3986 results about "Vehicle safety" patented technology

The invention provides a vehicle collision risk early warning method, system and device and a medium, and belongs to the technical field of vehicle safety control. The method comprises the steps that surrounding environment information of a vehicle is collected in real time through a vehicle built-in sensor and a GPS module; a multi-sensor fusion device technology is used to carry out comprehensive processing on surrounding environment information of a vehicle, and an environment perception model is generated after data integration; based on the environment perception model, target identification is carried out by using a computer vision technology based on a convolutional neural network so as to identify travelers, vehicles, traffic signs and obstacles; performing behavior prediction based on a deep learning model to predict a behavior track of a front vehicle or pedestrian; evaluating a potential collision risk in the current scene in combination with the target recognition result and the behavior prediction result; when a potential collision risk is detected, an early warning signal is sent to a driver, and when the driver does not take corresponding measures, active safety measures are automatically triggered to avoid collision or reduce accident damage.

The invention relates to the technical field of unmanned aerial vehicles, in particular to a mooring unmanned aerial vehicle safety management system which comprises a multi-source data acquisition module, a data fusion and analysis module, an intelligent risk assessment module, a control instruction generation module and an execution mechanism module. The multi-source data acquisition module acquires flight and mooring cable data of the unmanned aerial vehicle by using various sensors, and transmits the flight and mooring cable data to the data fusion and analysis module through a time division multiple access protocol with low packet loss rate and low delay; the intelligent risk assessment module judges a risk level by combining a risk assessment model based on a support vector machine and a Bayesian network algorithm according to the information, and generates prompt information if the risk level exceeds a preset range; the control instruction generation module generates instructions according to a risk prompt and a preset strategy, ranks the instructions and sends the instructions to the execution mechanism module; a flight control system in the execution mechanism module cooperates with a mooring cable electric take-up and pay-off device and the like, real-time adjustment and control over the flight attitude of the unmanned aerial vehicle and the cable state are achieved, and it is ensured that tasks are safely and stably executed in the complex environment.

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.

The invention discloses a mountainous area icy road vehicle driving speed early warning method based on physical information constraint, and relates to the technical field of intelligent traffic and vehicle safety, and the method comprises the steps: carrying out the multi-modal data collection and preprocessing, constructing a multi-modal feature extraction network to extract a high-dimensional vector, constructing a fusion module based on an attention mechanism to output a fusion feature vector, and carrying out the early warning of the driving speed of a mountainous area icy road. The method has the advantages that data such as environment, vehicle dynamics and road surface friction coefficients are collected through the multi-source sensor fusion technology, a multi-modal feature extraction and fusion network is constructed, and the real-time classification early warning is realized through the multi-modal feature extraction and fusion network; the method comprises environment, vehicle dynamic and road friction feature extraction sub-networks, deep extraction of different modal data features, a fusion network based on an attention mechanism, concerning of association, coupling and cooperative influence among data, and dynamic weighting of fusion features, so that the purposes of accurately sensing a complex environment, reducing false alarm and missing alarm and effectively improving early warning accuracy are achieved.

Techniques for clustering sensor data are discussed herein. Sensors of a vehicle may detect data points in an environment. Clustering techniques can be used in a vehicle safety system to determine connection information between the data points. Further, the machine learned model can be trained to output motion data representing velocity data or a cluster association score. The connection information and / or motion data can be used by a vehicle computing device to detect objects in an environment and / or to control operation of a vehicle.

The invention relates to the technical field of vehicle safety, and discloses a braking method and system based on vehicle automatic emergency early warning and braking and a storage medium. The method comprises the following steps: fusing multi-modal sensor data to generate a dynamic environment perception graph and calibrating the dynamic environment perception graph; generating a real-time risk distribution map and a potential collision probability prediction matrix based on the dynamic risk assessment model; carrying out matrix multi-stage decision optimization to generate emergency braking trigger conditions, and dividing early warning levels and scheduling according to the conditions and a risk map; and the braking system control parameters are adapted according to the early warning instruction set, and a final execution scheme is generated after verification. The system comprises a sensing module, an evaluation module, a decision module, an early warning module and an execution module. Electronic equipment and a computer readable storage medium can execute the method. Through multi-modal fusion, dynamic risk assessment, graded early warning and redundancy check, the safety performance of the vehicle in a complex environment is improved, accurate early warning and reliable braking are achieved, and the method is suitable for the technical field of vehicle safety.

The invention relates to the technical field of data processing, and discloses a motor vehicle safety performance classification method and system based on multi-factor coupling influence. The method comprises the steps of collecting multidimensional safety data of a motor vehicle, calculating a mutual information value and transfer entropy to generate a coupling relation network diagram, constructing a three-layer safety index system to form a comprehensive score, obtaining low-dimensional features through phase-space mapping, determining a dynamic boundary value by applying density clustering, and classifying real-time states to output risk early warning information. According to the technical method for realizing accurate classification and early warning, the defects of neglect of factor coupling relation, lack of scene adaptability, insufficient dynamic prediction capability and the like in the prior art are overcome, and the accuracy and practicability of safety performance evaluation and early warning of the motor vehicle are improved.

The invention relates to the technical field of automatic driving automobiles, in particular to a scene extraction, generation and evaluation method for a vehicle safety test, which adopts a semantic segmentation network to keep scene element features in original natural driving data to the greatest extent. Based on a high-level decision deep reinforcement learning algorithm, a rough set is used to extract an association generation rule of a typical dangerous scene, the typical dangerous scene can be automatically generated in combination with simulation software, and a certain design thought is provided for automatically extracting and generating the characteristics of the scene by referring to natural driving data; meanwhile, the overall intelligence of the self-driving automobile is evaluated in combination with two dimensions of natural driving evaluation and test scene complexity evaluation, and key index advantages in different aspects are combined, so that the multi-dimensionality of evaluation of the self-driving automobile is improved; and the scene generation efficiency and the intelligent level are greatly improved.

The present invention discloses a vehicle transport safety monitoring system for hazardous chemicals based on all-element management. A driver holography management unit performs multi-factor identity verification and multi-dimension driver status monitoring on a driver, and implements linkage with vehicle control according to an identity verification and / or status monitoring result. A vehicle safety monitoring unit automatically monitors a vehicle safety status, and forms warning or alarm information according to a monitoring result. An in-vehicle hazardous chemical safety monitoring unit comprehensively monitors physical and chemical safety parameters of in-vehicle hazardous chemicals and in-vehicle hazardous chemical anti-theft and robbery monitoring information during transportation, and forms warning or alarm information according to a monitoring result. This system can implement comprehensive real-time monitoring and risk warning for persons, vehicles, and materials for the in-vehicle hazardous chemicals during transportation, so as to ensure transport safety of the hazardous chemicals.

The invention relates to the technical field of vehicle safety, in particular to a pedestrian body model chest injury risk curve adjusting method. Comprising the following steps: analyzing the relationship between the speed and the chest injury risk, and drawing a first curve between the vehicle speed and the injury risk based on accident statistical data; fitting data of a rib cortical bone uniaxial stretching material mechanics experiment; constructing a vehicle-pedestrian side impact simulation experiment matrix under different working conditions, and generating a simulation sample number; extracting the rib strain of the human body model in the sampling model, calculating the fracture probability of a single rib of the chest of the human body model, and then calculating the maximum simple injury probability of the specified chest of the human body model; fitting the relationship between the vehicle speed in the simulation model and the chest injury risk of the human body model, and drawing a second curve between the vehicle speed in the sample and the chest injury risk of the pedestrian model; and constructing an objective function according to the difference between the first risk curve and the second risk curve. According to the technical scheme, the accuracy of accident damage prediction can be improved.

Techniques for accurately predicting and avoiding collisions with objects detected in an environment of a vehicle are discussed herein. A vehicle safety system can implement one or more models to output data indicating an intersection probability between the object and a portion of the vehicle in the future. The model may employ a blur filter to modify a representation of an object usable to determine the intersection probability. A vehicle can be controlled in an environment based at least in part on the intersection probability.

A system includes one or more processors that obtain raw data regarding one or more events of a vehicle. The system infers, within the raw data, an indication of a vehicle, vehicle attributes, an environment, and environmental attributes according to an ontological framework. The ontological framework defines linkages among the vehicle and the vehicle attributes, and relationships among the vehicle and other objects within the environment. The system transforms the raw data into a seed scenario according to the ontological framework. The seed scenario includes a file, and the seed scenario includes a description or a depiction of the vehicle, the vehicle attributes, the environment, and the environmental attributes. The system generates one or more additional scenarios by modifying the seed scenario. The modification of the seed scenario is based on modifications to the environmental attributes, the vehicle attributes, and navigation attributes.

A vehicle-mounted display device, a vehicle steering wheel assembly, and a vehicle. The vehicle-mounted display device comprises a decorative piece (1), wherein the decorative piece (1) comprises a connection portion (4), a touch-control display screen (2), a first mounting position (11) and a second mounting position (12); the decorative piece (1) is mounted on a base (13) of a vehicle steering wheel (3) by means of the connection portion (4); the first mounting position (11) is used for mounting the touch-control display screen (2), and the second mounting position (12) is used for mounting a vehicle airbag; and the first mounting position (11) and the second mounting position (12) are arranged side by side on the decorative piece (1), and the first mounting position (11) is located above the second mounting position (12) in the direction in which the first mounting position (11) and the second mounting position (12) are arranged.

The invention relates to the technical field of smart parking lots, in particular to a vehicle safety management method based on a smart parking lot, which comprises the following steps: S1, multi-source data acquisition: acquiring dynamic trajectory data of a target vehicle, parking lot environment parameters and adjacent parking space state data in real time, and generating a multi-source feature vector set; s2, data space-time alignment: carrying out space-time alignment processing on the multi-source feature vector set, and outputting a standardized vehicle behavior sequence; s3, abnormal behavior recognition: inputting the standardized vehicle behavior sequence into an abnormal behavior recognition model, and generating an abnormal behavior characteristic spectrum and a confidence coefficient parameter; s4, risk level assessment: constructing a risk assessment model based on the confidence coefficient parameter and the environmental parameter, and outputting the risk level of the parking lot in the current scene; and S5, security policy triggering: triggering a corresponding security policy execution instruction according to the risk level. According to the invention, potential risks can be effectively prevented, and the safety of vehicles and personnel is ensured.

The invention discloses a quality detection method and system for vehicle safety belt production and a storage medium, and relates to the field of manufacturing quality control of automobile passive safety parts. The method aims at solving the problems that an existing detection means is weak in tiny defect recognition capacity, insufficient in defect evolution modeling and lack of intelligent feedback regulation and control. According to the method, multiple detection means such as medium-wave infrared thermal imaging, laser confocal microscopy, structured light three-dimensional reconstruction and capacitance sensing are fused, and multi-mode defect sensing of key parts such as a braid, an insertion area and an adhesive layer is achieved; deducing a defect evolution process and generating a high-credibility pseudo label by constructing a thermal-stress-rheology coupling finite element model; the graph neural network and the AI model are combined to complete defect type identification, risk quantification and grading, and automatic optimization of process parameters and production line control response are realized based on the quality grade, so that the detection precision, the response efficiency and the product yield are remarkably improved.

The invention relates to the technical field of traffic safety, in particular to a highway risk map construction method and device fusing multi-source data, and the method comprises the following steps: S1, selecting a traffic conflict risk, a driving behavior risk and a vehicle safety risk as risk dimensions, selecting risk indexes according to the traffic conflict risk, the driving behavior risk and the vehicle safety risk, and calculating risk index data and the weight of the risk indexes; s2, calculating the total risk of the road section according to the weight of the risk dimension, the weight of the risk index and the risk index data; and S3, obtaining a risk map according to the total risk of the road section and the weight of the risk dimension. According to the method, by constructing the risk map and identifying the high-risk area, the application of multi-source data fusion in the aspect of highway risk assessment is enhanced, a scientific basis and technical support can be provided for traffic safety management, and the safety of the highway is improved.

The invention relates to the technical field of electric vehicle control, and discloses a stability control method and system for an electric vehicle, and the method comprises the steps: collecting vehicle driving data in real time, and dynamically estimating the state parameters of the vehicle through an unscented Kalman filtering algorithm; according to the state parameters of the vehicle, combined with the kinematic model and the foresight trajectory, the expected yawing moment in a short time in the future is calculated, and the longitudinal traction requirement of the vehicle is obtained; and according to the state parameters of the vehicle and the longitudinal traction requirement of the vehicle, the yawing moment and the traction force are decoupled and distributed to the four electric driving wheel ends, and stable control over the electric vehicle is achieved. Compared with a traditional stable control system only based on closed-loop feedback, the stable control system has the advantages that the control response is faster, the yawing intervention is more accurate, and better control performance and vehicle safety are shown under the low-adhesion road surface and high-load working conditions.

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.

Examples are disclosed for systems and methods for monitoring and filtering data transmitted to a vehicle connected to a wireless network. In one embodiment, a method for an edge node of a wireless network comprises routing traffic of the wireless network to a vehicle connected to the wireless network through the edge node; examining the traffic for potentially malicious content at the edge node; transmitting data packets of the traffic without potentially malicious content to the vehicle; and not transmitting data packets of the traffic with potentially malicious content to the vehicle.

The invention relates to the technical field of vehicle safety monitoring, and discloses a non-contact type in-vehicle driver respiratory rate detection method and device. Physiological signal data are collected through a millimeter wave radar, infrared thermal imaging and a microphone array, breathing frequency band signals are extracted through self-adaptive wavelet transform, and breathing signal waveforms are reconstructed through time-frequency joint analysis. A model formed by combining a multi-channel convolutional neural network and a bidirectional gating circulation unit is adopted to extract a respiratory rhythm feature vector, interference is eliminated based on a dynamic time warping algorithm, and a respiratory frequency value is estimated by applying methods such as variational mode decomposition. And processing abnormal points and missing data through a sliding window mechanism, and outputting a stable monitoring result. The invention further provides a sensor data synchronous calibration and model online updating method. According to the invention, the breathing frequency can be detected with high precision in a complex in-vehicle environment, and the safety and intelligent level of the vehicle are improved.

The invention provides a vehicle safety control method and device, electronic equipment, a vehicle and a storage medium. An electronic image of an intelligent driving perception picture is acquired in real time; wherein the electronic image is used for displaying the identification information of the vehicle and the identification information of the surrounding environment; receiving a current instruction input by a user for the identification information of the vehicle and the identification information of the surrounding environment in real time; according to at least one layer of security judgment mechanism, if it is determined that the current instruction is unreasonable, receiving a next instruction adjacent to the current instruction, and covering the current instruction with the next instruction; the safety judgment mechanism is used for enabling the vehicle to drive safely after judging that the current instruction is reasonable; and if it is determined that the current instruction is reasonable, the vehicle is allowed to be controlled to drive according to the current instruction.

The invention relates to the technical field of tire burst control, and discloses a vehicle tire burst control method and device, a vehicle, a storage medium and a program product.The vehicle tire burst control method includes the steps that after vehicle tire burst is detected based on vehicle condition information parameters, the tire burst information is adjusted according to an offset adjusting mode corresponding to the position of a tire burst wheel and the vehicle condition information parameters; the power-assisted steering torque and the yaw velocity of the vehicle are adjusted, so that the vehicle enters a stable state with the low yaw velocity, the driving torque of the non-tire-burst wheel is conveniently redistributed, vehicle transient control is cooperatively performed from multiple dimensions, and the real-time performance is high. And the method is not limited by the current working condition of the vehicle, so that the vehicle can be kept stable after tire burst and has the stable running capacity at a certain speed, potential safety hazards such as rollover and drifting of the vehicle after tire burst are avoided, and the safety of the vehicle is improved.

The invention relates to the technical field of risk assessment, in particular to a safe driving assessment method and system based on Beidou navigation. The method comprises the following steps: acquiring corresponding real-time position, speed and driving track data in a vehicle driving process through a Beidou navigation module, and acquiring vehicle state data and environment sensing data to obtain a vehicle multi-dimensional driving state data set; performing vehicle space-time and driving operation feature analysis and driving road matching analysis on the vehicle multi-dimensional driving state data set to obtain vehicle driving road attribute matching data; and carrying out vehicle driving risk estimation and vehicle safety over-rate evaluation based on the vehicle driving road attribute matching data, carrying out safety comparison verification evaluation, generating vehicle personalized driving improvement suggestions, and pushing the vehicle personalized driving improvement suggestions to the vehicle-mounted terminal to execute corresponding vehicle safe driving behavior improvement operation. According to the invention, real-time monitoring, accurate evaluation and active safety intervention of vehicle driving behaviors can be realized.

The invention discloses a light rail vehicle safety monitoring system based on multi-sensor fusion, and relates to the technical field of light rail vehicle safety monitoring, the system is composed of a plurality of functional modules, and the system comprises a multi-mode sensing module for capturing images of the surrounding environment of a light rail in real time and delimiting a visual mark area; performing three-dimensional scanning on the visual marking area, generating environment point cloud data in real time, and acquiring a dynamic object distance by using a Doppler compensation algorithm; the data processing module is used for calculating object deformation between adjacent frames by adopting an optical flow method and a CNN deformation detection network; carrying out object relative motion calculation according to object shape analysis; carrying out instance segmentation based on MaskR-CNN, and outputting an object contour; meanwhile, a dynamic target clustering algorithm is used for conducting multi-target tracking on the object contour, and three-dimensional coordinates and motion vectors of the object are output in combination with environment point cloud data; and the fusion and analysis module performs time synchronization based on the PTP protocol.

The invention relates to a vehicle dynamic adjustment method for a side collision accident, and the method comprises the following steps: S1, collecting vehicle body multi-dimensional information data information, vehicle environment information data information and vehicle passenger information data information, and building a real-time updated data model through data fusion; training a neural network according to virtual collision data generated by the vehicle collision simulation software to obtain a collision risk prediction model; s2, inputting data in the model into a collision risk prediction model to obtain the injury degree of a passenger and collision prediction position point information, dividing the state between a vehicle and a potential collision object into different risk levels, and generating a risk map; and S3, the vehicle adjusting system adjusts the height, the speed and the seat posture of a vehicle suspension system and a vehicle safety belt according to the different risk levels, risk prompting is carried out on vehicle passengers so as to reduce the risk levels, and the risk map is updated in real time. According to the invention, the safety of passengers in a side collision accident can be improved.

The invention discloses an intelligent vehicle automatic parking path planning optimization method, system and device and a storage medium. The method comprises the following steps: planning a parking path of a vehicle from a current position to a target parking space by using a search algorithm; a virtual parking environment is constructed, a simulation test is carried out in the virtual parking environment, different parking scenes and environment changes are simulated, and whether a parking path is safe or not is verified; when the parking path is verified to be safe, the vehicle is controlled to be parked according to the parking path; and when the vehicle is parked, the environment change is monitored in real time through a sensor, and the parking path is dynamically adjusted by using a path-finding algorithm and reinforcement learning. An A * algorithm is used for carrying out preliminary path planning and optimization, the reliability of the algorithm is verified through simulation testing, path planning and control strategies are continuously optimized, environment changes are monitored in real time and paths are dynamically adjusted by fusing data of various sensors such as a laser radar, a camera and an ultrasonic radar, and it is ensured that a vehicle safely avoids obstacles.

The invention discloses a vehicle tire burst control method and system, a vehicle, control equipment and a storage medium, and relates to the technical field of vehicle safety control. The method mainly comprises the steps of determining the position of a tire burst wheel according to a vehicle tire burst signal, and identifying a driving scene according to vehicle state information; if the linear driving scene is recognized, acquiring tire braking force pressure according to the position of the flat tire wheel, calculating a yaw torque compensation value according to the tire braking force pressure, performing rear wheel steering adjustment, transferring a driving torque based on the position of the flat tire wheel, and performing vehicle body posture adjustment based on the position of the flat tire wheel; if the scene is recognized as the curve scene, controlling and changing the steering angle of the rear wheel based on the position of the flat tire wheel so as to adjust the yawing torque, transferring the driving torque based on the position of the flat tire wheel, and adjusting the posture of the vehicle body based on the position of the flat tire wheel. According to the vehicle tire burst control scheme, safer driving experience can be provided for the user, and the vehicle using experience and satisfaction degree of the user are improved.

The invention discloses a vehicle positioning method and device based on fingerprint positioning and electronic equipment, and relates to the 5G technical field or other related technical fields, and the method comprises the steps: employing a plurality of receiving antennas of a base station to detect the characteristics of an uplink signal transmitted by a to-be-positioned cash truck, generating a channel impulse response fingerprint matrix CIR, a channel frequency response fingerprint matrix CFR and an angle-time delay channel frequency power fingerprint matrix ADCFP based on the uplink signal characteristics, and matching the extracted angle-time delay channel frequency power fingerprint matrix ADCFP with fingerprints in a pre-established fingerprint database to obtain a matching result; and according to a matching result, positioning the position coordinates of the to-be-positioned cash truck by adopting an improved power WKNN matching algorithm. The technical problem that in the prior art, an armor cash carrier is influenced by different environmental factors, the positioning precision is low, and the vehicle safety is reduced is solved.

An impairment analysis (“IA”) computer system for detecting an impairment is provided. The IA computer system is associated with a host vehicle, and includes at least one processor in communication with at least one memory device. The at least one processor is programmed to: (i) interrogate or otherwise scan a target vehicle by using a plurality of sensors included on a host vehicle to scan the target vehicle and a target driver; (ii) receive sensor data including target driver data and target vehicle condition data; (iii) analyze the sensor data by applying a baseline model to the sensor data; (iv) detect an impairment of the target driver or target vehicle based upon the analysis; and / or (v) output an alert signal to a host vehicle controller, or direct collision preventing actions (such as automatically engage vehicle safety systems), based upon the determination that the target driver or target vehicle is impaired.

Disclosed herein is a device for filtering a point cloud. The device may include processor configured to receive a plurality of sensed points representing distance measurements to points in an area around an entity. The processor may also be configured to determine, for each sensed point of the plurality of sensed points, one or more probabilities that the sensed point is associated with an obstacle in the area around the entity, wherein each probability of the one or more probabilities is based on a corresponding filter of one or more filters. The processor may also be configured to generate a filtered point cloud from the plurality of sensed points based on, for each sensed point, a hierarchical combination of the one or more probabilities.