1875results about "External condition output parameters" patented technology

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

The invention belongs to the technical field of path planning, and discloses an unmanned driving dynamic path planning method and system based on multi-source data fusion, and the method comprises the steps: collecting an ice and snow pavement friction coefficient, a curve curvature and an obstacle point cloud, constructing a sensor confidence coefficient matrix, generating a fused semantic map, and constructing a dynamic environment semantic model. Outputting a real-time friction coefficient field and a risk thermodynamic map layer; the roadside unit broadcasts coordinates of opposite vehicles in a blind area of a curve to a vehicle end, constructs an ice and snow pavement offset crowdsourcing map, and generates a global-local fusion topology; fusing the real-time friction coefficient field and the global-local fusion topology to generate a smooth trajectory set, and further generating a risk optimal path instruction set; a steering angle and torque instruction is decomposed, positioning drift is compensated in real time, and a normal mode for updating the vehicle positioning state and a degradation mode when the millimeter wave radar fails are constructed; and generating execution logs and health state vectors, and aggregating the execution logs and the health state vectors of multiple vehicles to form closed-loop iterative update.

Disclosed in the embodiments are a vehicle risk avoidance method and system, a vehicle, and a storage medium in the technical field of intelligent driving. The method includes the following steps: obtaining first motion state information of a target vehicle and obstacle information of a target obstacle at a plurality of consecutive moments, the obstacle information including two or more of an obstacle type, an obstacle location, obstacle boundary information, and second motion state information; executing a risk assessment strategy corresponding to the obstacle type according to the first motion state information and the obstacle information corresponding to each moment, to determine a risk assessment result of whether the target vehicle collides with the target obstacle; and determining, if a plurality of risk assessment results corresponding to the plurality of moments satisfy a risk condition, a target risk assessment result from the plurality of risk assessment results, and generating a target security protection strategy corresponding to the target risk assessment result to avoid a collision risk.

A system for navigating a host vehicle, includes at least one processor comprising circuitry and a memory, wherein the memory includes instructions that when executed by the circuitry cause the at least one processor to perform operations comprising: receiving from a camera onboard the host vehicle at least one captured image representative of an environment of the host vehicle; detecting, based on analysis of the at least one captured image, a presence of a road covering material on a road segment surface; determining, based on analysis of the at least one captured image, a distance between the camera onboard the host vehicle and the road covering material; and determining a thickness profile of the road covering material based on the determined distance between the camera onboard the host vehicle and the road covering material, and also based on stored three-dimensional profile information associated with the road segment surface.

System and methods are disclosed for navigating a host vehicle. In one implementation, at least one processing device may be programmed to receive an image representative of an environment of the host vehicle, identify, based on the image analysis, a target vehicle, select a safe navigation mode among a plurality of available safe navigation, each of the safe navigation mode being associated with a corresponding safe navigation distance, determine a planned navigational action for the host vehicle that if taken would result in a next-state distance between the host vehicle and the target vehicle greater than or equal to the selected safe navigation mode safe navigation distance, and implement the planned navigational action by controlling one or more actuators associated with the host vehicle. The at least one processing device may be further programmed to select a safe navigation mode based on a detected location or condition of the host vehicle.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

The present invention relates to an automated driving system for switching between automated driving mode and manual driving mode in an automated driving situation, and a fallback method for an automated driving failure situation in the automated driving system. According to the present invention, the method comprises the steps of predicting whether, on a route a vehicle is traveling, the vehicle will deviate from an operational design domain (ODD) in which automated driving is possible; and when deviation is predicted, performing a minimal risk maneuver (MRM) process for controlling the vehicle to respond to an automated driving failure situation at a deviation point. According to the present invention, the stability of the automated driving system in emergency situations may be increased, and by proposing a stable mode-switching method for the automated driving system, the best response method may be provided.

Techniques relating to determining regions based on intents of objects are described. In an example, a computing device onboard a first vehicle can receive sensor data associated with an environment of the first vehicle. The computing device can determine, based on the sensor data, a region associated with a second vehicle proximate the first vehicle that is to be occupied by the second vehicle while the vehicle performs a maneuver. Further, the computing device can determine an instruction for controlling the first vehicle based at least in part on the region.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods are provided herein for implementing dynamic, scalable platoon formations for traveling vehicles. Such platoons may be formed and modified on the fly by vehicles that have varying sensing or autonomy capabilities. Platoons may be negotiated among available vehicles, to designate roles including global platoon leader, local leader(s), and platoon member(s). While the platoons are in place and the vehicles traveling in coordination, platoon members may depart the platoon, and the remaining members may re-allocate roles among the remaining members.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

A computer system is provided and is programmed to: (1) receive sensor data associated with a primary vehicle traveling on a roadway; (2) detect an obstacle in the roadway; (3) determine a vehicular response to react to the obstacle in the roadway; (4) instruct the primary vehicle to execute the vehicular response; and (5) electronically transmit information associated with the obstacle in the roadway to one or more additional vehicles on the roadway.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

A system for processing multi-modal data representing an environment to generate scene graphs of the environment is described. The system can obtain sensor data associated with a vehicle operating in the environment. In examples, the system can determine a set of features from the sensor data, including one or more objects and one or more agents present in the environment, and can generate a scene graph that represents the poses and velocities of these objects and agents relative to the environment. In some examples, based on generating the scene graph, the system can generate a knowledge graph by encoding the relationships among the identified objects and agents. In some examples, the system can generate a control signal, using attributes that represent the states of objects and agents in the knowledge graph, and provide this control signal to the vehicle in order to adjust or cause the operation of the vehicle.