183 results about "Driver Distractions" patented technology

The invention relates to the technical field of driving, and particularly discloses a mobile phone management and control method and a control system in a driving process. Vehicle running state data and mobile phone multi-mode sensing data are obtained, vehicle CAN bus information and mobile phone sensor data are combined, a motion coupling degree value is calculated on the basis of deviation of a steering wheel angle and mobile phone acceleration, the operation synchronization rate is analyzed through accelerator operation and gyroscope response, hand action coordination is evaluated, and the hand action coordination is evaluated. The method comprises the following steps: calculating a spatial constraint deviation value by combining a magnetometer orientation and a driving position coordinate offset degree; monitoring an abnormal posture of a driver; generating a risk assessment value by fusing three indexes; dynamically controlling a mobile phone function; according to the system, through multi-source data fusion and a hierarchical management and control strategy, emergency communication requirements are balanced on the premise of ensuring driving safety, distracted driving behaviors are accurately identified, and driving potential safety hazards caused by use of a mobile phone are solved.

The invention provides a lightweight driver distraction behavior detection method, and belongs to the technical field of computer vision. The technical problems that a distraction driving detection algorithm model is complex in structure and large in parameter quantity, and model light weight and detection precision are unbalanced are solved. According to the technical scheme, the method comprises the following steps: S1, collecting distraction driving behavior data pictures, and constructing a distraction driving behavior data set; s2, a lightweight distracted driving detection network ASU-YOLO is constructed on the basis of the YOLOv8n model; s3, training the constructed ASU-YOLO network model by using the manufactured distraction driving behavior data set to obtain a lightweight distraction driving detection model; and S4, detecting distraction driving behaviors through the trained model. The method has the beneficial effects that the detection precision of the model is improved while the parameter quantity and the calculation quantity of the algorithm are reduced, the light weight of the model is realized, and the requirement of real-time detection is met.

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.

A system (1) for efficiently determining a driving behaviour of a driver driving a vehicle. The system includes a 3-axis accelerometer (2) generating accelerometer data (3), a direction sensor (4) generating direction data (5) and orientation sensor (6) generating orientation and angular velocity data (7), all placed inside the vehicle. The system (1) also includes a processing unit (8) which processes the accelerometer data (3), the direction data (5) and the orientation data (7), and calibrates the accelerometer data (3) to generate a calibrated accelerometer data (9). The calibrated accelerometer data (9) is used in determining the driving behaviour of the driver of the vehicle and generate Driving Deficiency Alerts, including but not limited to Over-speeding. Hard acceleration. Sudden Braking, Fast Cornering, Quick Lane Changing, Distracted Driving, Dangerous Driving over bumps and Crash Detection.

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.

The invention provides a driver distracted driving detection method and device, terminal equipment and a program product, and the method comprises the steps: obtaining a real-time image of a vehicle driven by a driver when the driving speed of the vehicle is greater than a preset speed threshold value; determining a real-time head pose and a real-time sight angle corresponding to each real-time image; the real-time head pose comprises a real-time head pitch angle and a real-time head yaw angle; the real-time sight angle comprises a real-time sight pitch angle and a real-time sight yaw angle; if the continuous preset number of the real-time head poses are within the preset head pose range and the real-time sight angles exceed the preset sight angle range, it is judged that the driver has the distracted driving behavior. According to the method, whether the driver has the distracted driving behavior or not can be judged by combining the real-time head posture and the real-time sight angle of the driver in the vehicle driving process, whether the driver has the distracted driving behavior or not can be more accurately determined, and misjudgment is avoided.

The invention discloses an auxiliary display method and system for an automobile central control large screen, and the method comprises the steps: firstly, collecting the real-time state of an automobile after ignition, reading the biological characteristics of a driver, and carrying out the data monitoring of an environment to judge a driving scene; dynamically distributing a screen area according to the scene risk level and the task priority; judging the priorities of the voice, the gesture and the touch screen according to the scene risk level; according to the method, early warning information is improved and automatic reminding is carried out when a driver is distracted to drive, priority display content on a screen is distributed according to different scenes, unnecessary content is folded and hidden in a specific scene, information priority display in different scenes is improved, the distraction scenes of the driver are reduced, and the driving safety is improved in a high-risk scene of a highway section. The screen improves the priority of voice interaction, and the risk that a driver is distracted during high-speed driving is avoided.

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.

A method for detecting distraction of a driver in a driver monitoring system is disclosed. The method includes receiving a first image including the driver within a vehicle. The method includes determining a gaze class corresponding to the first image among a plurality of gaze classes by using a first model in response to the receiving the first image. The plurality of gaze classes includes a first gaze class indicating that the driver is gazing in a front direction and a second gaze class indicating that the driver is gazing in a non-front direction. The method includes determining whether the distraction of the driver is present in the first image based on the gaze class corresponding to the first image.

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.

Disclosed are a system and method of regulating access to devices that can distract a driver that is operating a motor vehicle based on received Vehicle-to-Everything communications including Vehicle-to-Vehicle and Vehicle-to-Infrastructure information. One example method may include determining a vehicle is approaching a vehicle movement restriction location requiring a vehicle movement restriction, determining an amount of time associated with the vehicle movement restriction, and determining whether a device located inside the vehicle will be made accessible to a user during the vehicle movement restriction based on the amount of time associated with the vehicle movement restriction.

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.