15results about "Traffic control systems" patented technology

The invention relates to air route quantitative evaluation, in particular to an unmanned aerial vehicle air route dynamic quantitative evaluation method based on comprehensive risk avoidance, and the method comprises the steps: constructing a multi-dimensional air route risk quantitative evaluation system, and enabling each dimension to comprise a plurality of specific and quantifiable risk evaluation factors; mapping the observation value of each risk assessment factor into a standardized risk score; determining the weight of each risk assessment factor according to a dynamic weight adjustment strategy, and calculating a route comprehensive risk score by combining the standardized risk score of each risk assessment factor; executing the evaluation process on all the candidate paths, calculating an air route comprehensive risk score for each candidate path, preferentially recommending the candidate path with the highest air route comprehensive risk score, and generating an air route risk evaluation report at the same time; according to the technical scheme provided by the invention, the defect that accurate dynamic quantitative evaluation of the route of the unmanned aerial vehicle is difficult in the prior art can be effectively overcome.

An object of the invention is to maintain autonomy of each traffic service provider and enables efficient cooperation between traffic services. A traffic service cooperation support system 1 includes a plurality of traffic service planning devices that manage traffic service plan information including transportation plan information between boarding and alighting points of a passenger, and a traffic service cooperation support device 100. The traffic service cooperation support device 100 executes processing of calculating a transportation service evaluation value for a passenger at a transfer point based on the traffic service plan information received from each traffic service planning device and travel information of a passenger by each traffic service, and processing of transmitting a target value of a transportation service of a transportation mode of a traffic service at the transfer point to the traffic service planning device of a traffic service through which a passenger gets on and off at the transfer point when the evaluation value does not satisfy a predetermined condition. The traffic service planning device controls the transportation mode based on the traffic service plan information generated based on the target value.

The invention discloses a space target long-time-sequence intelligent collision early warning method, and belongs to the technical field of space. The implementation method comprises the steps of constructing a double-branch multi-scale time convolution network long-time-sequence orbit forecasting model, and on the basis of a traditional time convolution network, establishing a relation between the model and perturbation of different time scales by establishing two branch expansion convolution structures and setting different expansion coefficients, so that the forecasting precision of the model is improved. On the basis, a voxel hash mapping rapid screening method is established, a Gaussian negative logarithm likelihood loss function is introduced, statistical characteristics of forecast errors are utilized, a voxel cube is constructed, only voxels where satellites are located and the distance between targets in neighborhoods of the voxels and the satellites need to be calculated during screening, the screening calculation scale is greatly reduced, and the screening speed is increased. The method does not depend on a high-precision dynamic model, can sense and extract the features of different time scales, improves the track forecasting precision and the collision risk screening efficiency, and accelerates the collision early warning speed.

An anomaly management system includes one or more processors. The one or more processors are configured to execute: video acquisition processing of acquiring a video captured by a camera mounted on a moving body; anomaly event recognition processing of automatically recognizing an anomaly event shown in the video and content of the anomaly event by using a machine learning model; notification destination determination processing of automatically determining a notification destination according to the content of the anomaly event; and notification processing of automatically transmitting to the notification destination, anomaly event information including at least information indicating the content of the anomaly event and position information indicating a position of the anomaly event.

A system includes a vehicle system provided with an autonomous vehicle.The vehicle system includes one or more computing devices configured to define a shared virtual secret key with an infrastructure server using a public server key obtained from the infrastructure server, generate a light code pattern to identify the autonomous vehicle using the shared virtual secret key and a code length, transmit a vehicle public key and flashing characteristics of the light code pattern, control one or more selected lighting devices of the autonomous vehicle to perform a flashing sequence indicative of the light code pattern, and transition to a maneuvering mode of operation to have the autonomous vehicle controlled by the infrastructure server in response to the autonomous vehicle being identified based on the flashing sequence.

A method includes identifying a route for passengers seeking to enter a building. The route includes at least one section of road for automobiles to access a building; or at least one access point inside the building for pedestrians to pass through. The method includes receiving traffic flow information corresponding to the route. The method includes determining whether the received traffic flow information corresponds to a condition that impedes the route, from among a set of pre-trained conditions. The method includes identifying and displaying a list of user-selectable state alternatives related to the condition that impedes the route, based on a determination that the received traffic flow information corresponds to the condition.

A vehicle control system and method may receive state information from a vehicle group having one or more vehicles. The state information may indicate an operational parameter of one of the vehicles. The controller may direct a display device to present one or more statuses of the one or more vehicles based at least in part on the state information that is received by the controller. This state information may be used to notify an operator of a potential component fault before continued operation of the vehicle group further damages the component or a vehicle. In another example, the state information may be used to control or change operation of one vehicle based on the state or status of another vehicle, such as by changing movement of one or more drones in a swarm responsive to a lower state of charge of another drone in the same swarm.

This specification provides an embodiment of a smart city location recommendation method and system based on the Internet of Things (IoT), executed by a management platform of the smart city location recommendation IoT system. The method includes acquiring location information and pedestrian flow information for at least one location; wherein, the location information includes the number of locations, the distance between locations, and the control measures for entrances and exits and passageways in each location; the control measures include whether the entrances and exits and passageways in each location are closed; based on the location information and pedestrian flow information of at least one location, determining multiple candidate control schemes for at least one location; and based on the multiple candidate control schemes, determining a target control scheme for at least one location.

This invention discloses a large-scale space target collision early warning combination screening method, belonging to the field of space situational awareness. The implementation method is as follows: Preliminary screening of similar targets is performed based on the distance between intersection points on the orbital plane intersection line to quickly reduce the number of satellite combinations to be analyzed; by decomposing the analytical solution considering the minimum relative distance between satellites under J2 perturbation, it is divided into long-term terms and exact solutions; based on the evolution characteristics of the long-term terms, a second screening is performed using the trend characteristics of the long-term terms; a third screening is performed using the analytical exact solution considering the relative distance between satellites under J2 perturbation; based on the results of the third screening, satellite combinations with collision risk are determined, realizing large-scale space target collision early warning combination screening, thereby protecting the safety of space assets. This invention fully considers the long-term evolution characteristics of orbits under perturbation, significantly reducing the calculation time for early warning, and is suitable for rapid screening of long-term collision early warnings for large-scale space targets.

In a condition where a construction machine is in operation in an area where communication means for transmitting operation data to a server is not available, processing for accumulating operation data of the construction machine in the server is executed. A vehicle-side control unit (17) determines whether long distance communication means (W) is available or not, when determining that the long distance communication means (W) is available, controls a first vehicle-side communication unit (14) so as to transmit the operation data via the long distance communication means (W), and when determining that the long distance communication means (W) is not available, causes a vehicle-side storage unit (15) to store the operation data, and further determines whether a transmission start command is received from a tablet (3) or not, and when determining that the transmission start command is received, controls a second vehicle-side communication unit (16) so as to transmit the operation data stored in the vehicle-side storage unit (15) via short distance communication means (N).

To provide a group control system and a group control method that allow machine learning by taking acceleration and deceleration data into account and group controlling based on correct position information.SOLUTION: A group control system controls a plurality of moving objects 10 that are capable of traveling autonomously within a predetermined area. The group control system includes a position information estimation unit 101 that estimates position information of each moving object, a route planning unit 102 that creates a route plan for each moving object based on the estimated position information, an acceleration data acquisition unit that acquires acceleration data acquired from an acceleration sensor 11, and a moving object position acquisition unit that acquires an actual position of each moving object using a position sensor. A deep reinforcement learning model is trained so as to correct the amount of deviation between the actual position of the moving body obtained using the position sensor and the estimated position of the moving body based on the obtained acceleration data.SELECTED DRAWING: Figure 4

An apparatus, method, and computer program product for: communicating with a first proximity detection sensor and a second proximity detection sensor; detecting the presence of a collision detection device of the moving object within a first proximity detection range based on first information received from the first proximity detection sensor; in response to detecting the presence of the collision detection device, determining a vehicle speed parameter corresponding to a second proximity detection range; controlling a speed of the autonomous mining vehicle based on the vehicle speed parameter; and in response to detecting the presence of the collision detection device within the second proximity detection range, controlling the autonomous mining vehicle to perform at least one collision avoidance operation based on second information received from a second proximity detection sensor.

Method for detecting track anomalies and associated device This method (100) includes, in configuration, the steps of: training a neural network (110) on training tracks to predict a log-likelihood value of a data track; calculation (120) of an average log-likelihood value for validation tracks and calculation (125) of a median; calculation (130) of a score on the average log-likelihood value of test tracks with respect to the median and calculation (135) of a statistical coefficient; definition (140) of an anomaly threshold from the median and the statistical coefficient.The process includes, in inference, the steps of: acquiring (150) at least one lead of interest; processing (160) the lead of interest by: calculating a mean log-likelihood value of the lead of interest; calculating a score of the mean log-likelihood value of the lead of interest relative to the median; and comparing the score of the mean log-likelihood value of the lead of interest to the abnormality threshold, the lead of interest being "abnormal" when the score of the mean log-likelihood value of the lead of interest is greater than the abnormality threshold, and "normal" otherwise. Figure for the abstract: Figure 3.

An emergency driving situation control apparatus using a brain wave signal includes a sensor configured to collect a brain wave signal for at least one passenger of a mobility in a predetermined channel region, an analyzer configured to determine, by analyzing the brain wave signal collected in the predetermined region, whether or not an emergency situation has occurred to the mobility, and a controller configured to control an operation of the mobility based on a result of the determination.