5261 results about "Road traffic" patented technology

Techniques are described for automatically detecting anomalous road traffic conditions and for providing information about the detected anomalies, such as for use in facilitating travel on roads of interest. Anomalous road traffic conditions may be identified using target traffic conditions for a particular road segment at a particular selected time, such as target traffic conditions that reflect actual traffic conditions for a current or past selected time, and/or target traffic conditions that reflect predicted future traffic conditions for a future selected time. Target traffic conditions may be compared to distinct expected road traffic conditions for a road segment at a selected time, with the expected conditions reflecting road traffic conditions that are typical or normal for the road segment at the selected time. Anomalous conditions may be identified based on sufficiently large differences from the expected conditions, and information about the anomalous conditions may be provided in various ways.

The invention relates to a road traffic control method and corresponding traffic facilities. The method includes: setting roads mounted or vehicles mounted devices for controlling queens passing; setting associated traffic facilities which assist controlling queens; guiding, including and controlling to vehicle flows in various directions by the above devices and facilities; arranging the vehicle queens into sequential array at the same time or by turns in roads and crosses according to preset procedure. By the method, the vehicle flows can pass continuously or non-continuously without crossing or with less crossing in multi-driveway road. The invention has small investment, wide application and can greatly improve the cross passing ability.

The invention provides a four-dimensional live-action traffic road condition perceiving and early-warning monitoring management system based on radar tracking and positioning. According to the four-dimensional live-action traffic road condition perceiving and early-warning monitoring management system, a tracking and positioning technology, a license plate snapping technology, a vehicle and pedestrian three-dimensional simulation technology, a road monitoring technology, an early-warning and alarming technology, a traffic condition simulation technology, a network communication technology, a meteorological simulation technology, a computer graphic technology, a video rendering technology, a three-dimensional high-precision GIS map technology, a virtual reality fusion technology, a vehicle-road collaborative technology and a navigation technology are perfectly fused together; and through the system, the various demands of road traffic operation managers for road real-time monitoring orarea monitoring and the information demands of traveling of drivers and passengers for real-time and real traffic conditions are greatly met, and the various drawbacks which are imperfect and cannot be solved in an existing monitoring system are overcome, so that the management efficiency of the road managers is improved, the firm foundation is laid for automatic management of roads, and the important safety guarantee is also provided for traveling vehicles on the road.

The invention relates to a bus arrival time prediction technique based on bus GPS data, which comprises sending out from the existing bus GPS data, making full use of auxiliary information such as pulling in, door opening and the like of a bus GPS apparatus, adopting a space dividing method to divide zones for bus lines, accounting the history arrival time of partitioned areas to each station in front according to the actual arrival time of a bus, and leading the bus arrival time to continuously do self-adapting update as the accumulation of history data, and meanwhile, predicating the bus arrival time by means of road traffic status which is calculated by GPS information of a taxi or road traffic status calculated by other systems, and improving the predicating accuracy.

Method for detecting concealed objects in road traffic in which the surroundings of a vehicle and movement variables of the driver's vehicle are sensed by sensors, said variables are transmitted as information to vehicles which are located in the surroundings by an interface for vehicle-to-vehicle communication and are received from the vehicles which are located in the surroundings, wherein the following steps are executed:

• • the data from the sensors expand a surroundings model,
  • the expanded surroundings model is represented in updated form by a display in the driver's vehicle,
  • a situation analysis of the surroundings and an evaluation of the situation are carried out in the driver's vehicle,
  • objects which represent an accident hazard on the display are displayed with a high priority,
  • predefined steps for reducing accident hazard are activated in the driver's vehicle,
  • information relating to the pre-defined steps are transmitted to the surroundings by the communication system.

The invention discloses an unmanned perception based unmanned aerial vehicle route planning method for rapidly planning the vehicle driving route. According to the invention, based on the unmanned flight perception system, road video images around a target vehicle are photographed by utilizing an unmanned flight to be sent to a ground station; the ground station receives the video images and processes the video images so as to obtain the vehicle and road messages, route planning is carried out according to the vehicle position, and the route is transmitted to the unmanned aerial vehicle; and a computer module of the ground station calculates a flight control signal to control the unmanned aerial vehicle to follow the vehicle to proceed, and corrects the route in real time. According to the invention, based on an unmanned flight platform, the operation state is stable; and the road environment around the vehicle is photographed from a great height, the coverage area is wide, and the observed road traffic information is distinct and clear, so that the road distribution condition and the traffic condition around the target vehicle can be obtained rapidly, the reasonable route planning can be obtained, the route is more precise, and the road condition analysis is more timely and accurate.

The invention provides a self-adapting quick response system for urban traffic congestion, comprising a center system, a roadside detector, a roadside detection controller, a dynamic display board on the road and an information subsystem, wherein the center system is used for processing information which is transferred by the roadside detector and the roadside detection controller which are connected with the center system and returning results after analyzing the information; the roadside detector is used for detecting the road traffic state by utilizing a comprehensive inductor and sending detection results back to the center system as well as the adjacent roadside detector and roadside detection controller; the roadside detection controller is used for detecting and analyzing the relevant traffic flow and the congestion state of the road section or the road junction and controlling the traffic light length, a wireless shirt-distance information publishing device and the dynamic information board; the dynamic display board on the road is used for intercommunicating information with the center system and the adjacent roadside detection controller and dynamically displaying the information after being analyzed; and the information subsystem is connected with the center system and used for pertinently broadcasting traffic information to drivers through various ways. By means of the system provided by the invention, the possible congestion can be rapidly detected and controlled to ensure that the congestion is eradicated at the beginning of forming.

The invention relates to a multi-information-fusion-based high precision localization method for an unmanned vehicle, and the multi-information-fusion-based high precision localization method can be applied to environment perception and intelligent decision-making of the unmanned vehicle. Through cooperation of an offline map and online sensing information, high-precision real-time positioning can be realized. The offline map records road traffic information of a driving area of the unmanned vehicle. The online sensing information includes lane lines and road boundaries. When the unmanned vehicle runs in a map area, according to location information given by an inertial combined navigation system, an approximate location of the vehicle is determined, a local map near the approximate location is obtained, the lane lines in front of the vehicle and the road boundaries on both sides of the vehicle can be detected by a vehicle sensor, the relative position of the vehicle and the lane lines and the road boundaries can be determined, by contrast of the location of the vehicle in the map, deviation is calculated, a location error is corrected, and high-precision localization can be achieved.

The invention discloses a method for recognizing a road traffic sign for an unmanned vehicle, comprising the following steps of: (1) changing the RGB (Red, Green and Blue) pixel value of an image to strengthen a traffic sign feature color region, and cutting the image by using a threshold; (2) carrying out edge detection and connection on a gray level image to reconstruct an interested region; (3) extracting a labeled graph of the interested region as a shape feature of the interested region, classifying the shape of the region by using a nearest neighbor classification method, and removing a non-traffic sign region; and (4) graying and normalizing the image of the interested region of the traffic sign, carrying out dual-tree complex wavelet transform on the image to form a feature vector of the image, reducing the dimension of the feature vector by using a two-dimension independent component analysis method, and sending the feature vector into a support vector machine of a radial basis function to judge the type of the traffic sign of the interested region. By using the method, various types of traffic signs in a running environment of the unmanned vehicle can be stably and efficiently detected and recognized.

The invention discloses a harbor district road traffic demand predicting system which is based on a TransCAD macro simulated platform and is used to obtain harbor district road traffic generation amount in an objective year. The predicting system at least comprises a storage module, a harbor district road network model, a road network model application module, a road network loading distribution unit, an analysis evaluation module and a planning module, wherein the storage module is used to store data basis for predicting harbor district road traffic generation amount; the harbor district road network model inputs a harbor district project map into a TrarsCAD model platform through a harbor district project geographical information database so as to establish the harbor district road network model according to road traffic circulation in a harbor district; the road network model application module optimizes and selects traffic parameters by means of genetic algorithm to obtain a harbor district objective year OD matrix; the road network loading distribution unit is used for obtaining the traffic flow distribution state and traffic circulation state of the entire road network; the analysis evaluation module combines with the traffic distribution result to carry out traffic adaptability analysis evaluation on a future road network planning scheme; and the planning module is used to put forward guidance instructions and overall measures with regard to harbor district road traffic planning.

The invention discloses an automobile intelligent speed limiting system and an automobile speed limiting method, and relates to the technical field of automobile safety driving assistance. The automobile intelligent speed limiting system and the automobile speed limiting method can improve accuracy of speed limiting identification recognition and improve driving safety of a driver. The automobile intelligent speed limiting system comprises a camera, an intelligent navigation device, an automobile body sensor and a controller, wherein the camera can recognize road traffic identification boards and extract road traffic identification information through the image processing technology, the intelligent navigation device can provide current road information, navigation route information and national or regional traffic road speed limiting law related information in real time, the automobile body sensor can provide automobile body information in real time, a traffic speed limiting identification database is set up in the controller, the input end of the controller is connected with the camera, the intelligent navigation device and the automobile body sensor, and the output end of the controller is connected with an automobile instrument and an engine ECU. The automobile intelligent speed limiting system is used for assisting in limiting the speed of the automobile.

The invention is a taxi operation safety and dispatching control system consisting of vehicle device, GSM/GPRS or 3G mobile communication platform, road traffic information module, calling module, preserving and calling module, passenger position identifying module, monitor module, geographic information module, safety module, guide module, charging module and so on. The system sends the passenger on and off information into dispatching center and records vehicle coordinate and line to dispatching center data base.

The invention provides a method and device for detecting road traffic abnormal events in real time. The method includes the steps of monitoring a road, obtaining a plurality of frames of continuous monitor images, extracting bright white segments from the monitor images, obtaining lane lines and lane end points through processing, building a lane model, determining a bidirectional detection area of a lane according to the lane model, detecting a moving object in the bidirectional detection area according to a Gaussian mixture model background subtraction method, determining the position of the moving object, building the mapping relation between the moving target and an actual vehicle according to the position of the moving target in the multiple frames of continuous monitor images by the adoption of a posterior probability splitting and merging algorithm and a feature point matching and tracking method, obtaining the running track and running speed of the actual vehicle, detecting the lane model and the running track and running speed of the actual vehicle according to a prestored road traffic abnormal behavior semantic model, and judging whether the road traffic abnormal events exist or not. The method has the advantages of being intelligent, high in accuracy and the like.

The invention discloses an urban road traffic information detecting and issuing system, which comprises a traffic flow information collection sub-system used for collecting the road traffic information; a traffic flow analyzing and processing sub-system which is connected with the traffic flow information collection sub-system through a communication sub-system and used for extracting the traffic information with the same characteristic to conduct analysis and processing; a dynamic traffic information platform which is connected with the traffic flow analyzing and processing sub-system, used for storing real-time and historical data and can implement information sharing and exchanging; and a traffic information issuing sub-system which is connected with the dynamic traffic information platform through the communication sub-system and used for issuing the processed result internally and externally in a form of road network traffic state index system. The urban road traffic information detecting and issuing system can detect and issue the road traffic information in real time and is convenient for the effective management of the road traffic.

The invention discloses a light stream based vehicle motion state estimating method which is applicable to estimating motion of vehicles running of flat bituminous pavement at low speed in the road traffic environment. The light stream based vehicle motion state estimating method includes mounting a high-precision overlook monocular video camera at the center of a rear axle of a vehicle, and acquiring video camera parameters by means of calibration algorithm; preprocessing acquired image sequence by histogram equalization so as to highlight angular point characteristics of the bituminous pavement, and reducing adverse affection caused by pavement conditions and light variation; detecting the angular point characteristics of the pavement in real time by adopting efficient Harris angular point detection algorithm; performing angular point matching tracking of a front frame and a rear frame according to the Lucas-Kanade light stream algorithm, further optimizing matched angular points by RANSAC (random sample consensus) algorithm and acquiring more accurate light stream information; and finally, restructuring real-time motion parameters of the vehicle such as longitudinal velocity, transverse velocity and side slip angle under a vehicle carrier coordinate system, and accordingly, realizing high-precision vehicle ground motion state estimation.

Techniques are described for assessing road traffic conditions in various ways based on obtained traffic-related data, such as data samples from vehicles and other mobile data sources traveling on the roads, as well as in some situations data from one or more other sources (such as physical sensors near to or embedded in the roads). The assessment of road traffic conditions based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest from the data samples. In some situations, at least some of the mobile data sources are configured to frequently acquire and store data samples, and to occasionally make multiple such data samples available together for use in the road traffic condition assessment (e.g., by acquiring a data sample every minute and by transmitting a group of stored data samples every 15 minutes).

In order to be able to undertake a reliable prediction of a movement trajectory of an object moving in road traffic even for relatively long prediction periods, a method is proposed in which there is firstly determined (203) with the aid of an evaluation of a determined movement variable of the object a driving maneuver that is executed by the object. A model (205₁; . . . ; 205_N) of the movement of the object is then selected as a function of the determined driving maneuver, and the movement trajectory of the object is calculated with the aid of the selected model. There is also proposed an apparatus for predicting the movement trajectory of an object that is suitable for carrying out the method. The object can be both a motor vehicle and an object in the surroundings of the motor vehicle.

A navigation device has a GPRS receiver for receiving real-time information about slow traffic flow or slow average speed on a stretch of motorway, indicating congestion. The device calculates a new itinerary to avoid the congestion, based on historically recorded speeds on secondary roads weighed by the current average speed in the congestion area.

The invention discloses a pedestrian identification method under a road traffic environment based on improved YOLOv3. The method comprises the following steps of: S1, acquiring and pre-processing an image, and making a pedestrian sample set; 2, calculating the length-width ratio of the pedestrian candidate frames by using a clustering algorithm and the training set; 3, inputting the training set into the YOLOv3 network for multi-task training and saving the trained weight file; S4, inputting a picture to be recognized into the YOLOv3 network to obtain a multi-scale characteristic map; S5, using a logistic function to activate the x, y, confidence degree and category probability of the network prediction, and obtaining the coordinates, confidence degree and category probability of all prediction frames by judging the threshold value; S6, generating a final target detection frame and a recognition result by carrying out the non-maximum value suppression processing on the above result. The method of the invention solves the problem of low detection accuracy of the prior method, realizes the multi-task training, does not need additional storage space, and is high in detection accuracyand fast in speed.