141 results about "Intelligent vehicle technologies" patented technology

The invention discloses an intelligent screen control system based on a large scenarized model, which belongs to the technical field of intelligent vehicle-mounted devices, and comprises a semantic understanding agent used for identifying user requirements according to user text contents and generating operation requirements; the scenarized interface analysis agent is used for performing analysis according to the screenshot image, identifying an interface menu and interface elements and generating a scenarized operation list; the matching agent is used for matching the operation demand with the scenarized operation list, generating an operation instruction and performing interaction determination on the operation instruction and a user; the simulation interaction agent is used for simulating the operation of a user on a vehicle machine screen according to the operation instruction and converting the simulation operation into an operation sequence which can be executed by a vehicle machine operation system; the multi-mode execution agent is used for sending the operation sequence to the vehicle machine; according to the system, through cooperative work of the multi-agent module, accurate recognition and response to user requirements are achieved, and corresponding vehicle-machine interaction operation is generated.

The invention discloses a scene understanding and intelligent decision-making method for a planet probe vehicle in a complex unknown environment, and relates to the technical field of deep space exploration and intelligent vehicles, and the method comprises the steps: obtaining multi-dimensional environment information; constructing a slump function based on the geometric morphology, motion characteristics and material attributes of the obstacle, and dynamically quantifying the risk of the obstacle; smoothing the potential field function by adopting Gaussian filtering, and dynamically adjusting the weight of a global potential field and the weight of a local potential field; in combination with an improved clustering algorithm, fuzzy regions of obstacles capable of being crossed and obstacles not capable of being crossed are accurately divided; screening passable areas by constructing a credibility mean square value function; carrying out multi-objective optimization by adopting a genetic algorithm, and generating an optimal path matched with the environment in cooperation with an offline data set and online reasoning; and inputting the optimal path into a bottom layer control system of the probe vehicle, controlling execution of a motor driving and steering mechanism, monitoring the state of the probe vehicle in real time, and triggering dynamic re-planning. And the autonomous obstacle avoidance capability and the path planning efficiency of the probe vehicle in a complex environment are improved.

The invention provides a warning method and system of an intelligent tripod warning lamp, and relates to the technical field of intelligent vehicle-mounted technologies. When a vehicle is in danger, vehicle state information is collected to automatically generate a warning trigger instruction, and a rear target projection point is determined based on a road type and a vehicle position; projection parameters are calculated in combination with vehicle postures, a vehicle-mounted projection light source is driven to generate a triangular warning graph on the ground at the target distance, meanwhile, light beam output is adjusted in a self-adaptive mode according to environment illumination and visibility, and it is guaranteed that the graph is clear and visible and conforms to human eye safety; the projection position and parameters are dynamically updated to maintain stable warning during the duration of the dangerous state, and the system is automatically closed and reset when the danger is relieved or manual termination is performed, so that the risk of manual arrangement is avoided, and long-distance stable warning is realized.

The invention discloses a vehicle, a vehicle control method and an intelligent driving control unit, relates to the technical field of intelligent vehicles, and solves the problems of complex link topology, long data transmission link and high time delay of data transmission of an intelligent headlamp in a current vehicle. The vehicle comprises at least one sensor, an intelligent driving control unit and a driving projection device. The output end of the at least one sensor is coupled with the input end of the intelligent driving control unit, and the first output end of the intelligent driving control unit is coupled with the first input end of the driving projection device. The at least one sensor generates external environment data according to the external environment of the vehicle and sends the external environment data to the intelligent driving control unit, and the intelligent driving control unit generates projection image data according to the external environment data and sends the projection image data to the driving projection device. And driving the projection device to output a projection image according to the projection image data.

The invention belongs to the technical field of intelligent vehicles, and discloses an autonomous mobile robot multi-target tracking method and system based on a dynamic adaptive motion model. Detecting object information in the environment, and obtaining detection frame data of each frame of laser radar and camera; laser and camera data are projected to the ground to calculate the Euclidean distance so as to realize multi-sensor fusion and different frame data association operation. A corresponding adaptive state transition matrix is set for a previous frame trajectory, and a prediction bounding box and a covariance matrix of the previous frame trajectory in a current frame are calculated by using a Kalman filtering technology. The long-term and short-term memory module stores historical information of a management target and dynamically adjusts a tracking strategy and parameters. According to the invention, the tracking effect of the multi-target tracking algorithm on the autonomous mobile robot is successfully improved, powerful technical support and guarantee are provided for the intelligent robot to realize accurate multi-target tracking and efficient operation in a complex environment, and further development of the intelligent robot technology in practical application is powerfully promoted.

The invention relates to the technical field of intelligent automobiles, in particular to an intelligent cabin multi-AI Agent cross-domain collaborative super-brain system and an interaction method, and the system comprises a sensor cluster, a central decision AI Agent, a five-domain AI Agent and a cross-domain synchronization mechanism. The sensor cluster is used for collecting multi-dimensional data and transmitting the multi-dimensional data to the central decision AI Agent; the central decision AI Agent is used for processing the data, including analyzing fused data to establish a mapping relation corresponding to scenes and services, performing cross-domain priority arbitration and realizing inter-domain data synchronous scheduling; the five-domain AI Agent comprises a cabin domain AI Agent, a power domain AI Agent, a vehicle body domain AI Agent, a chassis domain AI Agent and an automatic driving domain AI Agent and is used for executing specific functions. According to the invention, the problem of function islands of a traditional intelligent cabin is effectively solved, deep coupling and dynamic linkage of cross-domain functions are realized, the function utilization rate is increased from less than 20% in the prior art to more than 78%, and the utilization rate of system resources is remarkably improved.

The invention discloses a vehicle-mounted emotion feedback method, device and equipment based on quantum emotion calculation and a storage medium, and relates to the technical field of intelligent automobiles, and the vehicle-mounted emotion feedback method based on quantum emotion calculation comprises the steps: obtaining user physiological information and vehicle environment information; obtaining various physiological information index values according to the user physiological information, and processing the various physiological information index values through a preset quantum emotion calculation model to obtain emotion state information; and adjusting the scene and environment in the vehicle according to the emotional state information and the vehicle environment information. The emotional state of the user is judged according to the physiological information of the user and the environment information of the vehicle, so that the scene and the environment in the vehicle are adjusted according to different emotional states, emotional feedback is completed for the user, and the emotional experience of the user is improved.

The invention discloses a multi-view perception method for 3D point position coding and memory queue time sequence modeling based on depth priori, and belongs to the technical field of intelligent vehicles. According to the method, continuous multi-frame multi-view images of a nuScenes data set and camera parameters are used as input, the whole process comprises the steps that an image backbone network, a depth prediction module, a 3D position coding module and a 3D Transform decoder cooperatively operate, and perspective view features are generated firstly; converting the 3D point coordinates into 3D point coordinates with depth priori, converting the 3D point coordinates into embedded features, fusing the embedded features with image features, and inputting the fused features into a lightweight 3D Transform decoder; and finally outputting target classification and 3D bounding box parameters. According to the method, the consistency and expression ability of position features are effectively enhanced, efficient time sequence modeling is realized, the detection precision is improved, dense feature calculation overhead is avoided, the model convergence speed is increased, and the long-distance small target detection performance is improved.

The present invention relates to the field of intelligent vehicle technology, and provides a vehicle terminal access method, device and system, the method comprising: receiving an access request corresponding to a target client, allocating a corresponding dynamic port for the current access request; sending the dynamic port to the target client, and instructing the target client to establish a first data channel between the target client and a reverse proxy server; sending the dynamic port to a target vehicle terminal through a target control channel, and instructing the target vehicle terminal to establish a second data channel between the reverse proxy server and the target vehicle terminal; based on the first data channel, receiving an external network request sent by the target client; based on the second data channel, sending the external network request to a target service associated with the external network request in the target vehicle terminal. The present invention can improve the security of communication and ensure the stability of system operation.

The invention discloses an automobile cabin intelligent control method and system based on image acquisition, which is used in the technical field of intelligent automobiles, and comprises the following steps: acquiring original image data in a cabin, and preprocessing the original image data based on a PID control algorithm to obtain standard image data; using a multi-task loss function to train an SSD model, and analyzing the standard image data to obtain a target detection result; constructing a decision model based on the target detection result and the environmental data, and optimizing the decision model in combination with user preference to obtain a reinforcement learning decision model; and obtaining a real-time decision result according to the real-time standard image data in combination with the reinforcement learning decision model and the safety guarantee rule, and adjusting the cabin according to the real-time decision result. According to the method, the state in the cabin and passenger behaviors can be accurately recognized, more accurate environment adjustment and personalized setting are provided, and the user experience is improved by combining the reinforcement learning model, the adaptive changing environment and user habits.

The invention discloses an evaluation method and device for a vehicle-mounted control unit and a vehicle, and relates to the technical field of automatic testing technologies and intelligent vehicles, and the evaluation method for the vehicle-mounted control unit comprises the steps: obtaining vehicle use scene data corresponding to the vehicle-mounted control unit, the vehicle using scene data is used for representing scene working condition characteristics of the vehicle-mounted control unit in a whole vehicle using process, and the whole vehicle using process comprises a travel preparation stage, a travel advancing stage and a travel ending stage; driving a vehicle-mounted control unit to execute control operation by using the vehicle use scene data to obtain test data; and performing function evaluation on the test data to generate an evaluation result which is used for representing whether the function performance of the vehicle-mounted control unit meets the release standard or not. According to the method and the device, the technical problem of poor accuracy of an evaluation result caused by a method for evaluating the vehicle-mounted control unit based on a modular function case in related technologies is solved.

The invention provides a driving assistance method and device and a vehicle, and is applied to the technical field of automatic driving technologies and intelligent vehicles. The driving assistance method comprises two main stages, in the first stage, a first accelerator mistaken stepping evaluation result is obtained at least according to first accelerator stepping information of the vehicle, in the second stage, second accelerator stepping information is obtained, and the vehicle is emergently braked in combination with the first accelerator mistaken stepping evaluation result and the accelerator stepping information obtained again. Through the judgment of the two stages, the accuracy of mistaken stepping judgment can be improved, the vehicle can be braked in time under the condition that mistaken stepping is confirmed, and the probability of traffic accidents caused by mistaken stepping of an accelerator is reduced. And when the driver normally accelerates urgently, even if mistaken stepping is preliminarily judged, through secondary confirmation of mistaken stepping, the misjudgment rate can be reduced, the driving freedom degree of the driver is prevented from being affected, and the driving experience is improved.

The invention provides a vehicle trajectory prediction method and system, and relates to the technical field of intelligent vehicles, and the method comprises the steps: obtaining the current trajectory data of all vehicles in a target road section; performing driving intention classification on the current trajectory data of the target vehicle through an intention prediction model constructed based on LSTM to obtain intention features; according to the current trajectory data of all vehicles, through a feature aggregation mechanism of a graph convolutional neural network, feature information of the target vehicle and neighbor vehicles is aggregated, and spatial interaction features are extracted; and based on the intention features and the spatial interaction features, generating future trajectory coordinates of the target vehicle in the target road section by using Transform. According to the method, the prediction precision and robustness are improved, meanwhile, it is ensured that the generated trajectory conforms to the driving intention and the real traffic dynamics constraint, the high-accuracy and high-rationality vehicle trajectory prediction precision in different scenes is remarkably improved, and more reliable environment perception and decision support is provided for an automatic driving vehicle.

The invention discloses an SOA-based personalized scene service customization method, apparatus and device, and a storage medium, relates to the technical field of intelligent vehicles, and discloses an SOA-based personalized scene service customization method, which comprises the steps of obtaining a preset service storage table and a preset capability storage table, issuing a preset service set of a preset service storage table and a preset capability set of a preset capability storage table; querying a preset service set corresponding to the vehicle type according to the vehicle type of the vehicle, and obtaining a plurality of preset types of preset services according to the preset service set; querying a preset capability set corresponding to the preset service according to the preset services of different preset types; obtaining preset scene information, and combining the preset capability set according to the preset scene information to obtain a target preset capability set; and activating the personalized scene according to the target preset capability set and the scene activation instruction to obtain a personalized scene service. A personalized driving scene can be dynamically combined according to user requirements and vehicle type characteristics.

An interaction method and device, a terminal and a storage medium are applied to the technical field of interaction technologies and intelligent vehicles. The control object can be selected based on the voice information input by the user, and the state of the control object is adjusted based on the hand movement of the user. Wherein the voice can directly determine a control object, the gesture can enable a user to freely express quantitative meaning, interaction is jointly completed through combination of the voice and the gesture, and efficient interaction can be achieved. Moreover, the user does not need to get close to the operation equipment (such as a touch screen and a key) in voice and gesture operation, so that the user can complete direct selection and free adjustment of the operation object at a relatively low cost under a comfortable condition, and the use experience of the user is remarkably improved.

This application discloses a vehicle control method, system, electronic device, and storage medium, belonging to the field of intelligent vehicle technology. In this method, when the in-vehicle terminal recognizes a keyword for starting the vehicle based on voice data, and if a driver is present in the driver's seat, it sends the voice data and a video feed from the driver's seat to the server for further security verification. Once the server confirms that the voice data and the driver match the relevant information provided by the target user, it notifies the in-vehicle terminal to control the vehicle start. This method, while enabling voice-controlled vehicle start, ensures the security of vehicle starting and avoids situations where the user forgets their car key or the key malfunctions, thus improving the user experience.

The invention discloses a security situation awareness method based on an intelligent networked automobile, and relates to the technical field of new energy intelligent automobiles. S2, establishing a vehicle real-time communication link; s3, early warning communication of adjacent vehicles; and S4, protecting the driving operation. According to the security situation awareness method based on the intelligent networked automobile, a vehicle-mounted external sensor network is constructed through a Sudoku topology, the awareness range of a single vehicle is expanded to the whole domain covered by the Sudoku, and information interaction is performed between the vehicle in the Sudoku and the adjacent vehicle through a communication link. Therefore, the sensing range is greatly expanded, the sensing blind area is effectively eliminated, and the early warning information is transmitted through V2X, so that the vehicle in the middle lane can respond in time when the vehicle in the side lane encounters the ghost probe condition, and the vehicle in the middle lane does not need to respond when sensing the ghost probe condition. Therefore, the advanced response time is greatly prolonged according to the early warning information of the surrounding vehicles.

The invention discloses an unmanned vehicle obstacle sensing method and system for a complex cross-country environment, and belongs to the technical field of intelligent vehicles. The method comprises the following steps: acquiring terrain parameters and vehicle control parameters, and constructing a deep network model; obtaining maneuverability indexes of the vehicle based on the terrain parameters, the vehicle control parameters and a deep network model; the uncertainty and the risk of the maneuverability index are quantified; acquiring image data based on a binocular camera stereoscopic vision system, processing the image data, and performing non-standard obstacle identification and trafficability risk probability assessment based on the processed image data; and performing global path planning and local dynamic planning on the off-road vehicle. According to the method, the precision and adaptability of dynamic modeling can be remarkably improved, real-time identification and risk assessment of non-standard obstacles are effectively realized, the intelligence, reliability and real-time performance of a planning and decision-making mechanism are enhanced, and the viability and task completion rate of off-road vehicles in a complex non-structural environment are improved.

The application provides a calibration method and device of a vehicle-mounted surround view system, and relates to the technical field of intelligent vehicles. The method comprises the following steps: acquiring an image of a calibration object collected by a vehicle-mounted camera, the calibration object comprising multiple regions, at least one region comprising a pattern containing identification information, identifying the identification information of the pattern and the two-dimensional coordinates of the corner points of the pattern, and determining the calibration external parameters of the camera according to the identification information of the pattern and the two-dimensional coordinates of the corner points. Thus, the external parameter calibration of the camera is performed by identifying the corner points of the pattern of the calibration object, the distortion generated when the edge region of the image is identified is reduced, the requirement for ambient light and the arrangement difficulty of the calibration object are reduced, the identification rate of the corner points is improved, and the efficiency and accuracy of the calibration are improved.

The invention discloses a network attack detection system and a network attack detection method for a digital-aware-fused intelligent connected automobile, and relates to an intelligent vehicle technology. The system comprises a vehicle end data generation module, a road side unit data acquisition module, a cloud end attack detection module and a detection result display module. The method comprises the two steps of online real-time detection and offline training, during offline training, a training database containing multiple attack types is firstly constructed, then a data driving model is constructed based on a hierarchical vector Transformer model, and finally constraints in vehicles, between vehicles and between vehicles and lanes are fused, so that the data driving model is constructed. Weak, medium and strong constraints are divided according to the relevance between the constraints and the attacks, and a differential fusion scheme is designed. According to the method, deep fusion of data driving and knowledge guiding is realized, spatio-temporal features are extracted in a layered manner, high-precision map information is fused, attack detection precision and interpretability are improved, various network attacks can be effectively detected, and safe and efficient operation of an intelligent networked automobile is guaranteed.

This invention belongs to the field of intelligent vehicle technology, specifically a method for generating key scenarios for intelligent vehicles based on a personalized interactive car-following model. It includes the following steps: Step 1, selecting and preprocessing a natural driving dataset; Step 2, constructing a personalized interactive car-following model; Step 3, generating key scenarios; and Step 4, evaluating the key scenarios. This invention constructs a personalized car-following model with highly realistic interactive characteristics. Based on this model, through multi-weight allocation, it diversifies the interaction features between vehicles and expands the coverage of scenario parameters. This reduces the difficulty of scenario generation while achieving high coverage, high rationality, and high diversity in key scenario generation, solving the problems of low scenario coverage, poor scenario rationality, and limited scenario types in current methods when generating key scenarios based on natural driving data.

The embodiment of the present application provides a kind of single-layer planar dipole antenna, wherein the antenna is applied to intelligent vehicle technical field, wherein the single-layer planar dipole antenna includes: base material, planar dipole antenna, planar balun, differential double-wire transmission line and band ground coplanar waveguide;Wherein, planar dipole antenna, planar balun, differential double-wire transmission line and band ground coplanar waveguide are arranged on the same layer of base material surface;Band ground coplanar waveguide includes center signal conductor and left and right two side reference ground, two metal arms of planar balun are connected to left and right two side reference ground of band ground coplanar waveguide respectively, and the upper end of differential double-wire transmission line is respectively connected to two radiation arms of planar dipole antenna, while the lower end of differential double-wire transmission line two transmission lines are respectively connected to one of center signal conductor and left and right two side reference ground of band ground coplanar waveguide.The embodiment of the present application can realize the integrated molding of antenna by the structure of single surface arrangement antenna, and reduce the integration difficulty of antenna.

The present invention discloses a smart vehicle audit method and system based on big data edge computing equipment, which relates to the field of smart vehicle technology, including collecting environmental information and vehicle operation status data through sensors on smart vehicles, and preprocessing the data; based on the identified situational features, fog nodes collaborate with cloud databases to generate audit strategies for specific situations; sending the audit strategy instruction set back to the smart vehicle, the vehicle implements strategy adjustments and feeds back effect data; the fog node receives feedback data to evaluate strategy performance, optimizes the existing audit strategy based on the evaluation results, and sends the optimized strategy to the smart vehicle. The present invention optimizes the personalized strategy of smart vehicles by integrating edge computing and cloud databases, greatly improving environmental adaptability, driving safety and decision-making efficiency.

The invention discloses a pet emotion recognition method, device and equipment and a storage medium, and relates to the technical field of intelligent vehicles, and the method comprises the steps: obtaining the local state information and behavior state information of a pet leaving a vehicle; constructing a local feature vector according to the local state information and the local weight matrix, and constructing a behavior amplification coefficient according to the behavior state information and the amplification coefficient matrix; constructing a state analysis quantity based on the local feature vector and the behavior amplification coefficient; and determining whether the emotion of the car-leaving pet is in an abnormal state according to the state analysis quantity. According to the method and the device, whether the current emotion of the car-leaving pet is in the abnormal state or not can be determined by comprehensively identifying the body local state and the behavior state of the car-leaving pet, and the user is notified to pay attention in time when the current emotion is in the abnormal state, so that the user only needs to pay attention to the corresponding notification without worrying about anxiety all the time.

The invention belongs to the technical field of artificial intelligence, and discloses a multi-purpose teaching and training intelligent vehicle based on machine vision. The specific use steps of the multipurpose teaching and training intelligent vehicle in a training field given by a user are as follows: S1, initialization and starting, S2, track tracking operation, S3, detection of sidewalks or upslopes, S4, operation in a deceleration zone, S5, acceleration to pass through roundabout, S6, detection of a yellow square frame and traffic light identification, and S7, arrival at a destination: a run6 is started for acceleration in a green light, and the vehicle continuously drives to a terminal point to complete a driving task. Through theoretical pushing and field verification, the rotating speed of the motor of the intelligent vehicle is successfully optimized to 300 revolutions per minute, perfect matching of the speed and the torque is achieved, the running stability of the intelligent vehicle is remarkably improved through the adjustment, the intelligent vehicle can steadily turn at different curves, stable and smooth racing track performance is ensured, and the running safety of the intelligent vehicle is improved. The innovation not only overcomes the centrifugal force problem of the original motor at a high speed, but also enhances the controllability of the vehicle, and establishes a new benchmark for the technical development of the intelligent vehicle.

The application relates to the technical field of intelligent vehicles and discloses an intelligent reconnaissance propaganda vehicle adapted to a plateau extreme environment, which comprises a propaganda vehicle body, a multi-modal data acquisition module used for acquiring data, a fusion processing module used for performing weighted fusion on the multi-modal data and generating comprehensive state information, a fault state simulation module used for generating abnormal state parameters related to the plateau extreme environment, a decision control module used for generating vehicle operation control instructions according to the comprehensive state information containing the abnormal state parameters, a propaganda control module used for determining the priority and distribution strategy of propaganda content according to the vehicle operation state and task demand, and a cooperative communication and parameter updating module. Through the multi-modal data acquisition module, the fusion processing module and the stability evaluation and feedback adjustment mechanism, the comprehensive state information can truly reflect the vehicle operation state under the plateau extreme environment, so that the state determination deviation caused by environmental mutation is reduced.

A communication method and related apparatus are disclosed, applicable to the fields of vehicle networking and intelligent vehicle technology. In this application, a first terminal requests a first credential from a first server to prove its identity to a first vehicle. Interaction information sent from the first terminal to the first vehicle is signed with a first private key corresponding to the first public key in the credential to prevent tampering and forgery. Information exchanged between the first terminal and the first vehicle is forwarded through a second server. This application designs the first and second servers as different servers, remotely implementing secure verification of the identity and permissions of the vehicle control terminal and remote communication between the vehicle control terminal and the vehicle through dual servers. This provides a high level of security protection for remote interaction between the terminal and the vehicle, ensuring convenience while enhancing security.

The invention discloses a panoramic image calibration method and system based on algorithm fusion, and relates to the technical field of intelligent vehicles. The method comprises the following steps: acquiring a to-be-calibrated image sequence, and preprocessing the image sequence; constructing a parameter calibration model by utilizing an expert network, constructing a plurality of sub-networks according to different calibration algorithms, calibrating an image sequence by utilizing the sub-networks, scoring calibration results of all the sub-networks, and taking the image with the highest score as a preliminary calibration image; and constructing a parameter correction model by using a deep learning network, and performing parameter correction on the preliminary calibration image by using the parameter correction model according to other calibration results except the preliminary calibration image to obtain a final calibration image. According to the invention, through combination of the expert network model and the deep learning model, various calibration algorithms can be evaluated, the advantages of various algorithm calibration processes can be learned, and a more accurate panoramic image can be obtained.

The application discloses a kind of panoramic image calibration method and system based on algorithm fusion, it is related to intelligent vehicle technical field.The method includes the following steps: obtaining the image sequence to be calibrated, pre-processing image sequence;Parameter calibration model is constructed using expert network, according to different calibration algorithm, multiple sub-networks are constructed, and the image sequence is calibrated using sub-network respectively, the calibration results of all sub-networks are scored, and the image with the highest score is used as the preliminary calibration image;Parameter correction model is constructed using deep learning network, and the preliminary calibration image is corrected according to the remaining calibration results except the preliminary calibration image using parameter correction model, to obtain the final calibration image.The application combines expert network model and deep learning model, not only can evaluate multiple calibration algorithms, but also can learn the advantages in the calibration process of multiple algorithms, and can obtain more accurate panoramic image.

The invention discloses a vehicle anti-slip control method, device and equipment, a vehicle, a storage medium and a program, and is applied to the technical field of intelligent vehicles. The method comprises the steps that it is determined that the vehicle enters a driving state, and a slip position point is obtained according to the current vehicle position; determining an anti-slip adjustment strategy according to the relative position distance between the current vehicle position and the slip position point; and adjusting the driving state parameters of the vehicle before the vehicle passes through the slip position point according to the anti-slip adjustment strategy. According to the embodiment of the invention, active skid resistance can be realized, the vehicle driving state is adjusted before the vehicle slips, the influence of a wet and slippery road surface on vehicle driving is reduced, and the vehicle driving safety can be improved.