170 results about "Impaired driving" patented technology

The invention discloses a dangerous driving critical state identification method, and relates to the technical field of intelligent driving safety. According to the method, multi-mode signals of eye movement, electrocardio, skin electricity, vehicle operation and the like are collected and converted into a unified phase field, and the synchronous coherence of the unified phase field is analyzed; the individual phase dynamics manifold of the driver is learned on line by using a Shenchang differential equation, and a system instability precursor is identified by detecting the behavior that a state point escapes from a steady state attractor; further, multi-dimensional indexes such as synchronous collapse and topological fracture are fused, collapse time is estimated in combination with a Lyapunov index, and an advanced early warning instruction is generated; and finally, based on the model predictive control and the personalized phase response curve, generating and executing targeted multi-mode phase reset intervention, and forming a sensing-early warning-intervention active safety closed loop. According to the invention, normal form transformation from post-event alarm to beforehand regulation and control is realized, and early warning advancement and intervention accuracy are improved.

The invention relates to the technical field of driver monitoring, in particular to a dangerous driving behavior detection method based on heterogeneous federal ensemble learning. The client extracts a feature vector of the driving behavior data, and maps the feature vector into a low-dimensional category prototype by using a prototype adapter network; a local dynamic differential privacy technology is adopted to carry out noise injection encryption processing on parameters of the prototype adapter and then upload the parameters to a server; the server performs cross-client prototype alignment and aggregation to generate a global prototype; the client downloads global prototype parameters and updates a local model, incremental learning is carried out based on a dynamic prototype library, and when a novel dangerous driving behavior is detected, a newly-added prototype and a historical prototype are separately stored; and the client performs similarity matching with the global prototype according to the driving behavior characteristics acquired in real time to obtain a detection result. According to the method, an extensible and high-reliability federal learning framework is provided for landing of an intelligent automobile safety system from three dimensions of heterogeneous compatibility, privacy-efficiency balance and personalized dynamic updating.

The invention provides an early warning method and system for the toppling risk of goods transported by a vehicle, and the method comprises the steps: obtaining the driving state of a first vehicle and / or the road state of the first vehicle; according to the running state of the first vehicle and / or the road state of the first vehicle, a first running mode of the first vehicle is determined, and the running modes of the vehicle comprise one or more of a starting and accelerating mode, a braking mode, a bumping mode, an uphill and downhill mode, a turning mode and a constant speed mode; determining a first cargo type to which the cargo transported by the first vehicle belongs, and determining a first dumping risk early warning model corresponding to the first operation mode under the first cargo type; and performing early warning on the driving state of the vehicle by using the first toppling risk early warning model. According to the invention, goods can be effectively prevented from toppling over, and dangerous driving behaviors of a driver are reduced.

The invention discloses a risk superposition-considered dangerous driving risk quantitative evaluation and early warning grading method, and relates to the field of aircraft driving safety, and the method comprises the steps: selecting a pilot dangerous driving single-source risk characterization index, and carrying out the quantification; constructing a pilot dangerous driving single-source risk quantitative evaluation model and a pilot dangerous driving multi-source risk superposition model; predicting the dangerous driving comprehensive risk of the pilot; grading the dangerous driving comprehensive risk of the pilot; sending a grading early warning signal to the pilot according to the prediction structure; the pilot dangerous driving risk quantitative evaluation model is constructed, graded early warning is performed on the result, the pilot dangerous driving behavior characteristics are comprehensively analyzed, and the pilot dangerous driving risk can be efficiently sensed, predicted and early warned, so that the aircraft accident rate is reduced, and the air transportation safety is improved.

The invention provides a vehicle dangerous driving evaluation method based on a large model, and relates to the field of traffic control systems. The vehicle dangerous driving evaluation method based on the large model comprises the following steps: step 1, data acquisition: setting a data acquisition layer to acquire multi-source heterogeneous data, and acquiring driving state data of a vehicle in real time through cameras and sensor equipment deployed along a highway; 2, data preprocessing: setting a data preprocessing layer, and performing whole-chain data management and alignment; step 3, data extraction: setting a feature extraction layer to construct a multi-dimensional feature engineering system; step 4, identification: setting a large model identification layer: establishing a large model based on a Transform architecture; and step 5, outputting a result: outputting a result judged and identified by the large model for evaluating dangerous driving of the vehicle. According to the scheme, the dangerous driving behavior of the vehicle is accurately identified, the danger index is provided, and a reliable risk assessment result is provided.

The invention provides an intelligent identification and classification method and system for a traffic dangerous driving scene. The intelligent identification and classification method for the traffic dangerous driving scene comprises the following steps: extracting each accident characteristic value corresponding to each preset accident characteristic in a traffic accident video; converting each accident characteristic value into a numerical value form; carrying out dimension reduction by utilizing classification principal component analysis; using an elbow method and a K-means clustering method to obtain an optimal clustering number and a clustering result, and dividing the traffic accident video into a plurality of different categories according to the clustering result; and determining the main features of each category and the dangerous driving category corresponding to each category by using statistical analysis. According to the method, different types of traffic dangerous driving scenes can be intelligently identified, and driver risk perception training and scene identification and decision making of an auxiliary automatic driving system can be better carried out.

The invention relates to the technical field of automobile data recorders, in particular to an AI intelligent driving behavior analysis automobile data recorder, which is used for acquiring the real-time vehicle condition in front of a driving vehicle and the driving behavior of a driver in the vehicle and acquiring the driving behavior data of the driver based on the acquisition of the driving behavior data of the driver. The feature extraction module is used for extracting hand actions and eye actions in the driving behaviors of the driver, the hand actions are used as judgment criteria for standardizing the driving behaviors, and the eye actions are used as compensation basis for judging the concentration of the driver; the driving behavior is scored and predicted through a driving behavior scoring model pre-constructed by a risk prediction module, a scoring threshold value of dangerous driving and a scoring threshold value of distracted driving are set, risk-based judgment is carried out according to the score of the driving behavior, and warning is carried out through a warning module according to the judged driving behavior risk-based judgment. Therefore, the driver is reminded to correct the driving behavior in time, and traffic accidents are avoided.

A method for operating a driver assistance system of a vehicle includes initiating an autonomous or semi-autonomous driving process of the vehicle, collecting data associated with surrounding vehicles within an external environment of the vehicle, and transmitting the data associated with the surrounding vehicles to a Central Processing Unit (CPU) comprising one or more processors. The method further includes determining, with one or more machine learned models, safety concerns of the surrounding vehicles based on the data associated with the surrounding vehicles, determining, with the one or more machine learned models, a safety score of each surrounding vehicle of the surrounding vehicles located within a predetermined distance from the vehicle based on the respective safety concerns of each surrounding vehicle, and updating, with the one or more machine learned models, a driving trajectory of the autonomous or semi-autonomous driving process of the vehicle based on the safety scores of the surrounding vehicles.

The invention discloses a driver dangerous driving behavior visual identification method and device, a terminal and a medium. The method comprises the following steps: acquiring image data when a driver drives; detecting and identifying the image data of the driver during driving by adopting a dangerous driving behavior identification model, and outputting whether the driver has a dangerous driving behavior or not; the dangerous driving behavior recognition model is obtained through the following steps: collecting image data of a driver during dangerous driving, and constructing a driver dangerous driving state data set according to the image data; a dangerous driving behavior recognition model is constructed, the dangerous driving behavior recognition model is an improvement on a YOLOv8n model, a C2f module in a backbone network of the YOLOv8n model is reconstructed into a C2f-DC module by using a DC module, and a loss function is modified into an SAIOU loss function; and training the dangerous driving behavior identification model by using the driver dangerous driving state data set. The method has the advantage of being higher in driver dangerous driving behavior recognition accuracy.

The invention discloses a vehicle key cabinet based on driver health examination and a using method thereof, and relates to the field of vehicle management. The vehicle key cabinet based on driver health examination comprises a vehicle key cabinet unit, a touch operation screen, a multi-mode verification module, a health detection module and a key sensing module, the vehicle key cabinet unit comprises a key cabinet main body and a key placing lattice, and the key placing lattice is used for placing a vehicle key; the multi-mode verification module is used for verifying the identity of a driver, the health detection module is used for detecting the health state of the driver, and the key sensing module is used for sensing whether a key is in place or not. According to the vehicle key cabinet based on driver health examination, the multi-mode verification module is utilized to pass face, fingerprint and card triple-modular redundancy verification, the safety risks of false claim and embezzlement are effectively prevented, the health detection module is utilized to monitor the health state in real time, and dangerous driving behaviors of drunk driving and fatigue driving are completely eradicated from the source of physiological indexes.

The invention provides a method for automatically monitoring and identifying dangerous driving behaviors based on a driving scene, and the method achieves the comprehensive monitoring of a vehicle driving state and a vehicle driving road section through the installation of a satellite positioning device, the collection of CAN bus data, the deployment of high-precision map road network data, and the data fusion. By setting a behavior threshold value and combining time sequence analysis and map data, the judgment accuracy is enhanced, a real-time monitoring algorithm engine is developed, and dangerous driving behaviors are accurately monitored and recognized in a complex and changeable driving scene. According to the method, dangerous driving behaviors of the driver can be corrected in time, dangerous driving events are recorded and managed at the same time, the running safety risk is reduced, the driving safety is improved, and losses caused by traffic accidents are reduced. Various dangerous driving scenes are covered, a driver is helped to reasonably control the vehicle speed, digitization of motorcade management and transparency of risk management are achieved, and the requirement for supervision compliance is met.

The invention discloses an intelligent traffic system and an operation method thereof, and relates to the technical field of vehicle traffic, and the method comprises the steps: obtaining vehicle driving data and physiological signal data in real time through a sensor, and carrying out the preprocessing; constructing an emotion recognition model according to the vehicle driving data and analyzing behavior characteristics of a user; identifying an emotional state in real time according to the user behavior characteristics and the physiological signal data; and performing emotion recognition calibration on the driver regularly, and optimizing the emotion recognition model according to a calibration result. By recognizing the emotional state and behavior characteristics of the driver in real time, potential dangerous driving behaviors can be warned in advance, an alarm can be given in time, the driver is reminded to be cold and quiet, and traffic accidents caused by out-of-control emotion are avoided; a traffic management department can more accurately know traffic conditions and driver emotion distribution conditions of different road sections and different time periods.

The invention relates to the technical field related to intelligent traffic driving safety, in particular to a dangerous driving behavior recognition method and system based on multi-modal data fusion. According to the method, driver behaviors and vehicle operation data are collected in real time through a vehicle-mounted sensing system, hardware-level synchronization of multi-source data is achieved based on GPS-PPS pulses, and a unified timestamp system is established; extracting multi-modal features of vision, vehicle dynamics and biological signals under a unified time sequence; cross-modal feature deep fusion is realized by using a graph neural network and an attention mechanism; performing risk quantification by adopting a secondary decision model in combination with an environment adaptive weight; and finally, according to the risk score, triggering graded early warning and linkage with vehicle control, and meanwhile, generating an event report and uploading the event report to a cloud platform to form closed-loop management. According to the method, the problem of misjudgment caused by data asynchronization in a traditional scheme is effectively solved, and the recognition accuracy and the system reliability in a complex environment are remarkably improved.

The application discloses a kind of dangerous driving behavior monitoring method, comprising: obtaining the real-time driving data of driver end, and the driving order that hits preset driving rule is marked with exception label;Real-time traffic data of current driving path is obtained, and traffic rule is generated according to real-time traffic data;Real-time driving data carrying exception label is detected by traffic rule;If real-time driving data hits traffic rule, the exception label is removed;If real-time driving data does not hit traffic rule, the exception label is converted into dangerous driving label.By the way of marking exception label, first find out those real-time driving data of driver end that exceed normal driving data range, and by calling real-time traffic data, the generation scene of real-time driving data marked as exception label is further verified by traffic rule generated by traffic data, effectively identify the real-time driving data generated under unexpected accident, avoid to bring misjudgment to driver end and cause punishment.

The invention discloses a driver behavior monitoring method and device and a storage medium, and belongs to the technical field of vehicle control. The method comprises the steps of obtaining a driver detection result in a video image of a vehicle driving position in response to a vehicle in a starting state, a forward gear and a steering angle of a steering wheel greater than a first angle; responding to the obtained detection result indicating that the driver exists in the video image, and obtaining a fatigue driving detection result, a distraction detection result, a dangerous driving detection result and an abnormal emotion detection result according to the expression and behavior of the driver; and in response to at least one of a detection result indicating that the driver is in a fatigue driving state, a detection result indicating that the driver is in a distraction state, a detection result indicating that the driver is in a dangerous driving state or a detection result indicating that the driver is abnormal in emotion, sending prompt information based on the priority level. The driver is prompted according to the order of the danger degrees, and the driving safety is guaranteed.

The invention discloses an automatic alarm method and system for dangerous driving behaviors of a driver, and the method comprises the steps: collecting real-time video data of the driver through a vehicle-mounted camera, obtaining an undetermined video frame through preprocessing, and inputting the undetermined video frame into a dangerous driving behavior recognition model to obtain a recognition result. And if the result shows that the dangerous driving behavior exists, the vehicle-mounted terminal alarm gives an alarm and uploads the related target video frame to the preset alarm server, so that automatic monitoring and early warning of the dangerous driving behavior are realized, and improvement of road traffic safety is facilitated.

The embodiment of the invention provides a method for adjusting a human-computer interface in a vehicle, the vehicle and a computer readable storage medium. The method comprises the steps that a crisis perception test is conducted on a test object based on a preset test device, a first adjustment strategy is obtained, the preset test device comprises an electroencephalogram instrument, an eye movement instrument and an electrocardio instrument, and the first adjustment strategy is used for adjusting the opportunity that a human-computer interface of a vehicle transmits dangerous driving scene information to a driver; driving distraction tests are conducted on the test object based on a preset test device, a second adjustment strategy is obtained, and the driving distraction tests comprise a visual distraction test, an auditory distraction test, a cognitive distraction test and a manual distraction test; determining a human-computer interface adjustment strategy based on the first adjustment strategy and the second adjustment strategy; and adjusting the human-computer interface based on the human-computer interface adjustment strategy. According to the method and the device, the technical problems that the safety assessment of the HMI design is not accurate enough and the HMI design still has potential safety hazards in related technologies are solved.

The invention relates to the technical field of safe driving, in particular to a driver action analysis method and device, a vehicle and a storage medium, and the method comprises the steps: determining a plurality of key skeleton joint points corresponding to the current driving action of a driver under the condition that the vehicle is in a target driving working condition, determining a target risk skeleton joint point of the driver according to the target coordinate information of each key skeleton joint point and the coordinate information of the target standard action joint point; and analyzing a target spatial distance and a target relative angle between the target risk skeleton joint point and a preset target dangerous action joint point, so as to determine at least one dangerous driving action of the driver according to an analysis result. Therefore, the problems of inaccurate data acquisition, high complexity of a multi-sensor data fusion algorithm, reduction of real-time performance and accuracy of driver action analysis and the like due to the fact that in-vehicle sensors are easily influenced by environmental factors in related technologies are solved.

The invention provides a system for guaranteeing safe driving of a heavy truck at night, and the system comprises a scene analysis module which is used for determining a current scene condition according to environment information transmitted by a sensor assembly and a high-definition map after the truck is started; the geographic position analysis module is used for receiving the road information sent by the high-precision map so as to determine the current vehicle position information; the driver monitoring system is used for systematically recognizing driver identity information, driver fatigue degree and dangerous driving behaviors and sending recognition results to the driving behavior detection and analysis module; the driving behavior detection and analysis module is used for determining the state of a driver according to the recognition result, the current scene condition and the driving duration after the vehicle starts to run; and the safe driving module is used for adjusting the following distance and the speed according to the current scene condition, the current vehicle position information and the driver state so as to ensure the night driving safety of the vehicle.

The present invention provides a method, device, equipment and readable storage medium for generating a dangerous scene. The method comprises: extracting a target scene segment based on target information; generating a target object list in the target scene segment; generating a target vehicle driving trajectory and a high-precision road map in the target scene segment based on the target object list; weighting based on a weighting factor to generate a first preset number of driving behavior parameters of a host vehicle and a target vehicle driving behavior parameter; combining to obtain a second preset number of driving behavior combinations; mapping the driving behavior combinations to the high-precision road map to generate a target scene; and performing simulation re-play on the target scene, with the target scene in which an accident occurs in the simulation re-play and the target scene in which an accident is dangerous but no accident occurs as the dangerous scene. The present invention makes it possible to obtain dangerous scenes without artificially creating dangerous driving, and reduces costs while improving the efficiency of dangerous scene generation.

The present invention discloses a method, device, storage medium and apparatus for dangerous driving intervention. The method includes: when establishing a connection with a smart wearable device, obtaining the physical state information of the driver and passenger uploaded by the smart wearable device; when it is determined according to the physical state information that the driver has an abnormal physical state, collecting the driving behavior information and driving image information of the driver, and determining whether to activate the intelligent driving function according to the driving behavior information and the driving image information; since the present invention obtains the physical state information of the driver through the smart wearable device, obtains the driving behavior and driving image information of the driver when the driver has an abnormal physical state, and determines whether to turn on the intelligent driving mode according to the driving behavior and driving image information, it can know the unexpected situations that occur during the driving process of the driver and take reasonable relevant measures in time to prevent the occurrence of danger.

The invention relates to the technical field of new energy automobiles, in particular to a transverse protection control method and system for an intelligent rail vehicle in a manual mode. The invention provides a transverse protection control method for an intelligent rail vehicle in a manual mode, and the method comprises the steps that a vehicle network control system obtains transverse deviation parameters according to multi-source data fusion, and the transverse deviation parameters comprise a transverse deviation value and a transverse deviation threshold value provided by a vehicle-mounted signal system; the transverse deviation value is compared with the transverse deviation threshold value, the vehicle network control system outputs a corresponding result to achieve transverse protection of the vehicle in a manual driving mode, the transverse deviation value comprises a transverse deviation value based on visual recognition and / or a transverse deviation value based on combined inertial navigation, and the transverse deviation value comprises a transverse deviation value based on visual recognition and / or a transverse deviation value based on combined inertial navigation. Potential safety hazards caused by distraction or dangerous driving behaviors of the driver are effectively avoided, and vehicle driving safety is guaranteed.

The invention discloses a traffic factor evaluation method and device for road traffic safety, relates to the technical field of road safety, and mainly aims to improve the accuracy of traffic factor evaluation. According to the main technical scheme, the method comprises the steps of obtaining networked vehicle driving data corresponding to each to-be-evaluated traffic area within a preset evaluation time range; determining the total number of dangerous driving behaviors and the total driving mileage of the networked vehicles in a preset evaluation time range according to the driving data of the networked vehicles; determining a road safety risk index within a preset evaluation time range according to the total number of dangerous driving behaviors and the total driving mileage of the networked vehicles; according to a preset road safety risk influence factor analysis model, a safety influence coefficient corresponding to each traffic factor in the plurality of traffic factors is calculated in combination with the road safety risk index; and determining the road traffic safety influence degree of each traffic factor by using the safety influence coefficient corresponding to each traffic factor according to a preset evaluation model.

The invention discloses a dangerous driving behavior detection method and device, equipment and a storage medium, and relates to the technical field of vehicle control. The method comprises the following steps: acquiring multi-modal data of a target vehicle, wherein the multi-modal data comprises data of at least two modals; according to the multi-modal data, the dangerous driving behavior is predicted, a prediction result is obtained, and the prediction result is used for indicating the behavior type of the predicted dangerous driving behavior; and under the condition that the prediction result meets the alarm condition, an alarm action is executed, and the alarm action is a prompt action aiming at the dangerous driving behavior. According to the method, the safety of public transportation is improved.

The invention provides a driving assistance method and system for a vehicle and the vehicle, and the driving assistance method comprises the steps: collecting the voice features of a driver when a first vehicle runs, and judging whether the voice features are matched with a preset voice database or not; if yes, acquiring first vehicle environment information of a vehicle-mounted sensor, calculating a driving state parameter based on the first vehicle environment information, and judging whether the first vehicle is in a dangerous driving environment or not according to the driving state parameter; if yes, the braking opportunity and the braking force of the first vehicle are calculated according to the driving state parameters, and a braking system of the first vehicle is controlled to execute active braking according to the braking opportunity and the braking force. The technical problems that an existing driving assistance system depends on sensor data to achieve automatic braking, but in an emergency, voice response often reflects danger faster than sensor data or a manual starting system, but in the prior art, a voice starting system cannot be combined in time, intervention lags, and potential safety hazards exist are solved.

A detection function control device includes: a vehicle situation determining unit configured to determine a situation of a vehicle on the basis of detection results of one or more of an ignition state, whether a driver is seated in a seat, the acceleration of the vehicle, the position of the vehicle, the speed of the vehicle, and sound which is collected; and a detection function control unit configured to determine which of one or more detection functions for detecting dangerous driving is to be activated on the basis of the situation determined by the vehicle situation determining unit.

The embodiment of the invention relates to the field of open pit coal mine transportation safety, and discloses a dynamic early warning method, system and device for the driving risk of a mining truck and a medium. The dynamic early warning method for the driving risk of the mining truck comprises the steps that driving information of a vehicle is collected in real time based on a vehicle-mounted positioning system, the driving information comprises driving time, a geographic position, a driving state and topographic features; extracting key risk features for reflecting driving risks based on the driving information, wherein the key risk features include dangerous driving behavior features and terrain-related features; and constructing a risk prediction model based on the key risk features so as to carry out classified early warning on the driving risk of the vehicle. According to the technical scheme disclosed by the invention, the limitation of a single data dimension is broken through, the evaluation comprehensiveness is improved, and real-time perception, multi-dimensional analysis and accurate early warning of the driving risk are realized.

The travel information sensor (14A) detects travel information of the own vehicle. The living body information sensor (13A) detects living body information of the driver. The camera section (12A) detects an expression of the driver. The communication section (32A) acquires, via the network (4), a degree of involvement indicating a degree to which the own vehicle is being subjected to an involvement behavior by other vehicles. The involved degree calculation section (313A) calculates an involved degree indicating a degree to which the own vehicle is being subjected to an involvement behavior from other vehicles. The danger degree judgment section (312A) calculates a danger degree including whether the own vehicle is being subjected to an involvement behavior from other vehicles, based on the involved degree and the degree of involvement. The prompting section (33A) warns of the danger degree when it is judged that the involvement behavior is being subjected to.

The present invention relates to the field of new energy vehicle technology, and more specifically, to a lateral protection control method and system for a smart rail vehicle in manual mode. The present invention provides a lateral protection control method for a smart rail vehicle in manual mode, the method comprising: a vehicle network control system obtains a lateral deviation parameter based on multi-source data fusion, the lateral deviation parameter including a lateral deviation value and a lateral deviation threshold provided by an on-board signal system; the lateral deviation value is compared with the lateral deviation threshold, and the vehicle network control system outputs a corresponding result for achieving lateral protection of the vehicle in manual driving mode, wherein the lateral deviation value includes a lateral deviation value based on visual recognition and / or a lateral deviation value based on combined inertial navigation, effectively avoiding safety hazards caused by driver distraction or dangerous driving behavior, and ensuring vehicle driving safety.