361 results about "Single vehicle" patented technology

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 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 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 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 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 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 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 provides an expressway lane dynamic optimization management method, system and device and a medium, and belongs to the technical field of intelligent traffic management. The method comprises the following steps: firstly, collecting traffic flow, environment and vehicle credit data through high-speed key node multi-source sensing equipment and a data center, and fusing to generate a time-space fusion data set; inputting the data into a space-time diagram convolution network model, dynamically mapping a road topology into a variable weight directed graph, and generating future lane-level traffic flow, vehicle speed and occupancy rate prediction data through convolution operation; inputting the prediction data into a hierarchical reinforcement learning model, generating a single-vehicle differential management and control instruction in combination with vehicle credit data, and determining an optimal strategy through digital twinborn simulation; then, a modularized lane controller is used for executing a strategy and collecting feedback data; and finally, inputting feedback data into a causal inference engine to analyze the effect, optimizing the reinforcement learning model, and dynamically updating credit data according to vehicle behavior data.

The invention relates to the technical field of intelligent traffic, in particular to a traffic abnormal event dynamic detection method and system based on trajectory analysis, and the method comprises the steps: receiving original space-time trajectory data; preprocessing the original spatio-temporal trajectory data, and mapping the original spatio-temporal trajectory data to a road section sequence of a road network through a map matching algorithm; constructing a behavior model based on the track sequence after map matching, learning the track sequence of the normal behavior mode on the road section, and establishing an observation emission model and a state transition matrix; in the online stage, the log-likelihood value of an observation sequence and / or the similarity between the observation sequence and a normal behavior pattern cluster are / is calculated for a real-time track in a set sliding window, and when the log-likelihood value and / or the similarity exceed a set threshold value, the track is marked as abnormal; performing time-space aggregation on abnormal trajectories of the same road section or intersection in unit time according to single vehicle abnormality judgment, and triggering group abnormal event alarm when aggregation data exceed a preset value.

The invention relates to a vehicle lamp control method, computer equipment and a computer readable storage medium, and relates to the technical field of vehicle light control, and the method is applied to a server, and comprises the steps: generating a single vehicle control file of each vehicle participating in a vehicle lamp combined control scene according to the global vehicle lamp configuration information of each frame of picture of the vehicle lamp combined control scene; wherein the single vehicle control file of each vehicle comprises local vehicle lamp configuration information, corresponding to each frame of picture, of the vehicle, and the local vehicle lamp configuration information of different frames of pictures is arranged according to a picture presentation time sequence of the vehicle lamp combined control scene; and sending the corresponding bicycle control file to each bicycle, so that the bicycle performs bicycle lamp control according to the local bicycle lamp configuration information in the corresponding bicycle control file and the global time parameter of the bicycle lamp joint control scene. By adopting the method, the real-time network quality of the vehicle lamp combined control scene performance is not depended on, the requirements on network delay and network bandwidth are reduced, and the performance effect of the vehicle lamp combined control scene is improved.

The invention discloses a traffic information real-time path planning system and method for vehicle-road cooperation. Comprising a vehicle-road cooperative perception and information fusion module, a vehicle-road cooperative communication and information interaction module, a dynamic traffic state analysis and prediction module, a multi-target real-time path planning module, a path dynamic correction and obstacle avoidance decision module and a vehicle control and execution feedback module. According to the method, vehicle-mounted and roadside data are fused through vehicle-road collaborative sensing, a non-line-of-sight blind area is covered, the limitation of single vehicle sensing is solved, and the integrity of environment information is improved; based on a short-term traffic prediction and real-time correction mechanism, path planning can be dynamically adjusted along with traffic state changes, and the problem that a planned path is disjointed from the reality is avoided.

The invention relates to an airport snow sweeper cooperative formation control method, electronic equipment and a medium, and the method comprises the steps: building a vehicle transverse and longitudinal coupling kinematic model in an airport snow removal environment based on environmental parameters, the vehicle transverse and longitudinal coupling kinematic model comprises transverse and longitudinal physical quantity cross terms, enabling the longitudinal speed to affect the transverse posture change, and enabling the longitudinal speed to affect the transverse posture change; the transverse path characteristics and the control input restrict the longitudinal motion; constructing a formation constraint model based on the vehicle transverse and longitudinal coupling kinematics model; constructing a distributed communication topology model based on a directed graph and an adjacent matrix; constructing a single vehicle local optimization control problem model; and iteratively solving the single vehicle local optimization control problem model based on a path guiding type distributed model predictive control algorithm to obtain the optimal control input of each vehicle. According to the method, control input can be optimized while accurate path tracking of the snow sweeper is ensured, energy consumption and cost are reduced, and collaboration, safety and efficiency of snow removal operation of an airport can be improved.

The invention discloses an intelligent network connection vehicle formation distributed cooperative control method, system and device based on mode switching, a vehicle driving mode is divided into a single vehicle cruising mode and a formation following mode, and in the single vehicle cruising mode, a driving risk field model and model prediction control are utilized to enable a vehicle to move along a risk minimum path; and in the formation following mode, the safe distance between each vehicle and the front vehicle is kept through distributed model prediction control, and stable following motion is realized. Meanwhile, a state set is constructed in combination with a sensor and the Internet of Vehicles, the collision risk of adjacent lanes is detected in real time based on risk assessment of a fuzzy theory, and when the risk reaches a threshold value, the vehicle is converted from following to single vehicle cruising; after the queue is divided into two sub-queues, the front pilot car generates an attraction potential field to the rear pilot car, the two sub-queues gradually approach each other, and when the following condition is met, the rear pilot car is converted from single car cruising to formation following. And finally, based on a finite-state machine thought, a cooperative control system is constructed to flexibly adjust the driving mode.

The invention discloses a hybrid electric vehicle energy distribution method based on dynamic load prediction, which is applied to the field of vehicle power management. According to the scheme, a vehicle-mounted central control system is connected with a roadside traffic system to acquire information equipment to form a data transfer library; according to the method, driving records of other vehicles which are highly matched with the driving condition of the vehicle on the same road condition can be obtained through the data transfer library to serve as references so as to predict the load on an unfamiliar road condition, prediction accuracy is higher, energy distribution is optimized, the problems that in the prior art, load response is lagged, and adaptability to the unfamiliar road condition is poor are solved, and the load prediction efficiency is improved. In addition, for the whole congested road condition, by establishing vehicle-vehicle direct connection temporary data communication, vehicles in all road conditions can share driving data backwards, sudden braking and sudden acceleration caused by blind following of a single vehicle are avoided, the overall fuel consumption rate of the road conditions is reduced, optimization of mass energy distribution is achieved, and meanwhile engine abrasion is reduced.

The invention discloses a holographic road surface state modeling and cloud sharing method and system based on an intelligent tire group, is suitable for feedforward control of an intelligent chassis of a vehicle, and belongs to the technical field of vehicle engineering.The method comprises the steps that an original signal output by an intelligent tire sensor, a vehicle motion state signal and a positioning signal are collected; calculating the collected data through a vehicle-mounted edge calculation unit to obtain road surface elevation value information, and uploading the road surface elevation value information to a cloud end; the cloud server receives and processes the data uploaded by the vehicle end, constructs a holographic road surface, responds to a request of the vehicle end, and issues corresponding road surface information; and the vehicle end sends a road surface demand, receives a road surface elevation value information data sequence issued by the cloud end, and assists the vehicle to carry out dynamic feed-forward control. The method provided by the invention can accurately collect road surface state information, break a single vehicle data island, realize road surface state information sharing, and assist the vehicle to carry out intelligent chassis feed-forward control.

This invention discloses a method and apparatus for vehicle trajectory tracking and false trajectory identification based on multiple geomagnetic sensors. The method includes: geomagnetic sensors deployed on both sides of a road acquiring data of passing vehicles; a base station storing and cleaning the location of the geomagnetic sensors and the vehicle data acquired by those sensors, and packaging and sending the data to a data processing center at fixed time intervals; within a fixed time period, the data processing center processes the received vehicle data acquired by the geomagnetic sensors to generate a set of vehicle trajectories; based on the time difference between adjacent vehicle trajectories passing the same geomagnetic sensor, it determines whether there is a false trajectory among adjacent vehicle trajectories. If so, the adjacent vehicle trajectories are weighted and merged into a single vehicle trajectory, the vehicle trajectory set is updated, and the updated vehicle trajectory set is output; if not, the vehicle trajectory set is output. This invention can output reliable vehicle trajectory information.

The invention provides a fuel cell automobile energy management strategy fused with traffic perception, which constructs a future speed prediction model based on a Transform network, can effectively represent a dynamic interaction relationship between a main automobile and a plurality of surrounding automobiles, and has higher accuracy and stability compared with a traditional automobile speed prediction mode based on historical data or single automobile information; a deep integration model structure is also introduced for speed prediction, the uncertainty of a prediction result can be quantified, and good prediction credibility judgment and output adaptive adjustment can be realized in combination with a dynamic threshold mechanism, so that the robustness of the method in a complex traffic scene can be remarkably improved; and a reinforcement learning energy management strategy is constructed based on the predicted future speed sequence, so that the future traffic change can be perceived in advance and quickly coped, and the method has obvious advantages in the aspect of fuel economy.

The invention relates to the field of vehicle safe driving, in particular to a collision risk detection method and device. According to the invention, through receiving the traversing target motion data and the vehicle position uploaded by each vehicle in the control area, the cooperative sensing network is formed by means of the detection range coverage of multiple vehicles, when a certain vehicle has a sensor blind area due to the shielding of a static vehicle, other vehicles can upload the detected traversing target information to the cloud server, so that the information of the traversing target can be transmitted to the cloud server. Target omission of a single vehicle due to view limitation is avoided; the cloud server constructs a dynamic target sequence based on multi-source data, can more comprehensively track the movement track of the traversing target, analyzes the collision risk in combination with the position of each vehicle, can pre-judge the threat of the traversing target to the vehicle through the information of other vehicles when the traversing target is located in a certain vehicle blind area, and then carries out control operation on the corresponding vehicle in time. The problems that a blind area exists in a sensor, and when a static vehicle exists in the view field, the vehicle cannot find the crossing pedestrian in time, and injuries are easily caused are effectively solved.

The invention provides a parking path planning method and device of a semitrailer and terminal equipment. The method comprises the steps that the initial vehicle state, the target parking position and the parking area of the semitrailer are obtained; converting the semitrailer system model corresponding to the semitrailer into an equivalent single vehicle model; wherein the trailer rear axle center of the semitrailer is determined as the rear axle center of the single-vehicle model, the center distance between the hinged point of the tractor of the semitrailer and the trailer and the rear axle center of the trailer is determined as the single-vehicle axle distance in the single-vehicle model, and the tractor is determined as the single-vehicle wheel of the single-vehicle model; determining a model node path of the single vehicle model in the parking area based on a mixed A * algorithm by using the initial vehicle state and the target parking position; and determining the parking path of the tractor according to the model node path, the corresponding relationship between the semitrailer system model and the single vehicle model and the kinematics relationship between the tractor and the trailer. According to the method, the parking safety and efficiency of the semitrailer can be improved.

The invention relates to a self-supervised multi-modal fusion and collaborative optimization method suitable for a curve and ramp scene. The method comprises the following steps: acquiring an original perception image; generating four paths of geometrically consistent recovery images; constructing a multi-modal fusion model, and generating a depth confidence map aligned with the restored image; uniform ground plane parameters are obtained; collecting all coordinate points to construct an initial 3D lane candidate point set; generating a high-density locally enhanced BEV lane representation; and outputting the fused global consistency 3D lane map. And outputting the structured 3D lane line marking data. Non-planar structures such as curve superelevation and longitudinal ramps can be accurately depicted, and geometric consistency in a complex road scene is greatly improved; the problems of single vehicle shielding, sensor noise and the like are effectively relieved, the precision, the integrity and the system-level reliability of 3D lane reconstruction are remarkably improved, meanwhile, dependence on a high-precision map or 3D true value marking is avoided, and the method has high engineering landing value.

The invention relates to the technical field of intelligent traffic and automatic driving, in particular to a vehicle obstacle avoidance method and device, a vehicle and a storage medium. Identifying attribute information of at least one target obstacle according to the pavement environment information, and determining a risk level corresponding to each target obstacle based on the attribute information of each target obstacle; and determining a target obstacle avoidance strategy according to the risk level corresponding to each target obstacle, and controlling the current vehicle to perform obstacle avoidance operation based on the target obstacle avoidance strategy. Therefore, the problems of recognition lag, high false report and missing report rate, limited single vehicle sensing range, untimely data sharing and the like in the existing road surface obstacle detection technology are solved, and remote and high-precision road surface obstacle recognition and real-time information sharing are realized through multi-source data fusion and vehicle-road cooperation. The more intelligent and humanized driving care experience is realized, and the user experience is improved.

According to the method for evaluating the urban trunk traffic signal control effect based on the single-vehicle theory provided by the invention, the obtained gate data is used for deducing to obtain a time theoretical value sequence for all vehicles on a certain lane to arrive at a downstream intersection within a certain time period needing phase positioning; and further obtaining a green light phase arrival proportion and an upstream intersection signal control phase evaluation index. According to the evaluation method provided by the invention, the Platon Ratio index calculation model which is more suitable for the actual situation and is based on the bicycle identification data is provided; the theoretical arrival time of the vehicle arriving at the parking line at the intersection is deduced by using the vehicle matching characteristics of the single vehicle identification data, and the new model has practical guiding significance; the TAD trunk line coordination evaluation index based on the single vehicle identification data is provided, the PCD index for evaluating the trunk line coordination control effect provided in the prior art and a calculation method are improved, the limitation of the PCD index in domestic application is improved, and the method has more practical guiding significance in China.

A fire early warning method based on multi-source information fusion, a system, and a medium. The method comprises single vehicle model training, model deployment, fire risk assessment, BIM display and anomaly information notification. In the fire early warning method based on multi-source information fusion, the system, and the medium, a vehicle temperature fault mode can be quickly and accurately identified by performing deep fusion and model training on image and location and temperature information of the vehicle, thereby improving the accuracy of fire early warning; and multi-source information is acquired in real time, such that when over temperature occurs in the vehicle, workers can quickly localize the vehicle and the vicinity thereof, so that measures can be taken promptly for the over-temperature vehicle, thereby preventing the occurrence of fires or reducing the severity of fire damage, and providing strong support for fire prevention and control work. By establishing a bridge BIM model, a bridge fire risk map is displayed on a display device or a user terminal, and a command department, workers and drivers can quickly ascertain information regarding the location, cause, severity, and vehicle density of a current bridge fire by means of the bridge fire risk map; the system has high environment adaptability, fire risk assessment can be carried out in different environments, the decision-making efficiency of the command department is improved, the workers can quickly arrive at a rescue site to carry out firefighting and rescue operations, and drivers in low-risk areas can quickly evacuate from the bridge or assist the workers in evacuating others, thereby reducing casualties and economic losses caused by fires as much as possible.

The invention relates to the technical field of vehicle intelligent management, in particular to a fault data driving analysis method and system for a new energy commercial vehicle, and the method comprises the steps: collecting vehicle operation data, cloud environment data and external working condition data to form multi-source time series data; preprocessing the data and generating a sliding time window data set; basic features, time-frequency domain features and environment coupling features are extracted from the data set; splicing the features into a sequence, inputting the sequence into a pre-trained anomaly detection model, outputting an anomaly score, and triggering early warning according to a preset rule; and analyzing the contribution degree of each feature to the exception score, and performing rule matching with a single-vehicle fault mode library in which a fault evolution path is stored to obtain a predicted exception type and generate a diagnosis report. According to the invention, accurate detection and real-time early warning of low-frequency gradual change faults under complex working conditions can be realized, an interpretable diagnosis report is generated, and the maintenance decision-making efficiency is improved.

The application discloses an AGV multi-vehicle linkage control system and method, which is composed of multiple independent AGV electrical control systems, each of which comprises a controller, a remote controller, a driver and a motor. The controller is provided with multiple interfaces for being connected with upstream and downstream AGV systems, drivers and remote controllers. A single AGV or multiple AGVs can be controlled through a selection switch remote controller. In a single-vehicle mode, a connection line is cut off and a single-vehicle control gear is selected; in a multi-vehicle linkage mode, the connection line is connected and a multi-vehicle linkage gear is selected, so that synchronous control is realized. The application can not only realize single-vehicle motion control, but also realize multi-vehicle linkage control after two or more AGVs are connected in a head-to-tail mode through a butt joint cable. The application effectively solves the problem of linkage control of two or more AGVs and is suitable for transferring loads with super large tonnage. In addition, a wireless communication network does not need to be established in a use site, so that production cost is reduced.

The invention discloses a parking control method and electronic equipment, and relates to the technical field of vehicles. The method comprises the steps that in response to an automatic parking request sent by a target vehicle, vehicle end sensing data collected by the target vehicle and road end sensing data collected by road end equipment in a parking area corresponding to the automatic parking request are obtained; performing fusion processing on the vehicle end sensing data and the road end sensing data to generate global environment information corresponding to the parking area; based on the global environment information, distributing a target parking space for the target vehicle, and planning a global parking path leading to the target parking space; performing cooperative scheduling by using the global parking path and the real-time state information of the associated vehicle, and generating a cooperative control instruction; and sending the global parking path and the cooperative control instruction to the target vehicle to control the target vehicle to perform automatic parking. The technical problems that the view field is limited and the cooperation efficiency is low due to the fact that an existing automatic parking technology depends on bicycle perception are solved.

The invention provides a path planning and management system for enterprise logistics scheduling, and the system comprises a vehicle resource database which is used for storing and managing the resource information of vehicles in an enterprise, and the resource information comprises a vehicle ID, a vehicle type, a load upper limit, an energy consumption type, and a real-time state; the task management module is used for receiving logistics transportation tasks, and the tasks comprise cargo information, starting places, destinations and time requirements; the scheduling decision module is configured to match vehicles from the vehicle resource database according to the task information; for a task of which the traffic volume exceeds the load of a single vehicle, automatically splitting the task into a plurality of sub-tasks and distributing a vehicle for each sub-task to generate a multi-vehicle collaborative transportation scheme; and for the matched vehicle, calling a path optimization method to generate a path plan.

The invention discloses a parking identification method and system based on a multi-modal fusion identification curbstone machine, and belongs to the technical field of parking management, and the method comprises the following steps: detecting a vehicle entry event through a built-in sensor of the curbstone machine, and triggering the collection of sensing data; when the adjacent curbstone machines detect vehicles within similar time, a collaborative sensing network is automatically established; each tooth machine broadcasts the acquired stereo image and point cloud data to a collaborative awareness network in combination with geographic position codes, carries out fusion, reconstructs a three-dimensional scene model, and analyzes and judges the number of actual vehicle units through a connected domain; and if the vehicle is a single vehicle, selecting an optimal recognition unit to execute a license plate recognition task according to the relationship between the three-dimensional contour of the vehicle and the spatial position of the curbstone machine. According to the invention, the identification problem during cross-position parking of the vehicle is effectively solved, repeated charging or missing detection is avoided, and the accuracy of license plate identification and the charging reliability in a complex scene are improved.