6614 results about "Driving mode" patented technology

The invention discloses a digital human emotion recognition and feedback system based on a large model, and relates to the technical field of large models.During operation of the system, multi-modal data of a driver are collected in real time through an embedded sensor network in various driving environments, and the emotion, driving behaviors and driving environment parameters of the driver are obtained; the method comprises the steps of constructing fatigue, anger and other multi-dimensional emotion features of a driver to form an emotion feature vector, analyzing an emotion change trend through threshold comparison, obtaining an evaluation state, dynamically generating a feedback strategy for optimizing a driving state based on an emotion state and a driving scene of the driver, and embedding a generative large model into a module. Diversified feedback responses including audio reminding, driving mode adjustment and entertainment content recommendation are automatically generated, feedback strength and form are adaptively adjusted through multi-dimensional parameters, a feedback generation effect is dynamically adjusted, and an emotion state analysis result and vehicle behavior monitoring data are integrated, so that a user experience is improved. And evaluating the potential dangerous state of the driver in real time.

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.

Methods and apparatus to adjust a steering angle of a vehicle in a self-driving mode are described herein. An example vehicle disclosed herein includes a steering controller including instructions and programmable circuitry to execute the instructions to access a path follower (PF) angle request, generate a virtual boost curve (VBC) angle request based on a torque input to a steering wheel, determine an angle blending weight based on the torque input and a speed of the vehicle, determine a final angle request based on the PF angle request, the VBC angle request, and the angle blending weight, and convert the final angle request to a torque request to be used to adjust a steering angle of the vehicle via a motor.

The invention relates to the technical field of intelligent transportation control, in particular to a tomato transportation speed self-adaptive adjustment method based on path road condition feedback, which comprises the following steps: acquiring road images, vibration waveforms and altitude data through a multi-source sensing unit, and constructing real-time road condition information; identifying a driving mode and extracting a corresponding bumping parameter; detecting the maturity grade of the tomato by combining multispectrum and thermal imaging, and querying a maturity-compressive strength corresponding table to calculate a cargo damage threshold value; constructing a dynamic mapping model under multiple working conditions, predicting vibration response and converting the vibration response into equivalent pressure; comparing the equivalent pressure with a damage threshold value to obtain a safety margin, constructing a speed adjustment decision tree and generating a maximum allowable speed value of each road section; and dynamically generating a segmented variable-speed control instruction based on the speed decision matrix, and controlling a throttle valve and a braking system to cooperatively change speed. The method has the advantages of accurate working condition identification, dynamic fruit adaptation, closed-loop speed regulation and control and the like, and is suitable for fine speed control of a high-sensitivity fruit and vegetable transportation scene.

The invention relates to the technical field of power grid planning, in particular to a charging pile planning method based on dynamic analysis. The method comprises the following steps: obtaining electric vehicle use data in a target area, and carrying out charging demand prediction analysis according to the electric vehicle use data to obtain a charging demand prediction model; based on the charging demand prediction model, performing multi-scene simulation to obtain charging demand distribution map data, multiple scenes including different traffic flows, different driving modes and different charging demand scenes; obtaining geographic information data of the target area; and constructing a spatial analysis model according to the geographic information data, and evaluating the candidate position of the charging pile according to the charging demand distribution map data, thereby obtaining position suitability score data. According to the method, different scenes are simulated, so that the adaptability and robustness of planning are enhanced.

The invention discloses a driving style quantitative evaluation method based on multi-modal perception and a knowledge graph, and the method comprises the steps: employing a multi-modal data source, which comprises various sensor data, vehicle motion data, and physiological reaction and behavior data (such as heart rate change and operation intervention frequency during emergency braking) of a driver and a passenger in a real scene; a user emotion mapping algorithm is constructed by the model, subjective experience is converted into quantifiable numerical indexes (such as a trust index and a comfort score), a driving style deviation index (SDI) is calculated by using an algorithm in combination with a constructed driving behavior knowledge graph, quantitative evaluation of brand driving genes is realized, and a data driving basis is provided for function and algorithm optimization. And a personalized intelligent driving mode is realized. Therefore, the driving style quantitative evaluation method based on the multi-modal perception and the knowledge graph has the advantages of being high in intelligent degree and high in individuation degree.

There is provided a control system for an electric vehicle comprising a power train, the control system comprising: a first controller configured to automatically select a drive mode for the vehicle based on a whole vehicle operation model; and a second controller coupled to the first controller, the second controller configured to adjust a set of tuning parameters for the vehicle, whereby the second controller is configured to receive a signal from the first controller to adjust the set of tuning parameters such that the vehicle operates in the selected drive mode. Thus enhancing the operation and durability characteristics of the vehicle.

The invention relates to the technical field of orchard unmanned vehicles, in particular to an orchard unmanned vehicle intelligent navigation and control method based on multi-sensor fusion, and the method comprises the following steps: S1, global path planning: generating discrete plane path points, and then pre-fitting a prior navigation global path; s2, three-dimensional environment perception: extracting static obstacle and dynamic obstacle information in a working path in real time, detecting and identifying a target obstacle at the same time, and performing sensor information fusion; s3, path tracking control: controlling the orchard unmanned vehicle to run according to a priori navigation global path; and S4, dynamic path optimization: realizing dynamic planning of an obstacle avoidance trajectory, synchronously establishing a trajectory overlap ratio evaluation mechanism, and adjusting a driving mode to avoid the obstacle. According to the invention, the reliability of environment perception and path dynamic adjustment is improved, and the three-dimensional motion control model is constructed, so that the unmanned vehicle can safely and accurately complete autonomous navigation work.

The invention provides a vehicle control method, a storage medium and a vehicle, and belongs to the technical field of vehicles. When the vehicle is in the low-speed four-wheel drive state, the vehicle state information is obtained, and when the vehicle state information meets the mode switching condition, the vehicle can be controlled to be switched to the low-speed direct drive mode from the current drive mode, and the vehicle is controlled to run in the low-speed direct drive mode; wherein in the low-speed direct drive mode, a clutch of the vehicle is in a sliding friction state, and an engine is in a driving state. By developing the low-speed direct-drive mode, on one hand, the engine can drive the vehicle in a low-speed scene, the problem that the power consumption of the vehicle is too fast when the vehicle runs in winter is effectively solved, and then the power protection capacity of the vehicle is improved; and on the other hand, the vehicle can be always kept in a four-wheel-drive state in a low-speed state, the phenomenon that the vehicle slips and drifts in snowy days is effectively avoided, and then the driving safety of the vehicle is guaranteed.

The invention discloses a path planning method based on an improved artificial potential field method, and the method comprises the steps: generating a corresponding planning path according to a driving mode selected by a driver; according to the driving mode, a gravitational potential field, a repulsive potential field, a road boundary potential field and a lane line potential field in the corresponding mode are obtained in real time; the gravitational force, the repulsive force, the boundary force and the lane force borne by the main vehicle are calculated in real time, then the resultant force is calculated through a gradient descent method, and the driving direction of the vehicle along the planned path is adjusted in real time according to the resultant force. By introducing the concept of the dynamic risk field, the complex dynamic traffic environment can be flexibly dealt with, and collision with static or moving obstacles is effectively avoided, so that the driving safety and adaptability are improved; different driving modes are designed, the transverse and longitudinal weight factors and the gear parameters are adjusted according to the requirements of a driver, the path planning scheme meeting the personalized requirements is generated, and the comfort and diversity of driving experience are enhanced.

A power supply system is installed in a vehicle having an autonomous driving mode. The power supply system includes a first load circuit connected to a first load that operates using electric power from a main battery; a second load circuit connected to a second load that operates using electric power from the main battery or an additional battery and that is necessary to continue the autonomous driving mode; and a first circuit connection / disconnection mechanism provided on a power supply line for electrically connecting the first load and the second load, the first circuit connection / disconnection mechanism that electrically connects or disconnects the first load circuit to or from the second load circuit. The first load circuit is located on an upstream side of the first circuit connection / disconnection mechanism. A current path on a downstream side of the first circuit connection / disconnection mechanism includes a first path for allowing a current flowing from the first load circuit to the first circuit connection / disconnection mechanism to flow from a branch point located on the downstream side of the first circuit connection / disconnection mechanism to the second load, and a second path for allowing the current to flow from the branch point to the additional battery. A current value allowed for conduction through the first path is higher than a current value allowed for conduction through the second path. The second load circuit includes the second load and a third load that is not necessary for traveling in the autonomous driving mode. The third load is connected to the second path between the branch point and the additional battery.

The invention discloses a multi-mode energy recovery optimization control system of a hybrid electric vehicle, and relates to the technical field of energy recovery control. The state sensing module is integrated with a sensor to collect vehicle speed, acceleration, battery state and road condition signals, and data accuracy is ensured through Kalman filtering fusion; the driving mode recognition module is used for recognizing modes such as urban congestion and high-speed cruise through an algorithm based on driving behaviors and road conditions; the energy recovery strategy decision module is used for dynamically adjusting recovery priorities and parameters according to different modes, and balancing efficiency and smoothness; the power distribution execution module converts and stores energy through cooperative control of double clutches and a gearbox, and is linked with an ABS (Anti-lock Brake System) to guarantee safety; and the feedback module is optimized in real time, battery and motor states are monitored, strategies are dynamically corrected, and efficiency and component protection are both considered. The energy recovery efficiency is improved, the driving smoothness and the braking safety are improved, the system adapts to multiple scenes, the service life of the battery is prolonged, and the performance of the hybrid electric vehicle is comprehensively improved.

The invention relates to the technical field of battery control, in particular to a new energy automobile battery device and a control method thereof. The method comprises the following steps that dynamic parameter monitoring is conducted on an automobile battery, a battery state parameter set is obtained, and the battery state parameter set comprises a residual electric quantity continuous parameter, a voltage continuous parameter, a current continuous parameter and a temperature continuous parameter; according to the battery state parameter set, working condition identification is carried out on the automobile battery, a battery working condition type is obtained, and the battery working condition type is any one of a discharging working condition, a charging working condition and a standing working condition; if the battery working condition type is a discharge working condition, performing driving behavior data acquisition on the target automobile to obtain a driving behavior data set; and performing driving behavior pattern recognition on the target automobile based on the driving behavior data set to obtain a driving pattern type. The use efficiency and the safety of the battery are improved.

The invention relates to the technical field of driving analysis, in particular to an unmanned intelligent analysis method and system, and the method comprises the following steps: S1, collecting multi-dimensional data in real time through a vehicle-mounted sensor, a traffic information network and meteorological data, building a short-term energy consumption prediction model based on the multi-dimensional data, the short-term energy consumption prediction model predicts energy consumption fluctuation in a future driving route; s2, dynamically adjusting a driving mode of the unmanned vehicle according to an energy consumption fluctuation prediction result of the short-term energy consumption prediction model and based on a multi-mode energy efficiency optimization mechanism; and S3, through a self-adaptive friction coefficient feedback mechanism, road surface friction coefficient changes of the tires and the ground under different road conditions are dynamically evaluated, and power output and braking response of the vehicle are adjusted. Acceleration, deceleration, sliding and regenerative braking modes of the vehicle can be adjusted in real time, and it is ensured that the vehicle is in the optimal energy efficiency state all the time.

The invention provides a simulation test scene parameter generation method of an automatic driving system and electronic equipment. The method comprises the following steps: acquiring road condition information transmitted by at least one road monitoring device in a target road in real time; the road condition information comprises a long-tail problem identification result; reconstructing road scene parameters based on pre-stored road attribute data and road condition information; searching a target vehicle running on a target road in an automatic driving mode by accessing an automatic driving system, and acquiring sensor configuration parameters of the target vehicle; and generating simulation test scene parameters according to the sensor configuration parameters and the road scene parameters, so as to generate a simulation test scene for the target road, the target vehicle and the automatic driving system based on the simulation test scene parameters. According to the invention, the simulation test scene covers a wide range of driving environments and emergencies, and the diversity and comprehensiveness of the scene are ensured, so that the method is used for realizing the efficient offline verification of the automatic driving system.

A safety strategy determining method, applied to a hybrid transmission control unit (HTCU), and comprising: monitoring an environment signal in a hybrid transmission; when an error occurs to the environment signal, determining an abnormal event corresponding to the environment signal; analyzing the abnormal event, and determining a safety state corresponding to the abnormal event; on the basis of the safety state and the current gear of a vehicle, determining a safety strategy corresponding to the abnormal event, wherein the current gear comprises one gear in different driving modes, and the safety strategy comprises at least one of the following: maintaining the current gear, reverting the gear in the hybrid transmission to the current gear, and switching from the current gear to a corresponding downshifted gear; sending a gear switching request carrying the safety strategy to a vehicle electronic control unit (VECU), wherein the gear switching request is used for requesting the VECU to switch to a target gear on the basis of the safety strategy; and switching the gear in the hybrid transmission to the target gear. The problem of over-protection or under-protection in the hybrid transmission is avoided, and therefore the driving safety is improved. Also provided is a security strategy determining apparatus.

The invention provides a gear switching method, a vehicle and a storage medium, the method is applied to the technical field of hybrid power control, and the method comprises the steps that in the process that the vehicle runs based on a direct drive mode, if it is determined that the vehicle needs to switch gears, the whole vehicle driving power corresponding to the direct drive mode and the discharging power of a power battery are determined; based on the whole vehicle driving power, the discharging power and the gear change state of the vehicle, a target torque reduction gradient is determined; based on the target torque reduction gradient, the actual torque of the engine is controlled to be reduced to the target torque; and under the condition that the actual torque of the engine is equal to the target torque, the clutch is controlled to be in an open state, so that the vehicle is shifted under the condition that the clutch is in the open state. The method can ensure that the power is not interrupted in the vehicle gear shifting process, the vehicle stability is improved, and the user driving experience is improved.

The invention discloses a nail gun. The nail gun comprises a shell; the motor is arranged in the shell; the percussion assembly is arranged in a nailing period, can move from an initial position to a percussion position so as to knock out a nail to a workpiece, and can move from the percussion position to the initial position; the guide assembly is arranged to at least guide the percussion assembly to conduct nailing in the nailing direction; the trigger switch is arranged on the shell and is configured to be operated when the percussion assembly is driven; the push rod switch is arranged at the lower end of the percussion assembly and is opened when the push rod switch is pressed downwards to abut against the workpiece in the nailing direction; the controller is configured to be capable of working in a single driving mode or a continuous driving mode; when the trigger switch is operated and the push rod switch abuts against the workpiece, the controller works in the continuous driving mode. In the continuous driving mode, the controller is configured to control the percussion assembly to conduct nailing when the distance between the lower end of the guide assembly and the surface of the workpiece is smaller than or equal to the preset distance.

The invention provides a two-wheeled electric vehicle control system and method, and the system comprises a user side control platform which is used for detecting the operation of a user on a reverse turning handle, and generating a corresponding control instruction according to the pre-configured function setting; the communication module is used for bidirectionally transmitting instruction signals and vehicle state information in real time between the user end control platform and the vehicle end control system; and the vehicle end control system is used for receiving and analyzing the control instruction, judging the running or static state of the vehicle, executing kinetic energy recovery braking or driving mode switching in the running state, executing a reversing function or a self-defining function in the static state, and feeding back a vehicle execution result or vehicle state information through the communication module. The technical problems that in the prior art, the control function of a control terminal is single, the operation mode is not convenient enough, and the integration degree of the control terminal and vehicle functions is low are at least solved.

The invention belongs to the technical field of a computer system based on a specific calculation model, relates to a virtual and real dynamic interaction-based digital twin bearing fault diagnosis method, and aims to solve the problem that a bearing system in large rotary mechanical equipment is large in fault hidden danger and avoid serious consequences caused by faults. A generally used bearing is in a high-precision standard, so that system signal data under bearing fault operation is not easy to collect in a factory, and the unbalanced data sample distribution directly leads to finiteness of fault diagnosis in a real scene only based on a big data driving mode classification method, so that the system signal data cannot be easily collected in the factory. The method has good scene working condition self-adaption, can be applied to fault diagnosis of a bearing system in different industrial scenes, fully excavates limited industrial data information and rich physical priori knowledge, and ensures safe and reliable operation of an important rotating mechanical system through a digital twinning technology.

The invention provides a wind power plant unit state monitoring and fault early warning system and method based on deep learning, and belongs to the field of wind power generation and artificial intelligence. According to the system, a cloud edge collaborative architecture is adopted, an edge computing terminal operates a data-driven space-time prediction model and a physical digital twinborn model in parallel, and abnormity is preliminarily screened by calculating a double-track residual error and comparing the double-track residual error with a dynamic early warning threshold value. And when an exception occurs, the cloud platform receives multi-modal data including a sensor, a model state and an operation and maintenance text, performs deep root cause analysis by using a diagnosis model fused with a wind power fault knowledge graph, and generates an interpretable diagnosis report. According to the method, deep fusion of data and a physical model is realized, and the accuracy of fault monitoring, the interpretability of diagnosis and the intelligent level of operation and maintenance decision are remarkably improved through a data-physical double-track driving mode.

The invention relates to the technical field of intelligent driving, and particularly discloses a driver behavior evaluation and prediction method and system based on a driving scene, and the method comprises the steps: monitoring and extracting the driving scene, the information of people in a vehicle, and the state information of a driver; a driving scene and environment model is established according to the driving scene, a vehicle using scene model is established according to information of people in the vehicle, and a driver model, intelligent driving performance parameters and driver behavior evaluation parameters are generated according to driver state information of a driver in different driving modes; comprehensively evaluating the driving behavior and the driving style of the driver in the manual driving process based on the driving scene and environment model, the vehicle using scene model and the driver model so as to adjust and predict intelligent driving performance parameters and driver behavior evaluation parameters; and a driving strategy in the intelligent driving process is optimized and adjusted based on the driving performance parameters and the driver behavior evaluation parameters, so that the interactive experience of the driver is improved, and the safety of intelligent driving is improved.

The invention discloses a decision support method for time-space dynamic data of Internet of Vehicles based on knowledge retrieval enhancement, which comprises the following steps: collecting perception data inside and outside a vehicle, and separating general data and private data; preprocessing the data, and compressing and encrypting the private data; performing data processing by adopting a data knowledge dual-drive mode and a large language model, and converting the data into structured data; constructing a super-relation knowledge graph, and carrying out vectorization processing based on an embedded vector model; performing node importance evaluation based on the super-relation knowledge graph, and performing data updating and increment; storing the private data locally, uploading the general data to a public database of the cloud, and defining a dynamic access range of the public database; and extracting a user questioning entity by using the local entity recognition model, matching data existing in the local and the cloud, obtaining background knowledge contents, and generating an enhanced retrieval result. According to the method, the problems of acquisition of decision support information and use safety of data in an Internet of Vehicles environment are solved.

The invention discloses a UWB-based multifunctional integrated vehicle-mounted safety control method and system, and the method comprises the following steps: collecting the dynamic environment data around and in a vehicle in real time based on a plurality of sensors, carrying out the path adjustment and driving mode optimization through generating a multi-level three-dimensional environment model, and carrying out the control of the driving mode. The system performs risk assessment and dynamic adjustment by using external data such as obstacles, road conditions, weather and the like collected by a sensor and combining a behavior mode of a driver and a health state of a passenger, and in a collision prediction and protection mechanism, the system gradually activates a protection system by analyzing factors such as vehicle speed and acceleration, so that the safety is guaranteed, and the safety is ensured. The system optimizes the energy distribution of a key system through an energy scheduling model, improves the energy efficiency, and preferentially guarantees the operation of a safety system in an emergency. According to the invention, a self-adaptive learning algorithm and a multi-scene optimized driving assistance function are integrated, and self-adaptive adjustment and safety control of the vehicle in different driving environments are realized.

A chain binder to connect and apply tension to chains, straps, or the like used to secure cargo on any type of platform or vehicle. The chain binder includes a lever body; a first side portion; a second side portion; and gears that effectuate two modes of operation. Namely, a power driven mode of operation or a manual lever mode of operation. Rotations of the gears causes hooks or connectors, via movement of eye-bolts, toward each other to place tension in the chains or strap. Opposite rotations of the gears causes hooks or connectors, via movement of eye-bolts, away from each other to relax the chains or strap. If components are damages during use, they may be replaced on an as-needed basis to reduce operational cost of the chain binder since the entire binder does not need replaced when only one component fails or is damaged.

An electric vehicle battery control system including a first electric motor configured to propel a vehicle in a drive mode in response to a drive current, a second electric motor operative to generate a back electromotive force voltage in a fault mode, a battery, having an open circuit battery discharge voltage, for generating the drive current for coupling to the first electric motor, and a vehicle controller for limiting the drive current in response to a difference between the back electromotive force voltage and the open circuit battery discharge voltage being less than a threshold voltage difference.

The invention discloses a vehicle driving optimization method based on a road surface friction coefficient intelligent prediction model, and belongs to the technical field of urban traffic road engineering and vehicle control. Collecting real-time state parameters of a front road surface in real time through a vehicle vision system, wherein the state parameters comprise road surface types and road surface conditions; according to the real-time state parameters of the front road surface, the running state parameters of the vehicle and the environment state parameters, a real-time prediction friction coefficient value between the vehicle and the road surface is output through the intelligent friction coefficient prediction model; and transmitting the real-time predicted friction coefficient value to a vehicle control system, and adjusting the power distribution of the vehicle. The road friction coefficient value can be predicted in real time, and the power distribution of the vehicle is reasonably adjusted in combination with the current state of the vehicle. A power source of the vehicle is reasonably used, so that the trafficability, the driving economy and the running stability of the vehicle are improved, the driving mileage of the vehicle is prolonged, and the switching of the driving modes of the vehicle is guaranteed to have a basis.

The invention discloses a vehicle operation control system, method and equipment, a medium and a product. The system comprises an operation state determination subsystem used for determining predicted operation situation information associated with a vehicle; the operation constraint subsystem is used for determining vehicle operation constraint parameters based on the predicted operation situation information and determining a to-be-driven mode of the vehicle; the motion control subsystem is used for determining an actuator reference instruction based on the predicted operation situation information, the to-be-driven mode, the vehicle operation constraint parameters and the to-be-driven track; the control distribution subsystem is used for determining an actuator distribution instruction of a plurality of actuators in the vehicle based on the actuator reference instruction and the predicted operation situation information; and the coordination control subsystem is used for determining target actuator instructions of the plurality of actuators based on the to-be-driven mode and the actuator distribution instruction, and controlling the actuators to operate based on the target actuator instructions. The control consistency, the response real-time performance, the operation safety and the comfort of the whole vehicle in a complex dynamic scene are improved.

A vehicle (22) being operable in an autonomous driving mode having a parking brake (17) with a first actuation unit (9) configured to apply or release the parking brake (17); an activation device (10) configured to activate or deactivate the autonomous driving mode; an interlock (11), wherein the interlock (11) is configured to prevent an activation of the autonomous driving mode unless the parking brake (17) is applied by the first actuation unit (9).

The invention discloses a fully-mechanized coal mining equipment group sensing-control integrated twinning device and method, and relates to the technical field of coal mine intelligent mining digital twinning. The method comprises a sensing information driving mode, a physical instruction driving mode and a dual-driving mode. When the sensing information driving mode lacks physical instruction information, real-time sensing data of the hydraulic support are collected, sensing-control integration is achieved, and the shape is consistent; the physical instruction driving mode is used for speculating a current posture through a speed parameter training model and an optimal force training model when sensing information is lost, so that virtual-real synchronization is realized; and the dual-drive mode is used for realizing real-time consistent action and regulation of the virtual monitoring model and the virtual control model according to an adaptive control strategy when the physical instruction transmission path and the sensing information transmission path are both intact. According to the method, high-fidelity actions of the virtual control model and the virtual monitoring model can be driven through the physical entity, and virtual reconstruction of the working space is completed in the virtual space.