1939 results about "Safe driving" patented technology

The invention discloses a fatigue driving monitoring and early warning method based on multi-dimensional feature fusion, and relates to the field of intelligent traffic and safe driving, and the method comprises the steps: carrying out the feature extraction of a collected face image of a vehicle driver through a feature extraction model based on a convolutional neural network, and obtaining a face key point; then calculating the eye closing rate, the mouth opening rate, the head posture data and the facial muscle data to finally obtain multi-dimensional features, and fusing the multi-dimensional features by using a multi-layer perceptron to obtain fused facial features; and then, a fatigue state detection model obtained based on recurrent neural network training is utilized to detect the facial feature time sequence at a plurality of continuous sampling moments so as to detect whether a fatigue driving state occurs and trigger alarm feedback. According to the method, the fatigue state of the driver can be identified and judged more accurately through combination of multi-dimensional data fusion and a time sequence analysis algorithm, and the monitoring and early warning accuracy and reliability are improved. And the system can keep high robustness even in an illumination change or complex driving environment.

The invention provides a vehicle driver dangerous driving behavior early warning method based on deep learning. The vehicle driver dangerous driving behavior early warning method comprises the steps that vehicle lamp brightness and angle data collected by a vehicle-mounted sensor in the vehicle driving process are acquired; preprocessing the vehicle lamp brightness and angle data to obtain standardized vehicle lamp brightness and angle data; according to the standardized vehicle lamp brightness and angle data, the current driver visibility is output through a deep learning model; obtaining a dangerous driving behavior prediction result through a dangerous driving behavior prediction model in combination with the visibility of the driver, the vehicle driving state data and the road environment condition data; according to the dangerous driving behavior prediction result and the vehicle lamp brightness and angle data, whether driving is dangerous driving is judged; if dangerous driving is judged, matching an early warning strategy from a preset early warning strategy library, generating an early warning measure, simulating execution and evaluating an early warning effect, and if necessary, adjusting the early warning strategy until a safe driving requirement is met.

The invention relates to an automatic driving YOLO target detection method and system for coping with light influence, and the method comprises the steps: employing a vehicle-mounted camera to shoot the actual condition images of streets and roads on a plurality of streets, obtaining the vehicle actual condition images of different streets and different roads in different time periods, and forming an image data set; marking the image data set by using a LabelImg tool, and marking a vehicle target in the image, including a bounding box and category information of a vehicle; constructing an automatic driving YOLO target detection network model coping with light influence; and training an automatic driving YOLO target detection network model coping with the light influence by using the obtained image data set, wherein the trained automatic driving YOLO target detection network model coping with the light influence is used for coping with vehicle target detection under the light influence. Accurate vehicle distance adjustment and safe driving decisions can be provided for the automatic driving vehicle, and driving safety and efficiency under various light conditions are ensured.

The invention provides a driving training method and system for a robot vehicle by an intelligent agent based on auxiliary driving, and relates to the technical field of intelligent driving, and the method comprises the steps: obtaining the multi-sensor fusion sensing data of the robot vehicle, inputting the multi-sensor fusion sensing data into a preset deep reinforcement learning model, according to a driving decision output by the deep reinforcement learning model, in combination with a preset safe driving rule base, generating driving guidance subtasks in three dimensions of transverse control driving guidance, longitudinal control driving guidance and traffic rule driving guidance; and sensing the execution state and the environment of the robot vehicle in real time, inputting a sensing result into the hierarchical finite-state machine model so as to trigger a corresponding state transition condition, and outputting a corresponding driving guidance action sequence according to a current state node until a corresponding driving guidance sub-task is completed. And converting the driving guidance action sequence into a specific vehicle control instruction.

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.

A control method for safe driving in a zebra crossing intersection scene, comprising: making a trend prediction based on V2X recognition of status information of a pedestrian and surrounding object on the zebra crossing; communicating in real-time with V2X before a vehicle passes through the zebra crossing to obtain the status information of the pedestrian and the surrounding object on the zebra crossing, making a trend prediction and making a judgment, and controlling the vehicle's operation based on the judgment result. Through the fusion perception of the on-board perception technology and the V2X technology, the vehicle obtains the beyond-visual-range perception ability of the environment, perceives the pedestrian and other object on the zebra crossing in advance and predicts their motion trajectories, and the vehicle makes a judgment and responses in advance for potential risk to avoid collision between the vehicle and the pedestrian and other object on the zebra crossing; on the basis of ensuring the safety of traffic participants, the comfort of the driver and passengers is guaranteed to the maximum extent.

The present invention relates to the field of dynamic monitoring technology, and more specifically, to a dynamic monitoring system for safe driving behavior and safe production of mobile machinery on-site based on an AI large model. It includes a multimodal data acquisition unit, which collects real-time video data, motion data, control instruction data, and obstacle data around the vehicle; a multimodal data fusion unit is used to achieve time domain alignment of video data and motion data through a dynamic time offset compensation model and an interpolation algorithm, and then establish a causal association model between control instruction data and motion data based on a structural equation model, and reversely correct the time domain alignment parameters; a safety monitoring unit detects abnormal driving behavior in real time, and triggers an early warning mechanism when an abnormality is detected. The system can accurately adjust the alignment parameters of the time domain alignment module, while improving the sensitivity and accuracy of abnormal driving behavior detection, and can also improve the adaptability of the safety monitoring system to dynamic environments.

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 vehicle driving track prediction method based on a large language model, and belongs to the technical field of automatic driving. The invention aims to provide a vehicle driving track prediction method based on a large language model, which is used for inputting a vehicle track into the large language model in a text format and evaluating the capability of the large language model in understanding the time dynamic aspect of a traffic agent. According to the method, tracks of different vehicles in a traffic scene are encoded by using a track encoder, track encoding features are mapped into a text embedding space of a large language model by using a reprogramming adapter, and the encoding features are translated into lexical combination which can be understood by the large language model. According to the method, powerful support is provided for realizing reliable and safe driving decisions by the large language model.

The invention discloses a new energy automobile safe driving detection method and system, and relates to the technical field of safe driving detection, and the method comprises the steps: collecting vehicle state data, and carrying out the preprocessing of the vehicle state data; constructing a health state evaluation model, and predicting a vehicle health state score; dynamically adjusting scene detection parameters according to the vehicle health state score; environment data are collected in real time through the adjusted scene detection parameters, and a scene risk score is evaluated based on the environment data; and according to the vehicle health state score and the scene risk score, generating an optimal driving strategy, and executing an active safety linkage mechanism. According to the invention, the health state evaluation model is constructed, the multi-dimensional state data of the vehicle is comprehensively analyzed, and the health state of the vehicle is accurately quantified; the environment detection parameters are dynamically adjusted according to the health state score, the detection sensitivity and frequency are improved when the health state is poor, and the adaptability to complex road conditions and severe weather is enhanced.

The invention discloses an intelligent emergency control system of an arrangement ship steering oar, and belongs to the technical field of arrangement ship steering oar control. An intelligent emergency control system of an arrangement ship steering oar comprises a main control module, a steering oar driving module and a steering oar control module. The problem that in the prior art, when a main steering system breaks down, a ship is prone to being out of control, colliding or other safety accidents are caused is solved, the steering oar driving module is used for conducting driving operation on the arrangement ship steering oar in an emergency mode, the state of the arrangement ship steering oar is monitored in real time, and the safety of the arrangement ship steering oar is improved. When potential problems and fault trends exist in the arrangement ship steering oar, a control instruction of the arrangement ship steering oar is adjusted so as to ensure safe operation of the arrangement ship steering oar, quick response can be made and an emergency mode can be started in emergency, dangerous situations such as out-of-control of the ship can be effectively avoided, remote monitoring and management can be achieved, and the safety of the arrangement ship steering oar is improved. Safety and management efficiency of the ship are improved, and safe driving of the ship is effectively guaranteed.

The invention relates to the technical field of intelligent networked vehicles, in particular to an intelligent networked vehicle remote control and monitoring method. The method comprises the following steps: acquiring real-time running state information and roadside sensing data of a vehicle, and generating a remote takeover request; receiving a control instruction sent by the cloud control platform, and feeding back a control execution result; calculating instruction interaction delay and network transmission delay; the stability of the communication environment is evaluated by combining the two, and the vehicle driving mode is dynamically adjusted. Furthermore, time delay is calculated according to heartbeat signals and control instruction timestamps in the communication link, the stability index of the communication environment is evaluated, and the safe driving speed is adjusted according to the stability. And when the communication environment stability is lower than the threshold value or the instruction interaction delay exceeds the threshold value, an emergency stop mechanism is triggered. According to the method, the safety and the stability of the intelligent network connection vehicle under remote control can be improved.

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.

An autonomous vehicle (AV) includes features that allow the AV to comply with applicable regulations and laws for performing safe driving operations. An example system for an AV includes acquiring, by a computer located in the AV, an image from a camera located on the AV, wherein the image characterizes an area towards which the AV is driven on a road or a lane on a highway; determining that the pedestrian or the rider is located beside the road or the lane on the road according to the image; and in response to the determination, performing a driving operation on the AV, such as turning from a center of the lane to a first side of the lane away from the center of the lane and away from the position of the pedestrian or rider, and / or decelerating the AV in response to certain conditions.

A driving analysis server may be configured to receive vehicle operation data from vehicle sensors and telematics devices of a first vehicle, and may use the data to identify a potentially high-risk or unsafe driving behavior by the first vehicle. The driving analysis server also may retrieve corresponding vehicle operation data from one or more other vehicles, and may compare the potentially high-risk or unsafe driving behavior of the first vehicle to corresponding driving behaviors in the other vehicles. A driver score for the first vehicle may be calculated or adjusted based on the comparison of the driving behavior in the first vehicle to the corresponding driving behaviors in the other vehicles.

The invention relates to the technical field of intelligent traffic, in particular to an integrated fog measurement type highway safe driving guidance device and method, and provides the following scheme that local visibility data is acquired based on a plurality of highway safe driving guidance devices and uploaded to a region controller to generate guidance information. And the regional controller performs research and judgment calibration and gradient calibration on the data, corrects measurement errors and generates road visibility space distribution information by screening calibration anchor points and adopting an exponential attenuation function and iterative gradient calculation. Based on the information, a connectivity algorithm is used to identify a fog area, and in combination with road environment and historical accident data, an optimal induction strategy is matched, and induction information is dynamically adjusted. The fog area detection accuracy is improved, the induction information matching is optimized, and the driving safety and the passing efficiency under the low-visibility condition are improved.

System, methods, and other embodiments described herein relate to implementing cooperative management strategies. In one embodiment, a method includes determining a severity score and a risk area. The method may then determine whether vehicles should belong to a first vehicle set, which may perform a cooperative vehicle response action, or a second vehicle set, in which individual response actions may be performed. The method may then generate a cooperative response action or an individual response action corresponding to the cooperative or individual vehicle sets, provided such vehicle sets are not empty.

The invention relates to the technical field of signal adaptive noise reduction, and provides a PPG signal adaptive noise reduction method based on wavelet transform and an auto-encoder. The method combines adaptive variational mode decomposition, stationary wavelet denoising, multi-dimensional consistency optimization, a synchronous updating technology and a Lagrangian multiplier constraint adjustment technology to remove high-frequency noise of PPG signals, and can better adapt to individual differences of drivers and complexity of the signals; the total loss function of the auto-encoder is optimized by adopting contrast loss, weighted reconstruction error and sparsity regularization, so that the sensitivity and the judgment capability of an abnormal health state are enhanced; according to the method, the PPG signals can be accurately restored and evaluated in multiple dimensions, reliable technical support is provided for continuous monitoring of the health state of the driver, high stability and expandability are achieved, and important guarantee can be provided for safe driving of vehicles and health management of the driver.

The invention provides a control method, device and equipment, a vehicle, a chip and a medium, relates to the artificial intelligence technology and can be applied to the field of vehicles, and the method comprises the steps that scene information of a scene where a movable target is located and input data of a planning track are determined; motion risk prediction of the movable target is carried out according to the input data, and a risk prediction result is determined; the risk prediction result is used for indicating whether the movable target has a risk when moving according to the planned trajectory; and according to the risk prediction result, determining whether to update the planning trajectory of the movable target. Therefore, a motion risk prediction technology is introduced into the trajectory planning scene of the movable target, potential motion risks are predicted, dangerous points or collision risks possibly existing in the planning trajectory are recognized in advance, and early warning is provided for safe driving. Once the potential motion risk is detected, the planning trajectory can be adjusted, so that the efficient and safe trajectory can be planned, and the possibility of risks such as collision of the movable target in actual driving can be reduced.

The invention discloses an unmanned vehicle azimuth end-based object autonomous detection obstacle avoidance intelligent identification system, which comprises the following modules: a sensing data acquisition module, which is used for acquiring and preprocessing multi-source sensing data and generating an environment sensing data set; the feature extraction module is used for inputting the environment perception data set into the Motion-DETR dynamic perception model to generate a target object feature set; the perception graph construction module is used for constructing a dynamic environment perception graph; the threat evaluation module is used for calculating threat levels, generating an azimuth end threat level matrix and establishing a dynamic risk distribution diagram; the path extension optimization module is used for dynamically selecting an extension direction by adopting a path extension optimization method introducing dynamic risk cost; the path cost optimization module is used for dynamically optimizing the expansion structure according to the principle of minimum accumulated path cost value; and the path output module is used for generating an optimal safe driving path in the dynamic environment. The invention provides an intelligent obstacle avoidance path planning method fusing perception mapping and risk optimization.

The invention discloses an intelligent electric vehicle driving state safety estimation method under network attacks, and relates to automobile intelligent safety and automatic driving. Firstly, a three-degree-of-freedom dynamic model of the intelligent electric vehicle is established, the model comprehensively considers transverse, longitudinal and yaw dynamic characteristics of the vehicle, and accurate description is provided for subsequent state estimation. And designing an attack signal and state security estimation method based on an adaptive tolerance extended Kalman filtering algorithm. According to the method, the real-time and accurate estimation of the vehicle state is realized by fusing the information of various sensors such as GPS, LiDAR, IMU and the like, and malicious data introduced due to network attack can be effectively eliminated. Meanwhile, the method also has the capability of tolerating non-ideal network environments such as sensor data packet loss and the like, and the safety and reliability of the self-driving automobile are further enhanced. The intelligent electric vehicle can still maintain accurate driving state estimation when subjected to network attack, and safe driving of the vehicle is ensured.

The invention discloses a driving scene human factor safety monitoring experiment platform and method, and belongs to the technical field of safe driving, the driving scene human factor safety monitoring experiment platform is composed of a plurality of acquisition devices, an experiment machine, a server and a display, the plurality of acquisition devices are used for acquiring monitoring data in an experiment process of a user, and the monitoring data correspond to current simulation driving scene information; according to the method, the current behavior mode and the physiological signal of the driver are comprehensively monitored and correspond to the current simulated complex driving scene information, and the problems that the human factor safety monitoring is poor in universality, and the human factor safety monitoring experience is poor are solved. And the physiological signal of the driver cannot be comprehensively monitored, so that the human factor safety monitoring experiment result is more accurate and reliable.

The invention relates to the technical field of automobiles, in particular to a control system and method for self-adaptively adjusting the position of an outside rear-view mirror based on a DMS. The control system comprises a static judgment module, a dynamic judgment module, a DMS main module, a seat adjusting module, a memory module, an information acquisition module, a data analysis module, a rearview mirror adjusting module and a feedback optimization module. The information acquisition module and the data analysis module work cooperatively, so that the system can calculate the optimal view angle and position according to real-time road and vehicle driving information, and finally, the feedback optimization module continuously optimizes the system performance by collecting the adjustment effect and the driver satisfaction degree, so that the system performance is improved. According to the method, a closed-loop continuous improvement process is formed, and the method not only improves the efficiency and accuracy of adjustment, but also ensures that the system can continuously adapt and learn along with the passage of time so as to provide more personalized and safer driving experience.

The invention relates to the technical field of driving control, in particular to a new energy automobile safe driving control method and system. The method comprises the following steps that a historical urgent danger avoiding data set and a corresponding user misoperation braking state are obtained through a new energy automobile control center, sample undersampling processing is conducted firstly, and an urgent danger avoiding balance sample is obtained; thirdly, analyzing user misoperation braking abnormity by using a balance sample, and performing disordered vector intensity calculation under a time dimension based on braking abnormity data to generate disordered vector segmentation clustering data; then, braking time sequence intervention correction sensing is carried out according to the clustering data, optimized braking intervention correction data are generated, finally, the optimized data are transmitted to a new energy automobile control terminal, and safe driving control is executed. According to the invention, the driving control technology is optimized, so that the driving control technology is more perfect.

The invention discloses an intelligent automobile path tracking system for urgent danger avoidance, and particularly relates to the technical field of intelligent automobiles, the system comprises an environment sensing module, the output end of the environment sensing module is connected with a danger assessment module, and the output end of the danger assessment module is connected with a path planning module. The output end of the path planning module is connected with a decision planning module, the output end of the decision planning module is connected with a vehicle control module, and the output end of the vehicle control module is connected with a communication and interaction module. Through cooperative work of all the modules, perception of the surrounding environment of the vehicle, danger assessment, path planning, decision making, vehicle control and communication interaction with the outside are achieved, under the dangerous condition, the system can make a response rapidly, effective danger avoiding measures are taken, the driving safety of the vehicle and the accuracy of path tracking are ensured, and the driving safety of the vehicle is improved. And a comprehensive and reliable guarantee is provided for safe driving of the intelligent automobile.

The invention relates to the technical field of parking lot digital management, and discloses a digital twin enabling parking lot digital global management system. According to the system, a global digital twin model of a parking lot is generated, and multi-source sensing data such as parking space states, vehicle identities and environmental parameters are fused; generating a parking space distribution matrix with priorities and time windows in combination with the parking request of the user and the real-time parking space state; according to the parking space two-dimensional code hardware parameters, the digital twin model and the parking space distribution matrix, outputting a vehicle guiding path set with a timestamp; a real-time obstacle avoidance and dynamic adjustment system is constructed to generate a safe driving path set so as to dispatch vehicles; outputting a power distribution control instruction set and updating the digital twinborn model by combining the electric quantity of the energy storage unit, the power distribution network data and the safe driving path set; and finally, system knowledge evolution and end-to-end closed-loop optimization are realized through scheduling execution logs. According to the system, global efficient management of the parking lot is realized, and the parking space utilization, vehicle scheduling and energy utilization levels are improved.

The present invention discloses a safe driving early warning system for intervention based on driver emotions. The system obtains current driver facial expression dynamics through a driver emotion real-time recognition unit, performs intelligent emotional analysis based on the obtained facial expression dynamics, and controls a vehicle running status based on a real-time emotion recognition result. The system monitors driver emotions in real time during driving, and controls a vehicle driving status and / or issues a safety warning to a driver according to a monitoring result, so as to ensure the traffic safety of various vehicles from the perspective of the driver.

The invention provides a vehicle stability control method and device and a vehicle. The stability control method comprises the steps that the front road adhesion coefficient and the rear road adhesion coefficient of the vehicle are determined; and according to a first comparison result of the front road adhesion coefficient and the rear road adhesion coefficient, whether the vehicle runs on the butt joint road is judged. And when the vehicle runs on the butt joint road surface, determining the running state of the vehicle according to a second comparison result of the front road surface adhesion coefficient and the rear road surface adhesion coefficient. And the transverse and longitudinal stability control system and the vertical stability control system are adjusted according to the running state of the vehicle. Therefore, the driving stability of the vehicle in different driving states can be effectively improved, and more flexible, comfortable and safer driving experience is provided.

Systems and methods are provided for operation of a vehicle to prevent the vehicle from entering unsafe driving states. In examples, a current operational state of a vehicle is determined based on sensor data collected by sensors on the vehicle. Based on the current operational state, a safety-critical threshold value for a vehicle dynamic parameter at which the vehicle is predicted to breach a limit-of-handling is determined. A command to operate the vehicle in a requested operational state is received, which includes driving inputs for operating the vehicle in the requested operational state. The command is filtered by constraining the requested operational state according to a control barrier function based on the safety-critical threshold value and a requested value of the vehicle dynamic parameter determined from the requested operational state. A control signal is generated that controls the vehicle by modifying the driving inputs based on the filtered command.

To provide an elevator control device so that a user and an autonomous movable object can safely ride together in a car while not reducing elevator user convenience.SOLUTION: An elevator control device comprises an aptitude map storage part, a boarding candidate position determination part, and a movable body information processing part which are communicably connected to an autonomous movable object moving using an elevator. The aptitude map storage part stores aptitude map information showing the aptitude as the boarding position of the movable object by the position in the car defining the locations with a lower frequency of the user boarding as the boarding position of the movable object in the elevator car. The boarding candidate position determination part determines the boarding candidate position of the movable object in the car based on the stored aptitude map information. The movable object information processing part generates boarding instruction information to instruct the boarding into the determined boarding candidate position and transmits it.SELECTED DRAWING: Figure 1