1148 results about "Speed limit" patented technology

The invention discloses an expressway emergency management and control method and system, and the method comprises the steps: building a multi-dimensional heterogeneous data fusion model through a time-space diagram neural network according to the traffic flow, meteorological data and accident reports collected by roadside sensing equipment, and carrying out the dynamic prediction of the risk level of a road segment, and obtaining a real-time risk level map of a whole road network; based on the real-time risk level map, outputting a dynamic shunting scheme containing variable lane marks and a speed limiting strategy; according to the dynamic shunting scheme, generating a vehicle trajectory optimization strategy; and outputting an anti-interference optimization control strategy based on a vehicle trajectory optimization strategy in combination with sudden obstacle data polled by the unmanned aerial vehicle in real time. According to the embodiment of the invention, rapid and accurate path adjustment and cooperative control can be carried out, and the emergency management level of the expressway is improved.

The invention discloses a container wharf hazardous chemical transport vehicle intelligent tracking and positioning system comprising a data acquisition module used for acquiring vehicle positioning, driving behaviors and environment state data; the data fusion and time synchronization module is used for performing time alignment on the multi-source data; the behavior modeling and trajectory prediction module is used for extracting driving behavior characteristics and predicting a driving trajectory; the risk assessment and anomaly detection module is used for calculating an anomaly score and predicting a driving risk; the security strategy optimization module is used for constructing a zero-sum game security control model and optimizing speed limit, path and early warning strategies; the remote control and intelligent intervention module is used for executing remote speed regulation, emergency braking, path optimization and alarm pushing in the intelligent dispatching system; and the cloud monitoring and data analysis module is used for storing a track, an environment, a driving score and a safety strategy and predicting a long-term risk trend. According to the invention, based on intelligent game and multi-modal fusion, accurate tracking and safety management and control of the hazardous chemical substance transport vehicle are realized.

In various examples, systems and methods of the present disclosure correspond to speed limit fusion for automotive systems and applications. Systems and methods are disclosed that fuse speed limit data from both one or more map sources (e.g., nav-maps, HD maps, SD maps, etc.) and a perception system, and that employ rules to help resolve conflicts in speed limits from the perception system and the map(s). For example, if a conflict occurs, the rules may indicate whether to use the speed limit data from the perception data, the speed limit data from the map(s), and / or whether to use another type of speed limit data, such as cached speed limit data.

The invention discloses a train control method and a train control system based on credible interval entry check. The train control method comprises the following steps: starting a backup command; the temporary speed limit server starts a sending module used for sending authorization to a train self-protection system; performing interval credible entry check; determining the existence condition of the train in the interval; the temporary speed limit server updates a train existence condition list in the interval; the train obtains a front driving permission; the train runs according to the running permission. According to the train control method and the train control system based on the credible interval entry check, the axle counting equipment is arranged at the interval entrance, software of the temporary speed limit server only needs to adjust individual modules, and the software of the temporary speed limit server can be only used for recording the train condition in the interval and is not used for authorized calculation, so that the train control method and the train control system are convenient to use. The deployment cost of the temporary speed limit server is reduced, the transportation efficiency under the condition of a CTCS-2 system fault is improved, and the toughness of the CTCS-2 system is improved.

The invention provides an expressway variable speed limit control method, system and device and a medium, and belongs to the technical field of intelligent traffic management. The method comprises the following steps: collecting traffic flow data, road condition data and weather data in real time; preprocessing the collected data, and predicting a traffic jam trend and a safety risk index through a machine learning algorithm; determining weight factors of traffic flow, safety risk and congestion prediction through an analytic hierarchy process; dividing a road section speed range by using a clustering algorithm, and calculating a road section unit speed; constructing a target function which aims at maximizing the unit speed of the road section and minimizing the safety risk and the congestion time; and based on the target function, solving an optimal speed limit value by adopting a particle swarm optimization algorithm, and dynamically issuing speed limit information in different lanes. The highway traffic big data is analyzed in real time, the speed limiting strategy is intelligently formulated, the speed limiting strategy is published to the driver in real time through multiple channels, and the passing efficiency and safety of the highway are effectively improved.

The invention provides an accident liability intelligent determination method and system fused with video tracking, and the method comprises the steps: S1, carrying out the alignment evidence collection of a video, an IMU, a GPS, and an OBD, and forming an evidence chain through framing Hash, chain Hash, and a timestamp; s2, completing detection segmentation on the shared trunk multi-branch network; s3, in combination with appearance re-identification and a motion model, realizing cross-frame association, and outputting track, speed and shielding recovery; s4, mapping the trajectory to BEV according to depth and calibration, and constructing a space-time scene graph by fusing lanes, signals, speed limit and the like; s5, extracting events such as lane changing, line merging and signal rushing, and positioning a conflict point and a way giving relation; s6, generating error factors according to a rule base and priority judgment; s7, quantifying the responsibility proportion according to the weight, the collision participation time and the reaction time; and S8, generating a report evidence of the key frame and the rule list. The system is composed of an acquisition evidence obtaining module, a video understanding tracking module, a space-time reconstruction module, an event extraction module, a rule reasoning module, a responsibility quantification module and a report evidence storage module.

The invention discloses an expressway dynamic speed limit method based on multi-source data, and belongs to the technical field of expressway speed limit, and the method comprises the steps: obtaining accident data, constructing an accident risk degree prediction model, and generating an accident risk degree trend chart; acquiring real-time traffic flow data, constructing a traffic flow prediction model, and generating a traffic flow tendency chart; obtaining meteorological data, and generating a local meteorological state distribution diagram; performing space alignment and data fusion on the traffic flow tendency chart and the local meteorological state distribution diagram to generate a road section comprehensive risk index; performing time alignment on the accident risk degree trend chart and the comprehensive risk degree index of the road section to generate a real-time risk degree score of the current road section; smoothly adjusting the speed limit value by adopting a linear interpolation algorithm on the basis of the real-time risk degree score in combination with a preset speed limit adjustment rule; and comparing the current vehicle speed with the adjusted speed limit value, and early warning over-speed. According to the invention, accurate speed limit control based on multi-dimensional data is realized, and the road safety is effectively improved.

The invention relates to a road section risk early warning method based on thunder-vision fusion perception, and the method comprises the following steps: S1, obtaining the high-precision space-time position information of a vehicle in a tunnel through the multi-source information fusion of a millimeter-wave radar and machine vision; s2, mapping the vehicle position under the global coordinate system to a coordinate system expanded along a road center line, and representing the vehicle position as a longitudinal projection position and a transverse offset along the road direction; s3, under the coordinate system, taking anti-collision time as a core index, combining lane judgment and relative speed, constructing a dynamic longitudinal risk grading model, and performing linkage triggering with a warning system; s4, constructing a regional risk integral model; and S5, speed limiting information is issued to all driving-in vehicles in real time through the variable speed limiting screen, and the rear-end collision risk is actively prevented and controlled. According to the method, the sudden risk response time can be shortened, the speed limit control response precision can be improved, the rear-end collision rate of the diversion and convergence areas can be reduced, false alarm interference can be reduced, and the tunnel driving safety and the information guiding efficiency can be improved.

The invention relates to an unmanned surface vessel dynamic obstacle avoidance control method based on real-time nonlinear model predictive control, and the method comprises the steps: constructing a dynamic model, a static obstacle model and a dynamic obstacle model of an unmanned surface vessel; based on the dynamic model, the static obstacle model and the dynamic obstacle model, a multi-objective optimization model is constructed, and the multi-objective optimization model takes trajectory tracking cost, obstacle avoidance cost and control smoothing cost as objective functions and takes a motion range, speed limitation, control input limitation and a collision avoidance rule as constraint conditions; and obtaining the state of the unmanned surface vessel and the obstacle state in real time, inputting a multi-target optimization problem for solving, obtaining control input, and completing dynamic obstacle avoidance control of the unmanned surface vessel based on the control input. According to the invention, the adaptability and safety of the unmanned surface vessel in a complex navigation environment can be improved.

The invention discloses a group train tracking operation optimization method considering communication delay. The method comprises the following steps: establishing a heavy haul railway train group of a'navigation-front train following 'topological structure; a multi-target optimization model is constructed according to the characteristic constraint, the speed limitation and the safety distance constraint of the vehicle; generating a random communication delay in the model, calculating a time step number corresponding to the delay, and making a control decision on the following train based on state information of a previous time step of the preceding train; and solving the running speed of the following train in the multi-objective optimization model based on a particle swarm algorithm. According to the group train tracking operation optimization method, the problems of uncertainty and dynamic response lag caused by communication delay in the tracking operation process of heavy haul railway group trains are effectively solved, the tracking error between the trains can be remarkably reduced, the speed synchronism is improved, and the tracking operation efficiency is improved. And the distance fluctuation in the acceleration and deceleration stages can be effectively reduced, so that the train distance stability is ensured.

The invention discloses a deep reinforcement learning-based cooperative control method for an expressway confluence area, and the method comprises the steps: building a LiikeSim-Python joint simulation environment, and setting a coil detector in the simulation environment to obtain the traffic flow data of the upstream and downstream of the expressway confluence area; an EM algorithm based on Gaussian mixture distribution is used as a traffic state classifier, traffic flow data of the highway confluence area are used as input, and traffic states of the highway confluence area are divided; designing a state space, an action space and a reward function; taking the state space of the highway confluence area as input, taking the actions of the variable speed-limiting intelligent agent and the ramp metering intelligent agent as output, and constructing a network model of multi-agent sharing experience under time sequence characteristics; an independent experience pool is set for each of the variable speed limit agent and the ramp metering agent, and interaction experience of the agents and the traffic simulation environment is collected with the control period as the frequency; training an agent model by using the sampled samples; and realizing cooperative control of the highway confluence area by using the trained intelligent agent model. According to the invention, the traffic travel delay of the highway confluence area can be reduced.

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 relates to the technical field of road damage detection, in particular to an intelligent road damage recognition method and system based on image analysis, and the method comprises the following steps: obtaining road images, screening out substandard images, extracting local features of the images, executing enhancement processing, converting structural parameters, and matching a type template; and combining the vehicle axle load and the damage index to calculate a structure expansion trend, and generating damage expansion risk information. According to the method, the accuracy of damage type identification is improved by combining geometric parameters such as edge length, curvature range and closed region area value and performing scale conversion based on pixel resolution, and an evaluation mechanism of the influence of vehicle load on the damaged region is established by combining traffic flow and vehicle axle load data. The expansion trend is judged through the traffic frequency and the impact weight, the damage development situation is effectively predicted, the accident frequency, the speed limit information and the traffic obstruction factor are introduced into priority ranking calculation, and the urgency recognition capability of road maintenance is enhanced.

The invention discloses a vehicle remote control method, system and device based on a remote vehicle locking platform. The vehicle remote control method based on the remote vehicle locking platform comprises the steps that a cloud end receives vehicle remote control instructions, and the vehicle remote control instructions comprise a speed limiting instruction, a vehicle locking instruction and an unlocking instruction; verifying whether the remote control function of the vehicle is activated or not; if yes, a verification code is issued to the owner of the vehicle ownership, and after the verification of the owner of the vehicle ownership is passed, a vehicle remote control instruction is sent to the vehicle, so that the vehicle executes an action corresponding to the vehicle remote control instruction; and if the vehicle feedback is not received within the preset time, determining that the remote control of the vehicle fails, and ending the operation, otherwise, updating the vehicle state according to the vehicle feedback. By the adoption of the method and device, remote operation such as vehicle locking function activation, forbidding, speed limiting, vehicle locking and unlocking can be rapidly conducted on the vehicle, the method and device have the advantages of being high in success rate, safe and reliable, and convenience is provided for remote management of the vehicle.

The utility model discloses a safety protection system for a standing driving type piling car, which belongs to the technical field of storage car safety protection and comprises a controller, an interlocking switch, a lifting buffer switch, a descending buffer switch, an OPS switch, a transportation safety switch, a high-position speed limiting switch, an arm protection switch, a standing plate sensing switch and a handle. The lifting height H of the portal frame is judged through the controller, and the driving motor and the pump motor are controlled to work according to the signal states of the handle, the standing plate sensing switch, the OPS switch, the arm protection switch, the transportation safety switch, the high-position speed limiting switch, the lifting buffer switch and the descending buffer switch. According to the utility model, the associated control of the running speed and the lifting and stacking height of the standing type stacking vehicle can be realized.

The invention belongs to the technical field of roads and bridges, and discloses a bridge floor icing traffic safety intelligent monitoring and early warning method, which comprises the following steps: S1, bridge floor time-varying temperature multi-source prediction based on thermal environment change; s2, predicting the time-varying friction coefficient of the frozen bridge floor based on the temperature field; s3, icing driving safety prediction based on the friction coefficient and the line condition; and S4, bridge deck icing traffic safety comprehensive speed-limiting intelligent early warning is carried out. The core innovation of the invention lies in a full-chain intelligent early warning system, through a closed-loop process of multi-source temperature prediction, dynamic friction modeling, driving safety evaluation and intelligent speed limit decision, the early warning precision and practicability are significantly improved, and through a technical path of data driven prediction, accurate safety evaluation and intelligent decision output, the early warning precision and practicability are improved. The technical performance, the economic benefit and the social safety are all broken through, and a good solution is provided for road traffic safety in winter.

The invention relates to an ice and snow disaster variable speed limit control method and system based on a friction coefficient, and belongs to the field of intelligent traffic. According to the method, a set of variable speed limit control units are arranged on a road at intervals, and all the variable speed limit control units are in communication connection with a centralized decision-making unit through communication modules; each set of variable speed-limiting control unit detects traffic flow data, meteorological environment data and road condition data of a lane level in real time, uploads the data to the centralized decision-making unit, determines an optimal lane-level variable speed-limiting control scheme and an optimal deicing and snow-removing scheme through a decision-making optimization algorithm, and issues the optimal lane-level variable speed-limiting control scheme and the optimal deicing and snow-removing scheme to the variable speed release module; and the variable speed release module releases a lane level variable speed limit control scheme of a corresponding section, namely the lowest speed limit value, the highest speed limit value, the suggested speed and the like of each lane, and performs deicing and snow removal operation according to the deicing and snow removal scheme so as to ensure road traffic safety in ice and snow weather. The road traffic safety guarantee level is improved through variable speed limiting.

The invention discloses a vehicle speed limit control method and device, and the method comprises the steps: obtaining the first driving state information of a target vehicle in real time in the driving process of the target vehicle; the first driving state information comprises a first driving mode, a first charge state and a first vehicle speed of the target vehicle; determining whether to trigger speed limit control for the target vehicle according to the first driving state information; after speed limit control is triggered for the target vehicle, the vehicle speed of the target vehicle is controlled to be reduced to a first speed limit value from a first vehicle speed, and the running mode of the target vehicle is controlled to be switched to a second running mode from a first running mode; when the vehicle speed of the target vehicle is reduced to the first speed limit value, the gear of the target vehicle is controlled to be switched to the first gear, and second driving state information of the target vehicle is obtained; the second driving state information comprises a second driving mode, a second charge state and a second vehicle speed of the target vehicle; and determining whether to end speed limit control for the target vehicle according to the second driving state information.

The invention relates to the technical field of train operation optimization, and discloses an air braking efficiency uncertainty-oriented freight train operation optimization method, which comprises the following steps of: generating a state value function approximate value and a working condition selection set by adopting a constraint-based state value function table learning algorithm according to an initial state and a disturbance sampling value of a freight train; according to the state value function approximate value and the working condition selection set, a state value function table approximate algorithm based on operation time estimation is adopted to generate an operation time approximate value and an operation time estimation value; and according to the operation time approximate value and the operation time estimated value, a freight train operation sequence meeting the air braking constraint, the speed limit constraint and the operation time limit is generated by adopting an operation optimization algorithm meeting the time constraint. According to the method, the freight train operation sequence meeting various constraints can be calculated in a short time, meanwhile, the influence caused by uncertain disturbance is reduced, and the overall optimization effect of the calculation result is improved.

An apparatus of controlling autonomous driving in a transition demand (TD) process during the autonomous driving and a method thereof are provided. A controller is configured to determine whether there is a section where it is impossible to operate autonomous driving in front of an autonomous driving route of an autonomous vehicle, during autonomous driving of the autonomous vehicle, determines a maximum speed limit while a TD is output, based on a current speed of the autonomous vehicle, outputs the TD before a predetermined distance from the section where it is impossible to operate the autonomous driving, and is configured to control a speed of the autonomous vehicle based on the maximum speed limit. The apparatus enhances a problem in which a TD is initiated from a too long distance to the section where it is impossible to operate the autonomous driving.

A vehicle includes a windshield configured to transmit a front view of the vehicle to an interior of the vehicle, and a display device configured to display information set to overlap the front view on the windshield, and to divide the windshield into a first display area including a driving lane, both sides of the driving lane, and an upper settings area, and at least one peripheral display area located in a periphery of the first display area, display at least one of driving energy information, speed information, driving lane information, augmented reality (AR) turn by turn (TBT) information, traffic signal information, a distance to a vehicle ahead, warnings, and surrounding vehicles information on the first display area, and display at least one of destination arrival information, media content information, surrounding real image, audio video navigation (AVN) menu, speed limit information, and local geographic information on the peripheral display area.

Systems, methods, and devices described herein can be used to dynamically determine and modify driving parameters for a vehicle, such as an autonomous or semi-autonomous vehicle. An example vehicle system can be configured to: monitor a vehicle's geographic location; obtain data corresponding with the vehicle's geographic location; determine one or more traffic flow characteristics; determine one or more traffic flow characteristics based at least on the data; and determine one or more target driving parameters (e.g., a safe speed limit) for the at least one vehicle based at least on the one or more traffic flow characteristics.

The invention discloses a heavy haul train automatic driving curve planning method, electronic equipment and a storage medium. The method comprises the steps that a DP state transition table is loaded, and a plurality of initial states, a plurality of actions capable of being adopted in each initial state and a transition state capable of being reached by each action are defined in the DP state transition table; acquiring a protection curve as a speed limit line, and drawing according to the protection curve to obtain a guide line; performing curve planning by taking N DP step lengths as a cycle, for each DP step length, sequentially traversing each migration state corresponding to the initial state of the DP step length from top to bottom in the DP state migration table, and performing judgment based on a speed limit line and a guide line to obtain respective target migration states of the N DP step lengths; and generating a speed-mileage planning curve of the train according to the final speed of a differential interval obtained by performing dynamic differential calculation by adopting the action corresponding to the target migration state of each DP step length. According to the invention, the planning success rate and fault tolerance can be improved.

The invention discloses a bridge floor global driving wind environment early warning method, system and device based on limited measuring points and a storage medium, and relates to the technical field of wind speed early warning and artificial intelligence. Future short-time wind speed is predicted based on wind speed time history data, and a two-dimensional high-temporal-spatial-resolution prediction flow field of multiple planes is obtained according to the future short-time wind speed; main beam global wind-induced vibration response data are obtained based on the wind-induced vibration response data; obtaining three-dimensional high-temporal-spatial-resolution prediction flow field data according to the two-dimensional high-temporal-spatial-resolution prediction flow field and the girder global wind vibration response data; analyzing the three-dimensional high-temporal-spatial-resolution prediction flow field data to obtain a safe driving critical wind speed, and setting a wind speed-vehicle speed limiting criterion; and when the predicted wind environment exceeds the wind speed limit criterion, outputting the predicted data and the early warning level of the corresponding driving wind environment, and carrying out advanced early warning. According to the invention, the problem of low accuracy of existing driving safety early warning is solved, and wind speed multi-stage early warning for different vehicles in different driving environments is realized.

The invention relates to the technical field of intelligent cabins, in particular to a vehicle speed threshold value adjusting method and device, a vehicle, a medium and a program product. Obtaining a geo-fence boundary and a first speed threshold value in an area enclosed by the geo-fence boundary; and when the position of the vehicle is located in the area defined by the geo-fence boundary, adjusting a second speed threshold value of the vehicle to be the first speed threshold value. In this way, the speed threshold value of the vehicle is adjusted according to the position of the vehicle. On one hand, casualties or other losses caused by the fact that the vehicle is out of control due to too high speed in a public place can be avoided, and on the other hand, under the scene that speed limiting is not needed, it is ensured that the vehicle can be kept at a high speed, and the situation that the driving experience of a user is affected due to speed limiting is avoided.

The invention discloses a balanced two-wheeled vehicle trajectory tracking optimization control method based on an SCMG, and solves the trajectory tracking deviation problem under the SCMG physical boundary condition. The method comprises the following steps: adaptively adjusting a preview distance, calculating the preview distance according to a vehicle speed, a curvature and a transverse deviation value, and limiting a range; the maximum turning angle and the rotating speed of the handlebar are coupled with the vehicle speed, and large turning is preferentially decelerated. The method further comprises the steps of designing a reconnaissance distance on the basis of a pure tracking method, and limiting the speed in advance by calculating the reconnaissance distance, finding a reconnaissance point, calculating the reconnaissance curvature and solving Wherein the preview distance, the reconnaissance distance, the reconnaissance curvature, the speed limit value and the like have corresponding calculation modes, the deceleration, the constant value and the like of the vehicle are set according to actual situations, coupling of the handlebar and the vehicle speed is achieved through a mapping table or a function, and an empirical value is obtained through a real vehicle critical state test. According to the method, the trajectory tracking effect is optimized, and the driving control performance is improved.

The invention discloses a BIM-based highway construction period traffic guide and change optimization system, and relates to the technical field of road traffic management. According to the BIM-based highway construction period traffic guide and change optimization system, traffic management in the construction period is dynamically adjusted in real time through space-time modeling, data fusion and optimization decision. The system generates a road network space-time model, matches vehicle trajectory data with lane space data, counts the number and speed of vehicles, and optimizes traffic flow. According to the optimization result, the system outputs the tailgating length, the number of lanes and the speed limit value and generates a corresponding control instruction and a speed limit issuing instruction, traffic management of the highway construction period is accurately optimized through space-time modeling and real-time data fusion based on BIM, the system can obtain vehicle track data in real time, the lanes and the speed limit strategy can be dynamically adjusted, and the speed limit efficiency is improved. Traffic delay in the construction period is minimized, and traffic safety and smoothness of a construction section are effectively guaranteed by generating an accurate lane control and speed limiting instruction.

The invention discloses a multi-source data fusion rail train speed monitoring and early warning method, system and device, and relates to the related field of rail traffic safety monitoring, and the method comprises the steps: obtaining the line key parameters of a train test line, and constructing a train test line electronic map; matching a sensor array according to scene features of different operation sections; in the running process of the train, the real-time position and speed of the train are obtained through a real-time matching sensor, and multi-source sensor data are obtained; the ground dispatching center carries out fusion correction to obtain a corrected position and a corrected speed; performing prediction to obtain a predicted speed value and a position at the next moment; according to the train operation scene corresponding to the predicted position, scene speed limit parameters are extracted, and if the predicted speed value is not met, a train-ground cooperative early warning mechanism is triggered to conduct early warning. The technical problems that existing rail train speed monitoring and early warning are inaccurate in monitoring and not timely in early warning are solved, and the technical effect of improving the accuracy and timeliness of rail train speed monitoring and early warning is achieved.

The invention discloses a synergy control method for a confluence area based on Rainbow DQN and a driving risk field, relates to the technical field of traffic safety, and aims to solve the problems that the overall traffic quality of mixed traffic flow is reduced and the confluence risk is increased due to the fact that a control logic is not matched with a real-time risk in an existing signal control method for the confluence area of an expressway. According to the invention, by considering the CAV-containing mixed traffic flow of the main line of the confluence area, the main line traffic flow information and the ramp queuing information, the state space and the action space of the cooperative control agent are designed in a refined manner, and a reward function considering the traffic efficiency, the traffic safety and the cooperative interaction is provided; therefore, the main line speed limit value can be dynamically controlled, the ramp queuing vehicles are dynamically coordinated, the main line CAV vehicle driving track is induced, and the passing efficiency and the traffic safety are effectively improved. The overall traffic quality of the mixed traffic flow is ensured, and the confluence risk is reduced.

The invention discloses a lane-level congestion identification method, apparatus and device, and a computer program product. The method comprises the steps of receiving roadside sensing data and determining a sensing result of a vehicle target according to the roadside sensing data; dividing the perception result of the vehicle target to the corresponding lane to obtain the perception result of the vehicle target of each lane; based on a pre-created sliding window, respectively carrying out statistical analysis on the perception results of the vehicle targets of each lane to obtain the average speed of each lane; and determining lane-level congestion information according to the average speed of each lane and the road speed limit information. The vehicle sensing result is obtained by means of the strong sensing capability of the roadside equipment, the sliding window is used for carrying out statistical analysis on the data of each lane, and the lane congestion condition is accurately judged in combination with the road speed limit information for reference use of the vehicle, so that the problems of limited view and insufficient sensing capability of the vehicle are solved, and the driving safety is improved. Therefore, timely and accurate lane-level congestion information is provided for the automatic driving vehicle, and the driving efficiency and safety are improved.