128 results about "Distracted driving" 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.

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 discloses a driver distracted driving behavior recognition method based on comparative learning and real-time edge calculation, and relates to the technical field of intelligent traffic monitoring. Comprises: obtaining a target data set; a Yov5 detection model is constructed, and the Yov5 detection model is pre-trained; changing a pre-trained Yov5 detection model into a Yov5 classification model, and performing training again on the target data set to obtain a trained PT model; improving the PT model based on a comparative learning method; performing BN layer pruning on the improved PT model, and performing quantization processing on the PT model after pruning; converting the PT model into an ONNX model, then converting the ONNX model into an RKNN model, and pre-compiling the ONNX model; and finally, performing real-time reasoning on an edge computing platform by using the pre-compiled RKNN model. According to the method, the model learns the feature extractor with the characterization capability by utilizing comparative learning, so that the accuracy of driver behavior classification is effectively improved.

The invention relates to an assistance system (9) and to a corresponding method (15) for warning a driver (2) in an assisted or partially automated driving mode of a motor vehicle (1). Accordingly, when the driver (2) is first distracted, a distraction warning (14) is output after the expiry of a first warning period during which the driver (2) was distracted. If the driver (2) is distracted again within a predefined repetition period, then a distraction warning (14) is output to the driver (2) after a shorter second warning time. The invention also relates to a correspondingly configured motor vehicle (1).

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

A computer system is provided and is programmed to: (1) receive sensor data associated with a primary vehicle; (2) determine a current condition of a driver of the primary vehicle based upon the sensor data of the primary vehicle; (3) determine a threat level for the primary vehicle based upon the current condition of the driver of the primary vehicle and the sensor data of the primary vehicle; (4) activate at least one action in the primary vehicle based upon the threat level for the primary vehicle; and / or (5) electronically transmit the threat level of the primary vehicle to one or more additional vehicles.

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.

The invention relates to an assistance system (9) and a corresponding method (15) for warning a driver (2) in an assisted or partially automated driving mode of a motor vehicle (1). Accordingly, if the driver (2) is distracted for the first time, a distraction warning (14) is issued after a first warning period during which the driver (2) was distracted. If the driver (2) is distracted again within a predetermined repetition period, a distraction warning (14) is then issued to the driver (2) after a shorter second warning period. The invention also relates to a correspondingly equipped motor vehicle (1).

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.

The invention discloses a lightweight distracted driving detection method. Relates to the field of safety detection of an intelligent traffic system and an automatic driving assistance system, in particular to the technical field of distracted driving behavior detection based on deep learning. The problems that according to an existing detection method, the number of computing resources is large, lightweight application cannot be carried out, and inaccurate detection is likely to occur are solved. The method comprises the following steps: S1, data acquisition and division: collecting distraction driving behavior images, and dividing the distraction driving behavior images into a training set, a verification set and a test set; and S2, constructing a distracted driving detection model, and improving the existing YOLOv8s model framework by adopting a hierarchical improvement mechanism. S3, training the distraction driving detection model: training the distraction driving detection model through the training set and the verification set, and testing a training result through the test set to obtain the distraction driving detection model which can be applied; and S4, detecting the driving behavior by adopting the distraction driving detection model which can be applied.

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.

A distracted driving analysis system for identifying distracted driving events is provided. The system includes a processor in communication with a memory device, and the processor is programmed to: (i) receive labeled training data, the labeled training data including driving event records (a) each labeled as an actual distracted driving event or a passenger event and (b) including phone usage by a user that occurred within a time period of a driving event, (ii) identify common features of the actual distracted driving events and the passenger events by processing the training data using a supervised machine learning algorithm, (iii) generate a trained model based at least in part upon the identified common features, (iv) process a new driving event, (v) assign the new driving event based at least in part upon features of the new driving event, and / or (vi) determine whether the new driving event is an actual distracted driving event or a passenger event.

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.