559 results about "Single vehicle" patented technology

The invention provides an intelligent traffic vehicle behavior modeling method and system based on collaborative learning, and relates to the technical field of unmanned driving, and the method comprises the steps: obtaining traffic data in a target region; performing data calibration processing according to the traffic data to obtain a collaborative perception data set; a vehicle space-time trajectory function is generated according to the collaborative perception data set, and a conflict domain space-time distribution diagram is obtained; according to the vehicle space-time trajectory function and the conflict domain space-time distribution diagram, probability distribution characteristics of the vehicle driving intention and a multi-workshop competition cooperation relation matrix are extracted, and a multi-dimensional interaction characteristic vector is obtained; and behavior modeling is carried out according to the multi-dimensional interaction feature vectors, and a vehicle behavior model representing interaction behavior rules and decision logic between vehicles is obtained. According to the method, through multi-vehicle cooperative sensing data calibration and dynamic fusion of road geometric constraints, the problems of single-vehicle sensor shielding and cross-vehicle data heterogeneity are effectively solved, and the global consistency of vehicle position and motion state estimation is remarkably improved.

The invention discloses a multi-vehicle cooperative driving control method and system for unmanned vehicles, and relates to the technical field of intelligent driving control, and the method comprises the steps: carrying out the multi-dimensional sensing of a plurality of unmanned vehicles, and obtaining a multi-dimensional sensing data set; environment modeling is carried out, and a dynamic traffic scene model is constructed; multi-vehicle path conflict analysis is carried out through the multi-vehicle cooperation platform, and a first path combination is generated; optimizing the first path combination according to the plurality of single-trajectory tracking results to generate a second path combination; and performing multi-vehicle intelligent cooperative driving control on the unmanned vehicle based on the multi-vehicle cooperative strategy. The technical problems that in the prior art, the environment sensing range of a single vehicle is limited, communication between vehicles is insufficient, complex and changeable traffic scenes are difficult to deal with, and consequently the multi-vehicle cooperative driving efficiency and safety are poor are solved, intelligent control over the unmanned vehicle in the dynamic traffic scene is achieved, and the driving safety is improved. The technical effect of improving the multi-vehicle cooperative driving efficiency and the driving safety is achieved.

The invention discloses an intelligent driving cooperative collision avoidance system and method based on the Internet of Vehicles, and belongs to the technical field of automatic driving. Comprising a vehicle-mounted unit, a road side unit and a service platform, the vehicle-mounted unit is used for collecting vehicle state information and surrounding environment information and sending the vehicle state information and the surrounding environment information to the road side unit and the service platform. The road side unit is used for collecting road condition information, performing target detection based on the surrounding environment information and the road condition information, and obtaining obstacle information; and the service platform is used for evaluating a collision risk according to the vehicle state information and the obstacle information, predicting an optimal collision avoidance strategy based on the collision risk, and sending an instruction to the vehicle-mounted unit to perform collision avoidance control. Potential collision risks can be analyzed based on vehicle state information and road condition information which are shared in real time, and the safety of road traffic is improved. The problem that a traditional collision avoidance system mainly depends on perception and decision of a single vehicle and lacks cooperation with other vehicles and road infrastructures is solved.

The invention relates to a multi-source sensing fusion and dynamic resource allocation method based on vehicle infrastructure cooperation, and belongs to the technical field of mobile communication. The single-vehicle environment sensing is to complete the detection of the surrounding environment by using the sensing data of the single-vehicle environment sensing. However, the problem that heterogeneous data fusion efficiency is low due to the fact that the sensing range of single-vehicle sensing is limited exists. In order to solve the problem, the method comprises the following steps: firstly, constructing a vehicle-road double-layer cooperative sensing network, and improving the accuracy of shelter detection through a multi-source sensor space-time alignment and confidence weighting mechanism; secondly, designing an improved discrete dogvessel swarm optimization algorithm, balancing global exploration and local development by using a chain collaborative search strategy, and improving the perception fusion quality; and finally, developing a mixed action space depth deterministic strategy gradient algorithm, dynamically coordinating the resources of the intelligent network connection vehicle and the road side unit, and reducing the sensing time delay. The scheme is suitable for environmental perception optimization and edge computing resource scheduling of the vehicle in a complex traffic scene, and aims to improve perception fusion efficiency.

The embodiment of the invention relates to the technical field of unmanned vehicles, and discloses a multi-unmanned-vehicle cooperative path planning and scheduling method, device and equipment and a medium. According to the embodiment of the invention, on one hand, the joint search and global path reservation mechanism is introduced, so that the overlapping of the paths of multiple unmanned vehicles in time and space can be relieved fundamentally, the active collision prevention and congestion prevention of the global unmanned vehicles can be realized, and the system safety is improved. And on the other hand, the scheme of the embodiment of the invention can be moved up to the cloud, and the cloud control platform performs centralized decision making, so that the advantages of cloud computing can be inherited, the hardware cost of the single vehicle can be reduced, and the maximum total throughput and the shortest total task completion time of the system under multi-vehicle cooperation can be ensured (different from the target of realizing the shortest path of the single vehicle generally focused in the prior art); the global optimization value of cloud control is brought into play, and intensive and efficient utilization of computing power and road resources is realized.

The invention discloses a time-space alignment-based vehicle-road cloud integrated end-to-end automatic driving device and method, and aims to solve the problems of limitation of a single vehicle intelligent end-to-end algorithm and incompatibility of fusion after vehicle-road collaborative perception. The device comprises a roadside device and a vehicle end, the roadside device extracts roadside features and transmits the roadside features, and the vehicle end compensates transmission delay through a space-time alignment network module, projects the roadside features to a unified BEV space, and inputs the roadside features into a prediction planning network module after dynamic fusion. The method comprises the steps of road side processing, vehicle end processing, space-time alignment, feature fusion and end-to-end planning, and multi-task loss function optimization is adopted. According to the invention, deep fusion of vehicle and road information is realized, heterogeneous sensor scenes are adapted, the vehicle end load is reduced, the long-tail scene sensing and planning precision is improved, and the reliability and safety of automatic driving are enhanced.

The invention relates to the technical field of vehicle scheduling management, and particularly discloses a vehicle scheduling management method and system based on artificial intelligence, which are particularly suitable for decomposition and distribution of complex tasks. The complex tasks are analyzed through an artificial intelligence technology, whether the complex tasks can be independently completed by a single vehicle is judged, and if not, the vehicle scheduling management is completed. The method comprises the following steps of: if the vehicle scheduling task is a task, decomposing the task into a plurality of sub-tasks according to the constraint condition of the task, and identifying an efficient cooperative driver combination by screening the state of the vehicle and the social network of the driver so as to optimally distribute each sub-task, thereby improving the efficiency and accuracy of the vehicle scheduling system. The method is especially suitable for scheduling scenes with high task complexity and uneven resource distribution.

The invention relates to a vehicle cloud collaborative perception decision-making high-speed automatic auxiliary navigation driving method. The method comprises the following steps: S1, a vehicle end sends collected vehicle end data to a cloud end; s2, the cloud generates a first decision instruction based on the vehicle end data of all vehicles and other sensor data; s3, after the vehicle receives the first decision instruction, the current moment is recorded as a second timestamp, and accumulated time delay is calculated; s4, judging whether the accumulated time delay is smaller than a pre-configured time interval threshold value or not, and if yes, executing the step S5; s5, generating a first speed constraint boundary condition according to the instruction content; and S6, the vehicle end takes the first speed constraint boundary condition as one of constraint conditions of the decision model to generate a second decision instruction. Compared with the prior art, the method has the advantages that the problem of low driving safety of high-speed automatic auxiliary navigation caused by limited single vehicle detection distance is solved under the conditions that the vehicle end computing power requirement is hardly increased, the network condition is poor, and particularly the visibility is poor.

The invention discloses a fire early warning method and system based on multi-source information fusion and a medium. The method comprises single vehicle model training, model deployment, fire risk assessment, BIM modeling display and abnormal information notification. According to the fire early warning method and system based on multi-source information fusion and the medium, deep fusion and model training are carried out on the image, positioning and temperature information of the vehicle, the vehicle temperature fault mode can be rapidly and accurately recognized, the accuracy of fire early warning is improved, multi-source information is obtained in real time, and when the vehicle has an overtemperature phenomenon, the fire early warning efficiency is improved. A worker can quickly locate the vehicle and nearby the vehicle, measures can be taken for the overtemperature vehicle in time, fire disasters are prevented or the fire disaster damage degree is reduced, and powerful support is provided for fire prevention and control work. A bridge fire risk map is displayed on a display device or a user terminal by establishing a bridge BIM model, a command department, workers and drivers can quickly grasp information such as a disaster site, a fire reason, a disaster situation degree and a vehicle density degree of a current bridge fire through the bridge fire risk map, and the environment adaptability of the system is high; according to the invention, the fire risk can be evaluated in different environments, the decision-making efficiency of a command department is improved, a worker can quickly arrive at a rescue site to carry out fire extinguishing and rescue work, and a driver in a low-risk area can quickly evacuate from a bridge or assist the worker in personnel evacuation. And casualties and economic losses caused by fire disasters can be reduced as much as possible.

The invention relates to the technical field of traffic monitoring, in particular to a traffic monitoring method and system for vehicle tracking and early warning, and the method comprises the following steps: obtaining the real-time position, speed and acceleration data of all vehicles in a monitored road area, and setting according to the spatial distance between the vehicles and a time window; adjacent vehicles are aggregated into a dynamic traffic microscopic cluster, and a regional motion state reference is established; according to the method, the spatial distance and the time window between the vehicles are set, the dynamic traffic microscopic clusters are divided, refinement and dynamics of vehicle clustering are achieved, and the sensitivity and the response speed of road microscopic traffic environment changes are improved; and deviation calculation and weighted summation operation are carried out on a single vehicle motion vector by using a regional motion state reference, so that accurate quantification of individual driving behavior instability is realized, the abnormal driving state of the vehicle is captured, and the abnormal behavior recognition accuracy is improved.

The invention discloses an automatic driving path planning and tracking control method, which belongs to the technical field of automatic driving and comprises the following steps of: 1, generating a global path and a tracking path according to a current position and a planned target point in a vehicle driving process; 2, constructing a local cost map by adopting a multi-sensor fusion method, carrying out local path planning, and generating a vehicle real-time obstacle avoidance path; 3, evaluating and selecting a tracking path, an obstacle avoidance path and a lane keeping and changing path, and deciding an optimal local path; 4, performing transverse / longitudinal control on the vehicle by using a path tracking transverse / longitudinal control system; the method has the advantages that the tracking path conforming to the kinematics law can be planned for the vehicle, the vehicle can autonomously run according to the set speed and path according to the expected path output by the planning module and under the cooperation of the integrated navigation system, and the problems that in the prior art, single vehicle path planning and tracking control cannot be achieved are solved.

The invention discloses an elastic speed limiting and vehicle distance cooperative regulation and control method and system in an expressway agglomerate fog scene, particularly relates to the technical field of intelligent traffic control, and is used for solving the problem that a secondary accident risk is induced by neglecting a traffic flow phase change characteristic in an existing dynamic speed limiting method. The method comprises the following steps: collecting traffic density, vehicle spacing and visibility space-time distribution data in real time; performing weighted amplification on the traffic density based on the consistency of the visibility gradient direction and the traffic flow direction to generate corrected traffic density; calculating a phase change margin index representing the flow state stability by combining the corrected traffic density and the optical safety deviation degree; when the phase change margin is lower than a threshold value, dynamically constraining the maximum value of the speed-limiting decreasing amplitude according to the agglomerate fog moving trend, and keeping the positive correlation between the maximum value and the phase change margin; generating a target speed limit value based on the constraint decreasing amplitude value, and calculating a safe vehicle distance instruction by fusing the visibility data; and issuing an instruction to the vehicles in the agglomerate fog dynamic influence area to realize collaborative optimization of traffic system stability and single vehicle safety.

The invention discloses a dense warehousing operation optimization system, which takes storage efficiency, warehouse gravity center and goods allocation density as targets, optimizes warehouse goods allocation layout according to the initial goods quantity of a warehouse, reasonably allocates access points in orders, and effectively avoids collision during multi-vehicle collaborative operation while ensuring the safety and high-efficiency storage of the warehouse. The task allocation between the shuttle vehicle and the elevator is reasonably scheduled by taking the minimum order completion time and energy consumption as the optimization target, and the efficient utilization of resources and the balanced distribution of operation loads are ensured, so that the phenomenon that a single vehicle is excessively busy or idle is reduced; according to the method, the shortest path and vehicle avoidance are taken as targets, an intelligent algorithm is adopted to plan an optimal driving route for each vehicle, a lengthy path is avoided, the collision risk and the congestion phenomenon between the vehicles are reduced, and a dynamic avoidance strategy of multi-vehicle collaborative operation is considered in path planning so as to ensure that the vehicles can efficiently complete tasks in a dense operation environment.

The invention relates to a plug-in hybrid electric vehicle hierarchical energy management method based on historical data and real-time speed prediction, and belongs to the technical field of new energy automobiles, and the method comprises the following steps: S1, obtaining and preprocessing historical commuting data of a single vehicle, establishing a historical commuting data set, and predicting a future global driving condition; s2, constructing a vehicle part simplified model, and solving an optimal reference SOC track by using a dynamic programming algorithm; s3, constructing a real-time speed predictor by using a neural network to realize real-time speed prediction; and S4, optimizing a real-time speed prediction result in combination with the optimal reference SOC trajectory, and realizing energy management by using a PMP-MPC algorithm.

The invention, which relates to the automatic driving field, discloses a Transform-based networked automatic driving vehicle trajectory prediction method comprising the following steps: obtaining vehicle driving data and preprocessing the vehicle driving data to obtain time sequence information of each vehicle; a context vector of each vehicle is obtained by using a Transform encoder; inputting the context vector of each vehicle into a graph neural network to obtain an overall feature context vector I; obtaining a driving intention probability matrix of the current vehicle by using a random forest algorithm; splicing the driving intention probability matrix of the current vehicle and the overall feature context vector I, inputting the spliced driving intention probability matrix and the spliced overall feature context vector I into a decoder, and finally outputting the trajectory distribution probability of the vehicle; through combination of Transformer and GNN, not only can the dynamic state of a single vehicle be understood, but also the relative position and motion relation between vehicles can be understood, so that the vehicle trajectory can be predicted more accurately.

The invention discloses a bridge expansion joint abnormity diagnosis method and system based on voiceprint recognition and deep learning, and the method comprises the steps: arranging sensors on the bridge surface and under the bridge of an expansion joint, and obtaining the video data of a vehicle passing through the expansion joint and the audio data of the vehicle passing through the expansion joint; constructing a vehicle seam crossing discrimination model based on a neural network, inputting the video data of the vehicle passing through the expansion joint into the vehicle seam crossing discrimination model, and detecting a vehicle seam crossing scene; identifying a single vehicle seam crossing event in the vehicle seam crossing scene by adopting a data cleaning algorithm; cutting from the audio data of the expansion joint passing of the vehicle to obtain a bicycle seam passing audio of a time period corresponding to the bicycle seam passing event; extracting a Mel spectrogram of a single-vehicle seam-crossing audio frequency, and carrying out feature coding on the Mel spectrogram to obtain an enhanced Mel spectrogram; and constructing an expansion joint anomaly diagnosis model based on an unsupervised linear self-encoding network, inputting the high-dimensional Mel spectrum feature vector of the enhanced Mel spectrum into the expansion joint anomaly diagnosis model, and identifying the abnormal state of the expansion joint.

The invention discloses a multi-source fusion real-time traffic perception and emergency early warning system and method based on vehicle and road cloud cooperation, and relates to the technical field of traffic management. The vehicle end system comprises an information acquisition and transmission module, a local decision module, a data receiving module, an emergency response module and a data feedback module, and the information acquisition and transmission module acquires vehicle peripheral information through a vehicle-mounted radar and a vehicle-mounted camera. According to the invention, a vehicle end collects local information such as obstacles around a vehicle and dynamic states of adjacent vehicles through a vehicle-mounted radar and a camera, a road end collects regional information such as road section traffic flow density, road damage and traffic facility states through a roadside camera and a sensor, and a cloud end integrates data at the two ends and carries out time-space synchronization through a multi-source fusion technology. The defect of a single vehicle end or road end sensing blind area is overcome, and complete sensing of the traffic state of the whole road network is achieved.

The invention discloses a park access control system and method based on artificial intelligence, and the method comprises the steps: carrying out the license plate recognition and color classification of a visited vehicle through a license plate recognition model based on an artificial intelligence algorithm, obtaining a license plate number and a vehicle type, and judging the passing authority type of the visited vehicle according to the license plate number; constructing a carbon emission measuring and calculating model according to the vehicle type, and analyzing the carbon emission of the vehicle entering the park by using the carbon emission measuring and calculating model to obtain carbon emission data; according to the carbon emission data, counting the total carbon emission amount of each vehicle entering the park in a preset period, marking the vehicle of which the difference value between the total carbon emission amount of each vehicle and a preset carbon emission limit is smaller than a preset threshold value as a limited vehicle, and sending an early warning signal; and making a passing decision for an external vehicle according to the vehicle type and the carbon emission capacity of the current day. The invention relates to the technical field of deep learning, and solves the technical problem that a traditional access control system cannot accurately identify vehicle types and cannot make intelligent traffic decisions according to vehicle carbon emission.

The invention discloses a traffic jam state analysis method and system based on a dynamic pixel scale. The traffic jam state analysis method comprises the following steps: initializing and loading a model; acquiring a video frame and carrying out target detection; tracking multiple targets; traversing a tracking target and calculating the speed of a single vehicle; traversal of the tracking target and calculation of the single vehicle speed comprise the following steps of obtaining the current position, calculating the pixel displacement of the center point of the bounding box, dynamically estimating a pixel scale, calculating the real displacement and speed, outputting the smooth speed, and updating position information. Through a low-cost and high-precision video analysis method, on the premise that complex manual camera calibration is not needed, self-adaptive estimation of the vehicle speed on a road is achieved, and the vehicle congestion state is accurately judged in real time on the basis of the self-adaptive estimation.

The invention discloses a vehicle group energy collaborative sharing path planning method based on a space-time energy situation matrix, and belongs to the field of vehicle path planning. The method comprises the following steps: performing path planning on each single vehicle in a vehicle group based on a time window constraint of a distribution point, a vehicle dynamics constraint and an energy supply and demand balance condition to obtain a global optimal path of all vehicles in the vehicle group; constructing an energy situation four-dimensional matrix based on the optimal paths of all vehicles; calculating vehicle trajectory similarity based on the energy situation four-dimensional matrix to obtain a vehicle combination with high trajectory similarity; and performing energy transmission and path speed collaborative optimization on the vehicle combination with high trajectory similarity to obtain an optimized final planning path.

The invention discloses a double-lane road local road section single-lane alternate traffic signal dynamic timing method. The method comprises the following steps: determining a local road section traffic influence range of an event; collecting traffic flow data, and analyzing traffic flow characteristics; calculating a delay-based small vehicle equivalent quantity, and calculating a large vehicle adverse impact factor; constructing a signal dynamic timing method for alternate passage of road sections affected by double-lane road events; according to the method, a signal timing optimization model based on dynamic traffic flow characteristics is constructed, traffic flow data, including actual passing time, vehicle model proportion, actual arrival traffic volume and the like, of an event influence road section are collected in real time according to a special scene of an alternate passing road section of a double-lane road, and a signal period and a phase timing scheme are dynamically adjusted. The method can quickly adapt to the traffic fluctuation of the alternate passage road section, reduces the vehicle delay under manual command, improves the vehicle passage efficiency, and enhances the flexibility and dynamic adaptability of the signal timing of the alternate passage road section of the double-lane road.

The invention discloses an automatic driving vehicle test scene generation method, system and device and a medium, and relates to the technical field of dangerous scene generation, and the method comprises the steps: generating an initial scene based on a lane coordinate system; building a one-way single-lane car-following scene; the method specifically comprises: creating roads and vehicles based on an initial scene; the vehicle comprises an automatic driving vehicle and an intelligent body vehicle based on the IDM model; building a one-way double-lane vehicle switching scene; the method specifically comprises the steps of creating double lanes and vehicles based on an initial scene; the vehicle comprises an automatic driving vehicle and an intelligent body vehicle based on the IDM model; according to the established one-way single-lane car-following scene and the one-way double-lane car-switching scene, generating a dangerous scene for testing the automatic driving vehicle; according to the method, the dangerous scene of the automatic driving system is efficiently and accurately generated, so that the test is accelerated, and the performance and the safety of the automatic driving system in a complex and high-risk environment are verified.

The invention relates to the technical field of intelligent traffic and carbon treatment, in particular to a passenger car carbon emission monitoring and regulation system and method based on multi-source spatio-temporal data. According to the method, the multi-source spatio-temporal data is preprocessed and uniformly mapped, so that the problem of data scattering and irregularity is solved. And a vehicle-space unit-time period three-dimensional carbon emission calculation model is constructed, and accurate calculation of carbon emission of single-vehicle single-trip and high-carbon emission hot spot positioning are realized. And finishing differentiated division of the user groups by adopting a clustering algorithm. A dynamic early warning threshold value is generated based on an early warning threshold value prediction model, a regional carbon emission target and historical data, and false alarm and missing alarm are reduced. An early warning type-policy tool-adaptive object mapping rule base is established, the regulation and control strategy is generated in combination with early warning signals and real-time data, and automatic and accurate generation from problem recognition to the regulation and control strategy is achieved. Policy execution effect data is utilized to optimize model parameters, early warning threshold values and mapping rules, monitoring, early warning, regulation and control are formed, closed-loop management is optimized, and precise regulation and control are achieved.

The invention discloses a bridge dynamic weighing identification method based on a time sequence deep learning network, and the method comprises the steps: carrying out the data statistics, and obtaining a vehicle load spectrum; according to the vehicle load spectrum, using a Monte Carlo random sampling technology to extract a vehicle information sample, and forming random traffic flow information; establishing and correcting a bridge finite element model; according to vehicle information of a single vehicle in the random traffic flow, an axle coupling model is established based on the corrected bridge finite element model, bridge response of vehicle bridge crossing is simulated by using the axle coupling model, and a vehicle information mapping bridge response data set is established; building a neural network framework, and training a neural network based on the vehicle information mapping bridge response data set to obtain a vehicle information identification agent model; and identifying vehicle information from the actually measured vehicle-induced bridge response by using the proxy model. According to the method, the vehicle information can be accurately estimated from the vehicle-caused bridge displacement, and the vehicle space coordinate information does not need to be acquired priori.

The invention relates to the field of automatic driving, and discloses a bicycle intelligent automatic driving control system, which is configured to control each execution mechanism to complete actions through an action decision control module of the bicycle intelligent automatic driving control system, and the action decision control module comprises a map center line processing unit configured to obtain an initial map center line and generate a local planning track; a lateral control unit configured to construct and use a lateral predictive control model to output a lateral control signal; the longitudinal control unit is configured to output a speed predicted value by using a speed model established based on the acceleration response of the single vehicle under a preset condition; based on the deviation between the speed predicted value and the current actual state of the bicycle, estimating the current disturbing force of the bicycle; and calculating an expected accelerator force and an expected brake force for speed control according to the speed prediction value and the estimated current disturbance force. The problem that the steering wheel shakes due to the fact that the track is not smooth can be avoided, and the running stability and riding comfort of the vehicle are remarkably improved.

The invention relates to the technical field of intelligent traffic, in particular to a large-scale road network inter-region hot spot path identification method, which comprises the following steps: dividing a target road network into a plurality of regions, obtaining license plate identification data of a period on the target road network, extracting a single vehicle OD travel according to the license plate identification data, and screening out a cross-region single vehicle OD travel as to-be-analyzed data; grouping the to-be-analyzed data, wherein each group of data comprises a cross-regional single-vehicle OD travel in the same starting and ending region and in the same preset time interval; performing simplification and similarity calculation on each group of data to obtain a representative path of the data to be analyzed; aggregating the representative paths of the to-be-analyzed data to obtain a representative path list; and according to the representative path list, obtaining an occurrence frequency of each representative path, and according to the occurrence frequency, determining an inter-region hotspot path in the target road network. According to the method and the device, the hot spot paths among the regions can be efficiently, correctly and intelligently mined.

The invention discloses an expressway traffic flow speed prediction method considering motion state difference between vehicles and a hysteresis effect. The specific prediction method comprises the following six steps: step 1, calculating the disorder of the traffic flow caused by a single vehicle; 2, road unit distribution is carried out on disorder generated by a single vehicle to traffic flow; 3, calculating the total disorder of all road units; 4, constructing a hysteresis effect model; 5, determining the hysteresis phase number and coefficient of the hysteresis effect model; and 6, predicting the traffic flow speed. According to the method, the real-time track information of the vehicle is collected and the traffic flow speed is predicted according to the intelligent degree of the expressway assisted by detection equipment such as an induction coil and a vehicle-mounted GPS, so that traffic control measures can be taken in time, the purpose of preventing and relieving congestion is achieved, the operation efficiency and safety of a traffic system are improved, and the traffic safety is improved. And theoretical basis and model support are provided for expressway traffic flow speed prediction and traffic flow organization and control.

The invention provides an intelligent mining production management and control method based on an ADP algorithm, and relates to the technical field of intelligent mining, and the method comprises the steps: firstly obtaining vehicle data and wrong vehicle point data, carrying out the road division of a two-way single lane with a wrong vehicle point as the end points of the two ends of a road section, and carrying out the position judgment and display of a running vehicle in the two-way single lane; judging passing priorities of the vehicles, and checking whether vehicles running in opposite directions exist in adjacent road sections of the current vehicle or not; carrying out data preprocessing on the collected mine car scheduling data, dividing the data into a training data set and a test data set, carrying out classification processing on the preprocessed data, and establishing a classification model; identifying and dividing a naturally formed area of the underground mining area operation area by adopting a clustering algorithm; establishing a nonlinear system model, establishing a performance index, comprehensively considering transportation efficiency, cost saving and time benefit, measuring the effect of a scheduling strategy, and setting an optimization target; and establishing an ADP algorithm, and outputting an optimal scheduling scheme.

The invention discloses a vehicle-mounted millimeter-wave radar self-calibration method based on target tracking, and the main conception is that a calibration process is automatically activated according to the driving speed of a vehicle and the tracking effect of a front vehicle, and the trajectory point cloud direction of a single lane or multiple lanes is fitted based on the linear features formed by the trajectory point cloud of the front tracked vehicle. Therefore, the millimeter-wave radar deviation value of the vehicle is judged. According to the method, the characteristic of the millimeter wave radar good at dynamic target detection is effectively utilized, and radar calibration is realized by tracking the vehicle running in front, so that the problem of static target detection is effectively avoided. On one hand, the method does not need to depend on any fixed facility, enables the calibration process not to be limited by a specific environment or facility, and can smoothly carry out the calibration task in various complex and changeable roads and traffic environments. And through flexible and abundant front vehicle tracking scenes, the calibration process is more visual and easy to trigger, the computing power is saved, and the operation convenience and the intelligent level are greatly improved.

The invention provides a vehicle cluster multi-source fusion cooperative positioning method based on information geometry, and the method comprises the steps: firstly constructing a vehicle cluster heterogeneous multi-source fusion cooperative positioning system model, and obtaining navigation source information density functions of all vehicles; carrying out fusion calculation on a vehicle navigation source information density function, and respectively obtaining single-vehicle multi-navigation-source fusion positioning results of the to-be-positioned vehicle and the cooperative vehicle and corresponding three-dimensional covariance matrixes; and finally, constructing a vehicle cluster factor graph co-location model through distance and direction finding information between the to-be-located vehicle and the co-location vehicle, obtaining an internal factor graph model of the to-be-located vehicle, and carrying out confidence coefficient information iteration updating to obtain a multi-source fusion co-location result of the to-be-located vehicle and the co-location vehicle. According to the method, heterogeneous navigation source data is converted into a unified information probability function form, navigation source fusion is realized by using a theoretical framework of information geometry, the vehicle cluster positioning precision and positioning speed are improved, and the large-scale vehicle cluster navigation positioning requirements in a complex environment are met.