automata model (STNS) has been proposed that uses a set of rules to determine future lane-change behavior.
Predicting lane change based on a kinematic model: a kinematic model of lane change is proposed, which can plan the motion trajectory of the lane according to the characteristics of a polynomial. In addition, an infinite dynamic circle is applied to detect collisions during the lane change.
Predicting lane change based on a selection model: a highway lane selection model (FLS) has been proposed that will enable traffic professionals to more accurately simulate lane-change behavior on a highway. Thus, the traffic simulation software integrates the FLS algorithm into a commercial version of its software. The FLS algorithm includes target lane selection and gap acceptance decisions, with the goal of outputting the most accurate lane change decision.
The lane change planning algorithm mainly focuses on the safety, comfort and accuracy of lane change, but ignores the potential influence of lane change on other vehicles, so that the automatic driving vehicle cannot process the road environment more intelligently, and the efficient automatic driving cannot be achieved. Therefore, how to solve the problems in the prior art is a problem that needs to be solved urgently by those skilled in the art.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a decision-making method for lane changing behavior of an automatic driving vehicle based on a search technology, so that the automatic driving vehicle is more intelligent, and efficient automatic driving is achieved.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a decision-making method for lane change behavior of an automatic driving vehicle based on a search technology comprises the following steps:
(S1) after predicting the future track of the surrounding vehicles, automatically driving the automobile to decide the driving behavior of the automobile;
and (S2) in the behavior decision, making a decision on the lane changing behavior of the automatic driving vehicle through a behavior searching module and a behavior processing module respectively.
Further, the future trajectory prediction result of the surrounding vehicle in the step (S1) includes nine nodes, which are: the method comprises the following steps of (a) left lane change acceleration, (b) left lane change deceleration, (c) left lane change keeping speed, (d) lane change-free acceleration, (e) lane change-free deceleration, (f) lane change-free keeping speed, (g) right lane change acceleration, (h) right lane change deceleration and (i) right lane change keeping speed.
Further, the behavior searching module in the step (S2) is operative to search the system for optimal behavior of the discrete autonomous vehicle.
Further, the role of the behavior processing module in the step (S2) is that the system directly converts discrete behaviors into continuous accurate values as output results.
Specifically, the specific steps of the behavior decision in the step (S2) are as follows:
(S21) the behavior searching module acquires a track prediction result of a surrounding vehicle;
(S22) constructing a search tree by the acquired trajectory prediction result of the surrounding vehicles;
(S23) performing a behavior search on the constructed search tree;
(S24) converting discrete behaviors among the execution behaviors into continuous precise values through the behavior processing module;
(S25) the behavior processing module outputs the continuous accurate value as a result.
Further, the search tree in the step (S22) is a data structure based on a tree, the depth of the tree is Z, each non-leaf node has nine child nodes, where the root node represents the current state, and the other child nodes represent corresponding behaviors at a future time, and each node represents one behavior except the root node.
Specifically, each edge of the search tree in the step (S22) has two weights, namely, an influence factor F im And velocity V A Wherein the influence factor F im The calculation method of (2) is as follows:
formula (1) represents the summation result of the influence factors of all surrounding common vehicles, wherein t represents a certain time; ci represents the ordinary vehicles around the ith vehicle; c represents all surrounding ordinary vehicles;
representing the magnitude of an influence factor of the automatic driving vehicle on common vehicles around the ith vehicle at the time t;
representing the magnitude of the impact factor of the autonomous vehicle on all surrounding average vehicles at time t,
the values 1,2,3 represent the queuing case, the queue-insertion case and the crossover case, respectively.
Compared with the prior art, the invention has the following beneficial effects:
(1) The method comprises the steps of firstly obtaining a prediction result of a future track of a surrounding vehicle, constructing a search tree for the prediction result through a behavior search module, and then converting the search tree into a continuous accurate value through a behavior processing module to be output. The motion behavior of the vehicle is a continuous value, so that time is consumed for directly searching the vehicle, and the behavior decision comprises a behavior searching module and a behavior processing module, so that the search information is dispersed, the data processing time is shortened, the searching process is accelerated, and the behavior decision processing efficiency is improved. And by constructing a search algorithm and a pruning strategy of the search tree, an optimal decision sequence is effectively found, so that the automatic driving vehicle is more intelligent, and efficient automatic driving is achieved.
Drawings
FIG. 1 is a schematic diagram of the dynamic pruning of the present invention.
FIG. 2 is a schematic diagram of three influencing factors of the present invention.
FIG. 3 is a diagram illustrating the longest overlay path according to the present invention.
Detailed Description
The present invention is further illustrated by the following examples in conjunction with the figures and examples, and embodiments of the present invention include, but are not limited to, the following examples.
Examples
As shown in fig. 1 to 3, a decision method for lane change behavior of an autonomous vehicle based on search technology includes the following steps:
the method comprises the following steps that (S1) after the future tracks of surrounding vehicles are predicted, an automatic driving automobile decides the driving behavior of the automatic driving automobile, and (S2) in the behavior decision, the lane changing behavior of the automatic driving automobile is decided through a behavior searching module and a behavior processing module respectively.
The future track prediction result of the surrounding vehicle comprises nine nodes, namely nine nodes, which are respectively: the method comprises the following steps of (a) left lane change acceleration, (b) left lane change deceleration, (c) left lane change keeping speed, (d) lane change-free acceleration, (e) lane change-free deceleration, (f) lane change keeping speed, (g) right lane change acceleration, (h) right lane change deceleration, and (i) right lane change keeping speed.
In the step, the search tree is a data structure based on the tree, the depth of the tree is Z, each non-leaf node has nine child nodes (namely 3 × 3 discrete autonomous vehicle behaviors), wherein the root node represents the current state, the other child nodes represent corresponding behaviors at a future time, and each node represents one behavior except the root node, and the structure of the search tree is shown in fig. 1.
Each edge of the search tree has two weights, the influencing factor F im And velocity V A Wherein the speed calculation method is longitudinal travel distance/unit time in m/s, and the influence factor F im The calculation method of (2) is as follows:
formula (1) represents the summation result of the influence factors of all surrounding ordinary vehicles, wherein t represents a certain moment; ci represents common vehicles around the ith vehicle; c represents all surrounding ordinary vehicles;
representing the magnitude of an influence factor of the automatic driving vehicle on common vehicles around the ith vehicle at the time t;
representing the magnitude of the impact factor of the autonomous vehicle on all surrounding average vehicles at time t,
the values of 1,2,3 represent the queuing situation, the queue-jumping situation and the crossing situation, respectively, and the three lane-changing situations are shown in fig. 2.
In the behavior search part, a search is performed by using a BeamSearch algorithm, which is a heuristic search algorithm that uses breadth-first search to construct a search tree, which can reduce memory requirements but not necessarily reach a globally optimal solution. The BeamSearch system establishes a search tree using a breadth first strategy, sorts nodes according to heuristic cost at each layer of the tree, and then only leaves a predetermined number (Beam Width-bundling Width) of nodes, and only the nodes continue to expand at the next layer, and other nodes are cut off.
The method and the device have the advantage that the optimal lane change behavior is quickly found, and the whole search space is represented by using a decision tree structure. Using a dynamic pruning strategy, the manipulation tree can be kept within a manageable size so that searches can be performed efficiently.
The search process has two goals: the first is to maximize the average speed of the autonomous vehicle, which is calculated as the longitudinal travel distance divided by the duration. The second is to minimize the influence on surrounding ordinary vehicles, and the influence degree of the ordinary vehicles on the automatic driving vehicle can be simulated through the influence factor. And finally, outputting by a behavior decision function to obtain the optimal behavior of the automatic driving vehicle.
The behavior decision function consists of two submodules of behavior searching and behavior processing. In the behavior searching stage, the system searches to obtain an optimal behavior sequence of a discretization version. Specifically, the discretization behavior is discretization of the transverse lane change behavior and the longitudinal motion behavior, and the following three lane change behaviors are considered: left lane changing, right lane changing and no lane changing; and three longitudinal behaviors: accelerate, decelerate, and maintain speed. In the behavior processing stage, the discrete behavior obtained in the first stage is converted and an accurate value is generated, and finally, the transverse and longitudinal behaviors of the complete automatic driving vehicle are obtained.
Based on the behavior search tree, a search algorithm and a pruning strategy are used to effectively make the behavior decision of the optimal lane change. Once the trajectory prediction function section outputs the result of the trajectory prediction of the ordinary vehicle, the system may start searching for the optimum behavior of the autonomous vehicle and decide how to implement the behavior. Since the longitudinal movement distance of the vehicle is a continuous value, the system needs to discretize the longitudinal movement distance first to make the searching process feasible and then convert the longitudinal movement distance into a continuous value in the following behavior processing process.
The algorithm flows as follows:
1) Inserting the initial node into the list;
2) The target node (the node satisfying the target) is piled up, if the node is a leaf node, the algorithm is ended;
3) Otherwise, expanding the node and stacking the nodes with the bundling width. Then continuing to circulate in the second step;
4) The condition for the algorithm to end is to find the optimal behavior.
The target nodes are those that satisfy the maximization of the average speed of the autonomous vehicle while minimizing other common vehicle impact factors, and the search process is to search in the behavior tree and find those nodes (behaviors) that satisfy the target. For the searched target, the mathematical formula is as follows:
where M represents the optimal discrete behavior of the autonomous vehicle for the behavior search output, this equation (2) aims to find the discrete behavior that maximizes the speed while minimizing the influence factor. The BeamSearch algorithm used by the application can prune some paths with low reliability (pruning) under a proper condition and find out the optimal solution after pruning.
And finally, in the behavior processing part, directly evaluating according to the environmental information acquired by the sensor, continuously adjusting the discrete behavior value output by the behavior searching part, and finely adjusting according to the real-time condition of the external environment, so that the safe driving of the automatic driving vehicle is met.
The above embodiments are only preferred embodiments of the present invention, and are not intended to limit the scope of the present invention, but all changes that can be made by applying the principles of the present invention and performing non-inventive work on the basis of the principles shall fall within the scope of the present invention.
Claims (3)
1. A decision-making method for lane change behavior of an automatic driving vehicle based on a search technology is characterized by comprising the following steps:
(S1) after predicting the future tracks of surrounding vehicles, automatically driving the automobile to decide the driving behavior of the automobile;
(S2) in the behavior decision, making a decision on the lane changing behavior of the automatic driving vehicle through a behavior searching module and a behavior processing module respectively;
the behavior decision method comprises the following specific steps:
(S21) the behavior searching module acquires a track prediction result of a surrounding vehicle;
(S22) constructing a search tree by the acquired trajectory prediction result of the surrounding vehicles;
(S23) performing a behavior search on the constructed search tree; the goals of performing behavioral searches are: the first is to maximize the average speed of the autonomous vehicle, which is calculated as the longitudinal travel distance divided by the duration; the second is to minimize the influence on surrounding ordinary vehicles, and the influence degree of the ordinary vehicles on the automatic driving vehicle can be simulated through the influence factors; finally, outputting by a behavior decision function to obtain the optimal behavior of the automatic driving vehicle;
(S24) converting discrete behaviors among the execution behaviors into continuous precise values through the behavior processing module;
(S25) the behavior processing module outputting the continuous accurate values as a result;
in the step (S22), the search tree is a data structure based on a tree, the tree has a depth Z, each non-leaf node has nine child nodes, the root node represents the current state, and the other child nodes represent corresponding behaviors at a future time, and each node represents one behavior except the root node;
each edge of the search tree in said step (S22) has two weights, i.e. an influence factor F im And velocity V A Wherein the influence factor F im The calculation method of (2) is as follows:
formula (1) represents the summation result of the influence factors of all surrounding common vehicles, wherein t represents a certain time; ci represents the ordinary vehicles around the ith vehicle; c represents all surrounding ordinary vehicles;
representing the magnitude of an influence factor of the automatic driving vehicle on common vehicles around the ith vehicle at the time t;
representing the magnitude of the impact factor of the autonomous vehicle on all surrounding average vehicles at time t,
the values 1,2,3 represent the queuing case, the queue-insertion case and the crossover case, respectively.
2. The method for deciding on the lane-changing behavior of the autonomous driving vehicle based on search technology of claim 1, wherein the predicted future trajectory of the surrounding vehicle in step (S1) comprises nine nodes, which are: the method comprises the following steps of (a) left lane change acceleration, (b) left lane change deceleration, (c) left lane change keeping speed, (d) lane change-free acceleration, (e) lane change-free deceleration, (f) lane change-free keeping speed, (g) right lane change acceleration, (h) right lane change deceleration and (i) right lane change keeping speed.
3. The search-technology-based decision-making method for lane-changing behavior of an autonomous vehicle according to claim 2, characterized in that the behavior search module in step (S2) is used for searching the optimal behavior of the discrete autonomous vehicle.
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