15 results about "Traffic conflict" patented technology

The application discloses a kind of dangerous event chain extraction method and system based on complex traffic scene, including obtaining test scene data;Based on the key factors of dynamic attribute and interaction between vehicle and environment determined by preset natural driving data and sensor performance information, and the uncertainty of key factors is quantified;Based on the risk estimation multidimensional feature set and uncertainty information preset constructs driving risk estimation model for test scene;The driving risk estimation model is optimized, and the optimized risk estimation model for test is obtained;Based on the time evolution characteristics of dangerous event, a dangerous time chain model is constructed according to the optimized risk estimation model;Dangerous time chain model is solved, and dangerous event chain is obtained.The application uses traffic accident data and traffic conflict data to reveal the space-time evolution law of complex traffic scene, and improves the effect of later test scene reproduction and reconstruction by solving the dangerous time chain model.

This invention provides a short-term traffic conflict prediction method and early warning system based on RSU group collaboration and spatiotemporal graph neural network. It acquires vehicle trajectory and status data collected collaboratively by roadside unit groups, using the coverage area of ​​each RSU as a spatial analysis unit, and constructs an initial energy field distribution map of traffic conflict based on the spatial density of vehicle distribution and the intensity of speed temporal fluctuations. A pre-constructed spatiotemporal graph neural network is invoked to capture energy transfer paths between units, and a temporal cyclic layer is used to capture the energy change inertia within units, predicting and generating a dynamic evolution map of traffic conflict energy containing an energy evolution path network and peak times. Based on the map, potential conflict outbreak sources and expected affected unit sequences are identified, generating conflict propagation situation prediction information including spatial coordinates and propagation time sequences. Based on this information, differentiated collaborative working mode adjustment instructions are generated and distributed to each RSU. This invention achieves short-term prediction of traffic conflicts and collaborative scheduling of roadside resources.

This invention relates to the technical fields of tunnel safety control, intelligent transportation, vehicle-road cooperation, and tunnel safety early warning. Specifically, it relates to a tunnel active safety control system and method based on vehicle-road cooperation. Key technical points include: a multi-source data fusion module: integrating data from roadside equipment (including radar, cameras, and fiber optic sensors) and vehicle-mounted terminals to construct a four-dimensional perception network of people, vehicles, roads, and the environment; a dynamic risk assessment module: quantifying the probability of an accident (P) and collision energy loss (E) based on an equivalent traffic conflict number (ECN) model to achieve risk-level early warning; and a vehicle-road cooperative intervention module: triggering early warning and control strategies from both the roadside and vehicle perspectives through two-way communication between roadside smart guidance signs, information boards, and vehicle-mounted terminals. This invention improves the ability to respond to tunnel risks.

The present application relates to a kind of riding risk perception test methods based on immersive virtual reality interactive platform, the method is by constructing virtual reality traffic scene, simulation builds immersive virtual reality interactive test platform, let rider simulate real riding behavior in virtual scene, by recording the speed, acceleration and other riding data of rider, jointly use the objective data of riding behavior in virtual reality and risk perception subjective data to carry out risk assessment, construct dynamic risk assessment model applicable to immersive virtual reality interactive platform, and study the reaction behavior of rider under traffic conflict scene, test the perception and response ability of rider to riding risk.The present application can provide theoretical basis for slow traffic safety analysis, and the test results can be targeted to propose safety suggestions, so as to improve the traffic safety consciousness of rider and reduce the occurrence of traffic accident.

The application relates to the technical field of intelligent warehousing, and discloses a method for AGV and automated stereoscopic warehouse collaborative scheduling, which comprises the following steps: S1, collecting stereoscopic warehouse entrance and exit coordinates, channel layout parameters, AGV real-time positions, speeds, electric quantities and warehouse task data, and constructing a basic data pool; S2, based on historical task time length, stacker states and road conditions, adopting a time sequence prediction model to predict cargo arrival time, and demarcating a docking time window; S3, detecting AGV position deviation through visual recognition, and dynamically adjusting the vehicle body and the taking and placing device posture; S4, summarizing the states of multiple AGVs, the docking window and the task priority, adopting a global path planning and dynamic traffic right distribution algorithm to plan a collaborative path; S5, driving along the planned path, matching the docking time window and adjusting the vehicle speed, and avoiding traffic conflicts; and S6, counting the docking success rate and the collaborative traffic efficiency. The method improves the warehousing cargo flow efficiency and quality through data pool construction, accurate prediction, dynamic adjustment and collaborative planning.

The application relates to a traffic conflict risk assessment method combining multi-dimensional conflict indicators and a cloud model, which comprises the following steps: obtaining original trajectory data of a vehicle and cleaning the trajectory data; then, effectively identifying traffic conflicts based on a safety distance threshold and a time threshold; continuing to construct three-dimensional traffic conflict measurement indicators according to the effective traffic conflict trajectory data; determining the cloud model digital features of each conflict measurement indicator, establishing a traffic conflict severity assessment cloud model, calculating the mean value of the membership degree, and obtaining the weight of each conflict evaluation indicator in the comprehensive risk assessment by using the principal component analysis method; finally, based on the mean value of the membership degree and the weight value, the conflict is classified according to the weighted membership degree and the confidence degree criterion, and the traffic conflict severity classification is determined. The application can more comprehensively depict the real dynamic changes of the vehicle and the conflict risk severity, provides an efficient data processing flow and a modeling framework for the non-motor vehicle red light running conflict risk assessment, and provides scientific theoretical support and technical reference for the urban intersection traffic safety optimization and the control strategy formulation.

The present application relates to a non-stationary extreme value and Bayesian deep learning accident risk prediction method and system, belonging to the technical field of traffic engineering. The method comprises data acquisition, data processing after model construction, mapping transformation of original parameters and calculation of collision risk as a quantitative safety index. The system comprises an input module, a deep learning module, a GEV parameter mapping module and an accident risk prediction module. The present application combines deep learning with non-stationary extreme value modeling and proposes a Bayesian deep learning driven non-stationary generalized extreme value distribution (GEV) parameterization method, realizing real-time accident risk prediction based on traffic conflicts. The method can learn the complex nonlinear relationship between the covariates and the distribution parameters, provide a data-driven parameterization method, accommodate more covariates and consider the time dependence of the covariates.

This invention discloses a vehicle trajectory stitching method based on cross-correlation time synchronization and two-stage matching, comprising: 1. cleaning and fragment reconstruction of multi-source structured trajectory data; 2. achieving high-precision time synchronization by setting virtual detection lines in overlapping areas and analyzing the cross-correlation of traffic signals; 3. matching trajectories with time overlap using a dynamic time warping algorithm, and matching non-overlapping trajectories using a progressive relaxation method; 4. dynamically weighting and fusing the matched trajectories based on a smoothing weight function to ensure a smooth transition at the stitching point; and 5. applying Kalman filtering for overall trajectory smoothing and denoising. This invention achieves efficient and accurate trajectory stitching without relying on the original video, solving the technical problems of difficult spatiotemporal alignment of multi-source asynchronous trajectory data and easy jumps at the stitching point, providing high-quality long-distance trajectory data support for microscopic traffic behavior analysis, traffic conflict identification, and traffic safety assessment.

The present application relates to a kind of plane traffic system and optimization design method, also include with each two-way road of intersection, each the two-way road includes parallel vehicle exit lane and vehicle entry lane, the vehicle exit lane and vehicle entry lane are formed exit intersection and entry intersection respectively before vehicle stop line, and the exit intersection and entry intersection are symmetrically distributed in the two yellow line interruption place two sides, by controlling vehicle and pedestrian traffic to avoid space-time intersection of people flow and vehicle flow.The present application realizes vehicle and pedestrian no track intersection traffic by space-time separation, effectively reduces the traffic conflict of intersection, improves traffic efficiency and pedestrian safety.

The present application relates to the technical field of multi-vehicle cooperative scheduling, and particularly relates to a multi-unmanned vehicle path cooperative intelligent planning system, which comprises the following steps: constructing a local dynamic map with the vehicle as the center; forming a local dynamic passable area network; identifying key nodes in the local dynamic passable area network; calculating the dynamic conflict strength of the key nodes; marking the corresponding key nodes as high-conflict cooperative nodes when the dynamic conflict strength exceeds an adaptive threshold; and generating a flexible cooperative avoidance strategy. The present application accurately identifies the space-time traffic conflicts of multiple unmanned vehicles in complex scenarios, and realizes cooperative avoidance by using a distributed negotiation and flexible traffic control mechanism, thereby effectively improving the overall efficiency and running smoothness of multi-vehicle concurrent traffic while ensuring driving safety.

The present application relates to the technical field of unmanned aerial vehicle flight control and air traffic management, in particular to an unmanned aerial vehicle air traffic anti-collision route planning method based on a particle swarm algorithm, comprising obtaining real-time state data, inherent physical structure attributes and environmental meteorological data of a local unmanned aerial vehicle and adjacent unmanned aerial vehicles in a target airspace; fusing motion vector information, inherent physical structure attributes and environmental meteorological data to construct a dynamic fluid disturbance field model, and solving spatial form features and time decay features of a fluid wake danger zone; using an unmanned aerial vehicle dynamics model to evaluate maneuvering margin decay and generate an out-of-control probability prediction value, and constructing a composite artificial potential field containing entity geometric repulsion and wake fluid repulsion; combining the composite artificial potential field and a preset flight mission target to output an optimal four-dimensional flight trajectory; generating flight control instruction data packets based on the optimal four-dimensional flight trajectory to form an anti-collision route planning closed loop; the present application significantly enhances risk perception capability in complex scenarios.

This invention relates to the field of urban low-altitude airspace planning technology, and particularly to a method for constructing a hierarchical planning model for urban low-altitude airspace oriented towards traffic demand. The method for constructing this hierarchical planning model plans each low-altitude altitude layer corresponding to a specific heading angle range. Multiple low-altitude altitude layers can exist within the same heading angle range. The model construction method includes the following steps: First, acquiring aircraft operational demand data between low-altitude take-off and landing points within the study area, and standardizing the angles of direct flight routes between take-off and landing points; second, proposing a method to calculate the average probability of conflict between aircraft within the same layer of traffic flow and between adjacent layers of traffic flow based on the heading extension range; finally, providing an overall method for constructing a hierarchical planning model for urban low-altitude airspace oriented towards traffic demand. By using altitude layering to reduce low-altitude traffic flow operational conflicts, the proposed model considers both the total number of available low-altitude altitude layers and the control of airspace structural complexity while reducing traffic conflicts, thereby effectively improving the safety and manageability of urban low-altitude hierarchical airspace operation.