10552results about "Instruments for road network navigation" patented technology

A system and method of calculating an estimated time of arrival for a traveler is provided, along with a notification of this estimated time that is sent to a third party. The traveler is equipped with a navigation unit having a global positioning sensor and a wireless communications device. The navigation unit communicates with a service center equipped with a server capable of generating a preferred navigational route for the traveler. The service center also includes a traffic database for maintaining current traffic information. Once the traveler has selected a predetermined time and frequency of notifications based upon a selected criterion, the navigation unit transmits the traveler's position and velocity to the service center. The service center then calculates the estimated time of arrival based upon the traveler's position, velocity, distance to be traveled and traffic and weather conditions. Once this time is calculated, the service center sends a notification to the third party.

Disclosed are a high-performance loosely-coupled multi-modal data fusion system for a smart driving environmental perception system and a vehicle-mounted device, comprising: a fusion detection model based on a modality-independent feature interaction strategy, which is configured for converting a LiDAR point cloud, a camera image, and a millimeter-wave radar point cloud into a unified bird's-eye view representation, and performing multi-modal fusion; and a fusion tracking model based on a motion-appearance feature cascaded coupling data association strategy, which is configured for performing subsequent trajectory tracking and matching according to multi-modal fusion feature information. A VoD data set and a K-Radar data set are selected for training, verifying, and testing the comprehensive performance of the models, and a TensorRT accelerated inference model is applied, then quantized, and deployed to a vehicle-mounted computational testing platform. The present invention is compatible with mainstream sensor deployment solutions, and achieves the efficient complementary fusion of multi-source heterogeneous sensor information, significantly improving the reliability, accuracy, and adaptability of vehicle-mounted perception systems, thereby effectively responding to extreme operating conditions such as complex traffic scenarios and inclement weather.

The invention discloses an AR navigation system and method based on a visual language model, and the method comprises the steps: firstly carrying out video data collection, and constructing a memory database; secondly, querying a navigation target most related to a natural language request of a user in the constructed memory database to obtain a current AR equipment pose and a target pose; and then solving the shortest path from the current pose to the target pose by using the current AR equipment pose and the target pose according to the point cloud map, and optimizing the path direction to finally obtain an optimized path. And finally, guiding a user to move along the planned path through view superposition path indication and voice prompt in AR equipment by utilizing the optimized path, and updating the point cloud map and the memory database. According to the invention, high-precision real-time positioning and sparse point cloud map construction can be realized only by camera input in indoor and outdoor complex environments with low GPS precision, accurate navigation is carried out, and the flexibility and intelligent level of navigation are improved.

To inhibit a thief from taking away a vehicle to a distant place.SOLUTION: A vehicle 100 includes: an on-vehicle navigation device 10 for repeatedly obtaining a location of the vehicle 100 through a GPS 200; and an on-vehicle immobilizer device 20. The on-vehicle immobilizer device 20 turns on a remote immobilizer on the basis of the fact that the location of the vehicle 100, obtained from the on-vehicle navigation device 10, is outside a preset geographical region. The on-vehicle immobilizer device 20 prohibits operation of a drive system of the vehicle 100 while the remote immobilizer is in an on-state. Notification that the remote immobilizer has been turned on is provided to a terminal 300 of a legitimate user of the vehicle 100 via a server 400 or directly.SELECTED DRAWING: Figure 1

An apparatus and method relate to analyzing metrics relating to EV charging points and facilitating selection of an EV charge point based on metrics associated with the EV charge points. Methods may include: determining locations of one or more electric vehicle charging stations; determining a total power available at the one or more electric vehicle charging stations; determining, for the one or more electric vehicle charging stations, if dynamic load balancing is used; determining occupancy of charge points at each of the one or more electric vehicle charging stations; calculating an available power at a charge point of the one or more electric vehicle charging stations based on the total power available and the occupancy of charge points for the one or more electric vehicle charging stations; and calculating an estimated time to charge a vehicle at the one or more electric vehicle charging stations based on the available power.

The invention relates to the technical field of intelligent navigation, financial science and technology and medical health, and discloses a navigation method, device and equipment based on multi-modal knowledge enhancement and a medium, and the method comprises the following steps: obtaining a navigation instruction, and analyzing the navigation instruction to obtain a target constraint, a spatial constraint and an entity constraint; generating a structured target description text based on the target constraint, the spatial constraint and the entity constraint; an input image is acquired, a semantic saliency map is generated based on the input image and the structured target description text, and the semantic saliency map comprises a plurality of candidate areas; calculating a score of each candidate region of the semantic saliency map based on a preset scene knowledge graph to obtain a fusion score; and performing path planning according to the fusion score of each candidate region to obtain a target path. Navigation can be realized without navigation data training, and meanwhile, a potential target can be focused according to semantics, so that the navigation precision is improved.

The invention provides an unmanned system autonomous navigation method based on Beidou and multi-source information adaptive fusion, and belongs to the technical field of high-precision intelligent navigation. According to the invention, autonomous navigation is realized by constructing a three-layer navigation architecture of space-time alignment, intelligent fusion and behavior control. In the space-time alignment layer, performing time and space alignment on the acquired multi-source sensing data; in the intelligent fusion layer, environment types are identified through CNN, modeling is carried out on observation uncertainty of GNSS, LiDAR and IMU in combination with variational Bayesian reasoning, confidence weights are dynamically updated, and a layered federated filtering architecture is adopted to output a full-parameter navigation solution; and in the behavior control layer, the trigger threshold value and the safety margin of the behavior state are dynamically adjusted in the path planning execution process, the different behavior states are subjected to priority ranking and dynamic switching, linear speed and angular speed control instructions are output according to the behavior states, and autonomous navigation is achieved. The problems of low navigation precision, high fusion rigidity, decision disjunction and the like are solved.

The invention discloses a robot path planning method based on an artificial mouse travel algorithm. The method comprises the following steps: constructing a map; the method comprises the steps of parameter setting, initialization, initial path fitness evaluation, global search, local search, new path evaluation and local enhancement search, a dynamic adaptive search radius strategy is used for dynamically adjusting a search range C, and when an exploration-development switching threshold value E is larger than 1, namely Egt; the method comprises the following steps: 1, starting global search by adopting a dual-mode hybrid strategy, Elt; the method comprises the following steps: 1, starting local search, adopting an adaptive probability Levy flight or small-range disturbance strategy, introducing a local search enhancement module, carrying out refined search on a current global optimal path Xbest through a simplex method Nelder-Mead, and evaluating the fitness of the optimal path of iteration each time through the algorithm; and outputting an optimal path sequence according to the fitness value of the recorded optimal path. The method has the characteristics of improving the search capability, obtaining the optimal path and the like.

The invention discloses an unmanned agricultural machine path navigation method based on farmland environment perception judgment, and relates to the technical field of path navigation, and the method comprises the steps: continuously perceiving farmland topographic relief features, gradient changes, and agricultural machine current attitude and wheel speed information before and during the operation of an agricultural machine; identifying a terrain disturbance potential risk area based on the sensing data, and predicting the offset and rollover risk value of the terrain fluctuation to the agricultural machine trajectory; according to the method, a pre-correction model fusing terrain prediction data and agricultural machinery dynamic characteristics is called, a pre-correction path containing the terrain compensation amount is generated, and navigation control parameters are adjusted in advance, so that the pre-correction path containing the terrain compensation amount is actively generated by sensing terrain fluctuation and predicting risk and offset in advance; the problem of causal malposition of risk prior to control is avoided from the source.

The invention provides a target perception and avoidance decision-making method in visual navigation of intelligent equipment, and belongs to the technical field of intelligent decision-making. The method comprises the following steps: constructing a space-time diagram neural network, introducing semantic tag codes to define nodes and edges of the space-time diagram neural network, and predicting the trajectory of each obstacle through a regression network; an environment semantic label is defined, the environment semantic label and the predicted obstacle trajectory are coupled, and a semantic enhanced risk field is generated; and constructing a semantic road network map based on the risk field of semantic enhancement, generating a global path point sequence, and calling a dynamic step RRT * algorithm to avoid obstacles by taking the current global path point as a temporary target. According to the method, the risks of different areas can be evaluated more accurately, the risk value is adjusted according to the type and the position of the obstacle, the safety and the reliability in a complex environment are improved, and decision errors caused by environment changes are reduced.

The invention provides a driving track planning method and a driving track planning model training method and device.The method comprises the steps that target environment information around a target vehicle is obtained, and the target environment information at least comprises image information; and in response to the input of the target location, planning a target driving track of the target vehicle according to the target environment information around the target vehicle and the historical driving track of the target vehicle. According to the invention, the planning precision of the driving track of the target vehicle is improved, and the navigation precision of the vehicle based on the target driving track is further improved, so that the service quality of the navigation service provided by the target vehicle for a driver and a passenger is improved, and the use experience of the driver and the passenger for the vehicle is optimized.

The invention provides an end-to-end automatic driving track generation method and system, computer equipment and a medium, and belongs to the field of path intelligent planning, and the method comprises the steps: obtaining traffic environment three-dimensional perception information and a natural language instruction; converting the traffic environment three-dimensional perception information to obtain a structured semantic mark, and adjusting the weight of the structured semantic mark through a natural language instruction; based on the weight of the structured semantic mark, visual flow features are extracted from the structured semantic mark, and language flow features are extracted from the natural language instruction; based on a cross-modal attention mechanism, the visual flow features and the language flow features are fused, fusion features are obtained, and a simulation track is generated; real-time collision time is calculated, when the real-time collision time is larger than a collision time threshold value, the simulation track is corrected based on physical constraints and a traffic regulation library, and a corrected driving track is obtained. The automatic driving simulation track constructed by the method conforms to the intention of a user, and the system reliability and the man-machine cooperation efficiency are improved.

The invention belongs to the technical field of path planning, and discloses a path planning method and system fusing multi-modal terrain perception and vehicle dynamics, and the method comprises the steps: obtaining and fusing multi-modal geographic data to generate a terrain physical representation, and calculating the terrain complexity based on the terrain physical representation; predicting an overturning index and a stability margin of the vehicle based on the terrain physical representation, and generating a trafficability cost function; taking the trafficability cost function as a constraint condition, selecting a motion element according to terrain complexity to perform trajectory fitting, and generating a vehicle path; executing the vehicle path to obtain a vehicle state, correcting the trafficability cost function by using the vehicle state, and optimizing the vehicle path; and constructing a loss function, training the path planning model, and performing path planning by using the trained model. According to the invention, by establishing a terrain vehicle bidirectional coupling mechanism, safe and adaptive local path planning under a complex unstructured terrain is realized, and the traffic capacity and safety of the system in a dynamic environment are significantly improved.

A vision language model (VLM) may be used to evaluate signs that designate restricted or toll lanes, determine whether it is permissible (and / or the cost) to merge into a restricted or toll lane, and / or determine when to merge out of a restricted or toll lane based on the cost. Frames from one or more (e.g., front-facing) camera(s) may be evaluated for applicable signs (e.g., using a sign recognition DNN or a VLM). If detected, the (e.g., cropped) image of the sign may be provided as input to a VLM with a textual prompt instructing the VLM to determine whether to drive in the restricted or toll lane (e.g., whether it can be taken within budget) and / or what the cost would be. The generated response may be provided to an ADAS to trigger an initiation of a merge left or right or a determination to stay in the current lane.

The invention discloses an OVER-NAV vision-language combined navigation system and method, and belongs to the technical field of intelligent navigation.The system comprises a navigation server, a user handheld terminal and a navigation terminal.The navigation server comprises a vision module, a language obtaining module, a vision map module, a semantic map module and a multi-modal feature cross-modal interaction module; the core of the system is that a multi-modal feature cross-modal interaction module comprises a topology-aware heterogeneous modal representation unified framework, a differential geometry inspired dynamic attention guidance mechanism and a heterogeneous knowledge integration and collaborative optimization framework, and deep fusion of three modals of visual information, a language instruction and a semantic map is realized; according to the method, the navigation accuracy is remarkably improved, the natural language instruction understanding accuracy reaches 92%, the high recognition accuracy is still kept in illumination changing and noisy environments, and the method is particularly suitable for complex shopping mall environments, outdoor-indoor seamless transition navigation and other scenes.

The invention relates to the field of meteorological detection and analysis, and discloses a low-altitude complex wind field detection and inversion method based on a vehicle-mounted laser radar, and the method comprises the steps: determining a scanning point location based on the terrain, building distribution and simulation data; optimizing the driving path of the vehicle-mounted radar to cover the high-risk area; acquiring three-dimensional wind field data by adopting an RHI and PPI multi-mode scanning strategy; the influence of vehicle-mounted movement on speed measurement is corrected; calculating turbulence intensity and vortex nucleus position by using spectral width characteristics; and identifying the wind shear position and strength through a slope algorithm. According to the invention, low-cost and high-precision wind field detection is realized through maneuvering deployment and multi-mode cooperation, and reliable technical support is provided for safe flight of the unmanned aerial vehicle, low-altitude traffic management and meteorological early warning.

The invention relates to the technical field of tunnel and underground space robot autonomous navigation, and provides a tunnel path planning method and system for clearance corridor and elastic geodesic refinement, and the method comprises the steps: calculating the Euclidean distance from a structural terrain to a nearest obstacle or wall, taking the Euclidean distance as clearance, generating a direction-independent cost, and constructing a dual metric; running Riemannian geometry fast travel and backtracking geodesic under the condition that starting and ending point pixel coordinates are given to obtain a coarse path, after rasterization, calculating the distance from pixels to the coarse path, obtaining a widening radius by using clearance along the coarse path, performing nearest neighbor propagation to a global domain to obtain a radius graph, defining a corridor, and performing corner propagation by the coarse path to obtain an approximate curvature field; calculating to obtain a score, partitioning through thresholding, performing piecewise linear penalty, updating the total cost related to the direction, re-marking according to partitions, taking the reciprocal as a velocity field, and obtaining a final path through a solver. The problems of safe obstacle avoidance, curvature control and efficient solution need to be met at the same time in engineering are solved.

The invention relates to the technical field of terrain reconstruction and path planning, and particularly provides a night terrain reconstruction and path planning method and system based on multi-sensor collaboration, and the method comprises the steps: obtaining the multi-source sensing data of a night environment through a data collection module; performing multi-source data fusion processing on the multi-source sensing data to generate a night environment reconstruction model fusing multi-dimensional features; performing obstacle semantic analysis on the night environment reconstruction model, extracting static obstacle distribution information and a dynamic obstacle movement track, and constructing a composite environment map; according to the composite environment map, a passable path is generated through a dynamic optimization algorithm in combination with terrain passability parameters and kinematics constraint conditions of a mobile carrier, and path parameters are dynamically adjusted based on real-time update data of a night environment reconstruction model; when it is detected that the obstacle distance or the motion state exceeds a safety threshold value, path real-time correction and graded obstacle avoidance instruction output are triggered through multi-sensor confidence evaluation and path collision risk prediction.

The invention discloses an automatic guided vehicle path re-planning method, device and equipment and a storage medium, and the method comprises the steps: marking an area affected by an obstacle according to coordinates, a size and a safe distance, and obtaining an area not affected by the obstacle; abstracting a grid region in a region which is not influenced by the obstacle, traversing all feasible grid nodes in the grid region, and calculating the actual cost and the estimated cost of the current feasible grid node and the next feasible grid node; on the basis of the sum of the actual cost and the estimated cost, feasible grid nodes with the minimum cost are selected in sequence, and a detour path is obtained; simplifying the bypassing path into a key node, and fitting the key node into a spline curve; creating at least one temporary node and at least one temporary section based on the spline curve to form a dynamic temporary map; and performing path re-planning for the automated guided vehicle based on the dynamic temporary map. According to the method provided by the invention, the temporary path is automatically created when the scheduling system has no passing path, and the system-level deadlock is avoided.

A system for accidentology-based measuring of accident risk-indexing score values for a motor vehicle to be tested providing a measured accident probability value for an occurrence of an accident event having a physical impact to the tested motor vehicle. The system includes a driving scenario module, a test setting module, and a scoring module. The driving scenario module is configured for determining various driving scenarios for the motor vehicle by defining a set of measurable scenario characteristics, which include at least one ADAS variable. The test setting module is configured for determining a test setting in form of a multi-dimensional test matrix, which includes testing protocols for each of the measurable scenario characteristics for providing measured values of measurable scenario characteristics of the various driving scenarios. The scoring module is configured for generating the risk-indexing score by receiving the multi-dimensional test result signal and a historical data information signal.

The invention relates to the technical field of automatic driving, in particular to an automatic driving task processing method and device, equipment and a medium. According to the method, firstly, the navigation guidance information is acquired and directly incorporated into beyond visual range contents (such as remote steering and distance), so that the limitation of the sensing range of a vehicle-mounted sensor is broken through, and the problem that an existing system lacks beyond visual range information is solved. And secondly, semantic analysis is performed based on navigation information and multi-view visual data to obtain text probability distribution, so that association of global navigation context and real-time scenes is realized, and a semantic basis is provided for humanization-like planning. Then, performing cross-modal fusion on the aerial view angle features and the text probability features, so that global navigation logic and real-time environment perception are deeply coupled, and decision is ensured to consider both a short-term environment and long-term planning. And finally, fusing the features and inputting the features into a task network, so that sensing, prediction and planning are more suitable for an actual scene, the utilization capability of beyond-visual-range information in a complex scene is improved, and the adaptability and reasoning capability of the system are enhanced.

The invention provides a trajectory planning method and device, a vehicle and a computer readable storage medium, and the method comprises the steps: obtaining vehicle driving information, and generating a multi-modal trajectory according to the vehicle driving information, the multi-modal trajectory comprising a plurality of sub-trajectories; obtaining a hierarchical strategy network model, and inputting the multi-modal trajectory and the vehicle driving information into the hierarchical strategy network model to obtain a multi-dimensional score corresponding to each sub-trajectory; and determining a target trajectory in the plurality of sub-trajectories according to the multi-dimensional score. By setting a hierarchical strategy network model comprising a plurality of strategy layers, sub-tracks can be scored based on different scoring dimensions through different strategy layers, so that target tracks meeting different dimension requirements can be determined by integrating multi-dimensional driving requirements; therefore, the obtained target trajectory can meet vehicle driving requirements and adapt to a dynamic driving environment in an actual scene.

The invention provides a path planning method and system based on four-AGV cooperative carrying, and relates to the technical field of automatic logistics transportation. The method comprises the steps that a high-precision grid map is constructed, a global path is generated in combination with a simulation improved A * algorithm, dynamic weight factors are adaptively adjusted according to the obstacle density, and the path search efficiency is optimized; a time window constraint method is adopted to divide path segments and dynamically allocate time windows; real-time sensing of dynamic obstacles is fused, and a local path is adjusted based on Kalman filtering and a dynamic priority scheduling algorithm; a one-master and three-slave cooperative control architecture is adopted, a master AGV tracks a simulation path, and slave AGVs dynamically adjust control parameters through a fuzzy adaptive PID algorithm; the synchronous jacking operation of the four AGVs feeds back the state to the central server in real time through the Ethernet, so that the synchronism and safety of collaborative carrying are ensured. According to the invention, the technical problems of high path conflict rate, low trajectory tracking precision and slow obstacle avoidance response of a multi-AGV system in a complex environment are solved.

The invention provides a tunnel drilling and blasting slag removal vehicle autonomous path planning method and device based on a Naligation 2 behavior tree scheduling framework, and the method comprises the following steps: S1, carrying out the data collection and map construction of a tunnel scene after drilling and blasting; s2, identifying a blasting residue area and generating a target point; s3, constructing a behavior tree scheduling structure and planning a path; and S4, performing path search, execution and feedback evaluation based on a path planning algorithm. According to the method, multi-source sensor mapping and deep learning semantic segmentation technologies are combined, high-precision recognition and traffic area extraction of residual obstacles in an unstructured tunnel environment after drilling and blasting are performed are performed, complex scenes faced by slag removal path planning are fully considered, a behavior tree scheduling mechanism is adopted to fuse path planning, target scheduling and an anomaly recovery strategy, and the path planning efficiency is improved. And a path execution system of real-time response and fault-tolerant capability is further constructed, and the operation continuity and robustness of the robot in a complex operation environment are improved.

Embodiments of the present disclosure relate to multi-view LIDAR perception with motion cues for autonomous and semi-autonomous machines and applications. A DNN may be used to detect objects, a navigable space, weather or surface conditions, artifacts, and / or other parts or features of an environment based on multiple views of LIDAR data from multiple time slices. The DNN may include multiple input channels for processing multiple views of sensor data from multiple time slices to provide motion cues, and the extracted features from the different time slices may be geometrically projected from a first 2D view to a second 2D view, combined with features that were extracted from the second 2D view, and applied to a subsequent stage of the DNN. The data generated by the DNN may be provided to the drive stack of an autonomous vehicle or other ego-machine to enable safe planning and control of the vehicle.

In some embodiments, an electronic device concurrently presents a navigation user interface element with a respective physical location designated and a content element including content corresponding to the respective physical location. In some embodiments, an electronic device presents navigation from a first physical location to a second physical location with reduced visual prominence in a content element in response to an input corresponding to a request to present content corresponding to the second physical location.

The invention relates to the field of intelligent logistics transportation carbon emission real-time tracking, in particular to an intelligent logistics transportation carbon emission real-time tracking system and method. The method comprises the following steps: firstly, dividing a logistics transportation path into path sections, and generating a candidate path section set at the starting point of each path section to obtain candidate path sections; then, on the basis of the obtained traffic data, vehicle parameters and scheduling plans of the candidate path segments, the predicted carbon emission of the candidate path segments is calculated through a path segment carbon emission prediction algorithm; constructing an optimal path set based on the predicted carbon emissions of the candidate path segments; and finally, on the basis of the path segments in the optimal path set, obtaining actual operation data of the vehicle, and on the basis of the actual operation data of the vehicle, calculating the actual carbon emission. The technical problems that carbon emission prediction and optimization lack pertinence, obvious influences of speed change behaviors such as acceleration, deceleration and idling on energy consumption in actual driving are ignored, and comprehensive influences of vehicle load weight, driving speed and traffic conditions cannot be dynamically reflected are solved.

The invention discloses a global reference line generation method for an automatic driving vehicle and a related device, and relates to the technical field of automatic driving navigation, and the method comprises the steps: generating a collision-free original path of the vehicle based on an improved A * algorithm according to a starting point position and a terminal point position of the vehicle in a grid map; removing redundant points in the collision-free original path of the vehicle to obtain an original reference path; numerical smooth optimization is carried out on discrete points of the original reference path, and a cubic spline interpolation method is adopted to generate a basic reference line meeting the geometric continuity of the vehicle; establishing a Frenet coordinate system based on the basic reference line, and carrying out transverse and longitudinal motion primitive sampling to obtain a vehicle kinematics constraint and a boundary constraint; according to the constraint, establishing a quadratic programming multi-objective optimization function; and solving the quadratic programming multi-objective optimization function to obtain a global reference line. According to the invention, the robustness and security of reference line generation in a complex scene can be improved.

The invention discloses an end-side safe path planning method based on a small language model, which comprises the following steps: generating a task prompt by fusing the small language model SLM with an A * algorithm and a structured map token, storing the task prompt in a vector database, retrieving historical map information similar to the current task from the database when the path planning task is executed, and performing path planning according to the historical map information. The method comprises the following steps: constructing a COT logic chain prompt, guiding a small language model to gradually generate path key nodes and weights thereof in combination with the constructed COT logic chain prompt, then executing greedy path search by an A * algorithm by taking the key nodes as local targets, and finally generating a safe and efficient path. The method provided by the invention has end-side deployment capability, can be operated in scenes without network, resource limitation and the like, greatly reduces node traversal and improves planning efficiency compared with the existing A * algorithm, is particularly suitable for path planning tasks in a large-scale or high-density obstacle environment, gives consideration to intelligent reasoning and planning safety, and has wide application prospects. The method has good interpretability and expansibility, and has a wide application prospect.