11359results about "External condition input parameters" patented technology

The invention discloses a vehicle idling start and stop control system and method. The control system judges whether a vehicle has idling start and stop conditions by monitoring the water temperatureof an engine, the capacity of a storage battery, a safety belt and other information, judges whether the vehicle has conditions of stopping the engine by monitoring the rotating speed of the engine, the speed of the vehicle, the brake air pressure and other conditions, and judges whether the vehicle has conditions of starting the engine by monitoring the gear of a gearbox, the speed of the vehicle, a hand brake and other information. When the relevant conditions are met, start and stop work signals are sent to the engine though a CAN bus. In order to guarantee the reliability of frequent startand stop of the engine, the system strengthens flywheel gear rings of a starter and the engine. In order to ensure that the battery does not lose power, a storage battery capacity sensor is added tothe system on the basis of increasing the capacity of the storage battery, and the idling start and stop are allowed when the state of charge (SOC) of the storage battery satisfies a safe starting threshold. The idling start and stop control system has advantages of low cost and high reliability, and is especially suitable for commercial vehicles.

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.

A system and method for electric vehicle operational optimization is disclosed. The system comprises a memory storing processor-executable instructions and a processor, communicably coupled with the memory. The system obtains input data and predict health and performance parameters. The system generates computer simulated instances which emulate a behavior and a performance of the electric vehicle. The system, further, validates the health and the performance parameters by simulating the computer simulated instances in a virtual environment. The system determines a behavior status, a performance status and a health status of the electric vehicle. Thereafter, the system determines abnormality associated with the electric vehicle, followed by determining action for rectifying the abnormality. Consequently, the system controls an operation by performing the determined action at the electric vehicle.

Object detection and tracking systems may use machine-learned transformer models with self-attention for detecting, classifying, and / or tracking objects in an environment. Techniques described herein may include receiving sensor data generated by different sensor modalities of a vehicle, determining different bounding shapes based on the different sensor modalities, and using a machine-learned transformer model to determine associated and / or combined bounding shapes. The machine-learned transformer model may receive a variable number of input bounding shapes representing any number of objects and various sensor modalities. Multiple stages of the transformer may be used to determine associated bounding shapes and to assign attributes for the associated bounding shapes, based on the individual bounding shapes of the different sensor modalities and / or previous bounding shapes for objects detected and tracked in a previous scene in the environment.

A management server system may obtain data associated with a vehicle. The management server system may obtain the data from a plurality of data sources associated with the vehicle. The data may include a plurality of subsets of data that each correspond to correspond to a particular driver assignment category. Each subset of data may identify a particular persona as a driver of a vehicle. The management server system may determine a driver of the vehicle based on each subset of data and a hierarchical plurality of driver assignment categories. Based on the determination of the driver of the vehicle, the management server system can dynamically map the driver to a particular event. The management server system may generate a user interface identifying the driver and the event.

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 disclosure relates to transportation and related methods and systems including software-defined vehicles, vehicle operating states, an identity management system, an intelligent digital twin system that creates, manages, and provides digital twins for transportation systems using sensor and other data, and the integration of a transportation system with an AI convergence system of systems, providing a multi-layered system for intelligent automation and data-driven decision making across operational aspects of a transportation system.

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 provides an intelligent driving system and method and electronic equipment, relates to the technical field of intelligent driving, and realizes personalized improvement of driver ability and safety guarantee of a driving environment through three cooperative mechanisms including a teaching strategy module, a safety perception module and a behavior management module. Analyzing the multi-dimensional student data through a teaching strategy module by using a multi-modal large language model, and generating a personalized teaching strategy adaptive to the ability of the student; a driving environment is monitored through a safety sensing module, when a specific complex scene is detected, a multi-source heterogeneous sensor data fusion strategy is adjusted according to a scene type and a preset intervention degree, and then a safety coping strategy matched with environment interference is generated; the driving behavior data are recognized through the behavior management module, and multi-mode interaction feedback is generated in combination with the physiological data and the state data of the driver and used for guiding and correcting the driving behavior.

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 relates to the technical field of new energy vehicles, in particular to a multi-working-condition energy management method and system for a hybrid chariot, and the method comprises the steps: determining a corresponding working condition weight coefficient through obtaining the altitude, environment temperature and road condition data in real time, and the real-time working parameters of three power sources, namely an engine, a fuel cell and a lithium battery, so as to achieve the multi-working-condition energy management of the hybrid chariot; the system constructs a multi-objective optimization model based on the data to realize comprehensive fuel consumption minimization, lithium battery SOC balance and lithium battery life protection, and the model comprises establishment of a power source efficiency parameter mapping relation, determination of a corresponding relation between lithium battery power and cycle life, and construction of a membership function of an optimization objective. By adopting a method of combining Pareto local optimization and fuzzy evaluation, the optimal power distribution proportion among the engine, the fuel cell and the lithium battery is calculated, the overall efficiency of the hybrid chariot is effectively improved, and the service life of the hybrid chariot is effectively prolonged.

Techniques for determining a driving trajectory for an autonomous vehicle to follow are described herein. A vehicle may receive a destination associated with a region of an environment to which the vehicle is to navigate. Based on the destination, the vehicle can generate a local probabilistic graph that includes states, edges connecting the states, actions for the vehicle to perform along the graph, and / or probabilities associated with the actions. The vehicle may determine cost-to-go values for each state within the local probabilistic graph. While navigating to the destination, the vehicle can generate candidate trajectories. When determining the cost of following a candidate trajectory, the vehicle can determine the cost by projecting an ending state of the candidate trajectory onto the local probabilistic graph to determine an optimal action which considers a larger understanding of the goal of the vehicle. The vehicle can be controlled based on the candidate trajectory.

A dynamic traffic environment-oriented multi-lane driving decision-making method based on reinforcement learning, comprising: using state space, action space, and trajectory sampling information in a multi-lane driving decision-making scenario to establish a decision-making neural network model; using a comprehensive reward function to perform reinforcement learning training on the decision-making neural network model; continuously and deeply sensing the surrounding environment by means of a sensor array with which a vehicle is equipped, and capturing sensed environment information; inputting the sensed environment information into the decision-making neural network model, and, on the basis of current environment information, the decision-making neural network model predicting a vehicle trajectory and a recommended driving operation within a period of time; and converting the recommended driving operation outputted by the decision-making neural network model into a specific control instruction, and sending the instruction to a drive-by-wire chassis and an actuator of the 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.

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.

A perception system may be used to generate bounding boxes for objects in a vehicle scene. The perception system may receive images and feature maps corresponding to the received images. The perception system may correlate object queries from previous time steps with object queries from the current time step.

Apparatuses, systems, and techniques of using one or more machine learning processes (e.g., neural network(s)) to predict occupancy using an image input. In at least one embodiment, image data is processed using a neural network to predict occupancy in a 3D voxel space. In at least one embodiment, image data is processed using a neural network to detect objects in a 3D space.

The invention provides an intelligent driving control method, a vehicle and a storage medium, and relates to the technical field of intelligent driving. The method comprises the following steps: acquiring environment sensing data acquired by a sensor; the environment sensing data comprises rainfall detection data and target detection data; determining a real-time comprehensive rainfall level according to the rainfall detection data; dynamically adjusting the target detection weight of each sensor in data fusion according to the comprehensive rainfall level and the real-time target detection confidence of each sensor; based on the adjusted target detection weight, performing fusion processing on the target detection data collected by each sensor to obtain a target detection result; and adjusting an intelligent driving control strategy according to the comprehensive rainfall level and / or the target detection result. The environment sensing accuracy of the vehicle in a rainy environment is improved, the intelligent driving control strategy of the vehicle can adapt to the rainy environment change, and the driving safety and the driving experience are comprehensively improved from the sensing layer to the control layer.

The present disclosure discloses an anthropomorphic lane-changing control method and system based on driving risk quantification, and a vehicle. The method includes: determining whether a main vehicle has a lane-changing intention based on motion statuses of the main vehicle and a front vehicle in a current lane; if there is a lane-changing intention, generating an anthropomorphic lane-changing trajectory that satisfies safety constraints and reachability constraints; based on a motion status of a traffic participant that causes a risk to the main vehicle, calculating an overall risk level and determining whether the anthropomorphic lane-changing trajectory satisfies risk constraints; and if the risk constraints are not satisfied, regenerating an anthropomorphic lane-changing trajectory; or if the risk constraints are satisfied, performing tracking control based on a preview-following theory. In the present disclosure, a lane-changing intention is described and identified based on actually measured data, to generate an anthropomorphic lane-changing trajectory.

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.

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 discloses a new energy automobile hybrid power system cooperative control device and method.The new energy automobile hybrid power system cooperative control device comprises a multi-source data collection module, a cooperative control center and an execution unit, and the multi-source data collection module transmits collected information data to the cooperative control center; the cooperative control center comprises a data fusion unit, an energy management module, a heat management module and a cooperative decision-making unit, the data fusion unit receives data information of the multi-source data acquisition module, carries out fusion processing, and transmits the data information to the energy management module and the heat management module; the energy management module and the heat management module output a control strategy to the collaborative decision-making unit, and the collaborative decision-making unit judges a correction strategy and then sends a control instruction to the execution unit. The method has the advantages that cooperative control of energy management and heat management is achieved, various dimension data of road conditions, driving behaviors, power states and heat source states are fused, the problem of system unbalance caused by single module optimization is solved, and dynamic cooperation of energy management and heat management is achieved.

The invention discloses a chassis coordination control method and system based on tire side deviation state evaluation, and relates to the technical field of four-wheel steering four-wheel drive vehicle dynamics control. The method comprises the steps that an expected front wheel rotation angle is determined based on an expected steering wheel rotation angle input by a driver, and a front wheel rotation angle is determined; further determining a feed-forward front wheel steering angle, a feed-forward rear wheel steering angle and an additional yaw moment based on feed-forward control and feedback control, determining a first tire slip angle and a second tire slip angle based on a road adhesion coefficient, and further determining a wheel torque control weight, a feedback front wheel steering angle control weight and a feedback rear wheel steering angle control weight; and further determining a driving yaw moment, a feedback front wheel steering angle and a feedback rear wheel steering angle, determining a total longitudinal force based on an expected longitudinal acceleration input by a driver, and performing four-wheel torque distribution based on the total longitudinal force and the driving yaw moment to obtain a four-wheel driving torque. The driving stability of the vehicle under the limiting working condition can be improved.

The invention provides a vehicle lane changing planning method and system based on a graph neural network and multiple agents, and relates to the technical field of unmanned driving, and the method comprises the steps: obtaining a traffic scene graph structure with multiple agents; using a multi-agent reinforcement learning algorithm and a corresponding reward function to train the graph neural network fusion model, and updating parameters of the graph neural network fusion model by minimizing dominant function-based strategy gradient loss and value function loss to obtain a trained graph neural network fusion model; analyzing the traffic scene graph structure by using the trained graph neural network fusion model to obtain a multi-agent decision strategy; and performing decision-making prior fusion on the multi-agent decision-making strategy, performing dynamic interaction with the environment, obtaining a vehicle lane changing planning result, and completing the vehicle lane changing planning. According to the method, the problems of low multi-agent trajectory tracking precision and poor robustness in a complex scene are solved.

Methods of performing a dispatched logistics operation for delivery / pickup of items using a modular autonomous bot apparatus assembly (with a modular autonomy module, cargo storage system, auxiliary power module, and mobility base) and a dispatch server. The modular autonomy control module receives a dispatch command from the dispatch server and authenticates that each of the modular components in the assembly are compatible with the particular dispatched logistics operation. The bot assembly receives the transported item, autonomously moves from an origin location to a destination per the dispatch command using the mobility base autonomously controlled by the modular autonomy control module, receives authentication input that at least correlates to authentication information in the dispatch comment to allow selective access to the modular cargo storage system of the bot assembly, and then autonomously returns to the origin after the item is detected removed from the cargo storage system.

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 provides a vehicle driving control method and device for a backlight scene, equipment and a medium. The method comprises the steps that multi-source information of a target vehicle is acquired in real time; performing fusion calculation on the multi-source information to obtain a current backlight risk index of the target vehicle; determining a driving assistance strategy or an automatic emergency braking strategy based on the backlight risk index; and controlling the target vehicle according to the driving assistance strategy or the automatic emergency braking strategy. According to the vehicle driving control scheme for the backlight scene, the backlight risk index is generated by fusing the multi-source information, the backlight scene is accurately judged, the corresponding driving assistance or automatic emergency braking strategy is formulated, the driving safety and reliability of the vehicle in the complex backlight environment are improved, and the driving safety of the vehicle in the complex backlight environment is improved. The problems that in the prior art, due to dependence on a single sensor in a backlight scene, the target recognition precision is low, a dynamic adaptation mechanism is lacked, and a braking strategy is difficult to pre-judge and adjust in advance are solved.