Method and Internet of Things system for tidal lane opening management in smart city

    公开(公告)号:US12118882B2

    公开(公告)日:2024-10-15

    申请号:US18465147

    申请日:2023-09-11

    CPC classification number: G08G1/08 G08G1/0125

    Abstract: A method for tidal lane opening management in a smart city is provided. The method applied to the management platform includes issuing a data obtaining instruction to a sensor network platform to obtain a first traffic feature of a target road within a first time period from an object platform, wherein the first traffic feature is a feature reflecting a flow situation of the target road, determining a target tidal lane opening scheme of the target road within the first time period based on the first traffic feature using a preset algorithm, generating a scheme execution instruction based on the target tidal lane opening program, sending the scheme execution instruction to the target object through, and issuing a data upload instruction to send the target tidal lane opening scheme to the user platform through a service platform.

    Sensor abnormality estimation device

    公开(公告)号:US12024177B2

    公开(公告)日:2024-07-02

    申请号:US17689573

    申请日:2022-03-08

    Abstract: A sensor abnormality estimation device determines whether or not a position and a speed of a sensor-mounted vehicle at an intersection fulfill a predetermined performance condition. When the position and the speed fulfill the performance condition, the sensor abnormality estimation device acquires recognition results obtained by two of a plurality of external sensors, for a target object at a designated position associated with the position of the sensor-mounted vehicle. The sensor abnormality estimation device then acquires a degree of coincidence between the recognition results, and determines that there is an abnormality in at least one of the two external sensors when the degree of coincidence is lower than a predetermined determination value.

    Road traffic analysis methods and apparatuses

    公开(公告)号:US12014629B2

    公开(公告)日:2024-06-18

    申请号:US17646397

    申请日:2021-12-29

    CPC classification number: G08G1/082 G08G1/0125 G08G1/08 G08G1/083

    Abstract: Embodiments of the present disclosure can provide a road traffic analysis method and an apparatus. The method can comprise obtaining a traffic parameter of a road intersection by analyzing road traffic information of the road intersection, determining a reference adjustment length for each phase of a traffic signal cycle corresponding to each lane of the road intersection based on the road traffic information and the traffic parameter. The traffic signal cycle has one or more phases. The method can also comprise determining a first adjustment length when a difference between the reference adjustment length and the first adjustment length satisfies a condition associated with the lanes of the road intersection and the corresponding traffic parameter, and adjusting the phases of the traffic light cycle at the road intersection based on the first adjustment length for each phase.

    Regional dynamic perimeter control method and system for preventing queuing overflow of boundary links

    公开(公告)号:US11908321B2

    公开(公告)日:2024-02-20

    申请号:US17611225

    申请日:2021-01-07

    Inventor: Yajuan Guo

    CPC classification number: G08G1/08 G08G1/0125 G08G1/0145 G08G1/065

    Abstract: A regional dynamic perimeter control method and system for preventing boundary links queuing overflow. The method includes: estimating the number of queuing vehicles of boundary links using a Kalman filtering extension method using traffic flow information, and calculating a maximum of receivable vehicles; dividing the boundary links utilizing an estimated number of each boundary link's queuing vehicles and the maximum number of receivable vehicles obtaining a boundary link set with sufficient storage and a boundary link set with insufficient storage; obtaining a critical accumulation of a region according to a preset Macroscopic Fundamental Diagram (MFD) model of the region, and predicting the estimated region accumulation in a sampling period; and controlling a regional boundary intersection traffic flow operation using a deviation between the predicted and critical accumulation and boundary link sets. Deterioration of regional traffic flow is avoided, and the probability of overflow of boundary links is reduced.

    DISTRIBUTED ACOUSTIC SENSING OF TRAFFIC
    5.
    发明公开

    公开(公告)号:US20240046784A1

    公开(公告)日:2024-02-08

    申请号:US18265385

    申请日:2020-12-04

    CPC classification number: G08G1/04 G08G1/0145 G08G1/052 G08G1/08

    Abstract: Methods and apparatus for estimating a position of a boundary of a queue of traffic are disclosed, the queue extending along a roadway. Distributed acoustic sensing is used to generate, as a function of time and of position along the roadway, a signal representing acoustic vibration at a sensing optical fibre that extends along the roadway. A queue signature is detected in the signal of either the slowing down of a plurality of vehicles as they approach the boundary of the queue, or speeding up of a plurality of vehicles as they advance from the departed boundary of the queue. A position of the boundary of the queue is then estimated from the detected queue signature. Methods and apparatus are also disclosed for controlling one or more road traffic signals by using distributed acoustic sensing to detect acoustic vibration at one or more sensing optical fibres disposed along a roadway, as a function of time and of position along a roadway, and controlling the one or more road traffic signals responsive to the detected acoustic vibration.

    MULTI-INTELLIGENCE FEDERAL REINFORCEMENT LEARNING-BASED VEHICLE-ROAD COOPERATIVE CONTROL SYSTEM AND METHOD AT COMPLEX INTERSECTION

    公开(公告)号:US20240038066A1

    公开(公告)日:2024-02-01

    申请号:US18026835

    申请日:2022-08-04

    CPC classification number: G08G1/08

    Abstract: A multi-intelligence federated reinforcement learning (FRL)-based vehicle-road cooperative control system and method at the complex intersection use a vehicle-road cooperative control framework based on the Road Side Unit (RSU) static processing module and the vehicle-based dynamic processing module. The historical road information is supplied by the proposed RSU module. The Federated Twin Delayed Deep Deterministic policy gradient (FTD3) algorithm is proposed to connect the federated learning (FL) module and the reinforcement learning (RL) module. The FTD3 algorithm transmits only neural network parameters instead of vehicle samples to protect privacy. Firstly, FTD3 selects only specific networks for aggregation to reduce the communication cost. Secondly, FTD3 realizes the deep combination of FL and RL by aggregating target critic networks with smaller Q-values. Thirdly, RSU neural network participates in aggregation rather than training, and only shared global model parameters are used.

    MANAGING TRAFFIC LIGHT DETECTIONS
    7.
    发明公开

    公开(公告)号:US20240038065A1

    公开(公告)日:2024-02-01

    申请号:US17876340

    申请日:2022-07-28

    Inventor: Chong Meng Wong

    CPC classification number: G08G1/08 G08G1/095 G08G1/083

    Abstract: Provided are methods for managing traffic light detections, which can include: deriving a first state of a traffic light at an intersection a vehicle is approaching, according to first detection data acquired by a first traffic light detection (TLD) system; deriving a second state of the traffic light at the intersection, according to second detection data acquired by a second TLD system that is independent from the first TLD system; determining traffic light information at the intersection based on at least one of (i) the first state or (ii) a result of checking whether the first state is same as the second state; and causing the vehicle to operate in accordance with the determined traffic light information at the intersection. Systems and computer program products are also provided.

    METHOD, SYSTEM AND COMPUTER READABLE MEDIUM FOR PROBABILISTIC SPATIOTEMPORAL FORECASTING

    公开(公告)号:US20240012875A1

    公开(公告)日:2024-01-11

    申请号:US18365568

    申请日:2023-08-04

    CPC classification number: G06F17/17 G08G1/08

    Abstract: Probabilistic spatiotemporal forecasting comprising acquiring a time series of observed states from a real-world system, each observed state corresponding to a respective time-step in the time series and including a set of data observations of the real-world system for the respective time-step. For each of a plurality of the time steps in the time series of observed states, a hidden state is generated for the time-step based on an observed state for a prior time-step and an approximated posterior distribution generated for a hidden state for the prior time-step. The use of an approximated posterior distribution can enable improved forecasting in complex, high dimensional settings.

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