2890 results about "Vehicle safety" patented technology

The invention discloses a mountainous area icy road vehicle driving speed early warning method based on physical information constraint, and relates to the technical field of intelligent traffic and vehicle safety, and the method comprises the steps: carrying out the multi-modal data collection and preprocessing, constructing a multi-modal feature extraction network to extract a high-dimensional vector, constructing a fusion module based on an attention mechanism to output a fusion feature vector, and carrying out the early warning of the driving speed of a mountainous area icy road. The method has the advantages that data such as environment, vehicle dynamics and road surface friction coefficients are collected through the multi-source sensor fusion technology, a multi-modal feature extraction and fusion network is constructed, and the real-time classification early warning is realized through the multi-modal feature extraction and fusion network; the method comprises environment, vehicle dynamic and road friction feature extraction sub-networks, deep extraction of different modal data features, a fusion network based on an attention mechanism, concerning of association, coupling and cooperative influence among data, and dynamic weighting of fusion features, so that the purposes of accurately sensing a complex environment, reducing false alarm and missing alarm and effectively improving early warning accuracy are achieved.

The invention relates to the technical field of vehicle safety, and discloses a braking method and system based on vehicle automatic emergency early warning and braking and a storage medium. The method comprises the following steps: fusing multi-modal sensor data to generate a dynamic environment perception graph and calibrating the dynamic environment perception graph; generating a real-time risk distribution map and a potential collision probability prediction matrix based on the dynamic risk assessment model; carrying out matrix multi-stage decision optimization to generate emergency braking trigger conditions, and dividing early warning levels and scheduling according to the conditions and a risk map; and the braking system control parameters are adapted according to the early warning instruction set, and a final execution scheme is generated after verification. The system comprises a sensing module, an evaluation module, a decision module, an early warning module and an execution module. Electronic equipment and a computer readable storage medium can execute the method. Through multi-modal fusion, dynamic risk assessment, graded early warning and redundancy check, the safety performance of the vehicle in a complex environment is improved, accurate early warning and reliable braking are achieved, and the method is suitable for the technical field of vehicle safety.

The invention relates to the technical field of data processing, and discloses a motor vehicle safety performance classification method and system based on multi-factor coupling influence. The method comprises the steps of collecting multidimensional safety data of a motor vehicle, calculating a mutual information value and transfer entropy to generate a coupling relation network diagram, constructing a three-layer safety index system to form a comprehensive score, obtaining low-dimensional features through phase-space mapping, determining a dynamic boundary value by applying density clustering, and classifying real-time states to output risk early warning information. According to the technical method for realizing accurate classification and early warning, the defects of neglect of factor coupling relation, lack of scene adaptability, insufficient dynamic prediction capability and the like in the prior art are overcome, and the accuracy and practicability of safety performance evaluation and early warning of the motor vehicle are improved.

The invention discloses a quality detection method and system for vehicle safety belt production and a storage medium, and relates to the field of manufacturing quality control of automobile passive safety parts. The method aims at solving the problems that an existing detection means is weak in tiny defect recognition capacity, insufficient in defect evolution modeling and lack of intelligent feedback regulation and control. According to the method, multiple detection means such as medium-wave infrared thermal imaging, laser confocal microscopy, structured light three-dimensional reconstruction and capacitance sensing are fused, and multi-mode defect sensing of key parts such as a braid, an insertion area and an adhesive layer is achieved; deducing a defect evolution process and generating a high-credibility pseudo label by constructing a thermal-stress-rheology coupling finite element model; the graph neural network and the AI model are combined to complete defect type identification, risk quantification and grading, and automatic optimization of process parameters and production line control response are realized based on the quality grade, so that the detection precision, the response efficiency and the product yield are remarkably improved.

The invention relates to the technical field of electric vehicle control, and discloses a stability control method and system for an electric vehicle, and the method comprises the steps: collecting vehicle driving data in real time, and dynamically estimating the state parameters of the vehicle through an unscented Kalman filtering algorithm; according to the state parameters of the vehicle, combined with the kinematic model and the foresight trajectory, the expected yawing moment in a short time in the future is calculated, and the longitudinal traction requirement of the vehicle is obtained; and according to the state parameters of the vehicle and the longitudinal traction requirement of the vehicle, the yawing moment and the traction force are decoupled and distributed to the four electric driving wheel ends, and stable control over the electric vehicle is achieved. Compared with a traditional stable control system only based on closed-loop feedback, the stable control system has the advantages that the control response is faster, the yawing intervention is more accurate, and better control performance and vehicle safety are shown under the low-adhesion road surface and high-load working conditions.

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 discloses a light rail vehicle safety monitoring system based on multi-sensor fusion, and relates to the technical field of light rail vehicle safety monitoring, the system is composed of a plurality of functional modules, and the system comprises a multi-mode sensing module for capturing images of the surrounding environment of a light rail in real time and delimiting a visual mark area; performing three-dimensional scanning on the visual marking area, generating environment point cloud data in real time, and acquiring a dynamic object distance by using a Doppler compensation algorithm; the data processing module is used for calculating object deformation between adjacent frames by adopting an optical flow method and a CNN deformation detection network; carrying out object relative motion calculation according to object shape analysis; carrying out instance segmentation based on MaskR-CNN, and outputting an object contour; meanwhile, a dynamic target clustering algorithm is used for conducting multi-target tracking on the object contour, and three-dimensional coordinates and motion vectors of the object are output in combination with environment point cloud data; and the fusion and analysis module performs time synchronization based on the PTP protocol.

An impairment analysis (“IA”) computer system for detecting an impairment is provided. The IA computer system is associated with a host vehicle, and includes at least one processor in communication with at least one memory device. The at least one processor is programmed to: (i) interrogate or otherwise scan a target vehicle by using a plurality of sensors included on a host vehicle to scan the target vehicle and a target driver; (ii) receive sensor data including target driver data and target vehicle condition data; (iii) analyze the sensor data by applying a baseline model to the sensor data; (iv) detect an impairment of the target driver or target vehicle based upon the analysis; and / or (v) output an alert signal to a host vehicle controller, or direct collision preventing actions (such as automatically engage vehicle safety systems), based upon the determination that the target driver or target vehicle is impaired.

Disclosed herein is a device for filtering a point cloud. The device may include processor configured to receive a plurality of sensed points representing distance measurements to points in an area around an entity. The processor may also be configured to determine, for each sensed point of the plurality of sensed points, one or more probabilities that the sensed point is associated with an obstacle in the area around the entity, wherein each probability of the one or more probabilities is based on a corresponding filter of one or more filters. The processor may also be configured to generate a filtered point cloud from the plurality of sensed points based on, for each sensed point, a hierarchical combination of the one or more probabilities.

The invention provides a vehicle door anti-collision control method, electronic equipment and a vehicle, and relates to the technical field of intelligent vehicle safety, and the vehicle door anti-collision control method comprises the steps that vehicle door motion parameters and obstacle motion parameters are obtained; determining a predicted track gap distance between the vehicle door and the obstacle based on the obtained vehicle door motion parameter and the obstacle motion parameter; based on the predicted track gap distance between the vehicle door and the obstacle, the current collision risk level is judged, and an anti-collision strategy corresponding to the collision risk level is executed; wherein the anti-collision strategy is a strategy for intervening opening of the vehicle door based on the collision risk level so as to reduce the door opening collision risk. According to the method, motion parameters of a vehicle door and an obstacle are fused, a track gap is predicted, risks are evaluated, and graded protection such as prompting, speed limiting and angle limiting is implemented in combination with multi-stage threshold values; meanwhile, the intervention mode is dynamically adjusted, safety and user experience are both considered, and the door opening collision risk is effectively reduced.

The invention provides a vehicle safety early warning method and system for a highway network, relates to the field of traffic safety management and control, and solves the technical problem of inaccurate early warning in the prior art. The method comprises the following steps: acquiring environment data of a highway; dividing the expressway into a plurality of areas, and calculating an initial risk value of each area according to the environment data; calculating a risk propagation value of each region based on a dynamic risk propagation equation according to the initial risk value, and determining a risk level of each region according to the risk propagation value; and triggering early warning measures of each area according to the risk level. The method is used in the real-time safety management and control process of the highway network in severe weather, accurate prediction and graded early warning of risks can be realized through multi-dimensional data fusion and dynamic risk propagation modeling, and the safety and traffic efficiency of the highway network under complex meteorological conditions are improved.

A vehicle safety system of an autonomous vehicle may determine predicted velocity vectors for a potential collision, and use the velocity vectors to determine a trajectory for the autonomous vehicle to traverse the environment. The vehicle safety system may analyze sensor data to determine a likelihood of a potential collision with a dynamic object in the environment. Predicted velocity vectors may be determined for the autonomous vehicle and the dynamic object at a time and / or location associated with the potential collision. The predicted velocity vectors may be used to determine a point of impact, relative angle, and / or relative velocity between the autonomous vehicle and dynamic object at the potential collision. The likelihood of the potential collision and the predicted velocity vectors may be used to determine a trajectory for the autonomous vehicle, which may include following a current trajectory or transitioning to one or more contingent trajectories.

The invention discloses a traffic safety early warning method and device for a dangerous cargo transport vehicle, and the method comprises the steps: S1, monitoring the operation state of the dangerous cargo transport vehicle in real time, and collecting environment data in real time; and S2, based on the collected data, establishing a risk assessment model by using a convolutional neural network algorithm, analyzing the influence of extreme weather and complex terrains on the safety of the dangerous cargo transport vehicle, developing a risk assessment algorithm, and according to a risk assessment result, the risk assessment model established by using the convolutional neural network algorithm in the invention can be used for evaluating the safety of the dangerous cargo transport vehicle. And specific influences of extreme weather and complex terrains on vehicle safety can be deeply analyzed. The risk assessment model can learn various risk factors in a complex environment and comprehensively assess the risk factors, so that the accuracy and reliability of risk assessment are improved. The system can continuously monitor the change condition of the risk, timely adjust the early warning strategy according to the actual condition, and continuously improve the adaptive capacity and the early warning effect.

The invention discloses a reinforcement learning automatic driving decision control method based on a Lyapunov function, and relates to the technical field of automatic driving automobile safety control. The method comprises the steps that driving state information of a self-vehicle and driving state information of surrounding environment vehicles are received, the driving state information of the self-vehicle comprises the position, the speed, the acceleration and the course angle, and the driving state information of the surrounding environment vehicles comprises the position, the speed, the course angle and lane line information of the environment vehicles relative to the self-vehicle; and a decision control model of the automatic driving task based on reinforcement learning is constructed, and the decision control model takes the driving state information of the vehicle and the surrounding environment vehicles as output and takes the optimal automatic driving strategy as output. According to the method, the reinforcement learning strategy updating process is constrained through the stability condition proved by theories, the unification of safety and decision-making efficiency is realized in dynamic scenes such as highway lane changing and emergency obstacle avoidance, and the method has a good application prospect for training the safety and strategy stability of automatic driving by adopting reinforcement learning.

The invention belongs to the technical field of vehicle-mounted disaster prevention and early warning, and discloses a vehicle-mounted multistage disaster prevention and early warning method and system based on Beidou communication, and the method comprises the steps: obtaining vehicle position information and prediction path information, and determining a vehicle driving region; acquiring a detailed geographic information system data packet from a data service platform according to the vehicle driving area; according to the detailed geographic information system data packet, the current vehicle speed and the vehicle position information, the probability of occurrence of different types of natural disasters in the vehicle driving area and the degree of influence on vehicle safety are calculated, and a risk assessment result is obtained; according to a risk assessment result, carrying out risk grade division on the natural disaster risk; determining an early warning information presentation mode according to the risk level, the disaster type and the driver state, and generating and presenting early warning information according to the corresponding presentation mode; therefore, more accurate and more targeted natural disaster early warning can be provided, and the safety of vehicle driving is improved.

The invention discloses an engineering vehicle safety simulation and prediction system based on digital twinning, and the system comprises a data collection layer which collects multi-source heterogeneous data in real time; in the knowledge graph layer, a streaming inference engine is constructed based on an Apache Jena graph database, and an entity-relationship-attribute triple dynamic graph structure is adopted; according to the AI model layer, a physical rule serves as a loss function constraint term to be embedded into a neural network through a physical information neural network, a digital organ model concept is combined to split a vehicle into key organs for heterogeneous modeling, a simplified physical model is adopted in the core physical process, and an LSTM-AI model is adopted in external behaviors; the explanatory analysis layer is used for integrating an SHAP / LIME explanatory tool to output a visual evidence chain during fault prediction, and deploying an online incremental learning framework to allow the model to learn from new data and dynamically adjust normal range definition; and the visualization and application layer is used for performing three-dimensional visualization rendering based on WebGL or Three.js, and ensuring data transmission security through block chain evidence storage and end-to-end encryption.

A safety system for improving road compatibility of a vehicle includes a monitoring system and an integrated safety domain control unit. The monitoring system includes a vehicle external information monitoring module and a vehicle body posture monitoring module. The integrated safety domain control unit is configured to compute a collision condition between the vehicle and an obstacle based on data acquired by the vehicle external information monitoring module and the vehicle body posture monitoring module, the collision condition including a collision relative speed and a collision overlap rate, and determine, based on the collision relative speed and the collision overlap rate, whether to deploy an external airbag. A determination condition for controlling triggering of deployment of the external airbag includes determining whether the collision relative speed is less than a first speed threshold and / or whether the collision overlap rate is less than a first overlap rate threshold. The present disclosure further relates to a safety apparatus and method for improving road compatibility of a vehicle, and a medium, as well as a vehicle safety system, a vehicle safety apparatus, a method for enhancing safety of a vehicle, and a readable storage medium.

The invention provides a wire harness crimping quality intelligent detection and self-adaptive control method and system, and the method comprises the steps: calculating a stress distribution value of a crimping point through the obtained internal metal flow distribution characteristics in combination with a mechanical strength simulation algorithm, and obtaining a mechanical strength evaluation index; if the mechanical strength evaluation index is lower than a preset threshold value, triggering an alarm signal, extracting deviation type data from the alarm signal, and judging a quality judgment result; generating a feedback control instruction according to the judged quality judgment result, transmitting the feedback control instruction to crimping equipment to adjust crimping force parameters, and obtaining optimized crimping process data; updating a real-time monitoring database by adopting the obtained optimized crimping process data, and determining a geometric parameter reference value of a subsequent crimping batch; and matching the determined geometric parameter reference value with a vehicle safety requirement to obtain a final quality verification report.

A custom application system for safe vehicle operation is described. In one or more implementations, a single electronic control unit (ECU) of a vehicle controls safe operation of the vehicle and also executes applications, e.g., third party applications, within a partition that isolates execution of the applications from components responsible for the vehicle's safe operation. In accordance with the described techniques, the ECU includes a first processor to execute the applications and a second processor to maintain an operating state of the vehicle within a safe operating envelope of the vehicle. The second processor receives requests from the applications executed by the first processor and arbitrates them based on satisfying safety rules for the vehicle.

The invention provides an unmanned aerial vehicle route planning operation safety monitoring system and method based on virtual-real fusion, belongs to the technical field of unmanned aerial vehicle safety monitoring, and aims to solve the problems that the test training difficulty of an unmanned aerial vehicle is increased and the experiment cost of the unmanned aerial vehicle is high due to interference and terrain transformation in the task execution process of the unmanned aerial vehicle. The safety monitoring system comprises a ground distributed semi-physical virtual-real fusion simulation platform and an air experiment module. The ground distributed semi-physical virtual-real fusion simulation platform is used for constructing a virtual test scene of task execution of the unmanned aerial vehicle, optimizing a waypoint planning decision according to feedback information in a task execution process, and performing safety judgment and real-time obstacle avoidance on the current waypoint planning decision so as to optimize the waypoint planning decision; and the space-time experiment module is used for monitoring the flight state of the unmanned aerial vehicle and executing the optimized waypoint planning decision.

The invention relates to the technical field of driver monitoring, in particular to a dangerous driving behavior detection method based on heterogeneous federal ensemble learning. The client extracts a feature vector of the driving behavior data, and maps the feature vector into a low-dimensional category prototype by using a prototype adapter network; a local dynamic differential privacy technology is adopted to carry out noise injection encryption processing on parameters of the prototype adapter and then upload the parameters to a server; the server performs cross-client prototype alignment and aggregation to generate a global prototype; the client downloads global prototype parameters and updates a local model, incremental learning is carried out based on a dynamic prototype library, and when a novel dangerous driving behavior is detected, a newly-added prototype and a historical prototype are separately stored; and the client performs similarity matching with the global prototype according to the driving behavior characteristics acquired in real time to obtain a detection result. According to the method, an extensible and high-reliability federal learning framework is provided for landing of an intelligent automobile safety system from three dimensions of heterogeneous compatibility, privacy-efficiency balance and personalized dynamic updating.

The invention belongs to the technical field of industrial vehicle active safety control, and particularly discloses an industrial vehicle safety anti-collision early warning method and device, and the method comprises the steps: building a unified space-time perception reference through fusing ultrasonic wave, inertial navigation, laser radar and camera data; extended Kalman filtering is fused with inertial navigation prediction and laser radar poses, and high-precision positioning in the tunnel is achieved. Constructing a high-dimensional time sequence feature vector, and predicting a future collision risk in combination with a Bi-LSTM model; the early warning threshold value is dynamically adjusted according to the load, the gradient and the sensor state; the method has the following advantages that the early warning accuracy and response speed under the complex working condition are remarkably improved, and the operation safety of the industrial vehicle is guaranteed.

The invention relates to the technical field of industrial vehicle safety control, in particular to a forklift safety online industrial monitoring management system which comprises a data fusion unit used for collecting kinematics data, position and attitude information and load distribution data of a vehicle and conducting space-time alignment processing on the collected data to generate a real-time state vector; the margin calculation unit is used for calculating a plurality of independent safety margin components and carrying out fusion processing based on the safety margin components to generate a comprehensive safety margin; the risk prediction unit is used for predicting a future safety margin value based on the time sequence of the comprehensive safety margin, and comparing and analyzing the future safety margin value with a preset safety threshold and a preset warning threshold to generate a risk level; the intervention control unit is used for generating and executing a corresponding hierarchical intervention strategy in response to the risk level; according to the invention, a complete technical link from multi-source data sensing to closed-loop control is constructed, and the conversion from passive safety to active safety is realized.

The invention provides a vehicle control method and a vehicle, the method is applied to the technical field of vehicle safety, and the method comprises the following steps: obtaining a planned driving track of a target vehicle; on the basis of the planned driving track and the planned driving track of the current vehicle, under the condition that it is determined that the target vehicle will collide with the current vehicle, the collision time of the target vehicle and the current vehicle is calculated, and the minimum lateral collision avoidance distance for collision avoidance is calculated; and matching a protection action of the current vehicle according to the collision time and the minimum lateral collision avoidance distance, and executing the protection action before collision with the target vehicle. The method solves the problems that an existing collision early warning system cannot dynamically adjust the vehicle posture in a short time, a traditional chassis system cannot cooperate with a steering and suspension system to disperse impact energy before collision, the height of a vehicle is fixed, the living space of a passenger compartment is compressed, collision avoidance is difficult to achieve, and the safety is high. And the living space of the passengers cannot be optimized during unavoidable collision.

The invention relates to the technical field of automatic driving trajectory planning control, in particular to an optimal inter-vehicle time interval control method for cooperative cruise of a truck under a communication delay condition. According to the method, a layered control framework of truck cooperative cruise control is designed, and a vehicle safety inter-vehicle time interval model, a communication delay model and a queue collision safety model are constructed; the inter-vehicle time interval, the communication delay coefficient and the safety coefficient are quantified into space particles, optimization is carried out based on a particle swarm algorithm, and the optimal inter-vehicle time interval on the basis of guaranteeing the queue safety and the fuel economy is obtained; designing a longitudinal model predictive control (MPC) expected acceleration decision controller of truck cooperative cruise by considering multi-target constraints of safety and fuel economy in a vehicle following process to obtain an expected acceleration; and a load change-considered feedforward and active-disturbance-rejection control brake driving error compensation model is established, so that the anti-jamming capability of the model is enhanced, and the actual acceleration of the queue truck accurately follows the expected acceleration under the load change condition.

The invention relates to the technical field of unmanned vehicle scheduling, in particular to a park networking unmanned vehicle dynamic scheduling planning method and system, and the method comprises the steps: collecting dynamic obstacle data through a multi-source sensor and V2X equipment, generating a four-dimensional trafficability map, formulating an avoidance strategy weight, optimizing a multi-vehicle track, and carrying out the optimization of a multi-vehicle track. And evaluating a threat value to trigger the reconfiguration of the resident area by combining the edge node and the cloud adjusting path. The system comprises a sensing module, a decision-making module, a storage module, an allocation module, a regulation module and a verification module, and an exception handling module is linked with a threat value change rate to optimize resource allocation. According to the method, the avoidance strategy can be dynamically adjusted according to the threat value, the scheduling accuracy and the resource allocation accuracy are improved, the vehicle safety is guaranteed, and the task failure rate is reduced.

The invention relates to the technical field of vehicle safety, in particular to a vehicle autonomous emergency braking-oriented road surface attachment sensing method, which comprises the following steps of: acquiring all initial point cloud data in a preset sensing area in front of a vehicle by using a laser radar at the top of the vehicle, and segmenting the initial point cloud data to obtain a road surface point cloud data set in the preset sensing area; acquiring a reflected light intensity value of each piece of road surface point cloud data in a preset sensing area, and taking the road surface point cloud data of which the reflection intensity value is not in a high reflection intensity threshold range but is in a road surface reflection intensity threshold range as target point cloud data; based on the reflection intensity values of all the target point cloud data, generating a grayscale image, inputting the grayscale image into a classification model, outputting a pavement type prediction result, determining a pavement adhesion coefficient, and calculating brake pressure; and if the distance between the current vehicle and the front vehicle is smaller than the safe distance, the vehicle performs autonomous emergency braking based on the braking pressure. The safety and comfort of emergency braking are improved.

The invention relates to the field of transportation information, and discloses a ferry intelligent drop and pull dispatching and vehicle safety monitoring integrated device and method. The equipment integrates a multi-modal image acquisition unit, an edge computing server and the like, identifies the state of a vehicle through deep learning, generates a drop and pull scheme by using a multi-target optimization algorithm, and guarantees safety in combination with real-time trajectory tracking and an intelligent early warning mechanism. According to the invention, the drop-and-pull time of a single ferry can be shortened, the accident rate is reduced, the scheduling efficiency is improved, the throughput is increased, the problems of low efficiency and insufficient safety monitoring of traditional manual scheduling are solved, and the intelligence and safety of ferry transportation are improved.

The invention provides a vehicle safety risk threshold identification method for a heavy-duty truck, relates to the technical field of vehicle safety, and solves the problem of limited identification precision of a vehicle risk threshold in the prior art. According to the technical scheme, dynamic response data of a truck and environment variables under various external environments and different road conditions are obtained through a multi-source data acquisition system, and real-time key risk indexes of the heavy-duty truck are calculated; preprocessing the real-time key risk indexes to obtain cleaned real-time data; determining candidate thresholds corresponding to the real-time data through a quantile statistical method; the candidate threshold is fed back to the vehicle dynamics model for forward verification, the dynamic response of the vehicle under the threshold boundary condition is simulated until the simulation result passes forward simulation verification, and the candidate threshold is written into a dynamic threshold library. According to the technical scheme, accurate early warning can be achieved, and the accident rate is reduced.

The invention provides a driving route recognition system and method, electronic equipment and a readable medium, and belongs to the field of intelligent driving. According to the processed original image and preset navigation information, navigation and road condition information guidance are provided for a driver or intelligent driving is provided for the driver, and according to the intelligent driving technology provided by the patent, an intelligent driving auxiliary system with the core target of solving driver navigation confusion under complex road conditions is constructed. Through deep fusion of high-precision visual perception and map navigation information, strong road identification judgment logic is utilized, when a driver actively requests or a system pre-judges needs, an intelligent driving module provides accurate vehicle control assistance, and clear navigation prompts are combined to jointly ensure that a vehicle safely and correctly passes through a complex road scene, so that the driving safety of the vehicle is improved. And the driving convenience and safety are obviously improved. Voice input is a key link of man-machine cooperation.