69results about "Simultaneous traffic control systems" patented technology

A display system displays, on a display, positions of a work vehicle and one or more unmanned aerial vehicles flying around the work vehicle. The display system includes a processor configured or programmed to obtain position information of the work vehicle and the unmanned aerial vehicles, and, based on the position information, display the positions of the work vehicle and the unmanned aerial vehicles in a field shown on the display.

A computer-implemented method of using telematics data at a destination device is provided. The destination device may be a mobile device associated with a driver, or a smart vehicle controller of a destination vehicle. The telematics data is generated by an originating mobile device (i) having a Telematics Application (or “App”), and (ii) associated with a second driver / vehicle, the telematics data including acceleration, braking, speed, heading, and location data associated with an originating vehicle. The telematics data may be broadcast from the originating mobile device to the destination device that (a) analyzes the telematics data received, (b) determines that an abnormal travel condition exists, and (c) automatically take corrective action that alleviates a negative impact of the abnormal travel condition on the destination vehicle to facilitate safer travel. A usage-based or other insurance discount may be provided based upon insured usage of the telematics data-based risk mitigation or prevention functionality.

The present disclosure describes an autonomous warehouse package delivery system. The system includes a warehouse, a plurality of unmanned ground vehicles (UGV), a plurality of UAVs, and a plurality of servers. The warehouse includes a U-shaped conveyor belt, a plurality of shelving units, a plurality of package stacks, a first plurality of robots, and a second plurality of robots. Each UGV autonomously pick-ups a package from a shelf, navigates to the UAV landing pads, and places the package on the UAV landing pad. The plurality of UAVs travel to the UAV landing pad and pick up a package, autonomously fly to a package delivery address and release the package at the package delivery address. A real-time data distribution middleware network is connected to the U-shaped conveyor belt, the first plurality of robots, the second plurality of robots, the plurality of UGVs, the plurality of UAVs and a plurality of servers.

A computer-implemented method of using telematics data associated with an originating vehicle at a destination vehicle is provided. The method may include receiving telematics data associated with the originating vehicle by (1) a mobile device or (2) a smart vehicle controller associated with a driver or vehicle. The mobile device or smart vehicle controller may analyze the telematics data received to determine that (i) a travel event exists, or (ii) that a travel event message or warning is embedded within the telematics broadcast received. If the travel event exits, the method may include automatically taking a preventive or corrective action, at or via the mobile device or smart vehicle controller, which alleviates a negative impact of the travel event on the driver or vehicle to facilitate safer or more efficient vehicle travel. Insurance discounts may be provided to insureds based upon their usage of the risk mitigation or prevention functionality.

Determining a collision probability of a vehicle with an object. A three-dimensional position and orientation of the vehicle and the object is detected. The vehicle is approximated by at least one first geometrical element, that includes a part of the vehicle. The same is done for the object. A first error and a second error is determined in relation to the geometrical element for the vehicle or object. A Minkowski difference is formed for each combination of the at least one first geometrical element with the at least one second geometrical element. A third error is standardized via a transformation. The transformation serves to calculate calculation elements. The collision probability of the vehicle with the object is determined on the basis a spatial union of the calculation elements.

A computer-implemented method of using telematics data at a destination device is provided. The destination device may be a mobile device associated with a driver, or a smart vehicle controller of a destination vehicle. The telematics data is generated by an originating mobile device (i) having a Telematics Application (or “App”), and (ii) associated with a second driver / vehicle, the telematics data including acceleration, braking, speed, heading, and location data associated with an originating vehicle. The telematics data may be broadcast from the originating mobile device to the destination device that (a) analyzes the telematics data received, (b) determines that an abnormal travel condition exists, and (c) automatically take corrective action that alleviates a negative impact of the abnormal travel condition on the destination vehicle to facilitate safer travel. A usage-based or other insurance discount may be provided based upon insured usage of the telematics data-based risk mitigation or prevention functionality.

The present disclosure provides a kind of air-ground heterogeneous unmanned cluster traffic control and formation obstacle avoidance control method and system based on reinforcement learning, comprising: obtaining the stage privacy protection mechanism of air-ground heterogeneous unmanned cluster according to the unmanned vehicle dynamics model and unmanned vehicle dynamics model;Using the composite guide vector field that fusion is attracted force field and repulsive force field, determine the integrated error of the air-ground heterogeneous unmanned cluster formation;Construct non-fragile performance constraint function, and obtain error conversion function according to the integrated error;And according to the error conversion function, determine the distributed reinforcement learning virtual controller and actual controller;According to the distributed reinforcement learning virtual controller and actual controller, realize the air-ground heterogeneous unmanned cluster prescribed time consistency formation control.The present disclosure can prevent malicious nodes from deriving the initial position and speed trajectory of other nodes through state interaction.

This invention relates to a method for determining the collision probability of a vehicle (10) with an object (20, 25). The three-dimensional positions (x, y, z) and orientations of the vehicle (10) and the object (20) are detected. The vehicle (10) is approximated by at least one first geometry, wherein the at least one first geometry comprises a portion of the vehicle. The method is performed accordingly in the case of the object (20, 25). A first fuzziness (US1) and a second fuzziness (US2) are determined with respect to the geometry for either the vehicle (10) or the object (20, 25). A Minkowski difference (D') is formed for each combination of the at least one first geometry and the at least one second geometry. A third fuzziness (US3) is normalized by a transformation (TN). The transformation (TN) is used to calculate a computational volume (BK). The collision probability of the vehicle (10) with the object (20, 25) is determined based on the spatial union (D') of the computational volume (BK).

An autonomous transportation network (10) and method of operation (100a, 100b, 100c) are disclosed. The autonomous transportation network (10) includes a plurality of autonomous vehicles (20) having an on-board processor (27) and a vehicle memory (28) for locally computing (240, 310) a route (50) between an origin (30) and a destination (40), and a vehicle antenna (25) for transmitting the computed route (50). A control management center (100) includes a control management processor (120) and a central memory (140) and independently computes (250, 520) routes (50) for the plurality of autonomous vehicles (20). A plurality of beacons (17) are connected to and receive redirection information from the control management center (100) for transmission to one or more of the plurality of autonomous vehicles (20).

To provide a method for providing an inbound traffic alert.SOLUTION: A method includes receiving position sensor data for an aircraft. Position data is also received for target traffic. Based on the position sensor data for the aircraft, a position, a speed, and a direction of travel are determined for the aircraft. Based on the position data for the target traffic, a position, a speed, and a direction of travel are determined for the target traffic. The method includes determining that an aircraft 224 is on a runway 212 and determining that an aircraft 202 is an ownship. The method includes determining that the target traffic meets one or more target traffic alert criteria. An inbound traffic alert is output based on determining that the aircraft is on the runway, the aircraft is the ownship, and the target traffic meets the one or more target traffic alert criteria.SELECTED DRAWING: Figure 2

The invention relates to an unmanned vehicle optimization control method and scheduling system based on vehicle-road cooperation, and the method comprises the steps: obtaining a vehicle state, receiving A-CDM and A-SMGCS input data through the scheduling system, and combining with a scene operation situation after the fusion of roadside edge calculation equipment, carrying out the scheduling of the A-CDM and A-SMGCS input data, and carrying out the optimization control of the A-CDM and A-SMGCS. According to the method, unmanned vehicle trajectory planning is carried out, a multi-target cooperative scheduling and dynamic path planning model based on vehicle-road cooperation is assigned and established in real time, and an entropy weight model and a TOPSIS model are established to carry out risk evaluation and feedback on an output unmanned aerial vehicle scheduling scheme, so that various unmanned vehicles can rapidly arrive at an operation area. The operation delay of the unmanned vehicle in the flight time window is reduced, and the safe operation of the airport ground service vehicle is ensured.

A system to monitor vehicle accidents using a network of aerial-based monitoring systems, terrestrial-based monitoring systems and in-vehicle monitoring systems is described. Aerial vehicles used for this surveillance include manned and unmanned aircraft, satellites and lighter than air craft. Aerial vehicles can also be deployed from vehicles. The deployment is triggered by sensors registering a pattern in the data that is indicative of an accident that has happened or an accident about to happen.

The present invention relates to a system (1) for informing a driver of a means of transport (5) about an approach to an intersection point (12), comprising - a detection module (20) for detecting the approach of the means of transport (5) to the intersection point (10), wherein the detection module (20) sends a signal to a control module (30) upon approach; - the control module (30), which receives the signal, calculates a light stimulus (40) to be output based on the signal and displays the light stimulus (40) to be output on a lighting unit (50), so that the light stimulus (40) is perceived by the driver as an indicator that the means of transport (5) is approaching an intersection point (10).

Provided is an apparatus including: an area allocation unit which executes area allocation of allocating a plurality of robots to a plurality of areas, which are obtained by dividing a facility to be operated, based on specifications of the plurality of robots and requirements of a plurality of tasks to be executed in the facility; and a task allocation unit which executes, for a target area which is each of the plurality of areas, task allocation of allocating an intra-area task, which is at least one task belonging to the target area among the plurality of tasks, to a target robot which is at least one robot allocated to the target area among the plurality of robots.

In one embodiment, a micromobility transit vehicle may include a steering assembly. The steering assembly may include a steer tube configured to rotate within a headtube of the micromobility transit vehicle, and a stiffening brace positioned about the headtube. A stem may be coupled to the steer tube and the stiffening brace and configured to rotationally lock the stem and the stiffening brace together. A preload assembly may be coupled to the steering assembly and the stem and configured to adjust a preload of the steering assembly.

A processing system including at least one processor executing a traffic management application may detect a proximity to a traffic zone for vehicular traffic, the dual-mode vehicle having two modes of operation, the two modes of operation comprising a surface mode of operation and an aerial mode of operation, and may verify a rule set for vehicular operations within the traffic zone. The processing system may then identify a leader traffic management application from among a plurality of traffic management applications of a plurality of dual-mode vehicles within or near the traffic zone, obtain at least one navigation instruction from the leader traffic management application, and perform at least one of: executing a navigation action in accordance with the at least one navigation instruction, or displaying at least a portion of a permitted route that is in conformance with the at least one navigation instruction.

Systems and methods for improving aerial ride quality based on user feedback accessed from an aerial vehicle and devices associated with passengers are provided. A network system receives, from one or more devices associated with a passenger on an aerial vehicle, feedback data regarding a flight, whereby the feedback data is associated with an issue experienced by the passenger. The network system then identifies a root cause of the issue experienced by the passenger on the aerial vehicle. The identifying may include correlating the feedback data with other data associated with the flight. A mitigation action to mitigate the issue is determined. The network system may determine whether to trigger the mitigation action based on a corresponding threshold and can trigger the mitigation action to occur accordingly.

The embodiment of the invention provides an air-ground integrated multi-mode traffic cooperative scheduling method and system, and the method comprises the steps: collecting air-ground traffic real-time data, constructing a dynamic space-time diagram based on the collected data, and constructing a dynamic space-time diagram sequence; inputting a dynamic space-time diagram sequence into the space-time diagram neural network model to obtain a prediction result about a future traffic state; and candidate air-ground cooperative scheduling schemes are generated based on the prediction result, and a final air-ground cooperative scheduling scheme is determined from the candidate air-ground cooperative scheduling schemes. According to the technical scheme, through a space-time diagram neural network structure fused by GCN and GRU, space correlation features and time dependence features between air-ground nodes can be deeply excavated, mutual influence between ground congestion and low-altitude route conflicts can be captured, network cascade effects of ground road network congestion diffusion, low-altitude route conflict risks and logistics distribution delay can be accurately quantified, and the method and the device have the advantages of being high in reliability and high in reliability. And an active pre-judgment type collaborative decision is realized, so that the development requirement of the air-ground integrated multi-mode traffic system is met.

To allow a plurality of management companies for managing a space object flying in space to efficiently perform sharing execution of danger analysis.SOLUTION: Each of space transportation management devices 100 includes all of space transportation management rule information, a space information recorder 101, a danger alarm device 102, and a danger analysis device 103. Further, each of the plurality of space transportation management devices 100 includes all of a danger avoidance behavior support device 104 for displaying role sharing of an avoidance behavior of a space object, a danger avoidance behavior execution plan, a measurement error scrutiny device, and a security device 105 for preventing information falsification. The danger alarm device 102 notifies an approach or a collision of the space object, and even an anybody of a plurality of management companies quickly shares information among related companies with minimal effort in the case of foreseeing danger.SELECTED DRAWING: Figure 11

A computer-implemented method of generating and broadcasting telematics and / or image data is provided. Telematics and / or image data may be collected, with customer permission, in real-time by a mobile device (or a Telematics App running thereon) traveling within an originating vehicle. The telematics data may include acceleration, braking, speed, heading, and location data associated with the originating vehicle. The mobile device may generate an updated telematics data broadcast including up-to-date telematics data at least every few seconds; and then broadcast the updated telematics data broadcast at least every few seconds via wireless communication to another computing device to facilitate alerting another vehicle or driver of an abnormal traffic condition or event that the originating vehicle is experiencing. An amount that an insured uses or otherwise employs the telematics data-based risk mitigation or prevention functionality may be used with usage-based insurance, or to calculate or adjust insurance premiums or discounts.

A vehicle motion scoring device includes a processor configured to calculate at least one of a degree of variations in a distribution of a motion index or a maximum of the motion index, based on vehicle motion information indicating motion of a vehicle traveling along a predetermined road section, and set a lower score to the vehicle motion information as a calculated value of the degree of variations in the distribution of the motion index or the maximum of the motion index is greater. The motion index includes at least one of acceleration or deceleration of the vehicle, an amount of change in the acceleration or deceleration of the vehicle per unit time, an amount of change in a travel direction of the vehicle, or an amount of change in the travel direction of the vehicle per unit time.

This work management system for a flying work machine (FW) and a ground work machine (GW) in a work site comprises: a work flight information management unit (32) that manages work flight information including work flight characteristics of a flying work machine (FW) that performs ground work on a work site; a work travel information management unit (33) that manages work travel information including work travel characteristics of a ground work machine (GW) in the work site; and a work management unit that manages work flight behavior of the flying work machine (FW) and work travel behavior of the ground work machine (GW) on the basis of the work flight information and the work travel information.

System for predictive route monitoring from a vehicle, characterized by the following features: - a vehicle-integrated launch and landing device for an unmanned aerial vehicle for the automated deployment and return of the aircraft during vehicle operation, - an autonomously operated unmanned aerial vehicle with sensors for the airborne detection of road and environmental conditions, - a communication interface for bidirectional real-time data transmission between aircraft and vehicle, - a route planning module that generates a flight trajectory from a driving route specified in the vehicle, which follows the driving route at a defined advance distance, and derives control signals from this for the autonomous guidance of the aircraft, - an analysis and evaluation software that fuses sensor data transmitted by the aircraft in real time, classifies hazardous objects and road conditions, and generates warning signals and vehicle control parameters from this data, - an interface to vehicle-side display devices and driver assistance systems for outputting warning signals and for adapting at least one assistance system based on the vehicle control parameters.

In one embodiment, a micromobility transit vehicle may include a steering assembly. The steering assembly may include a steer tube configured to rotate within a headtube of the micromobility transit vehicle, and a stiffening brace positioned about the headtube. A stem may be coupled to the steer tube and the stiffening brace and configured to rotationally lock the stem and stiffening brace together. A cockpit housing is coupled to the stem to conceal one or more of an engagement of the stem with the steer tube or the engagement of the stem with the stiffening brace.

The invention discloses a high-speed traffic accident scene processing method and system based on an unmanned aerial vehicle nest, and belongs to the technical field of intelligent traffic systems, and the method comprises the steps: collecting accident data, matching an optimal nest, and controlling an unmanned aerial vehicle to autonomously fly to a scene; according to the on-site video stream, a remote watchman superposes an augmented reality virtual mark to carry out collaborative evidence obtaining, and an accident site three-dimensional digital twin model containing AR information is rapidly generated based on the AR mark and the video stream; synchronizing the twin model and the AR mark to multiple subjects for visual remote collaborative decision-making to obtain a disposal decision-making result; and based on the disposal decision result and the digital twinborn model, starting a traffic flow dynamic simulation model, and dynamically generating an optimized lane-level early warning and shunting strategy. According to the method, AR collaborative evidence obtaining, digital twin modeling and traffic flow dynamic simulation are adopted, rapid response, high-precision remote investigation and refined traffic intervention of an accident scene can be achieved, and the efficiency and safety of traffic accident disposal are improved.

The application discloses an air-ground collaborative management method based on mutual sensing verification, relates to the technical field of intelligent sensing, and comprises the following steps: determining a target area, arranging multiple groups of sensors, collecting and acquiring air parameter data and ground parameter data; transmitting the data to a data processing center through wireless communication technology to extract key information, acquire air key information and ground key information; presetting mutual sensing verification indexes, verifying the air key information and the ground key information, and acquiring verification results; if the verification results are accurate, inputting an air-ground collaborative management decision model to acquire a collaborative management decision; and performing air-ground collaborative management based on the collaborative management decision. The technical problem that the traditional air-ground collaborative management method in the prior art cannot comprehensively reflect the reliability of data and further leads to time delay of the collaborative management decision because the traditional air-ground collaborative management method relies on manual monitoring and scheduling is solved, and the technical effects of improving the accuracy and timeliness of data processing and management decisions are achieved.

A display system displays, on a display, positions of a work vehicle and one or more unmanned aerial vehicles flying around the work vehicle. The display system includes a processing device configured to obtain position information of the work vehicle and the unmanned aerial vehicles, and based on the position information, display the positions of the work vehicle and the unmanned aerial vehicles in a field shown on the display.

A method for aligning a vehicle to an ADAS calibration target may include providing a support bearing a camera arrangement and the calibration target, positioning the vehicle and the support so that the calibration target and the camera arrangement are in front of the vehicle, and moving the vehicle until a front license plate of the vehicle is detected by the camera arrangement. The method may also include acquiring one or more images and in response to detecting the front license plate in the images, identifying the corresponding alphanumeric code. The method may further include querying a first database to retrieve a model of the vehicle associated with said alphanumeric code, querying a second database to retrieve a predetermined position that is associated with the model of the vehicle, and providing commands to a driver for driving the vehicle so as the vehicle reaches said predetermined position.

A common command and control architecture (alternatively termed herein as a “universal control architecture”) is disclosed that allows different unmanned systems, including different types of unmanned systems (e.g., air, ground, and / or maritime unmanned systems), to be controlled simultaneously through a common control device (e.g., a controller that can be an input and / or output device). The universal control architecture brings significant efficiency gains in engineering, deployment, training, maintenance, and future upgrades of unmanned systems. In addition, the disclosed common command and control architecture breaks the traditional stovepipe development involving deployment models and thus reducing hardware and software maintenance, creating a streamlined training / proficiency initiative, reducing physical space requirements for transport, and creating a scalable, more connected interoperable approach to control of unmanned systems over existing unmanned systems technology.