105 results about "Traffic conflict" patented technology

The invention relates to an automatic driving risk quantification method based on a conflict risk field. Comprising the steps of 1, constructing a basic risk field; step 2, under a basic risk field framework, constructing a conflict risk field by taking an ADV as a center; step 3, regarding the conflict risk field as a repulsive force acting on the ADV, and using the repulsive force to quantify the dynamic influence of the environmental elements and various TP states on the driving safety of the ADV; the method comprises the following steps: firstly, constructing a basic risk field on the basis of traffic vehicle distribution and motion characteristics, further forming a vehicle forward, lateral, backward and traffic regulation constraint multi-dimensional conflict risk field on the basis, and converting an abstract traffic conflict relationship into a computable repulsive force model; dynamic quantitative characterization of driving risks in a state dimension, a space dimension and a time dimension is realized, and continuous optimization and evolution of an end-to-end automatic driving algorithm are supported by means of a repulsive force model constructed based on a conflict risk field.

The invention provides a vehicle-road cloud collaborative mixed traffic flow optimization method, system and device, and relates to the technical field of intelligent traffic, and the method comprises the steps: extracting a mixed traffic flow feature set containing a road environment, a vehicle state and a driver behavior through obtaining and synchronously fusing the multi-source traffic data of a cloud end, a road end and a vehicle end; according to the method, the intention of a driver is predicted by using a double-layer LSTM model, a vehicle trajectory is predicted in combination with an RNN-LSTM model, confidence fusion analysis of the intention and the trajectory is performed on this basis, accurate prediction of traffic conflict events is realized, and global optimization of traffic flow is supported. According to the method, the intention recognition and trajectory prediction precision in a complex mixed traffic environment is effectively improved, the intelligent decision-making capability of a traffic management system is enhanced, and an efficient technical means is provided for relieving traffic congestion, improving the traffic efficiency and guaranteeing traffic safety.

The invention provides a multi-AGV bidirectional channel dynamic avoidance method and system based on conflict prediction, and relates to the field of industrial logistics automation control, when a vehicle-mounted laser radar of a first AGV detects an opposite second AGV in a preset fan-shaped early warning interval, double-AGV priority quantification is carried out, avoidance role distribution is carried out according to a quantification priority comparison result, and an avoidance AGV is positioned; and the way-giving AGV receives the standard driving track of the non-way-giving AGV, calculates a smooth way-giving track, and carries out two-way traffic conflict dynamic resolution. The technical problem that the logistics efficiency of a high-beat precise assembly production line is seriously restricted due to the fact that the accuracy of collision conflict prediction and avoidance processing and the response timeliness are insufficient due to the fact that the autonomous collaboration of multi-AGV passing control is weak in a narrow industrial channel in the prior art is solved. Full-autonomous cooperative passage control of multiple AGV clusters in a narrow industrial channel is realized, and zero-interruption execution of high-value logistics tasks is ensured.

The invention discloses a method and a system for identifying traffic conflicts in a road interleaving area, particularly relates to the field of traffic management, is used for solving the problems of low identification precision and early warning delay of the traffic conflicts in the interleaving area, and is used for accurately capturing risk events and filtering non-intentional behavior interference based on a composite trigger mechanism of turn light activation and transverse displacement rate; a behavior feature vector is matched with a conflict type template library to realize self-adaptive interception of an asymmetric time window, and risk evolution cycles of scenes such as sharp cut-in and slow lane change are completely covered; introducing a dynamic window expansion mechanism driven by a conflict energy gradient to ensure the whole-process capture of a risk accumulation stage; multi-scale spatio-temporal features are extracted in combination with a convolutional network to generate a thermodynamic diagram, and refined positioning of risk spatio-temporal distribution is achieved; an early warning signal grading mechanism and a closed loop feedback optimization system are established, the system adaptability is continuously improved, real-time and accurate decision support is provided for an intelligent traffic management system, the secondary accident risk is effectively restrained, and the road network traffic efficiency is optimized.

The invention discloses an interpretable high-speed ramp intelligent driving vehicle confluence control method. The method comprises the steps that a state space is constructed, wherein the state space comprises the position and speed of a vehicle, lane information and the relative position and speed of adjacent vehicles; human-like decision guidance: training a human-like behavior discriminator based on real driving data by using a random forest model, screening action selection of reinforcement learning, and constructing a sample set containing state vectors and human-like action labels; reinforcement learning control; and carrying out interpretability analysis. Through an SHAP analysis method, an explainable DQN longitudinal acceleration control model in an automatic driving vehicle ramp merging process is provided, and an explainable automatic driving vehicle ramp merging control model is constructed in combination with an merging decision-making model constructed based on a random forest. According to the explainable high-speed ramp automatic driving vehicle confluence control method based on the confluence urgency degree and the environmental historical information, traffic conflicts during highway confluence can be relieved, and therefore the road passing efficiency is effectively improved.

This invention discloses a lane-level conflict prediction method for expressway weaving areas based on real-time traffic flow data, comprising the following steps: S1, selecting a suitable expressway weaving area as the survey location and conducting on-site investigation; S2, collecting traffic flow data and traffic conflict data for each lane, and defining thresholds for different conflict types; S3, building a traffic simulation model using the traffic parameters obtained from the survey, defining different conflict types according to the conflict thresholds obtained in step S2, and obtaining traffic conflict data for different types; S4, extracting feature variables from the data obtained in step S3, and constructing datasets for prediction models of different conflict types; S5, constructing a conflict analysis model; S6, constructing a conflict prediction model. This invention can achieve real-time lane-level traffic conflict prediction in expressway weaving areas, thereby providing more accurate lane-level conflict risk information for vehicles in each lane, helping drivers choose safer lanes and routes.

The invention discloses a visual constraint-based vehicle safety domain modeling and conflict risk assessment method, which comprises the following steps of: analyzing and selecting a non-signalized intersection with frequent traffic accidents by using accident black spots, and acquiring multi-dimensional traffic information of the intersection through an unmanned aerial vehicle; building three vehicle safety domain models of straight running, right turning and left turning; by predicting a vehicle trajectory equation, determining an intersection conflict position, dividing intersection conflicts into two categories of cross conflicts and confluence conflicts according to a trajectory interaction mode, and in different conflict scenes, according to a constructed vehicle safety domain, performing quantitative calculation on a conflict risk of vehicles in a non-signalized intersection to obtain traffic conflict exposure time; and based on the traffic conflict exposure time data, determining an optimal clustering number K of a K-means algorithm through an elbow rule, clustering results of the traffic conflict exposure time by using the K-means algorithm, and grading conflict risks. According to the invention, the accuracy of vehicle conflict detection at the non-signalized intersection is improved.

The invention discloses a traffic conflict risk evaluation method, system and device based on simulation modeling and a storage medium, and relates to the technical field of traffic safety evaluation method research, and the method comprises the steps: collecting road data and vehicle driving data, and constructing a traffic data set; carrying out traffic simulation modeling based on the traffic data set; carrying out traffic conflict index calculation based on traffic simulation modeling; evaluating the traffic conflict risk according to the traffic conflict index; according to the method, the traffic conflict is dynamically analyzed, the occurrence of the traffic conflict can be more accurately judged, the traffic conflict risk problem can be effectively solved, and a new thought is provided for judging the regional traffic conflict risk condition.

The invention discloses a distributed motorcade collaborative shockwave absorption method based on multi-objective optimization, and the method comprises the steps: S1, obtaining a scene of a mixed flow motorcade passing through a shockwave region, dividing a long motorcade, and determining key parameters; s2, constructing an optimization control model to solve an optimal motorcade splitting point, and splitting the hybrid motorcade; and S3, based on an improved AS-OCD distributed trajectory optimization algorithm, performing synchronous control on each sub-motorcade so as to completely absorb traffic shockwaves. The problem that traffic shockwaves frequently occur in a high-saturation traffic flow or an urban expressway is solved, state sharing and boundary coordination among the sub-motorcades are achieved through distributed collaborative optimization, the adaptability and robustness of the system in a high-density complex traffic environment are enhanced, speed coordination and safe distance between the motorcades are guaranteed, the risk of traffic conflicts is reduced, and the traffic safety is improved. And the stability and the operation efficiency of the whole traffic system are improved.

The invention discloses an intelligent targeted dredging method for an expressway confluence area, which belongs to the technical field of intelligent traffic and comprises the following steps: S1, acquiring traffic flow, vehicle speed, lane occupancy and weather parameters of a main road / ramp through a vehicle-mounted communication unit; s2, constructing a traffic capacity baseline value based on a random forest model; s3, when the traffic capacity is lower than a dynamic threshold value, calling a deep reinforcement learning model through an edge computing node; s4, a hierarchical pushing mechanism is adopted to distribute a control instruction, and variable information board speed limit values are issued to non-network-connected vehicles; and S5, when a residual error between a predicted value and an actual value of the sensor data confidence coefficient continuous time window exceeds a historical mean value standard deviation, switching to a time sequence prediction model to generate a conservative control strategy. According to the method, a multi-index feedback mechanism based on the carbon emission, the average delay time and the number of traffic conflict points is constructed, the weight is dynamically distributed through the entropy weight method, and quantitative evaluation of the dredging effect is achieved.

The embodiment of the invention discloses a mixed driving risk identification method and system for a road interleaving area and a computer storage medium, and the method comprises the steps: respectively quantifying the contribution value of each index to a traffic accident and a traffic conflict for each index in a multi-dimensional risk identification index system; sorting the indexes according to the contribution values to obtain a traffic accident contribution value sorting result and a traffic conflict contribution value sorting result; according to the traffic accident contribution value sorting result and the traffic conflict contribution value sorting result, indexes of which the contribution values to the traffic accidents and the traffic conflicts exceed a preset threshold value are selected and combined to form a high-contribution index system; screening the indexes of which the correlation among the indexes is smaller than a threshold value from the high-contribution index system; training a double-risk identification model based on the screened high-contribution index system; and constructing an accident conflict two-dimensional risk matrix so as to detect real-time traffic flow data by using the trained double-risk identification model and the accident conflict two-dimensional risk matrix.

The invention provides a mine laneway traffic scheduling method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a continuous vibration signal monitored by a distributed optical fiber sound wave sensing system in a mine laneway; performing filtering and signal identification on the continuous vibration signal to identify a vehicle driving signal; monitoring the triggering time of the calibration point in the mine laneway according to the vehicle driving signal, and determining the position and the driving direction of the vehicle; according to the vehicle driving signal, the position of the vehicle and the driving direction, determining whether vehicle early warning needs to be carried out in the mine laneway; and in response to the need of vehicle early warning, generating a vehicle scheduling and early warning instruction so as to perform vehicle scheduling. Therefore, by analyzing the vibration signals monitored by the distributed optical fiber sound wave sensing system in the mine laneway in real time, the position, the speed and the vehicle driving direction of the vehicle are accurately recognized, so that before potential traffic conflicts occur, dispatching and early warning instructions are sent to related vehicles, and the passing efficiency and the safety of the mine laneway are effectively improved.

The invention discloses a traffic risk analysis method and system based on multi-source perception data, and relates to the technical field of traffic risk management. The traffic risk analysis method based on the multi-source perception data comprises the following steps of performing noise interference optimization judgment; quantifying the timeliness of traffic risk capture; traffic conflict probability prediction; and carrying out traffic risk decision response analysis. According to the method, whether noise interference optimization is carried out or not is judged through the obtained high-frequency signal amplitude, whether timeliness optimization is carried out or not is judged based on the obtained capture timeliness quantification result, and when the timeliness is qualified, whether interaction parameter step length optimization is carried out or not is judged based on the predicted traffic conflict probability; meanwhile, whether decision response optimization is carried out or not is judged based on the decision response analysis result, and the effect of improving the accuracy of dynamic traffic jam risk assessment in the road transformation area in the urban road transformation process is achieved; the problem that the accuracy of dynamic traffic jam risk assessment is not high in the urban road transformation process in the prior art is solved.

The invention discloses an automatic driving non-signalized intersection traffic conflict control method based on a space-time network, and the method comprises the steps: carrying out the preprocessing: determining an intersection traffic conflict control range, and carrying out the grid division, after the vehicle arrives at the control area, the speed, the position, the acceleration, the lane entering time, the intentional steering time and the intersection control area time of the vehicle are reported to the central manager; a request is sent when the vehicle arrives, and the central manager judges whether the vehicle is allowed to enter; solving the space-time network control model and the trajectory reconstruction model, and feeding back the specific driving trajectory to the vehicle; the vehicle runs according to the running track fed back by the central manager and safely passes through the intersection; according to the invention, the path of the autonomous vehicle at the intersection is changed based on the path of the unconventional intersection, and the traffic conflict control of the non-signalized intersection in the pure automatic driving environment is explored, so that the traffic efficiency of the intersection is better improved.

The invention provides a low-altitude and ground traffic intelligent cooperative control method based on multi-agent reinforcement learning. The method comprises the steps that traffic state information is acquired, and scene classification is performed through a multi-agent reinforcement learning model; if the scene is a low-altitude congestion scene, executing unmanned aerial vehicle path dynamic planning, airspace resource allocation and conflict resolution, and fusing to generate a low-altitude control instruction; if the scene is a ground conflict scene, vehicle priority ranking, signal lamp optimization and road right distribution are executed, and a ground control instruction is generated through secondary fusion after a conflict resolution weight matrix is constructed; and finally, fusing low-altitude and ground instructions through a double-layer attention mechanism, and outputting a global cooperative scheduling scheme. According to the method, the airspace resource utilization rate and the road traffic efficiency can be improved, the traffic conflict risk is reduced, and a global integrated optimal scheduling scheme is formed.

The invention belongs to the technical field of intelligent traffic safety and risk prediction, and particularly relates to a traffic conflict risk spatio-temporal evolution prediction method based on a vehicle interaction relationship, and the method comprises the steps: taking a target vehicle as a main vehicle, and extracting the positions and speeds of the main vehicle and surrounding vehicles in a unified coordinate system through multi-source data; the method comprises the following steps: calculating a relative position and a relative speed, forming interaction relationship representation data, constructing a vehicle risk potential field on a space-time continuous domain on the basis, mapping a discrete interaction state into continuous and differentiable risk intensity distribution, further forming a sequence by the risk potential fields at each moment, introducing a space-time coding module based on Transform, and coding the vehicle risk potential fields at each moment by using a time-space coding module based on the Transform. And combining position coding, self-attention and graph attention to represent time sequence dependence and multi-vehicle risk propagation coupling, and outputting future risk field evolution distribution. According to the method, the problems of information loss and insufficient spatio-temporal expression of a traditional single time interval index and fixed time window method are solved, conflict risk dynamic prediction is realized, and a reliable basis is provided for traffic safety management and active intervention.

The invention discloses a method for identifying the driving style of an automobile driver. The method comprises the steps that driving feature data and environment feature data in the automobile driving process are acquired; preprocessing the driving feature data and the environment feature data, and extracting time sequence feature data; performing model training on the time sequence feature data to establish a driver style identification model; identifying the driving style of the driver in real time according to a driver style identification model; and the driving style of the driver is visualized outside the automobile. Through the mode, the identification precision of the driving style can be remarkably improved, the system can dynamically adjust the identification logic, it is ensured that the real style of a driver can still be accurately reflected in different scenes, and the robustness and practicability of the method are enhanced; according to the method, the driving style of the driver is visualized outside the automobile, the information transparency between traffic participants can be remarkably improved, traffic conflicts are reduced, and finally collaborative optimization of road traffic efficiency and driving safety is achieved.

The invention provides an airport flying area crossing traffic command system based on an A-SMGCS system, and belongs to the technical field of intelligent traffic, and the system comprises a sensor layer which is used for collecting aircraft dynamic data, to-be-passed authorized object data, environment meteorological data, and dynamic obstacle data; the data processing and decision-making layer is used for carrying out standardization processing and space-time mapping processing on the collected data, judging existing potential conflicts in combination with a dynamic threshold adjustment mechanism, and generating an issuing instruction based on a multi-target optimization decision; and the execution layer is used for controlling crossing traffic management and control equipment according to the issued instruction and feeding back an execution result to the data processing and decision-making layer in real time to form closed-loop control. The traffic conflict risk of the airport flight area crossing is effectively reduced, the crossing passing efficiency is improved, the vehicle waiting time and the aircraft taxiing delay are reduced, the fine scheduling requirement for scene movement is met, and reliable support is provided for safe and efficient traffic operation of the airport flight area.

The invention discloses a motor vehicle chain conflict identification method based on a rear vehicle avoidance characteristic, and the method comprises the steps: 1, collecting the driving track data of motor vehicles on an urban road, calculating the collision time, and identifying a single traffic conflict between the motor vehicles on the same longitudinal lane and between the motor vehicles on adjacent transverse lanes; 2, on the basis of a stimulation-reaction model, determining a vehicle avoidance reaction threshold by combining a signal detection theory, and identifying an avoidance behavior of a vehicle after the conflict; and 3, merging conflicts with continuous avoidance behaviors, dynamically delimiting the influence duration of the conflicts, and constructing a complete chain conflict chain based on causal association between the conflicts. According to the method, the defect that space-time proximity is equivalent to causal relevance in the prior art is overcome, the accuracy of chain conflict recognition is improved, and reliable support is provided for road traffic safety early warning and management and control.

The invention discloses an intersection state sensing and traffic safety evaluation method based on an unmanned aerial vehicle and a large model, and belongs to the field of traffic safety evaluation. Comprising the following steps: constructing a target detection and tracking model based on an unmanned aerial vehicle visual angle, collecting intersection state and traffic flow data by using an unmanned aerial vehicle aerial video, counting traffic conflict data, constructing intersection potential conflict indexes, constructing a comprehensive grade evaluation model, improving an initial model by using a large model, and outputting an intersection comprehensive safety evaluation grade. Using an improved RT-DETR algorithm and a Deepsort model to improve the detection precision of the traffic participants and carrying out training; the intersection state and traffic flow data are extracted, and the overall risk quantification of the intersection is realized through a plurality of potential conflict indexes and by adjusting the weight of each index through a large model. According to the method, dynamic regulation and control are carried out on the safety index weight in combination with a large model, comprehensive evaluation of potential risks, abnormal behaviors and the overall safety level of the intersection is achieved, and the method has important significance.

This invention relates to an AEB (Autonomous Emergency Braking) triggering method, device, and medium based on traffic conflict, comprising the following steps: identifying at least one target vehicle in the environment; determining the vehicle type of the target vehicle based on a preset target detection model; matching a preset target vehicle mass based on the vehicle type; calculating the difference in braking distance based on the acquired relative distance between the vehicle and the target vehicle and the target vehicle speed; calculating the collision loss momentum based on the target vehicle speed, the target vehicle mass, the acquired vehicle speed, and preset vehicle mass information; calculating the risk level based on the difference in braking distance and the collision loss momentum, and according to the vehicle type; and triggering AEB when the risk level meets predetermined conditions. Compared with the prior art, this invention ensures vehicle driving safety while also considering comfort.

The invention discloses an intelligent road cone and a vehicle dynamic guiding method, and belongs to the technical field of intelligent traffic facilities, the intelligent road cone comprises a cone structure, a counterweight base, a sensing unit module, a microcontroller, a communication module and a warning module.The inclination state is monitored through a posture detection assembly, a sensing unit collects multi-dimensional data, and the warning module sends the multi-dimensional data to the microcontroller; according to the guiding method based on the road cone, coherent electromagnetic waves are emitted through the intelligent road cones on the two sides of a road to form an interference enhancement area, a repulsive force-gravitational force virtual force field model is constructed in combination with multi-channel signal detection, and the interference enhancement area and the repulsive force-gravitational force field model are combined. And guiding the vehicle to drive towards a safe area by using the control logic that the gravitation weight is dynamically higher than the repulsive force. According to the invention, hot plug expansion is realized by adopting a modular architecture, the structural stability and the function expansibility are enhanced, the vehicle guiding precision is improved through dual-source electromagnetic wave interference and an intelligent control algorithm, the traffic conflict risk in a construction area is effectively reduced, and the road construction safety and the management efficiency are improved.

The invention belongs to the technical field of urban traffic control, and particularly relates to an intersection far-leading U-turn and shift left-turn cooperative control method, which comprises the steps of S1, intersection channelization design; s2, designing intersection signals, including designing an intersection main signal and an intersection pre-signal; s3, designing a traffic organization; s4, designing a phase sequence of an intersection phase; step S5, determining full red time; s6, determining pre-signals, including determining a turning-around pre-signal and a shifting pre-signal; and step S7, determining the length from the opening to the intersection, including determining the minimum value of the length of the shift lane and the minimum value of the length of the turn-round lane. By optimizing signal timing and space resource allocation, cooperative operation of far-guiding turning-around of left-turning vehicles on the secondary road and tailgating left-turning of the main road is achieved, the passing efficiency of the intersection is improved, and traffic conflicts are eliminated.

The application discloses a kind of dangerous event chain extraction method and system based on complex traffic scene, including obtaining test scene data;Based on the key factors of dynamic attribute and interaction between vehicle and environment determined by preset natural driving data and sensor performance information, and the uncertainty of key factors is quantified;Based on the risk estimation multidimensional feature set and uncertainty information preset constructs driving risk estimation model for test scene;The driving risk estimation model is optimized, and the optimized risk estimation model for test is obtained;Based on the time evolution characteristics of dangerous event, a dangerous time chain model is constructed according to the optimized risk estimation model;Dangerous time chain model is solved, and dangerous event chain is obtained.The application uses traffic accident data and traffic conflict data to reveal the space-time evolution law of complex traffic scene, and improves the effect of later test scene reproduction and reconstruction by solving the dangerous time chain model.

This invention provides a short-term traffic conflict prediction method and early warning system based on RSU group collaboration and spatiotemporal graph neural network. It acquires vehicle trajectory and status data collected collaboratively by roadside unit groups, using the coverage area of ​​each RSU as a spatial analysis unit, and constructs an initial energy field distribution map of traffic conflict based on the spatial density of vehicle distribution and the intensity of speed temporal fluctuations. A pre-constructed spatiotemporal graph neural network is invoked to capture energy transfer paths between units, and a temporal cyclic layer is used to capture the energy change inertia within units, predicting and generating a dynamic evolution map of traffic conflict energy containing an energy evolution path network and peak times. Based on the map, potential conflict outbreak sources and expected affected unit sequences are identified, generating conflict propagation situation prediction information including spatial coordinates and propagation time sequences. Based on this information, differentiated collaborative working mode adjustment instructions are generated and distributed to each RSU. This invention achieves short-term prediction of traffic conflicts and collaborative scheduling of roadside resources.

The invention provides an open block traffic flow intelligent distribution method and system, and relates to the technical field of data processing, and the method comprises the steps: carrying out the processing of a basic triangular unit, so as to define the interaction range of multi-mode traffic flow; according to the interaction range, mapping the point cloud data on the virtual analysis plane to a corresponding actual road network area so as to extract mutual interference parameters of multimode traffic flows in the branch network, and generating a Gaussian mixture model initial parameter set; and based on the initial parameter set of the Gaussian mixture model, carrying out iterative optimization distribution on the open block OD pair, dynamically adjusting the path selection probability to obtain a final flow distribution scheme, and outputting the real-time flow distribution proportion of each path. According to the method, key features can be extracted from mass trajectory data, traffic conflict areas are accurately positioned in space, and interference intensity is quantified.

The invention relates to a safety information auxiliary method and system based on expressway individual vehicle risk short-term and imminent prediction, the method comprises two stages of risk prediction and risk early warning, the risk prediction uses a vehicle risk short-term and imminent prediction model, the risk prediction is carried out based on the preprocessed traffic state data, and the risk early warning is carried out based on the vehicle risk short-term and imminent prediction model. The vehicle risk short-term and temporary prediction model comprises a Resnet module and an LSTM module which are improved based on a plug-and-play attention mechanism and a bidirectional pyramid structure; the traffic state data comprises vehicle track data and road section geometric data; the risk early warning is based on the result of the vehicle risk short-term and temporary prediction model, traffic conflict indexes of all vehicles in a preset range of the target vehicle are calculated in real time, and risk early warning is carried out; the system is used for implementing the method. Compared with the prior art, the two-stage auxiliary driving method provided by the invention can reduce effective information loss and guarantee the accuracy of a risk prediction result.

The application provides a traffic conflict identification method for full linear road sections based on collision conditions, collects high-precision trajectory data based on a multi-source heterogeneous sensor network, and then uses a denoising method of adaptive Kalman filtering (AKF) to extract more accurate trajectory data. Based on the processed trajectory data, a multi-factor fusion transformer model is used to predict the future vehicle trajectory, and then a TTCA model under Frenet is constructed, without pre-judging the conflict type and identifying the traffic conflict of the driving road section, and the vector cross product method is used to consider the collision conditions between vehicles. The application fills the gap of the existing conflict identification technology in multi-type conflict identification in complex scenes, and improves the accuracy of the conflict identification technology.

The invention relates to the technical field of intelligent traffic management and planning, and discloses a lane design method and system based on mixed traffic flow lane traffic capacity, and the method comprises the steps: constructing a traffic feature basic data set of a target road scene; classifying the vehicles and the following states of the vehicles; calculating occurrence frequencies of various car-following states; calculating the average time headway of the mixed traffic flow lane; calculating the traffic capacity of the mixed traffic flow lane according to the average time headway of the mixed traffic flow lane, and deducing the traffic capacity of other lanes; obtaining a lane design scheme according to the traffic demand and the number of lanes in the target road scene; and based on the obtained lane design scheme, according to vehicle following and lane changing rules, constructing a simulation platform and a comprehensive evaluation method based on an entropy evaluation method, and determining an optimal lane design method. According to the invention, a reasonable layout scheme is provided for the design of high-truck-ratio lanes in an intelligent network connection environment, refined lane management is realized, internal traffic conflicts are reduced, and the operation efficiency of the expressway is improved.

The application provides a kind of underground mine truck scheduling method and device based on state machine and reinforcement learning, it is related to underground mine transportation system scheduling optimization technical field.The method comprises: obtaining the scene model of underground mine transportation system, constructs topological structure model, constructs the underground mine truck fleet operation problem according to topological structure model and conflict type, and then constructs the fleet scheduling decision model based on state machine and reinforcement learning;Wherein, each truck is regarded as the agent of fleet scheduling decision model, and the state machine is equipped for the agent;With the goal of maximizing transportation efficiency, reducing waiting time and reducing conflict, the fleet scheduling decision model is solved, and the scheduling result of mine truck in the underground transportation working face with bidirectional single lane driving characteristics is obtained.The application realizes the intelligent scheduling of underground mine truck fleet by combining state machine and reinforcement learning technology, to improve transportation efficiency, reduce traffic conflict and reduce energy consumption.