262 results about "Impaired driving" patented technology

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 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 field of vehicle control, provides a vehicle early warning method and system for recognizing dangerous driving behaviors of a driver, and designs a comprehensive data analysis module and a multi-modal feature recognition network by monitoring and recognizing the driving behaviors and physiological and psychological states of the driver in real time. According to the method, the driving state and the driving behavior of the driver are comprehensively analyzed and judged, meanwhile, the method has more accurate analysis ability for repeated driving caused by personal emotion or out-of-control driving caused by physiological diseases of the driver, the recognition accuracy of dangerous driving behaviors is further improved, targeted active intervention is carried out in time, and the driving experience is improved. Meanwhile, dangerous driving alarm and dangerous driving feedback report are carried out, so that the safety of active protection is further improved, and the accuracy of dangerous driving behavior recognition and the safety performance of vehicle early warning 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 present application provides a continuous driving through automatic inspection method, system and medium, belonging to the field of intelligent transportation and big data technology. The method includes: obtaining the detection information of the vehicles traveling within the preset road section, including the license plate recognition information and the vehicle operation status monitoring information, and obtaining the driver behavior information and the driver physiological reaction monitoring information under the authorization of the vehicle owner. Then extract the peak speed data and acceleration data to judge whether there is speeding and dangerous driving respectively; judge whether the driver has closed his eyes according to the driver behavior information; process the driver's physiological reaction monitoring information to obtain the driver's physiological fatigue index and judge the driver's fatigue level. The present application monitors parameters from three aspects: vehicle driving status, driver's physiological reaction and driving behavior, analyzes and judges whether the vehicle driving status is safe and whether the driver's driving status is normal, avoids the occurrence of road vehicle accidents, and ensures personal life safety.

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 discloses a drunk driving judgment method, device and equipment, a storage medium and a product, and relates to the technical field of traffic safety monitoring, and the method comprises the steps: obtaining a parking position and a parking time period of a vehicle from a vehicle-mounted terminal; determining a current regional drunk driving risk of the parking position based on a preset corresponding relationship among the position, the time and the regional drunk driving risk; obtaining a parking time length and a dining place staying time length of the user in a parking period, and determining a current dinner party risk of the user in combination with the parking time period; after the user returns to the parking position, the current state of the user is obtained; determining a dangerous driving risk according to a comparison result of the current state and the state of the user before parking; the drunk driving behavior judgment result of the user is determined according to the current area drunk driving risk, the current dinner party risk and the dangerous driving risk, a large number of unmanned aerial vehicles do not need to be additionally configured, the risk of safety accidents caused in the drunk driving judgment process is reduced, and higher practicability is achieved.

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 road driving suitability evaluation method based on an XGBoost-SHAP and a random parameter multivariate Logit model, and relates to the field of road driving suitability evaluation analysis, and the method specifically comprises the following steps: S1, employing front automobile data recorder data and IMU data in an automatic driving automobile data set; s2, establishing a quantitative model of road environment aesthetics, and extracting related characteristic variables from four aspects of naturalness, vividness, diversity and uniformity; s3, an explainable machine learning method combining XGBoost and SHAP is adopted, occurrence of dangerous driving behaviors serves as a dependent variable, road environment aesthetics features serve as independent variables, a road driving suitability RRAV evaluation model of the automatic driving automobile is established, and the relation between road environment aesthetics and dangerous driving behavior RDBAV of the automatic driving automobile is researched; and S4, researching the influence of the random parameter heterogeneity on the RDBAV in the road driving suitability evaluation model by adopting a random parameter multivariate Logit model considering the mean value and variance heterogeneity.

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 discloses a dangerous driving grade determination system and method based on Internet of Vehicles data. The method comprises the following steps: acquiring a vehicle driving state and a self state index, a driver and passenger related index and a road related index based on the Internet of Vehicles data; performing index danger level division on each index according to the collected limit value condition of each index; and making a decision on each collected index and the index danger level corresponding to each index by using an integrated learning algorithm to obtain the dangerous driving level of the vehicle. According to the method, the vehicle, driver, passenger and road state indexes in the Internet of Vehicles data are comprehensively collected, the danger level of each index is divided carefully, the integrated learning algorithm is used for comprehensive evaluation, the dangerous driving level of the vehicle is accurately determined, and the road safety early warning and driving behavior management efficiency is effectively improved.

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 present invention provides a method for quantitatively assessing the risk of motor vehicle accidents at intersections, including: obtaining historical vehicle trajectory data within the intersection influence area and preprocessing to obtain preprocessed data; extracting vehicle dangerous driving behavior feature data including sudden acceleration and deceleration from the preprocessed data; respectively calculating the average daily frequency of dangerous vehicle behaviors under different turning labels; statistically calculating the average daily motor vehicle trajectory flow under different turning labels based on the vehicle historical trajectory data; calculating the spatiotemporal distribution of motor vehicle accident risk probabilities under different turning labels and generating a spatiotemporal map; and weighted calculating the overall motor vehicle accident risk level index at the intersection. The present invention utilizes the frequency characteristics of dangerous driving behaviors of networked vehicles to correlate with intersection accident probability characteristics, effectively making up for the difficulty in collecting traffic accident data, and effectively supporting safety warning services for vehicles passing through intersections.

A system for preventing an unauthorized and / or hazardous use of a vehicle. The system may include a first sensor; a driver recognition module in communication with the first sensor, the driver recognition module is configured to determine an identity of a driver based on readings of the first sensor; a second sensor; a driver condition determination module in communication with the second sensor, the driver condition determination module is configured to determine a condition of the driver based on readings of the second sensor; and a hazardous driving prevention module in communication with the driver recognition module and driver condition determination, the hazardous driving prevention module is configured to determine and perform one or more actions of a predetermined set of actions based on the determined identity and determined condition of the driver.

The invention discloses a vehicle control method, device and equipment based on the driver state and a storage medium, and relates to the technical field of vehicles, and the vehicle control method based on the driver state comprises the steps that the target driver state is determined according to the driver eye state and the driver behavior state; and when the target driver state is the target dangerous driving state, performing safe parking control of the vehicle according to current vehicle associated data. Whether the safe parking action is carried out or not is judged according to the eye state and behavior ability of the driver, safe parking is guaranteed under the condition that the driver does not have the ability to take over the vehicle (unconsciously), and the problem that accidents happen under the condition that the driver does not have the ability to take over the vehicle is solved.

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.

Disclosed is a vehicle path control apparatus including a memory provided to store danger driving vehicle data, a communication device provided to receive road information from an external server, and a processor configured to identify a danger driving vehicle driving on a road based on the road information and the danger driving vehicle data, select an unmanned vehicle driving within a preset distance from the danger driving vehicle based on the road information, generate a driving path of the unmanned vehicle for obstructing a course of the danger driving vehicle based on an expected path of the danger driving vehicle, and control the communication device to transmit information on the driving path to the unmanned vehicle.

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.