975 results about "Safe driving" patented technology

The invention relates to the technical field of risk assessment, in particular to a safe driving assessment method and system based on Beidou navigation. The method comprises the following steps: acquiring corresponding real-time position, speed and driving track data in a vehicle driving process through a Beidou navigation module, and acquiring vehicle state data and environment sensing data to obtain a vehicle multi-dimensional driving state data set; performing vehicle space-time and driving operation feature analysis and driving road matching analysis on the vehicle multi-dimensional driving state data set to obtain vehicle driving road attribute matching data; and carrying out vehicle driving risk estimation and vehicle safety over-rate evaluation based on the vehicle driving road attribute matching data, carrying out safety comparison verification evaluation, generating vehicle personalized driving improvement suggestions, and pushing the vehicle personalized driving improvement suggestions to the vehicle-mounted terminal to execute corresponding vehicle safe driving behavior improvement operation. According to the invention, real-time monitoring, accurate evaluation and active safety intervention of vehicle driving behaviors can be realized.

The invention relates to the technical field of intelligent networked vehicles, in particular to an intelligent networked vehicle remote control and monitoring method. The method comprises the following steps: acquiring real-time running state information and roadside sensing data of a vehicle, and generating a remote takeover request; receiving a control instruction sent by the cloud control platform, and feeding back a control execution result; calculating instruction interaction delay and network transmission delay; the stability of the communication environment is evaluated by combining the two, and the vehicle driving mode is dynamically adjusted. Furthermore, time delay is calculated according to heartbeat signals and control instruction timestamps in the communication link, the stability index of the communication environment is evaluated, and the safe driving speed is adjusted according to the stability. And when the communication environment stability is lower than the threshold value or the instruction interaction delay exceeds the threshold value, an emergency stop mechanism is triggered. According to the method, the safety and the stability of the intelligent network connection vehicle under remote control can be improved.

The invention relates to the technical field of intelligent traffic, in particular to an integrated fog measurement type highway safe driving guidance device and method, and provides the following scheme that local visibility data is acquired based on a plurality of highway safe driving guidance devices and uploaded to a region controller to generate guidance information. And the regional controller performs research and judgment calibration and gradient calibration on the data, corrects measurement errors and generates road visibility space distribution information by screening calibration anchor points and adopting an exponential attenuation function and iterative gradient calculation. Based on the information, a connectivity algorithm is used to identify a fog area, and in combination with road environment and historical accident data, an optimal induction strategy is matched, and induction information is dynamically adjusted. The fog area detection accuracy is improved, the induction information matching is optimized, and the driving safety and the passing efficiency under the low-visibility condition are improved.

The invention provides a control method, device and equipment, a vehicle, a chip and a medium, relates to the artificial intelligence technology and can be applied to the field of vehicles, and the method comprises the steps that scene information of a scene where a movable target is located and input data of a planning track are determined; motion risk prediction of the movable target is carried out according to the input data, and a risk prediction result is determined; the risk prediction result is used for indicating whether the movable target has a risk when moving according to the planned trajectory; and according to the risk prediction result, determining whether to update the planning trajectory of the movable target. Therefore, a motion risk prediction technology is introduced into the trajectory planning scene of the movable target, potential motion risks are predicted, dangerous points or collision risks possibly existing in the planning trajectory are recognized in advance, and early warning is provided for safe driving. Once the potential motion risk is detected, the planning trajectory can be adjusted, so that the efficient and safe trajectory can be planned, and the possibility of risks such as collision of the movable target in actual driving can be reduced.

The invention discloses an unmanned vehicle azimuth end-based object autonomous detection obstacle avoidance intelligent identification system, which comprises the following modules: a sensing data acquisition module, which is used for acquiring and preprocessing multi-source sensing data and generating an environment sensing data set; the feature extraction module is used for inputting the environment perception data set into the Motion-DETR dynamic perception model to generate a target object feature set; the perception graph construction module is used for constructing a dynamic environment perception graph; the threat evaluation module is used for calculating threat levels, generating an azimuth end threat level matrix and establishing a dynamic risk distribution diagram; the path extension optimization module is used for dynamically selecting an extension direction by adopting a path extension optimization method introducing dynamic risk cost; the path cost optimization module is used for dynamically optimizing the expansion structure according to the principle of minimum accumulated path cost value; and the path output module is used for generating an optimal safe driving path in the dynamic environment. The invention provides an intelligent obstacle avoidance path planning method fusing perception mapping and risk optimization.

The invention relates to the technical field of driving control, in particular to a new energy automobile safe driving control method and system. The method comprises the following steps that a historical urgent danger avoiding data set and a corresponding user misoperation braking state are obtained through a new energy automobile control center, sample undersampling processing is conducted firstly, and an urgent danger avoiding balance sample is obtained; thirdly, analyzing user misoperation braking abnormity by using a balance sample, and performing disordered vector intensity calculation under a time dimension based on braking abnormity data to generate disordered vector segmentation clustering data; then, braking time sequence intervention correction sensing is carried out according to the clustering data, optimized braking intervention correction data are generated, finally, the optimized data are transmitted to a new energy automobile control terminal, and safe driving control is executed. According to the invention, the driving control technology is optimized, so that the driving control technology is more perfect.

The invention relates to the technical field of parking lot digital management, and discloses a digital twin enabling parking lot digital global management system. According to the system, a global digital twin model of a parking lot is generated, and multi-source sensing data such as parking space states, vehicle identities and environmental parameters are fused; generating a parking space distribution matrix with priorities and time windows in combination with the parking request of the user and the real-time parking space state; according to the parking space two-dimensional code hardware parameters, the digital twin model and the parking space distribution matrix, outputting a vehicle guiding path set with a timestamp; a real-time obstacle avoidance and dynamic adjustment system is constructed to generate a safe driving path set so as to dispatch vehicles; outputting a power distribution control instruction set and updating the digital twinborn model by combining the electric quantity of the energy storage unit, the power distribution network data and the safe driving path set; and finally, system knowledge evolution and end-to-end closed-loop optimization are realized through scheduling execution logs. According to the system, global efficient management of the parking lot is realized, and the parking space utilization, vehicle scheduling and energy utilization levels are improved.

Systems and methods are provided for operation of a vehicle to prevent the vehicle from entering unsafe driving states. In examples, a current operational state of a vehicle is determined based on sensor data collected by sensors on the vehicle. Based on the current operational state, a safety-critical threshold value for a vehicle dynamic parameter at which the vehicle is predicted to breach a limit-of-handling is determined. A command to operate the vehicle in a requested operational state is received, which includes driving inputs for operating the vehicle in the requested operational state. The command is filtered by constraining the requested operational state according to a control barrier function based on the safety-critical threshold value and a requested value of the vehicle dynamic parameter determined from the requested operational state. A control signal is generated that controls the vehicle by modifying the driving inputs based on the filtered command.

The embodiment of the invention relates to the technical field of vehicles, and discloses a vehicle accident early warning method and device, electronic equipment and a storage medium, the method comprises the following steps: obtaining running state data in a vehicle based on a preset duration, the running state data being determined based on vehicle data obtained by at least one bus source in the vehicle; determining target time sequence characteristic data in the operation state data, wherein the target time sequence characteristic data indicates a change trend, a periodic rule and a sudden change condition of the operation state data in a time dimension and a dependency relationship among the data; and inputting the target time sequence characteristic data into a risk assessment model to obtain an assessment result of the accident risk of the vehicle after the preset duration. By applying the technical scheme of the invention, more accurate vehicle early warning can be realized, and the safety driving requirement of the vehicle can be better met.

The invention provides a heterogeneous motorcade prediction control method based on a path information protection mechanism, and relates to the technical field of network connection unmanned vehicle cooperative control and intelligent traffic. Firstly, a path information protection mechanism is provided for preventing an alien invading vehicle from tracking a motorcade and ensuring the safety of motorcade path information. In addition, a predictor model is established to improve the estimation performance of the adaptive law, and a variable gain nonlinear expansion state observer is designed to approach and compensate the total disturbance. Then, by introducing a variable-gain hyperbolic tangent tracking differentiator, the problem of'complexity explosion 'caused by repeated derivation of a virtual controller in a backstepping method is avoided; and finally, a distributed prediction cooperative controller is constructed, and stable and safe driving of the whole heterogeneous network connection fleet is ensured.

The invention provides a vehicle automatic driving path planning method, a path planning system and a vehicle, and relates to the technical field of intelligent driving. The method comprises the following steps: acquiring environmental parameters of a vehicle; the environment parameters comprise road environment parameters and road friction parameters; determining a safe driving area of the vehicle at least according to the road environment parameters, and determining an environment error coefficient of the vehicle at least according to the road friction parameters; and based on the environmental error coefficient, the steering angle variation of the vehicle in the path planning algorithm is constrained, the constrained path planning algorithm is utilized to generate an initial path of the safe driving area, and the vehicle is controlled to drive according to the initial path. According to the method, the environment parameters of the vehicle are obtained, the safe driving area and the vehicle steering angle constraint of the vehicle are determined according to the environment parameters, the initial path in the safe driving area is generated on the basis of the vehicle steering angle constraint, the vehicle is controlled to drive according to the initial path, and the safety of automatic driving of the vehicle is improved.

The invention discloses a bridge floor global driving wind environment early warning method, system and device based on limited measuring points and a storage medium, and relates to the technical field of wind speed early warning and artificial intelligence. Future short-time wind speed is predicted based on wind speed time history data, and a two-dimensional high-temporal-spatial-resolution prediction flow field of multiple planes is obtained according to the future short-time wind speed; main beam global wind-induced vibration response data are obtained based on the wind-induced vibration response data; obtaining three-dimensional high-temporal-spatial-resolution prediction flow field data according to the two-dimensional high-temporal-spatial-resolution prediction flow field and the girder global wind vibration response data; analyzing the three-dimensional high-temporal-spatial-resolution prediction flow field data to obtain a safe driving critical wind speed, and setting a wind speed-vehicle speed limiting criterion; and when the predicted wind environment exceeds the wind speed limit criterion, outputting the predicted data and the early warning level of the corresponding driving wind environment, and carrying out advanced early warning. According to the invention, the problem of low accuracy of existing driving safety early warning is solved, and wind speed multi-stage early warning for different vehicles in different driving environments is realized.

The invention discloses a safe driving map construction method based on binocular vision and IMU information fusion, and the method comprises the following steps: obtaining image data information through a binocular image collection unit, obtaining IMU data information through an inertial measurement unit, and carrying out the data preprocessing of the image data information and the IMU data information; performing joint initialization processing on a data preprocessing result based on binocular vision initialization and inertial measurement unit initialization; carrying out global pose nonlinear optimization processing on a joint initialization processing result based on a tight coupling optimization strategy of a sliding window; based on a BVAI-Fusion algorithm, generating a safe driving track map for a global pose nonlinear optimization processing result; initial pose estimation is improved through binocular vision and IMU multi-source heterogeneous information fusion, so that pose estimation precision and stereoscopic vision matching under multi-source heterogeneous fusion are improved, SLAM environment positioning accuracy is enhanced, and safety of driving map construction is guaranteed.

The invention discloses a hierarchical game-based energy and heat collaborative multi-objective optimization method for a pure electric vehicle. The method comprises the following steps: S1, establishing a coupling system and a collaborative optimization system # imgabs0 #; s2, acquiring the temperature and the current working condition; s3, establishing a hierarchical game system, and determining a game equilibrium solution; and S4, performing iterative adjustment. By constructing a collaborative optimization system and a coupling system, deeply fusing dynamic association between energy management and thermal management systems, and establishing a hierarchical game model, objective functions are established for the collaborative optimization system, the energy management system, the thermal management system and each subsystem of the thermal management system, mutual association is analyzed, the energy utilization efficiency is improved, and the energy utilization efficiency is improved. Energy distribution is more accurate and efficient; through layered game dynamic adjustment, multi-dimensional balance of overall energy consumption of the whole vehicle, battery safety and comfort of a passenger compartment is achieved, the limitation of single system optimization is broken through, and the comprehensive performance requirements for efficient operation, safe driving and comfortable driving of the vehicle under different environments and complex working conditions are met.

The invention provides a ramp energy-saving control method based on off-line speed planning and dual-mode model prediction tracking, and aims to solve the problems of insufficient precision of ramp driving energy consumption and safety cooperative control and frequent mode switching of an automatic driving vehicle. The method comprises the following steps: firstly, identifying a key road section based on high-precision road gradient data, and generating an optimal energy-saving reference speed matched with gradient characteristics in an off-line manner by adopting a dynamic planning and sequential quadratic planning hybrid optimization strategy; secondly, when the vehicle runs, relevant information is loaded after confirmation of a driver, the system takes over the longitudinal control right, two control modes are dynamically switched based on the front vehicle distance, and frequent mode switching is avoided through a hysteresis threshold value; and finally, taking the slope-fused vehicle longitudinal dynamics model as a prediction model, solving an optimal control instruction online by adopting a model prediction control algorithm, performing closed-loop execution, monitoring intervention operation of a driver, and immediately returning the driving right after the intervention operation is detected. According to the method, offline planning and online dual-mode tracking technologies are integrated, the method has the advantages of high energy consumption optimization precision, stable mode switching and good safety adaptability, and energy-saving and safe driving of the automatic driving vehicle in a ramp scene is realized.

The present invention relates to a safety steering device for manual intervention by a safety driver in an autonomous vehicle or a vehicle with a driver assistance system. The safety steering device comprises a control unit and a joystick operable by the safety driver, from which a steering intervention signal can be transmitted to the control unit, wherein the steering intervention signal overrides an autonomous steering signal. A safety control element is arranged on the joystick, which, when activated by the safety driver, generates a release signal to enable the transmission of the steering intervention signal to the control unit.

The embodiment of the invention discloses an automatic driving method and device, electronic equipment, a storage medium and a program product, a predicted driving track of a target vehicle is guided based on an anchor point track, the predicted driving track is used as a basic driving strategy, a basic reference track is provided for self-driving of the target vehicle, and the driving accuracy of the target vehicle is improved. The problem of distribution deviation of behavior clone in closed-loop deployment in related technologies is solved, multiple second driving parameters (including but not limited to prediction of aerial view states, prediction of operation actions and action reward signals) are predicted through the offline driving parameters and the first driving parameters, the multiple second driving parameters are used as alternative driving strategies, and the driving speed of the driving strategy is improved. And determining whether the alternative driving strategy is good or not through a preset regular penalty algorithm, a commentator algorithm and a basic driving strategy (predicted driving track) so as to determine a safer driving strategy, so that offline reinforcement learning is realized, and the problems of high calculation cost, low training efficiency, insufficient strategy robustness and the like existing in reinforcement learning are solved.

The invention discloses a real-time monitoring and early warning system for an abnormal driving state. The real-time monitoring and early warning system is applied to safe driving of road vehicles. The real-time monitoring and early warning system is provided with a vehicle-mounted data acquisition module, a cloud / edge calculation and analysis module and a non-positive driving behavior early warning and intervention module. The invention further discloses a monitoring and early warning method of the system. By adopting the technical scheme, multi-modal data fusion is adopted, so that the control is more accurate; angry or aggressive driving states are captured through real-time identification and behavior data; early warning intervention is performed to remind a driver or a traffic control department in time, so that accident risks are reduced; the control threshold value is dynamically adjusted, and false alarms are reduced; and through data analysis, a behavior pattern research basis is provided for traffic management.

The invention discloses a method, a system and equipment for enhancing an abnormal scene in a safety limit of an unmanned subway train and a medium, and relates to the technical field of safe driving. The method comprises the following steps: firstly, constructing a pre-trained basic diffusion model, and generating initial subway abnormal scene video data according to an initial frame image and a text prompt; then, a texture enhancement module is used for reducing the texture contrast and improving the trueness of the video; a reference pool containing a real luggage case image is introduced, luggage features are extracted through an object enhancement module, cosine similarity with reference pool features is calculated to serve as a target loss function, and image details are enhanced. And finally, constructing an orbit segmentation algorithm and training the model to realize multi-dimensional enhancement of the abnormal scene. According to the method, an abnormal data scene is generated by means of a diffusion model, and the problem of insufficient training data in subway abnormal object detection is solved through data enhancement.

The invention relates to the field of safe driving, in particular to a vehicle tire burst control method, electronic equipment, a storage medium and a vehicle. The vehicle tire burst control method comprises the steps that if it is determined that a first tire of a vehicle bursts, unbalance parameters of the vehicle at the current moment are obtained; the second tire is controlled to rotate towards the first tire side according to the steering angle, the second tire is a rear wheel non-burst tire, and the steering angle is determined according to the unbalance parameter. According to the invention, real-time and effective active safety control can be carried out on the vehicle, so that the safety of the vehicle is improved.

The invention provides a vehicle fault identification and driving behavior early warning method based on multi-modal feature fusion, and relates to the field of vehicle fault diagnosis and safe driving, and the method comprises the steps: collecting a vibration signal, an acoustic signal and a driving operation signal, extracting a fault feature and a behavior feature, calculating a time delay cross-correlation function to obtain a time delay influence spectrum, and carrying out the early warning of a driving behavior. And performing recursive calculation to obtain an evolution trajectory curve, calculating a fault-behavior coupling risk index by detecting the deviation of the evolution trajectory curve from a preset stable region, and generating a multi-modal early warning signal of a corresponding intensity level according to the risk index. The method has the advantages that correlation analysis of faults and driving behaviors is achieved, and the accuracy and timeliness of early warning are improved.

The invention discloses a safe driving early warning system based on big data and machine learning, and the system comprises the following steps: S1, data collection: collecting the vehicle position, parking time, vehicle door state, vehicle type and other data in real time through a roadside sensing device, and obtaining environment context information, such as road type, traffic flow, weather condition and other data; s2, data processing and fusion: matching and associating the real-time data acquired in S1 with historical data by using a multi-source data fusion technology, and constructing a complete vehicle behavior data set; s3, establishing a risk assessment model: inputting model related data based on a machine learning algorithm, constructing a door opening risk prediction model, and obtaining a probability value Pdoopen that the model output is a door opening behavior; s4, early warning: when a model early warning value is set to be Pthreshold and a probability value is greater than or equal to the early warning value, early warning about the door opening condition of a front parked vehicle is given to a rear vehicle through a vehicle-road cooperation system; the early warning system can effectively reduce the occurrence of traffic accidents.

The embodiment of the invention provides a vehicle interface information generation method and system and a vehicle, an interactive interface is deployed on the vehicle, in the process that the vehicle is safely driven by an interactive object, the interactive interface is presented in the visual range of the interactive object, and the method comprises the steps that the performance index type of the interactive interface is determined; determining test interface information associated with the performance index type; in the process that the interaction interface displays the test interface information, electroencephalogram data and / or eye movement data of the interaction object for the test interface information are / is collected; determining a test result of the test interface information based on the electroencephalogram data and / or the eye movement data; based on the test result, target interface information meeting the driving scene of the vehicle is generated for the interactive interface, and a performance index result of the target interface information under the performance index type meets a performance index threshold value under the performance index type. The technical problem that the interface information generation effect of the vehicle is poor is solved.

The invention discloses a train control system, method, equipment and medium based on active sensing and autonomous localization, and the system comprises a multi-source autonomous sensing system which collects and outputs the information of the environment in front of a train by fusing the sensing data of a plurality of sensors; the vehicle-mounted standby system is in communication connection with the multi-source autonomous sensing system, accesses a pre-stored digital track map and is used for dynamically generating route topology and movement authorization and generating a train control instruction according to the train front environment information, the train real-time position and the line data of the digital track map; a dual-mode communication switching unit is arranged in the vehicle-mounted standby system and used for monitoring the communication state of the main train control system, and when the main train control system breaks down, the train control right is automatically switched to the standby system. Compared with the prior art, after the main train control system loses efficacy, the train can be made to be independent of a central control system and a fixed electronic map, the real-time state of a line is automatically adapted, and the safe driving instruction is generated.

The present invention generally relates to a technology for recognizing objects in a Around View Monitoring (AVM) video for a vehicle. In particular, the present invention relates to a DNN-based object recognition method for multi-channel fisheye images of a vehicle AVM apparatus, by projecting multi-channel unit images of fisheye-lens cameras of the vehicle AVM apparatus onto a cylindrical projection plane so as to obtain multi-channel unit projection images, combining the multi-channel unit projection images on a single image so as to obtain a composite projection image, and inputting the composite projection image to a Deep Neural Network (DNN) model so as to obtain object recognition output. The invention has an advantage that AVM apparatus can effectively assist in safe driving by monitoring objects around the vehicle. Further, the present invention has an advantage that object recognition may be effectively achieved by the insufficient computing resources of vehicles.

The invention provides a safe driving method for a chemical goods freight vehicle, which comprises the following steps: judging whether a dynamic motor vehicle, a non-motor vehicle and a person enter a dangerous area or not when the vehicle reaches a turning area to turn by predicting the direction of a moving object, and marking the predicted dangerous vehicle and person. Therefore, the influence of a driving blind area on safe driving of a driver is reduced.

The invention provides a safe driving assistance method and system based on the Internet of Vehicles, and relates to the technical field of traffic, and the method comprises the steps: obtaining positioning information in a vehicle driving process; the reliability of the positioning information is judged, and if the reliability of the positioning information is lower than a preset threshold value, a multi-source information fusion mode is adopted to obtain a high-precision positioning result of the vehicle; according to the fused high-precision positioning result, whether the vehicle is located in a sensitive area is judged, and if yes, the vehicle is located in the sensitive area. According to the method, vehicle-mounted sensor data are fused, abnormal values are removed through median filtering, a comprehensive positioning result is obtained through a weighted fusion algorithm based on Kalman filtering, and meanwhile, a differential privacy protection mechanism is introduced, so that the positioning information security of a sensitive area is ensured. Besides, fault detection and automatic repair are carried out by adopting a distributed computing architecture and a deep learning model, resource allocation can be dynamically adjusted according to a system state, and real-time processing of positioning information is guaranteed.

The invention discloses a safety system and method for preventing drunk driving of a vehicle, the vehicle and a medium, and relates to the technical field of safe driving. The safety system comprises a vehicle-mounted terminal and a cloud server which are in communication connection, the vehicle-mounted terminal comprises a detection module, a collection module, a processing module and a control module, and the processing module is used for verifying the identity of a driver through face data and voice data continuously collected by the collection module and sending the identity of the driver to the cloud server after verification is passed. The behavior state of a driver is determined according to voltage data continuously output by the comprehensive detection module, if the behavior state is drunk driving, a first control instruction is generated, early warning information is uploaded to the cloud server, and the control module is used for intelligently controlling the vehicle through the first control instruction or a second control instruction. Wherein the second control instruction is obtained by analyzing the early warning information through the cloud server to obtain the vehicle operation data and combining the traffic environment around the vehicle and the vehicle operation data until the vehicle stably stops, and safety is ensured.

The invention relates to the field of intelligent driving, and particularly discloses a low-risk decision generation system and method in automatic driving based on causal inference, and the method comprises the steps: S1, data collection: employing a multi-sensor fusion system carried on an automatic driving vehicle, visual image data including a temporary road construction area, position and driving state data of a vehicle and driving data of surrounding vehicles are collected, and meanwhile environment information during data collection is recorded. S2, a content identification step: processing the collected visual image data, identifying a temporary indication board and a normal indication board by using a deep learning target detection algorithm and an OCR character identification technology, and respectively marking positions, characters and graphic information of the temporary indication board and the normal indication board; s3, a causal inference and scene understanding step; and S4, a path planning step. By adopting the technical scheme of the invention, the information in the temporary indication board can be accurately understood, and a safe driving path can be re-planned.

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.