3797 results about "Signal light" patented technology

An intelligent traffic management device for optimizing traffic flow at one or more intersections using reinforcement learning, comprising: a central control unit housed in a weatherproof enclosure and mounted on a structural support near an intersection; a sensor interface operatively connected to a variety of multimodal sensors, including at least radar-based vehicle detectors, inductive loop sensors embedded in the road surface, infrared pedestrian detection detectors, and high-resolution optical imaging modules; a powerful embedded computing unit consisting of a multi-core processor and a GPU configured for on-device inference; a reinforcement learning engine embedded in the computing unit, comprising a deep reinforcement learning model trained to map environmental states defined by traffic-related sensor inputs to optimized action policies for traffic light control; a signal actuation module electrically connected to traffic signal controllers, with action policies outputting signal phase durations and transitions in real time; a communications module supporting wireless connectivity and configured for device-to-device communication and cloud synchronization; a policy synchronization module configured to perform federated learning-based updates of model parameters by exchanging gradient weights or distilled traffic state vectors with neighboring intelligent traffic management devices deployed in a traffic network; where the reinforcement learning engine continuously observes traffic state vectors consisting of vehicle counts, average speeds, queue lengths, and pedestrian activity, and dynamically updates the policy to maximize cumulative traffic throughput and minimize average delay.

The invention discloses a road traffic signal control system and method, and the method comprises the steps: collecting real-time data through a sensor, a camera and a geomagnetic device, combining historical data with emergency analysis, and predicting a flow mode and a change trend; green light time distribution is dynamically optimized, a fixed timing, inductive and adaptive control strategy is adopted, and traffic light duration and priority adjustment is achieved; through regional cooperation and linkage control, green wave bands and branch intersection red light optimization are set, and peak period traffic signals are intelligently dispatched. A machine learning model is used to predict a flow peak value, environment and accident detection data are combined, a signal lamp period is dynamically adjusted, vehicle waiting time, traffic efficiency, carbon emission and traffic satisfaction are evaluated, and a traffic control strategy is comprehensively optimized. According to the road traffic signal control system and method, the traffic flow in different time periods is predicted by adopting the flow prediction model based on big data analysis and machine learning, and the signal period is adjusted in advance.

The invention provides an edge computing traffic light intelligent decision-making method integrated with AI video analysis, and the method comprises the steps: collecting a real-time traffic video stream of a target intersection through an edge computing device, and extracting a dynamic traffic feature set in the real-time traffic video stream, calling a pre-trained space-time analysis model to carry out multi-modal fusion processing on the dynamic traffic characteristic set, generating a traffic state vector of the target intersection, matching a candidate control strategy in a preset decision rule base based on the traffic state vector, and carrying out parameter adjustment on the candidate control strategy through a strategy optimization model to obtain a target traffic state vector; generating a target signal lamp control parameter; and issuing the target signal lamp control parameters to a traffic signal control terminal of the target intersection, and monitoring traffic state change data of the target intersection in real time to update weight parameters of the space-time analysis model. According to the invention, the traffic efficiency, pedestrian safety and traffic management intelligence degree of urban intersections can be improved.

The invention relates to a laser inter-satellite link test method, system and device and a computer readable medium, which are used for performing butt joint test on a laser inter-satellite link by using a laser inter-satellite link test system. The laser inter-satellite link test system comprises any one of a laser high-speed processor, a first laser terminal, a second laser terminal, a first collimator, a second collimator, a monitoring camera, a micro-vibration simulation galvanometer and a rotary table. The method comprises the following steps: carrying out a signal light capturing and tracking test; carrying out beacon light capturing and tracking and communication distance measurement test; carrying out a beacon light leapfrogging capturing and tracking test; carrying out a broken chain recapture test; performing a long-time link establishment assessment test; therefore, any one of the capture time, the link establishment time, the coherent fine tracking precision, the error rate, the ranging random error, the link reestablishment time, the link establishment interruption times and the recapture success times of the laser inter-satellite link is obtained. According to the invention, the matching performance and reliability of working processes among single machines can be effectively verified, and system risks can be effectively prevented.

The invention discloses a smart city traffic resource optimal configuration method, a platform and a storage medium, and relates to the technical field of traffic optimization. When a road accident is detected, accident information is collected, and a traffic accident position and traffic accident characteristics are obtained; time consumption prediction is carried out according to the traffic accident characteristics, and traffic control duration is obtained; calling real-time traffic data from an intelligent traffic system to obtain associated road data; executing multi-thread traffic flow back-stepping analysis to obtain associated traffic flow prediction information; carrying out shunting performance evaluation; and dynamically updating the signal lamp configuration of the plurality of shunting candidate roads by taking the associated traffic flow prediction information as a constraint. According to the invention, the technical problems of aggravation of traffic jam and low emergency management efficiency caused by lack of scientificity and dynamic adaptability of shunting planning and lagging of signal lamp regulation in a traffic accident scene in the prior art are solved, and the technical effects of optimizing traffic resource configuration, relieving traffic jam and improving traffic emergency management efficiency are achieved.

The invention discloses a smart city traffic signal lamp management and control system, and relates to the technical field of traffic signal control. The system comprises a region division module, a traffic flow detection sensor, a region traffic flow analysis module, a signal lamp management and control strategy generation module, an inter-region signal lamp cooperative control module and a variable information sign and information processing and generation module. The system can accurately monitor and analyze urban traffic flow conditions in real time by comprehensively applying an advanced region division algorithm, a traffic flow detection technology and an accurate traffic flow state evaluation model, and can intelligently generate a signal lamp management and control strategy according to an analysis result, so that the traffic lamp management and control efficiency is improved. Comprising the steps that a congestion relieving dynamic timing algorithm is adopted in a congestion area, the number of times of signal lamp switching is reduced, a flow balance guiding timing algorithm is applied in an area with small flow, in addition, cooperative control over signal lamps between the areas can be achieved, and efficient connection and balanced distribution of traffic flow between the areas are ensured.

The invention discloses an end-cloud cooperative detection method and system for traffic anomalies, and relates to the technical field of intelligent traffic control. The method comprises the following steps: collecting traffic video streams through edge equipment, and identifying abnormal behaviors and generating structured event data by using a lightweight YOLOv3-tiny model; when the confidence exceeds a dynamic threshold and the event type is a high-risk type, uploading a video clip and data to a cloud; the cloud integrates a historical road condition map, meteorological data and a real-time traffic flow state, reconstructs a three-dimensional event scene by adopting a space-time attention pyramid network, and verifies the authenticity of an event in combination with a traffic flow sudden change detection algorithm; and generating a signal lamp forced switching instruction for the risk level overrun event, and issuing the signal lamp forced switching instruction to roadside equipment within 3 seconds to execute emergency response. According to the invention, full-link closed-loop control of traffic accidents from identification to response is realized, and the false alarm probability is greatly reduced while the identification accuracy is guaranteed.

The invention provides a traffic participant behavior analysis-based edge calculation signal lamp control method and system, and the method comprises the steps: obtaining video stream data of a target intersection, carrying out the frame-by-frame behavior state analysis of the video stream data through a spatial-temporal feature coding network, extracting the behavior state features of each traffic participant, and carrying out the analysis of the behavior state features of each traffic participant; and inputting the behavior state characteristics into a preset behavior matching model, generating a behavior triggering identifier of the traffic participant in the current time window, generating a signal lamp control instruction of an edge computing node according to the time sequence relevance between the behavior triggering identifier and the phase state of the current signal lamp, and sending the signal lamp control instruction to the edge computing node. And finally, a signal lamp phase switching time sequence of the target intersection is adjusted based on the signal lamp control instruction, so that the traffic participants meeting the traffic rule conflict condition obtain the traffic priority under the signal lamp phase switching time sequence. According to the method, the traffic accident risk is reduced, and meanwhile, the overall traffic efficiency of the intersection is optimized by dynamically adjusting the minimum response period and the phase duration parameters.

The invention relates to the technical field of optical detection, in particular to semiconductor detection equipment, which comprises a reinspection light source for emitting reinspection light, a bright field light source for emitting bright field light and a dark field light source for emitting dark field light, the reinspection light, the bright field light and the dark field light are respectively guided to irradiate the surface of a wafer, the reinspection light is reflected by the wafer to form reinspection signal light, and the reinspection signal light is transmitted to the light source. The bright-field light is reflected by the wafer to form bright-field signal light, and the dark-field light source is scattered by the wafer to form dark-field signal light; the optical separation element is used for collecting the bright-field signal light, the dark-field signal light and the reinspection signal light and performing spatial separation, so that the dark-field signal light, the bright-field signal light and the reinspection signal light are emitted to different transmission paths for respective imaging; wherein a dark field illumination light spot formed by the dark field light on the surface of the wafer, a bright field illumination light spot formed by the bright field light on the surface of the wafer and a reinspection illumination light spot formed by the reinspection light on the surface of the wafer do not coincide with one another. The wafer defect detection device is at least beneficial to improving the detection speed and the detection precision of wafer defect detection.

The invention provides a traffic accident prediction method fusing multi-source features and a self-adaptive structure, and the method comprises the steps: extracting spatial features such as a geographic position, traffic flow and interest point distribution, combining the time features such as traffic flow change trend, periodicity and anomaly detection, and the external features such as weather and signal lamp density, and carrying out the prediction of a traffic accident. Node multi-dimensional feature representation is comprehensively constructed, a static adjacency matrix and a dynamic adjacency matrix are respectively constructed, geographic distance and node feature similarity information are fused, a self-adaptive adjacency matrix is generated by utilizing learnable parameters, and road network structure changes are dynamically described. Finally, traffic accidents are modeled and predicted based on a graph convolutional neural network, and accurate identification and early warning of accident risks in a complex traffic environment are realized. According to the method, the modeling capability of the prediction model for nonlinear and strong space-time correlation characteristics of traffic data is effectively improved, the accuracy and robustness of traffic accident prediction are remarkably improved, and the method has wide engineering application prospects and popularization value.

The invention discloses an intelligent traffic control system and method based on multi-agent near-end strategy optimization, and belongs to the field of intelligent traffic, Internet of Vehicles and deep reinforcement learning. The method comprises the following steps: firstly, constructing a fog-cloud collaborative three-layer architecture, and realizing real-time monitoring and dynamic regulation and control of traffic flow through cloud global decision and local sensing collaboration of a road side unit (RSU); secondly, designing indexes of'road section overlap ratio 'and'road section time overlap ratio', and solving the problem of secondary congestion caused by rerouting; then, a multi-agent near-end strategy optimization (MAPPO) algorithm is adopted, so that the traffic signal lamp is used as an autonomous agent to dynamically adjust the phase, and the limitation of single-point control is broken; and finally, through integrated optimization of rerouting and adaptive signal control, an original multi-objective optimization problem is converted into a layered multi-agent reinforcement learning problem. According to the invention, vehicle driving time and system energy consumption can be effectively reduced, road traffic efficiency is improved, and active avoidance and dynamic alleviation of urban traffic congestion are realized.

The invention, which relates to the technical field of intelligent traffic and illumination control, discloses a street lamp vehicle-road cooperative control system comprising a data acquisition module, a data processing module, a control execution module and a decision management module. The data acquisition module is used for acquiring traffic signal lamp phase, vehicle position and speed, street lamp working state and environment illumination intensity data; according to the invention, through a cross-system collaborative algorithm, the traffic flow direction is predicted in combination with the phase of the traffic signal lamp, the brightness of the street lamp illumination area is adjusted in advance, the energy distribution according to needs is realized, the energy consumption is effectively reduced, and through a vehicle-lamp-cloud three-level decision-making mechanism, the computing power priority is dynamically distributed according to the traffic condition. Traffic signal lamp control and vehicle driving path planning are optimized, the road passing efficiency is improved, traffic congestion is relieved, by pushing the lighting optimization path to the vehicle, the vehicle is driven under the proper lighting condition, the emergency brake risk is reduced, and the road traffic safety level is remarkably improved.

The invention relates to an intersection lane division and signal timing cooperative control method in an automatic driving environment, and belongs to the field of intelligent traffic. According to the method, lane function division schemes in corresponding directions are published by using lane function adjustment marks, signal timing schemes in corresponding directions are published by using lane-level signal lamps, and personalized suggestion schemes are pushed to network-connected automatic driving vehicles by using a network-connected communication technology, so that lane-level and vehicle-level refined intelligent management and control are realized, and the intelligent management and control efficiency is improved. The intersection traffic efficiency is improved, and intersection road traffic resources are utilized to the maximum extent. According to the invention, the problem of insufficient management refinement of the plane intersection can be overcome, the traffic capacity of the intersection is improved, traffic congestion is reduced, and the utilization rate of road traffic resources is improved.

The invention provides a regional boundary traffic signal control method and system based on model predictive control, and belongs to the technical field of intelligent traffic. The method specifically comprises the following steps: partitioning a road network region; collecting traffic parameters of each region of the road network, and fitting an MFD curve of each region based on the collected data; establishing an optimization model by taking minimization of the number of vehicles in a road network as a target; an optimal boundary control value is obtained through optimization solution of an MPC controller with a built-in SLSQP algorithm, so that boundary traffic flow and boundary traffic signal lamp timing of each region of the road network are controlled; a traffic flow is simulated based on SUMO, and from a current time step to a next time step, an MPC controller with a built-in SLSQP algorithm is used for continuous iterative optimization solution to realize rolling optimization. And modeling the traffic state of each region by using MFD to obtain the flow dynamic state of the region. By establishing a network structure to simulate traffic dynamics, the MPC method is realized to control transfer of boundary traffic flow and dynamically control timing of traffic signal lamps, so that traffic congestion is relieved.

The invention relates to the field of rail transit equipment, and discloses a rail transit equipment multi-source sensing data real-time fault-tolerant decision-making system, which comprises a multi-source sensing module used for collecting rail environment and locomotive state data through a 3D laser radar, a binocular camera and a Beidou positioning terminal, including obstacle, turnout, signal lamp and locomotive position information; the data fusion processing module is used for carrying out preprocessing, redundancy check and feature extraction on the multi-source sensing data to generate a fusion sensing result; and the fault-tolerant decision module is used for judging the safety state of the track based on the fusion sensing result, triggering an alarm / braking instruction and executing self-checking logic when the sensor is abnormal. Through the combination of double groups of redundant sensors and a three-level progressive fault-tolerant strategy, closed-loop control from data acquisition to safety decision is formed, the system robustness under complex working conditions is remarkably improved, and an omnibearing safety protection barrier is constructed for rail transit equipment.

The invention discloses a traffic signal lamp intelligent control method and system, and relates to the technical field of intelligent traffic systems, and the method comprises the steps: collecting traffic operation and environment data through deploying a sensor group in a target traffic area signal lamp; a self-adaptive analysis model including anomaly detection and congestion prediction is obtained through a traffic scheduling platform, traffic conditions are analyzed, and congestion parameters are obtained. And starting an emergency response mechanism according to the parameters, analyzing and determining signal lamp control strategy parameters, and finally realizing adaptive regulation and control of the traffic signal lamp. The technical problems that the real-time response capability of an existing traffic signal lamp control system is insufficient, and traffic abnormity and congestion cannot be effectively predicted are solved, and through real-time traffic data analysis and intelligent prediction, the real-time response capability of the traffic signal lamp system is enhanced, and the traffic safety is improved. And traffic abnormity and congestion can be effectively predicted and dealt with.

The invention discloses a multi-signal lamp linkage control method and system for intelligent urban traffic, and relates to the technical field of intelligent traffic, and the method comprises the steps: constructing an urban multi-level response system which covers the response architecture of three levels of intersections, regions and cities; a sensing acquisition network is accessed to collect weather information and traffic information, and traffic abnormal conditions in the response range of each level are identified; integrating the meteorological data, the traffic data and the traffic abnormal information into an urban multi-level response system according to the association of the response levels, and analyzing the traffic signal lamp strategy of each level so as to formulate the traffic control strategy of each level; and performing overall optimization on the traffic control strategy of each level in combination with the correlation characteristics of each level region, and finally forming a global collaborative control strategy. Therefore, the technical effects of fine control and global optimization, and enhancement of the adaptability and the overall operation benefit of the traffic system are achieved.

The invention relates to a traffic condition-based signal lamp timing method, system and device and a medium. The method comprises the steps that target traffic parameters of all intersections in the current time period are acquired through a multi-source sensor network, the association strength between the intersections is calculated based on a preset traffic flow transmission model, an intersection set with the association strength exceeding a threshold value is screened, and a regional linkage adjustment framework is constructed. And when it is detected that the traffic flow density at the intersection exceeds a preset threshold value, priority ranking is performed on timing adjustment requirements according to the association strength and the over-limit degree, signal lamp timing parameters of the affected adjacent intersection are adjusted, and a regional timing scheme is generated. According to the method, by dynamically sensing the traffic flow state and quantifying the intersection cooperation relation, regional cooperation optimization of signal lamp timing is achieved, and the road network traffic efficiency and the traffic pressure balance are effectively improved.

The invention relates to the technical field of intelligent network connection vehicles, in particular to an intersection signal lamp and intelligent network connection vehicle trajectory collaborative optimization method, which comprises the following steps: acquiring state information of all vehicles within a certain range of an intersection; obtaining mathematical expressions of signal lamp parameters of all lanes based on the entrance lanes of the intersection and a pre-established adaptive traffic signal model; generalized delays and vehicle tracks corresponding to different signal lamp parameters are obtained based on a pre-constructed vehicle delay estimation model; and solving an optimal signal lamp parameter and a corresponding vehicle track based on a heuristic search algorithm. Therefore, the problems that in the related technology, the real-time state of the traffic flow is not considered, the mixed traffic condition of mixed driving of the intelligent network connection vehicle and the human driving vehicle and the interaction relation between the signal lamp and the vehicle are not considered, so that the signal lamp parameter and the track of the intelligent network connection vehicle cannot be collaboratively optimized, the intersection traffic efficiency is low, and the traffic efficiency is poor are solved. And actual requirements cannot be met.

The invention discloses a processing method for dynamically adjusting a traffic light signal scheme according to traffic flow, which comprises the following steps of: deploying sensors including a ground induction coil, a camera and a pedestrian button at each traffic intersection, and preprocessing collected traffic flow and pedestrian flow data; and predicting trends of traffic flow, non-motor vehicle flow and pedestrian flow of each intersection in a short period, modeling traffic light states of each intersection into a dynamic system, calculating an optimal traffic light adjustment strategy, and adjusting the traffic light states in real time. By dynamically adjusting the traffic light signals, the system can more effectively respond to the actual on-site conditions including the flow of pedestrians and non-motor vehicles, more reasonable signal duration distribution is achieved, waiting time is shortened, traffic efficiency is improved, meanwhile, the pedestrians and the non-motor vehicles are considered, the safety of the system can be improved through an optimized signal control strategy, and the traffic light control method is suitable for popularization and application. The signal lamp is adjusted to provide more transition time for pedestrians and non-motor vehicles, so that the accident risk is reduced.

The invention discloses a road traffic information monitoring system and method, and the method comprises the steps: deploying a video monitor, a sensor, an unmanned plane and a satellite device to collect real-time road traffic data, and protecting the data safety through encryption transmission and multilayer identity authentication; the flow is monitored in real time by combining image recognition and sensor data, congestion is analyzed, traffic violation is recognized, and an alarm is given; future flow is predicted through machine learning and big data analysis, and the signal lamp period is intelligently adjusted; through combination of real-time and historical data, accident risk points are identified and early warning is carried out, and early warning is automatically issued and adjustment and control are carried out for severe weather. The AI technology automatically identifies accidents and notifies a management center, dispatches emergency resources, optimizes signal lamps and lane traffic, and dynamically adjusts surrounding flow; according to the road traffic information monitoring system and method, an automatic emergency processing system is established, accidents are automatically detected and alarms are triggered under the conditions of traffic accidents and emergencies, and emergency resources are quickly dispatched.

The invention discloses an urban traffic signal space-time adaptive flexible regulation and control method and system. The method comprises the steps of collecting traffic data such as the number of vehicles, the vehicle speed, the number of pedestrians and the pedestrian speed at an intersection in real time; performing cleaning, normalization and feature extraction on the acquired traffic data to generate standardized traffic parameters; based on the standardized traffic parameters, a signal lamp timing scheme is generated through a space-time diagram convolutional network and lightweight deep reinforcement learning hybrid algorithm; adjusting the traffic signal lamp in real time according to the signal lamp timing scheme; the method comprises the steps of storing traffic data and remotely monitored intersection states, integrating information of adjacent intersections to perform regional strategy optimization, integrating strategy data of each intersection through federal learning, and generating a global model to optimize a collaborative penalty coefficient; the method can quickly respond to the dynamic change of the traffic flow, optimizes the signal lamp timing scheme, and remarkably improves the data processing speed and decision-making efficiency.

The invention relates to the technical field of intelligent traffic control, in particular to a single-intersection signal lamp dynamic timing method and system based on holographic perception, and the method comprises the steps: collecting the information of a single intersection and surrounding traffic participants in real time through a multi-source holographic perception device, and carrying out the preprocessing, thereby obtaining a standardized traffic data set; based on the data set, combining vehicle, pedestrian and non-motor vehicle demand weights, constructing a traffic demand evaluation model, and calculating a demand priority coefficient of each entrance lane; constructing a signal lamp dynamic timing optimization objective function by taking minimization of intersection total vehicle waiting time, minimization of pedestrian crossing waiting time and maximization of intersection traffic volume per unit time as objectives according to the priority coefficient in combination with intersection traffic capacity constraint conditions, solving to obtain an optimal signal lamp timing parameter, and issuing the optimal signal lamp timing parameter to a control terminal; and traffic state feedback data of the intersection are collected in real time, and timing optimization is carried out again if the change exceeds a preset threshold value. The system can wait for a long time, improves the intersection passing efficiency, and relieves traffic congestion.

The invention provides a beam forming system which can be applied to the fields of wireless communication systems, automatic driving, artificial intelligence and the like. And small-scale, low-power-consumption and fast-response two-dimensional beam forming can be realized. The system comprises a first optical diffraction module and an array of second optical diffraction modules, the first optical diffraction module comprising at least one input port and a plurality of output ports, each second optical diffraction module in the array of second optical diffraction modules comprising one input port and a plurality of output ports. The first optical diffraction module is used for receiving at least one path of first signal light and generating multiple paths of second signal light with different phases based on the at least one path of first signal light. Each second optical diffraction module is used for receiving one path of second signal light in the multiple paths of second signal light and generating multiple paths of third signal light with different phases based on one path of second signal light, and the multiple paths of third signal light are used for generating multiple paths of first radio frequency signals.

The invention provides a traffic signal lamp intelligent control method based on a fiber bragg grating sensing network, and relates to the technical field of traffic signal lamps, and the method comprises the steps: obtaining the detection data of each fiber bragg grating sensing line, and enabling the fiber bragg grating sensing lines to be transversely laid at a selected traffic crossroad along a lane, three fiber bragg grating sensing lines parallel to the stop line are laid on each road; determining the length range of the motor vehicle queuing length of each lane according to the detection data of each fiber Bragg grating sensing line; and controlling the working state of the corresponding traffic signal lamp according to the length range of the motor vehicle queuing length of each lane. The actual traffic flow can be determined through the detection data of the fiber Bragg grating sensing lines, the green light time can be dynamically allocated based on the actual traffic flow, the vehicle queuing time is shortened, and the passing efficiency can be effectively improved.

The invention discloses a lateral transceiving integrated optical fiber ultrasonic endoscope and a preparation method, and belongs to the field of ultrasonic endoscopic imaging. Comprising a double-clad optical fiber, an excitation film, a dielectric mirror and a sensing film. A part of the inner cladding of the optical fiber is etched into a concave spherical surface and coated with an excitation film, excitation light transmitted in the inner cladding is absorbed, and focused ultrasonic waves transmitted in the lateral direction are excited. A part of an inner cladding layer and a fiber core of the optical fiber are etched, and a Fabry-Perot (Fabry-Perot, F-P) cavity is prepared. When an ultrasonic signal laterally transmitted into the ultrasonic endoscope acts on the sensing film of the cavity, the effective refractive index and the F-P cavity length of the sensing film are changed, and the change is demodulated through signal light in the fiber core to complete ultrasonic detection. According to the lateral transmitting-receiving integrated optical fiber ultrasonic endoscope, the inner cladding is etched and coated with the excitation thin film to excite laterally focused ultrasonic waves, an F-P cavity is formed through lateral etching of the optical fiber to achieve lateral ultrasonic detection, and the lateral transmitting-receiving integrated optical fiber ultrasonic endoscope is formed.

The invention provides an optical system, a control method, a vehicle optical signal device, a vehicle lamp and a vehicle, the optical system comprises at least one first light source assembly and a second light source assembly, and light emitted by the first light source assembly passes through an optical assembly and is emitted from a first light emitting area of a light emitting surface of the optical assembly; light emitted by the second light source assembly passes through the optical assembly and is emitted from a second light-emitting area of the light-emitting surface of the optical assembly, and at least partial area overlapping exists between the first light-emitting area and the second light-emitting area; wherein light emitted by the first light emitting area serves as a first light function, light emitted by the second light emitting area serves as a second light function, the first light function serves as a marker lamp of the driving automation system, and the second light function serves as other light functions with colors different from those of the first light function. Therefore, the structure and function multiplexing of the automatic system marker lamp and other signal lamps is realized, the cost of independently designing and manufacturing the automatic system marker lamp is effectively reduced, and the occupied space of the whole vehicle lamp is effectively reduced.

A wavelength cross-connect device performs a relay process in which multiple wavelength signal light beams that have been transmitted in multiple bands from a plurality of paths and demultiplexed into optical signals in the respective wavelength bands for each path are amplified, are switched to paths by contention WSSs, and are output to paths on the output side. A WXC unit performs the relay process on an optical signal in a specific wavelength band. An input-side conversion unit that converts a wavelength band other than the specific wavelength band into the specific wavelength band is provided on the input side, and an output-side conversion unit that converts the specific wavelength band after the conversion into the wavelength band prior to the conversion is provided on the output side. A directly-input optical signal in the specific wavelength band is directly output after the relay process at the WXC unit.

The invention discloses a sensing intelligent driving multi-mode traffic signal optimization method and system, and relates to the technical field of traffic signal optimization, and the method comprises the steps: collecting traffic flow data; constructing a multi-mode traffic flow model; connecting a plurality of traffic lights in the target area to obtain a street lamp control signal; based on the traffic flow data and the street lamp control signal, using the multi-mode traffic flow model to predict a traffic flow change trend; and configuring a timing optimization scheme of a plurality of traffic lights in the target area according to the traffic flow change trend prediction result. According to the method and the device, the technical problem of low traffic intersection passing efficiency caused by the fact that a traffic signal lamp timing scheme cannot be dynamically adjusted accurately according to real-time traffic conditions and multi-mode traffic behavior interaction relations in the prior art is solved, and the purposes of optimizing the traffic signal lamp timing of the target area according to the prediction result and improving the traffic signal lamp timing efficiency are achieved. And the traffic efficiency of the traffic intersection is effectively improved.

The invention discloses a trunk line intelligent network connection bus speed and signal timing collaborative optimization method, which comprises the following steps: acquiring basic information of bus lines, such as departure time interval, line length, station distribution and intersection signal phase; an intelligent networked bus speed control model is constructed, and dynamic speed guidance is implemented for different deviation types by calculating the deviation of the time headways of adjacent vehicles in real time; based on a vehicle speed control result, intersection signal lamp timing is optimized, and the negative influence of traffic signals on the driving speed of the public transport vehicle is reduced. By implementing accurate speed and signal lamp cooperative control at each bus station and intersection, the method can improve the overall operation efficiency of the trunk bus system to the maximum extent and reduce intersection delay while ensuring that the buses arrive on time, and compared with a traditional whole-line adjustment strategy, the method has the advantages that the overall operation efficiency of the trunk bus system is improved to the maximum extent, and the cost is reduced. According to the control strategy, the problem that the time headway deviation of a local road section is large can be solved more accurately.