98 results about "Distracted driving" patented technology

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.

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 a distraction driving detection model based on domain self-adaption, and aims to solve the problem that the performance of an existing distraction driving detection model is reduced due to the fact that distribution differences exist between a source domain and a target domain in the aspects of a driving environment, a shooting view angle, an illumination condition, driver characteristics and the like. According to the model, joint distribution alignment of feature and category prediction is realized through a label guiding domain adversarial network, and the adaptive capacity of the model in a cross-domain scene is remarkably improved. In order to further enhance the discrimination capability and generalization of the model, optimization is carried out from two levels of feature extraction and training strategy: a local feature enhancement module aims to improve the feature representation capability of key local behaviors; and according to the category structured CutMix strategy, the generalization ability of the model is enhanced by constructing a mixed sample. Experimental results show that the detection accuracy and robustness of the model under the cross-domain condition are effectively improved, a reliable technical scheme is provided for driver state monitoring, and the method has important application value for road traffic safety guarantee.

A system for detecting hazards for a vehicle includes a vehicle sensor for determining information about an environment surrounding the vehicle and a global navigation satellite system (GNSS). The system also includes a controller in electrical communication with the vehicle sensor and the GNSS. The controller is programmed to perform a plurality of measurements. The controller is further programmed to determine a plurality of classification scores of the first remote vehicle based at least in part on the plurality of measurements of the first remote vehicle and to determine an overall hazard score of the first remote vehicle based at least in part on the plurality of classification scores of the first remote vehicle. The controller is further programmed to take an action based at least in part on the overall hazard score of the first remote vehicle.

The invention discloses a truck driver safe driving evaluation method based on SEDEA, and the method comprises the steps: regarding the vehicle characteristics, behavior characteristics, road characteristics and environment characteristics of a vehicle in a driving process as external resources of a driver safe driving task, and regarding the characteristics of overspeed driving, fatigue driving, distraction driving and the like as risk results after the resources are utilized; the method comprises the following steps: taking resource utilization efficiency evaluation as a core means to measure the safe driving ability of a truck driver, and constructing a weight system containing multiple inputs and multiple outputs and a resource utilization efficiency evaluation model to calculate a safe driving score of the driver; and various safety indexes and quantitative classification management measures of the driver are given based on the model. The model is in close contact with the safe driving management situation of the truck driver, not only considers the resource input and the efficiency output of the safe travel task of the truck driver, but also sets the optimization direction between different inputs and outputs through a weight system.

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 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.

Disclosed is a simulation learning-based drowsy driving simulation platform system for detecting careless driving in conjunction with deep learning, the simulation learning-based drowsy driving simulation platform system comprising: a drive state warning device configured to determine a driver's careless driving from a captured image, determine a driver's careless driving determination level, and output the determined level; a smart cruise control interworking part configured to transmit the driver's careless driving determination level outputted from the drive state warning device; and a smart cruise control processing part configured to control a vehicle according to the driver's careless driving determination level transmitted by the smart cruise control interworking part, during a smart cruise control operation.

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.

A system and method for improved automotive safety, particularly aimed at the prevention of automobile, truck, and other vehicle accidents related to driving in a distracted manner while using a cell phone.

A system and method for improved automotive safety, particularly aimed at the prevention of automobile, truck, and other vehicle accidents related to driving in a distracted manner while using a cell phone.

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 (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.

To provide a device which prevents a driver from using a mobile phone when driving.SOLUTION: The mobile phone use restriction device of the present invention promotes a driver to put a mobile phone in a storage container, and prevents the driver from putting the mobile phone out of the storage container or prevents the driver from holding the mobile phone by a hand by mobile phone use restriction means of the storage container for the mobile phone when a vehicle moving speed detection shows that the vehicle is being driven, and acquires the record of a violation by violation responding means.SELECTED DRAWING: Figure 1

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 present disclosure relates to detecting distracted driving on a mobile device and mitigating distracted driving by determining whether a mobile device is used by a driver of a vehicle or by a passenger of the vehicle. The system can determine that the mobile device is in a vehicle and modify the functionality of the mobile device to transition the mobile device into an automobile mode. Vehicle data and / or mobile device data can be used to determine a number of individuals in the vehicle. An identity of the operator of the mobile device can be authenticated based on authentication information obtained by the mobile device. Based on the authenticated identity of the operator of the mobile device, a driving score associated with the driver can be modified, where the score indicates a number of instances of distracted driving by the driver.

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.

The invention discloses a driver fatigue and distraction driving state identification method based on a behavior map, and the method comprises the steps: data collection and preparation: extracting natural driving data from a vehicle-mounted global positioning system, and processing an abnormal value and a vacancy value; the method comprises the following steps of: constructing an abnormal behavior map: by taking five behavior variables in longitudinal and transverse dimensions as a data basis, firstly extracting and coding abnormal behavior data, secondly depicting abnormal behavior sheet characteristics, then constructing the abnormal behavior map, and extracting behavior map characteristic data; and fatigue and distraction driving state identification: using the behavior map characteristic data as an input variable of a machine learning model, and identifying different risk driving states of the driver through the machine learning model. The defects of a traditional driving state recognition method can be overcome, and the applicability of the method is improved on the premise that the driving state recognition precision is guaranteed.

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.

The invention discloses an emotion intervention method and device, a vehicle and a storage medium. The method comprises the steps that video data and audio data of a driver and passengers in the vehicle driving process are acquired; performing emotion state detection on the driver according to the video data and the audio data to obtain an emotion score of the driver; when it is determined that the driver and the passenger are in a negative emotional state according to the emotional score, an intervention strategy is determined according to the emotional score, and the intervention strategy is used for smoothing the negative emotion of the driver and the passenger; and performing emotion intervention on the driver and passengers according to the intervention strategy. According to the method, when it is determined that the driver is in the negative emotional state based on the detected emotional score of the driver in the vehicle driving process, emotional intervention is conducted on the driver according to the emotional score so as to flatten the negative emotions of the driver, the situation that the driver is distracted to drive, and consequently the driving risk of vehicle driving is increased can be avoided, and the driving safety is improved. And the driving safety of vehicle driving can be improved.

The invention discloses a safe driving control method based on steering wheel grip strength monitoring, which comprises the following steps: S1, acquiring grip strength data such as grip strength values and distribution areas in real time through array type pressure sensor groups arranged in the circumferential direction of a steering wheel; s2, preprocessing the data and extracting characteristic parameters such as an average grip strength value and a distribution balance degree; s3, inputting the characteristic parameters into a pre-trained deep learning model, and identifying three types of driving states including fatigue, distraction and sudden health abnormality; s4, triggering a corresponding strategy according to an identification result: starting graded early warning and adjusting vehicle power parameters in fatigue or distraction driving, starting emergency control in case of abnormal health, and automatically executing speed reduction, lane keeping and alarming; and S5, reserving a 3s driver feedback window period, judging whether to release the control strategy based on the recovery coefficient, and if no effective feedback is received, continuously executing until the vehicle stops. The grip strength characteristic can be accurately monitored, the abnormal state can be recognized, and the driving safety can be effectively improved by being linked with a vehicle active control system.

An acquisition unit acquires vehicle information and a moving image frame as image information captured by an imaging unit from a database. An extraction unit extracts a captured image in which at least one of a direction of a face of an occupant and a position of a gaze point satisfies a predetermined inattentive driving condition from among captured image in which a vehicle speed is equal to or higher than a predetermined threshold value and a steering angle is within a predetermined range.

The application relates to a light-weight distraction driving recognition method and system based on an improved ConvNeXt, and relates to the field of image classification of deep learning. The method comprises the following steps: 1. obtaining a driving scene image and performing pretreatment; 2. constructing an improved ConvNeXt model, and replacing a ConvNeXt Block module in an original ConvNeXt model with a CR-Former module; 3. iteratively training to obtain a distraction driving behavior recognition model; and 4. inputting an image into the distraction driving behavior recognition model to output a driving behavior recognition result. The system comprises an image acquisition and processing module, a model construction and configuration module, a model iterative training module and a behavior recognition output module which are connected in sequence. The application has the advantages that the model calculation cost is significantly reduced, the hardware resource occupation is reduced, and the capturing capability of the model for driving behavior detail features is effectively improved.

To more accurately detect the driver's condition. [Solution] The driver state detection device of the embodiment includes a recognition unit that recognizes at least one of the gaze or face direction of the driver of a moving object, and a determination unit that determines whether the driver is driving while distracted based on the recognition result of the recognition unit and a distraction determination condition. The determination unit makes a determination of distracted driving using a first distraction determination condition when the turning of the moving object is not predicted or the moving object is not turning, and makes a determination of distracted driving using a second distraction determination condition when the turning of the moving object is predicted or the moving object is turning. When the determination unit determines whether the moving object is turning, it changes the turning determination condition for determining whether the moving object is turning depending on whether the turning direction of the moving object is left or right.

Apparatuses, systems and methods are provided for determining vehicle driver distractions. More particularly, apparatuses, systems and methods are provided for receiving, via a vehicle interior data receiving module, vehicle interior data from at least one vehicle interior sensor, wherein the vehicle interior data is representative of an initial position of a body of at least one vehicle occupant; tracking, via a body tracking module, changes in the body of the at least one vehicle occupant from the initial position of the body; and predicting, via a driver action prediction module, one or more driver actions based at least on the changes in the body of the at least one vehicle occupant.

The invention provides a safe driving monitoring method based on multi-modal data, and the method comprises the following steps: S1, collecting video information during driving through a camera, separating audio information from the video information, and converting the audio information into text information through a voice recognition model; s2, inputting the video information into a target detection model, and when the driver is recognized to be in a fatigue or distracted driving state, generating a corresponding prevention measure; s3, synchronously performing the step S2, respectively extracting a visual initial feature, an audio initial feature and a text initial feature from the video information, the audio information and the text information, inputting the visual initial feature, the audio initial feature and the text initial feature into a multi-modal sentiment analysis model based on a mixed attention mechanism, and when the driver is recognized to be in an emotional driving state, executing the step S3; and corresponding prevention measures are generated. According to the invention, double-line detection is adopted to identify whether the driver is in fatigue, distraction or emotional driving, so that safe driving monitoring is more reliable and effective.

The present invention provides a method, device, electronic device, and storage medium for detecting distracted driving behavior, relating to the technical field of driving behavior detection. The method comprises: obtaining a driving behavior image; inputting the driving behavior image into a distracted driving behavior detection model, and obtaining a distracted driving behavior detection result output by the distracted driving behavior detection model. The distracted driving behavior detection model is trained based on sample driving behavior images and comprises a backbone network feature extraction module, an attention module, and a loss function module. The distracted driving behavior detection result is the result of multi-scale feature extraction, multi-scale feature fusion, and behavior detection performed on the driving behavior image by the backbone network feature extraction module, the attention module, and the loss function module in the distracted driving behavior detection model. The technical solution of the present invention improves the accuracy of distracted driving detection by performing distracted driving detection based on the distracted driving behavior detection model.