149 results about "Driver Distractions" patented technology

The invention provides a driver distraction behavior recognition method based on a deep learning optimization YOLO fusion network model. The method comprises the following steps: constructing a vehicle driver distraction behavior standard image data set G20 in a highway scene; a deep learning-based driver distraction behavior recognition method in an expressway scene is researched, three models of YOLOv5, YOLOv8 and YOLOv11 for driver distraction behavior recognition are constructed, and the comprehensive detection performance of the models is analyzed and compared; the method comprises the following steps: constructing driver distraction behavior recognition models YOLOv11n-ECA, YOLOv11n-BiFPN-GLAS and YOLOv11n-SIoU based on an optimization method, and analyzing the improvement advantages and comprehensive detection performance of the driver distraction behavior recognition models; the improved methods are fused to construct an optimized fusion network YOLOv11n-Driver, and the test accuracy, the test recall rate, the F1-Score, the average precision and the consumed time of each image on a constructed G20 data set respectively reach 93.1%, 95.4%, 94.2%, 98.3% and 3.4 ms / image, so that a feasible method is provided for optimizing the recognition performance of a YOLO model on driver distraction behaviors in an expressway scene.

An inside cabin sensor system, having an integrated, single hardware part comprising a camera sub-system and an integrated radar sub-system is proposed. A proposed system provides related information for covering the following applications inside a vehicle: Child Presence Detection (CPD), Driver Drowsiness & Fatigue (F), Driver Distraction (DD) as a safety-relevant function, complemented by Intrusion & Proximity Alert (IPA), Seat Occupancy Detection (SOD), Face Recognition (FR), and optional applications such as Driver Emotion (ES), Passenger Classification (PC), Airbag Suppression (AS), Airbag Activation (AA), Mobile Phone Detection (MP), Gesture Detection (GD) and Vital Signs Detection (VS). A proposed system utilizes Artificial Intelligence (AI)-related data processing for data processing, using radar point cloud data, calculated by said radar sensors. The proposed system utilizes Artificial Intelligence (AI)-related data processing for data processing, using video-captured data. The proposed system utilizes Artificial Intelligence (AI) -related data processing for sensor data fusion, using radar sensor and video sensor data.

The invention discloses a railway driver distraction behavior identification method based on a visual large model, and belongs to the technical field of railway safety monitoring. Comprises: constructing a data set; based on the data set, training the visual big language model by adopting a LoRA method to obtain a fine-tuning visual big language model; obtaining human body key point information on the original video data frame, and generating a skeleton diagram; and inputting the skeleton graph into the fine-tuning visual large language model, and outputting description information of the image and a corresponding cue word. According to the method, the technical route of lightweight posture recognition and visual large language model analysis is fused, and the posture of the driver is captured by adopting a character key point detection tool; meanwhile, a visual large language model is introduced, and correlation analysis of operation behaviors and environment equipment is achieved by constructing a driving scene knowledge graph; the performance bottleneck of a traditional monitoring system is broken through, a technical template capable of being copied and popularized is formed, and the method has important practical significance for improving the intrinsic safety level of railway transportation.

The invention relates to the technical field of vehicle interior light control, in particular to a vehicle interior light intelligent control device and a control method thereof.The control method includes the following steps that firstly, a front face image of a driver is collected through an in-vehicle camera, and working condition signals of the vehicle speed, steering lamps and the vehicle door state are obtained through a vehicle-mounted CAN bus; and 2, identifying a distraction action sequence of a driver by using a target detection model based on a target detection algorithm, and calculating an index of a fatigue state PERCLOS and an average eye closing duration # imgabs0 # in continuously acquired N frames of images. In the invention, based on image information acquired by a camera in a vehicle and a CAN bus working condition signal, the target detection algorithm and the fatigue identification index are fused, so that the driver distraction action sequence is identified, and the driver distraction action sequence is identified. Real-time identification of distraction and fatigue states of the driver is realized; when the state of a driver is abnormal, gradual prompting or warning can be rapidly carried out through lamplight, and the initiative, safety and comfort of man-machine interaction in a vehicle are effectively improved.

Some embodiments relate to driver or occupant monitoring using vision language models (VLMs). Any number of DNNs in a detection pipeline may be replaced with a VLM, and the VLM may be prompted to determine whether a corresponding feature is present in an image or sampled frames from a video. To facilitate using the VLM(s) to control one or more downstream actions, the VLM(s) may be prompted using structured inputs, and a designated output format for a corresponding structured output may be enforced in any suitable manner. As such, any number of VLMs may be used to perform any number of driver and / or occupant monitoring tasks (e.g., driver drowsiness detection, driver distraction detection, driver or occupant out-of-position detection, driver or occupant identification, seatbelt usage detection, occupant presence detection, occupant classification, child presence detection, gesture recognition, occlusion detection, and / or others).

The invention relates to the technical field of intelligent driving, in particular to a vehicle control method, device and equipment based on electroencephalogram signals, a medium and a program.The method comprises the steps that the electroencephalogram signals of a driver under at least one dynamic visual stimulus are obtained; generating a matching result according to matching of the electroencephalogram signal and a reference signal of the at least one dynamic visual stimulation, and determining target visual stimulation from the at least one visual stimulation according to the matching result; a target control intention expected by the driver is determined according to the target visual stimulation, and the vehicle is controlled to execute corresponding operation according to the target control intention. Therefore, the problems that in the related technology, the interaction mode of a driver and a vehicle control system mainly depends on physical buttons, touch screens or voice instructions to achieve control over vehicle parts, distracted driving is easily caused, driving safety is affected, and the user experience feeling is poor are solved.

The invention discloses an attention reminding algorithm for intelligent driving based on multiple modes, and relates to the technical field of intelligent driving and active safety, and the algorithm comprises the steps: firstly, collecting data in real time through a multi-mode sensor group, carrying out the alignment, and then calculating the quantization features, such as the fixation deviation degree, the steering wheel disturbance entropy and the heart rate variability; then, mapping scores by adopting an S-type function, analyzing a historical sequence by utilizing a long short-term memory network to generate a dynamic weight, and performing adjustment and weighted calculation on a single-mode score in combination with an environment complexity weight to obtain a comprehensive attention score; and finally, according to the continuous driving duration, calculating a linear decreasing dynamic reminding threshold value, executing dual logic judgment in combination with the environmental risk level, and triggering graded feedback through sound and light or vibration. According to the method, the problems of poor adaptability and high false alarm rate of traditional single-mode monitoring in a dynamic environment are effectively solved, the robustness of the system is improved, and adaptive evaluation and accurate early warning of driver distraction behaviors in a complex scene are realized.

The invention relates to the field of vehicle intelligent control, in particular to a vehicle control method and device and a vehicle. Compared with a traditional mode depending on visual perception and a machine learning model, the driving state of the driver and the vehicle driving risk data are detected at the same time, the misjudgment limitation of single perception is avoided, for example, potential risks can be judged in an auxiliary mode through abnormal states such as distraction and fatigue of the driver. Secondly, whether active safety intervention is triggered or not can be determined by analyzing driving risk data in real time, the passive situation that a traditional algorithm focuses on protection after collision is changed, and real-time monitoring of danger in the driving process is achieved. The target intervention strategy is matched according to the driving state and the driving risk data, corresponding safety operation is executed, a complete closed loop from detection and analysis to active intervention is formed, the risk is actively reduced before an accident occurs, and the defect that the traditional technology lacks an active prevention mechanism is overcome.

The invention provides a driving emotion early warning method and system based on voice analysis, relates to the technical field of voice emotion recognition, and effectively solves the interference problem of vehicle-mounted dynamic strong noise by deploying a microphone array and a noise sensor in a cockpit and combining multi-channel noise reduction algorithms such as beam forming and linear beam minimum variance. A high-quality voice instruction is extracted from a source; on the basis, the driving intention of the clear voice signal is accurately recognized by using the deep neural network, so that the accuracy is remarkably improved; more importantly, according to the method, the instability Ls and the high interactive friction probability Pf are recognized innovatively through calculation instructions, real-time quantitative evaluation on the man-machine interaction fluency is achieved, when the interactive friction probability Pf exceeds a preset threshold value, the system can automatically trigger self-adaptive strategy adjustment, and therefore the situation that a driver is distracted due to recognition errors is avoided, and user experience is improved. And the interaction experience and the driving safety are greatly enhanced.

The present application relates to a technology for monitoring driver attention in a vehicle. A driver distraction system collects vehicle data and scene information from the vehicle traveling on a route. The vehicle data and scene information are then processed to generate a reference heat map. Simultaneously, the driver distraction system can capture the driver's gaze to track the driver's gaze direction and duration while driving the vehicle on the route. The gaze direction and duration are processed to generate a driver gaze heat map. By analyzing the driver gaze heat map and the reference heat map, the driver distraction level of the driver in the vehicle is determined, and based on the driver distraction level, a suggestion or reminder warning is output to the driver of the vehicle.

The invention provides a distracted driving detection method based on depth separable convolution and multi-spectrum attention, and the method comprises the following steps: firstly, carrying out the efficient feature extraction of an input driver image through a feature extraction module based on depth separable convolution, so as to reduce the calculation complexity of a model and the number of parameters; secondly, a multi-spectrum attention mechanism is designed and integrated, and the capturing capability of the model on key features is enhanced through self-adaptive attention on different spectrum information, so that the accuracy of distraction driving detection is improved; secondly, carrying out diversified processing on training data by adopting a data enhancement technology so as to improve the generalization ability of the model; and finally, constructing a deep neural network architecture containing a maximum pooling layer and a classifier, and realizing real-time detection and classification of distraction driving behaviors. Compared with the prior art, the method has the following advantages: 1) by introducing the depth separable convolution, the calculation amount and the parameter scale of the model are significantly reduced, and the training and reasoning efficiency is improved; 2) a multi-spectrum attention mechanism enhances the attention capability of the model on key features, and improves the accuracy of distraction driving detection; 3) an optimized data enhancement strategy improves the generalization performance of the model and reduces the risk of overfitting; and 4) the whole model is simple in architecture and convenient to deploy and expand in practical application.

The invention relates to a vehicle-mounted intelligent service pushing method and device, a vehicle and a storage medium. Comprising the steps of obtaining a current state, a current position and current sensing data of a vehicle; calculating a current security score according to the current state data, the current position data and the current perception data, and determining a current security level according to at least one of the current state data, the current perception data and the current security score; under the condition that the current security level reaches a service pushing level, determining a pushable service type and a pushable content form according to the current security level, calculating the value of each service in the pushable service type, and determining target pushing content according to the value of each service and the pushable content form based on the current security level, and pushing the target push content to the user. Therefore, the problem that the service pushing opportunity is not matched with the driving safety requirement and the actual scene of the user in the related technology is solved, the risk that the driver is distracted to drive is reduced, and the experience feeling of the user is improved.

The invention provides a floating touch method, device and equipment for a vehicle-mounted screen, and is applied to the technical field of man-machine interaction. The method comprises the steps that when the vehicle-mounted screen is in a floating touch mode, the positioning position of a pointer on the vehicle-mounted screen is determined according to tracking information for the hand of a user. And judging whether the control is an effective control or not according to the positioning position and the suspension time of the pointer on the control at the positioning position. And under the condition that the control is determined to be the effective control, generating a to-be-executed operation instruction according to the operation action executed by the hand of the user. According to the current state information of the vehicle, whether the to-be-executed operation instruction is in the safe execution environment or not is judged so as to determine whether the to-be-executed operation instruction is executed or not, and the problems of low operation precision, high driver distraction risk and insufficient safety in the driving environment in a traditional interaction scheme can be solved.

This invention discloses a lightweight driver distraction behavior detection method based on CNN and ViL, belonging to the field of driver distraction detection. It constructs a hybrid model combining a Convolutional Neural Network (CNN) and a Visual Long Short-Term Memory (ViL) network for driver distraction behavior detection. The hybrid model performs five stages of processing. In the first two stages, local feature extraction is mainly performed. The DEC module is used to extract and downsample local features, while the PDC module further enriches the extracted local features. In the last three stages, the MlgViL module is used to process and fuse local and global information. This invention efficiently fuses local and global information, preserving fine-grained local information while capturing global contextual information, thus improving the overall performance of the model and achieving high accuracy and real-time performance with limited computing resources.

The application discloses a driving distraction intervention system based on a driver state and vehicle operation monitoring, which comprises a driver state information acquisition module, a vehicle operation state information acquisition module, an information processing module, a distraction state judgment module and an intervention information output module; the driver state information acquisition module acquires the state information of the driver; the vehicle operation state information acquisition module acquires the vehicle operation state information; the information processing module obtains a visual buffer time and a driving stability value according to the driver state information and the vehicle operation state information; the distraction state judgment module calculates the state parameters of the distraction state of the driver according to the result of the information processing module to judge the distraction state grade corresponding to the real-time state of the driver; and the intervention information output module provides the corresponding preset distraction intervention information to the driver according to the distraction state grade of the driver obtained by the distraction state judgment module. The application can help the driver to restore the normal driving state and improve the driving safety.

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.

The present invention discloses a driver distraction behavior detection method and system based on an improved YOLOv8 model, belonging to the field of information processing technology. First, based on the State Farm Distracted Driver Detection dataset, normal samples are eliminated to construct a driver distraction behavior dataset, and the driver distraction behavior dataset is preprocessed using Mosaic data enhancement technology. After manual annotation, the driver distraction behavior dataset is divided into a training set and a validation set. Secondly, an improved YOLOv8 model framework is constructed. The CSPLayer_2Conv module in Stage Layer 1, Stage Layer 2, Stage Layer 3, and Stage Layer 4 in the original YOLOv8 network backbone is replaced with a LarK module, a CAFM module is added to the neck part, and the original detection head Decoupled Head is replaced with an RT-DETR Decoder in the head part. Then, the improved YOLOv8 model is trained using the training set, and the model performance is tested using the Driver Distraction Dataset as a test set. Finally, the detection results are evaluated using evaluation indicators. The method and system provided by the present invention effectively resolve the dilemma of balancing accuracy and real-time performance of existing detection methods and reduce the risk of traffic accidents.

The invention discloses a driving state analysis method based on machine vision and a related device, and relates to the technical field of image processing, and the method comprises the steps: carrying out the definition optimization of each frame of driver image; performing feature point extraction on each frame of driver image after definition optimization based on a calibration network to obtain corresponding feature point information; based on the feature point information, an eye closing adaptive threshold value is analyzed, and fatigue driving state detection of the driver is carried out in combination with micro-expression analysis; carrying out hand-held object detection on the driver based on each frame of driver image to obtain hand-held object information; performing sight liveness analysis based on the feature point information so as to perform distracted driving state detection in combination with the handheld object information; and judging whether early warning information needs to be sent based on the fatigue driving state detection result and the distracted driving state detection result. According to the method, false detection or missing detection of the abnormal driving state can be effectively avoided, and the reliability of driving state analysis is improved.

A method of operation for a compute system comprising: monitoring a phone sensor array to detect a trigger; calculating a position of a cell phone based on the trigger and sensor data from the phone sensor array; predicting a driver distraction event by analyzing the sensor data and the position of the cell phone; compiling a driver distraction evaluation based on the driver distraction event and a sensor data packet from an in-vehicle sensor array; generating a distraction rating for display on a device based on the driver distraction evaluation.

The invention relates to a method and a system for situation-dependently handling an interaction of a driver (10) with an infotainment system of a moving vehicle, in which method and system content which is distracting for the driver (10) is identified and, as a result, is intended to be transferred to a passenger (12). For this purpose, it is checked whether the passenger (12) themselves is already interacting with the infotainment system and the transfer to them is implemented on the basis thereof.

The invention relates to the technical field of man-machine interaction, and provides an automobile information interaction display method and a storage medium, when a target display mode is considered, comprehensive analysis is carried out by fusing one or more types of target data, the scene adaptation degree of each preset display mode to a current driving scene is dynamically evaluated, and the display effect of the current driving scene is improved. Based on comprehensive analysis of multi-source information, intelligent collaboration and dynamic self-adaption of information display are carried out by effectively combining driving scenes, information priorities and user requirements, so that the driving safety and the situation awareness ability are comprehensively improved, highly-personalized intelligent man-machine interaction is realized, distraction and cognitive loads of drivers are reduced, and the driving experience is improved. And the driving safety is improved.

A method (400) for distraction driver detection and alert includes receiving vehicle speed data (20) from one or more wheel speed sensors (122) disposed on a vehicle (105), and receiving proximity data from one or more proximity sensors disposed on the vehicle, the proximity data indicating a distance (32) of the vehicle relative to any object in front of the vehicle. The method also includes executing a distracted driver detection algorithm (300) that uses the vehicle speed data and the proximity data to determine that the vehicle is interrupting the stop-time traffic flow and that a driver of the vehicle is distracted from operating the vehicle. The method includes instructing a system (270) of the vehicle to output an alert to reengage a driver based on determining that the vehicle is interrupting the stop-time traffic flow and the driver of the vehicle is distracted.

The present invention aims to provide a storage unit that can be attached to a bicycle handlebar, allowing for the storage of a smartphone while simultaneously preventing distracted driving. [Structure] To achieve the above objectives, the present invention: Firstly, a storage unit for bicycle handlebars is attached to the handlebars at the center of the handlebars by fastening means such as screws at the mounting point, with a storage compartment protruding from the mounting point toward the rider, the storage compartment being opened and closed with a lid, and the horizontally elongated storage compartment being a storage space for storing a smartphone horizontally. The above is my proposal.

The embodiment of the invention provides a driver distraction area boundary acquisition method, so as to accurately acquire a driver distraction area boundary, the method comprises the following steps: acquiring multi-view scanning data acquired by a depth camera for a cockpit, a plurality of diffuse reflection mark points being preset in a distraction attention area in the cockpit; based on the scanning data, performing three-dimensional reconstruction to obtain a cockpit three-dimensional model, and obtaining a first three-dimensional coordinate of each mark point in a depth camera coordinate system; based on a calibration plate in a common visual field of the two cameras, joint calibration is performed on the depth camera and the driver monitoring system camera, and a conversion relation from a depth camera coordinate system to a driver monitoring system camera coordinate system is established; according to the conversion relation, converting the first three-dimensional coordinates of the mark points into a camera coordinate system of a driver monitoring system to obtain second three-dimensional coordinates; and based on the second three-dimensional coordinates, determining distraction boundary information of the distraction region of interest in a camera coordinate system of the driver monitoring system.

The invention relates to the field of industrial vehicle hardware design, in particular to equipment structure optimization for monitoring the safety state of a driver of a vehicle. According to the technical scheme, the driver distraction early warning device suitable for the industrial vehicle comprises a vehicle connecting opening used for being connected with the industrial vehicle, a connecting rod connected with the vehicle connecting opening and a feature collecting module connected with the connecting rod, a mounting disc is rotationally connected to the connecting rod, and the feature collecting module is connected with the mounting disc. The mounting disc comprises a base disc and a moving disc in sliding connection with the base disc, the special acquisition module comprises a plurality of acquisition monomers mounted on the surface of the moving disc, and each acquisition monomer comprises a sensor. The invention aims to provide a driver distraction early warning device suitable for an industrial vehicle and the industrial vehicle. Flexible adjustment can be carried out according to heights and sitting postures of different drivers, and the recognition accuracy and universality are improved; the visual angle is wide and accurate, and targets are not easy to lose.

The invention discloses a driving force feedback distribution control system and method based on six-sense tactile sense, and relates to the field. Comprising an environment sensing module which is used for collecting obstacle information and road information around a vehicle and outputting an environment threat coefficient; the driver state monitoring module is used for detecting the hand position, the grip strength and the sight line direction of a driver and calculating the distraction degree of the driver; the central processing module is used for fusing environment and driver data and generating intensity and mode instructions of each tactile feedback point through a weight distribution algorithm; and the force feedback execution module comprises tactile generators distributed at a plurality of positions of the cockpit, and outputs vibration, pressure or temperature feedback according to instructions. The method has the advantages that the intuitionism and timeliness of danger perception and positioning are improved, the individuation and adaptive optimization of human-computer interaction are realized, and the dimension and robustness of information transmission are enhanced.

The invention discloses a user experience test method for an intelligent network connection heavy truck, and aims to comprehensively evaluate user interaction and performance of the intelligent network connection heavy truck in actual use. The experience, including availability, performance and safety of the system, of a heavy truck driver using the intelligent networking function is comprehensively evaluated and improved. In consideration of long-time driving and special operation requirements of a heavy truck driver, the method integrates static and dynamic use case analysis, dynamic driver distraction evaluation, system function and performance testing, system scoring and comprehensive evaluation performed by using a customized system availability scale. According to the invention, a manufacturer can more deeply understand the user experience of the intelligent network connection heavy truck in a real driving environment, so that valuable data support is provided for product design and function improvement. In addition, the method can help to improve the overall safety performance of the vehicle and the satisfaction degree of a driver, and has remarkable market and social values.

The invention discloses an in-transit collaborative navigation method and system based on visual real-time search, and relates to the technical field of intelligent cabins and man-machine interaction. According to the method, visual gazing data are collected in real time through a passenger end wearable device, space coordinate transformation is carried out in combination with vehicle positioning, and the visual gazing data are acquired in real time; and accurately locking the target interest point watched by the passenger in the three-dimensional map by using a ray projection technology. And the system background immediately calculates the travel time increment after the target interest point is set as the approach point, and generates a navigation modification suggestion in combination with the identity of the initiator. Navigation modification suggestions are presented on a vehicle-mounted interface in a floating window mode, after voice or touch confirmation, the system automatically executes node insertion and sequence recombination of navigation tasks, and an engine is called to dynamically re-plan a path. According to the method, WYSIWYG visual interaction is realized, the potential safety hazards that passengers are difficult to accurately describe places and drivers are distracted to operate are solved, and the cabin cooperation efficiency is improved.

The invention discloses wearable interaction hardware equipment, and relates to the technical field of man-machine interaction. S1, equipment wearing and setting; s2, reporting contact information; s3, automatically replying the voice message; s4, performing voice reply by the user; and S5, traffic light pause state notification. According to the wearable interaction hardware equipment, a user does not need to hold a mobile phone to check or edit messages, distracted driving is avoided through voice interaction of a Bluetooth headset, the traffic accident risk is reduced, meanwhile, road congestion caused by distraction of a driver is reduced, and timely transmission of emergency messages and delayed processing of non-emergency messages are ensured through emergency feature word recognition; not only are important cooperation or help information not missed, but also normal driving is not interfered, the non-emergency message is automatically replied and the optimal contact time is informed, the communication cost of a user is reduced, and the non-emergency message is broadcasted and replied on the premise of ensuring sufficient time in combination with a traffic light pause state, so that the message processing efficiency is improved, and message accumulation is avoided.

Raw sensor data from a device of a user is collected. The raw data does not include location data for the device. The data is preprocessed to identify driving trips in which the user had the device. The trip data is provided to machine-learning models (MLMs) as input and the MLMs provide as output predictions for each trip's estimated speeds, estimated number of hard brakes and a degree of each hard brake, and estimated degrees of distracted driving. A duration, time of day, and calendar date of each trip is identified. The predictions, duration, time of day, and calendar date are modified into four factor values. The four modified factor values are combined into an overall driver characteristic value and provided to a network service associated with the user for purposes of providing or modifying services provided to the user through the network service based on the overall characteristic value.