4168 results about "Driving safety" patented technology

The invention relates to the technical field of safety early warning, and particularly discloses a vehicle driving safety early warning method fused with meteorological data, which comprises the following steps: acquiring real-time multi-modal data of meteorological, traffic flow and vehicle state of a target road area, performing exception handling, space-time alignment and standardization to form a standardized data sequence, then constructing a multi-modal fusion tensor, and finally performing data fusion on the multi-modal fusion tensor. Extracting each modal dynamic mode, fusing cross-modal features, outputting a joint feature vector, inputting the joint feature vector into a safety risk prediction model to calculate a dynamic safety risk value, combining digital twin simulation risk conduction, generating graded early warning according to a preset threshold value, and performing management and control through vehicle-road collaborative network publishing and high-risk scene linkage traffic facilities. And finally, collecting feedback data evaluation effects, associating decision data to generate hash records, recording the hash records in the block chain, and carrying out federated learning incremental training optimization model based on feedback. According to the invention, accurate early warning under multi-factor coupling can be realized, data privacy is guaranteed, closed-loop optimization is formed, and road traffic safety and stability are improved.

The invention provides an intelligent driving control method, a vehicle and a storage medium, and relates to the technical field of intelligent driving. The method comprises the following steps: acquiring environment sensing data acquired by a sensor; the environment sensing data comprises rainfall detection data and target detection data; determining a real-time comprehensive rainfall level according to the rainfall detection data; dynamically adjusting the target detection weight of each sensor in data fusion according to the comprehensive rainfall level and the real-time target detection confidence of each sensor; based on the adjusted target detection weight, performing fusion processing on the target detection data collected by each sensor to obtain a target detection result; and adjusting an intelligent driving control strategy according to the comprehensive rainfall level and / or the target detection result. The environment sensing accuracy of the vehicle in a rainy environment is improved, the intelligent driving control strategy of the vehicle can adapt to the rainy environment change, and the driving safety and the driving experience are comprehensively improved from the sensing layer to the control layer.

The invention relates to an automatic driving risk quantification method based on a conflict risk field. Comprising the steps of 1, constructing a basic risk field; step 2, under a basic risk field framework, constructing a conflict risk field by taking an ADV as a center; step 3, regarding the conflict risk field as a repulsive force acting on the ADV, and using the repulsive force to quantify the dynamic influence of the environmental elements and various TP states on the driving safety of the ADV; the method comprises the following steps: firstly, constructing a basic risk field on the basis of traffic vehicle distribution and motion characteristics, further forming a vehicle forward, lateral, backward and traffic regulation constraint multi-dimensional conflict risk field on the basis, and converting an abstract traffic conflict relationship into a computable repulsive force model; dynamic quantitative characterization of driving risks in a state dimension, a space dimension and a time dimension is realized, and continuous optimization and evolution of an end-to-end automatic driving algorithm are supported by means of a repulsive force model constructed based on a conflict risk field.

The invention relates to the technical field of intelligent traffic and energy-saving illumination, in particular to an intelligent management and control method and system for energy-saving illumination of a highway tunnel and a computer program, and is suitable for an intelligent illumination control system for improving tunnel driving safety and energy efficiency of an illumination system. Dynamic response and fine energy consumption adjustment of tunnel lighting are realized through out-of-tunnel brightness multi-source fusion prediction, entrance section feedforward, closed-loop dimming control, a segmented intelligent lighting strategy based on vehicle detection and an in-tunnel brightness closed-loop and stepless dimming mechanism. The system has a multi-sensor redundancy mechanism and can automatically return to a safety mode when equipment fails, so that the continuity and the safety of illumination are guaranteed; and meanwhile, data summarization and energy-saving statistics are combined, control parameters are optimized in cooperation with a digital twinning or self-learning algorithm, the energy-saving effect and the operation and maintenance efficiency are further improved, and the purposes of traffic safety and low-carbon operation are considered.

The invention discloses a dangerous driving critical state identification method, and relates to the technical field of intelligent driving safety. According to the method, multi-mode signals of eye movement, electrocardio, skin electricity, vehicle operation and the like are collected and converted into a unified phase field, and the synchronous coherence of the unified phase field is analyzed; the individual phase dynamics manifold of the driver is learned on line by using a Shenchang differential equation, and a system instability precursor is identified by detecting the behavior that a state point escapes from a steady state attractor; further, multi-dimensional indexes such as synchronous collapse and topological fracture are fused, collapse time is estimated in combination with a Lyapunov index, and an advanced early warning instruction is generated; and finally, based on the model predictive control and the personalized phase response curve, generating and executing targeted multi-mode phase reset intervention, and forming a sensing-early warning-intervention active safety closed loop. According to the invention, normal form transformation from post-event alarm to beforehand regulation and control is realized, and early warning advancement and intervention accuracy are improved.

The invention discloses an automatic driving safety key simulation scene generation method based on adversarial generation and co-evolution. The method comprises the following steps: receiving a basic traffic scene described by a natural language, generating an antagonistic element scene containing security threats by using a large language model in combination with a traffic safety knowledge base, and analyzing the antagonistic element scene into an executable scene script; constructing a multi-agent confrontation collaboration diagram based on the meta-scene, and recognizing a key background vehicle through a cross-timing attention mechanism in combination with a time mask and time decay mechanism; and performing disturbance optimization on the key background vehicle trajectory to generate an automatic driving test scene. According to the method, a scientific and systematic solution with engineering operability is provided for safety verification of the automatic driving system when the automatic driving system faces real traffic challenges such as multi-source intervention and dynamic collaborative threat, and the method has wide adaptation capability and important industrial popularization value.

The invention provides a multi-dimensional driver capability evaluation and intelligent matching scheduling system, and the system comprises a data collection module which is used for obtaining driver driving behavior data, vehicle state data and environment data in real time; the preprocessing module is used for carrying out noise filtering, missing value filling and standardization on the acquired data; the multi-dimensional capability evaluation module is used for calculating a driving safety score, an efficiency score and an emergency response score of the driver through a dynamic weight distribution algorithm based on the preprocessed data; the demand analysis module is used for analyzing the route complexity, the time sensitivity and the special service demand of the passenger order; the matching scheduling module is used for generating a matching result according to the driver ability score and the passenger demand and outputting a scheduling instruction; the dynamic optimization module monitors the driver state and the road condition change in real time and adjusts the matching weight; and the interaction module is used for pushing real-time scheduling information and abnormal event early warning to the driver and the passenger. The scheduling efficiency and safety can be improved, and the passenger travel experience and the operation management level are improved.

The invention discloses a road line selection design scheme decision-making method based on multi-source fusion. The method comprises the following steps: collecting multi-source data of a road planning area, performing standardized preprocessing, identifying ground features through an improved target detection model, and extracting features; fusing multi-dimensional data to construct a constraint function aiming at cost, ecology and compliance, and determining a weight by using an analytic hierarchy process; and performing staged optimization on three-dimensional line selection based on deep reinforcement learning to generate candidate schemes, and determining an optimal scheme through comprehensive score and curvature comparison selection. The method solves the problems that traditional line selection depends on manpower, is low in efficiency and is one-sided in decision, improves the scientificity, economy and driving safety of a scheme, and is suitable for a road line selection design scene.

The invention provides an image processing method and device and electronic equipment, and the method comprises the steps: obtaining a continuous frame image, carrying out the motion detection of the continuous frame image, obtaining each motion region in the continuous frame image and the speed of each motion region, determining the target type of each motion region through semantic recognition, determining a priority corresponding to each motion area according to the speed and the target type of each motion area, and performing perception recognition on the motion areas with different priorities by adopting corresponding perception strategies to obtain a perception result; according to the method, perception identification is focused on a dynamic area and differentiated processing is carried out, a large amount of redundant calculation in a static or non-key area is effectively avoided, system calculation resources are saved, hardware loads are reduced, key targets can be ensured to be preferential, the real-time performance and response speed of the system are improved, and vehicle driving safety is ensured.

The invention belongs to the field of traffic control, and relates to a high-risk scene-oriented risk, dynamics and intention collaborative trajectory prediction method, which comprehensively utilizes an outward risk clue, vehicle dynamics characteristics and driving intention information (historical trajectory characteristics), and constructs a priori enhanced risk attention mechanism and a three-body interaction micrograph module to predict the risk, dynamics and intention collaborative trajectory of a high-risk scene. Early perception of potential risks is realized; meanwhile, a multi-scale dynamic time sequence encoder is adopted to capture the dynamic change of the vehicle from short-term disturbance to long-term behavior trend; an explicit intention recognition auxiliary task is introduced, and semantic constraint and optimization are carried out on the multi-modal trajectory; through collaborative modeling of the modules, the trajectory prediction precision and robustness of the automatic driving system under complex high-risk situations such as sudden cut-in and sudden brake can be remarkably improved, more reliable prior support is provided for a subsequent decision and control module, and therefore the driving safety of a vehicle in a complex traffic environment is effectively improved.

The invention relates to an intelligent driving safety system and method based on AI multi-source data fusion. The system comprises an environment modeling sensing module which obtains vehicle end multi-mode sensing data and vehicle size parameters to construct a surrounding environment model, and multi-vehicle environment data of collaborative sensing is formed through vehicle-vehicle communication; the behavior scene generation module analyzes the traffic flow characteristics of the multi-vehicle environment data, calculates the attention weight of the vehicle characteristics, associates the attention weight with the target size information, and generates a unified behavior scene representation. The collision risk judgment module fuses the unified behavior scene representation and the dynamic environment modeling data to generate multi-target fusion trajectory data, and if the uncertainty exceeds a threshold value, a historical database is checked to judge the risk level; and the path planning generation module performs classification according to risk levels and risk levels, and performs matching and speed adjustment instruction integration on the collaborative path optimization instruction to obtain a path planning rule. By adopting the system, the sensing precision, risk response capability and cooperative efficiency of the automatic driving vehicle can be improved, and the driving safety is guaranteed.

The invention relates to the technical field of automobile electric control, in particular to a new energy automobile electric control steering control method and system, and the method comprises the steps: obtaining and preprocessing a four-wheel vibration data sequence, carrying out the segmentation and consistency evaluation of the vibration data sequence of each wheel, and selecting a current data sequence in an optimal segment to calculate a road condition influence coefficient. And matching the current data sequence with the historical vibration data sequence, calculating the similarity to determine the influence weight, adjusting the influence coefficient in combination with the steering angle change rate of the historical vibration data sequence, inputting the adjusted pavement condition influence coefficient into the ECU, dynamically adjusting the steering auxiliary torque, and realizing accurate steering control. According to the method, the optimal segment is selected through segment processing and consistency evaluation to accurately reflect the current road condition, the steering auxiliary torque is dynamically adjusted in combination with historical vibration data sequence matching and the steering angle change rate, and the driving safety and comfort are improved.

The invention discloses a light passenger car chassis steering stability self-adaptive control method, which comprises the following steps of: collecting multi-dimensional working condition parameters in real time, and covering 12 types of key parameters such as a steering wheel rotating angle and a wheel speed; after preprocessing and abnormity diagnosis, double models are adopted to estimate the dynamic state of the vehicle in parallel; dynamically setting a control target, and realizing personalized control in combination with driver style recognition; the control quantity is optimized through multi-mode strategy dynamic switching and steering and braking system cooperative control; and actuator dynamic compensation, scenarized self-learning and multi-stage fault handling mechanisms are introduced. According to the method, the complex working condition adaptability is improved, the response speed is controlled, the subsystem cooperation efficiency is improved, the system robustness and the long-term use performance are enhanced, and the driving safety of the light bus is effectively guaranteed.

The invention provides a multi-source information fusion automobile light personalized interaction control system and method, and relates to the technical field of automobile intelligent light control. The system comprises a multi-source sensing module, a data preprocessing module, a data analysis processing module and a vehicle lamp driving module. The multi-source sensing module collects multi-source heterogeneous data, after the multi-source heterogeneous data is processed by the data preprocessing module, fusion analysis and intelligent decision are carried out by the data analysis processing module, a control instruction is generated, and finally, the instruction is executed by the vehicle lamp driving module and a state is fed back. According to the invention, the defects of the existing automobile light control technology in the aspects of intelligence, situational and individuation are overcome, the efficient, intelligent and individualized control of the automobile light is realized, and the driving safety, comfort and entertainment are improved.

The invention relates to the technical field of vehicle-mounted display, and discloses a vehicle-mounted HUD environment illumination adaptive brightness adjusting system, which comprises an illumination acquisition module, a parameter analysis module, a model construction module, a brightness calculation module, an error evaluation module, a calibration correction module, a threshold setting module, a result output module, a feedback optimization module, a system regulation module and the like. The illumination acquisition module analyzes the illumination parameter stability and interference difference and generates an illumination perception state value; the parameter analysis module screens an optimal brightness and light intensity combination; the model construction module is used for training samples to establish a brightness and light intensity relation model; the brightness calculation module generates a brightness threshold by combining real-time illumination data mapping; the error evaluation and calibration correction module optimizes a calculation rule; and the feedback optimization module operates feedback adjustment parameters to finally generate an environment illumination adaptive brightness adjustment scheme. The system realizes accurate and dynamic adjustment of the brightness of the vehicle-mounted HUD, effectively adapts to the environment illumination change, improves the display effect and the driving safety, and has the characteristics of self-learning and self-optimization.

The invention provides a vehicle driving speed planning method and device, a vehicle, a medium and a program product, and belongs to the field of automatic driving. The vehicle driving speed planning method comprises the steps that all obstacles around a target vehicle are traversed, and the types of the obstacles are determined based on the relative position relation between the obstacles and a target vehicle driving lane; calculating a first target vehicle speed according to the obstacle type and updating a global target vehicle speed; acquiring an obstacle prediction track, and if the confidence coefficient of the prediction track meets a preset requirement, determining a collision risk point based on the prediction track and the target vehicle planning path; calculating a second target vehicle speed based on the collision risk point and updating the global target vehicle speed; and after the traversal is completed, outputting the minimum global target vehicle speed in the traversal process as the reference vehicle speed. According to the technical scheme provided by the invention, the risk assessment based on the lane relationship and the collision risk assessment based on the predicted trajectory are fused, and the driving safety and comfort of the automatic driving vehicle in a complex dynamic scene can be improved.

The invention discloses a safe operation system and a safe operation method of a space-ground integrated rescue vehicle, which are used for the rescue vehicle working in a complex disaster environment and ensure stable and reliable two-way communication connection between the rescue vehicle and a command center by establishing an intelligent switching mechanism of a low-orbit satellite main link and a ground network standby link. Real-time transmission of key instructions and field information is guaranteed, and a foundation is laid for safe operation. Advanced risk early warning is achieved through a space-ground cooperation mechanism, dynamic decision making and path planning are carried out by integrating satellite information and local sensor data, and the global adaptive capacity of the rescue vehicle to the complex disaster environment is improved. Satellite pre-sensing information is combined with a local decision, and a pre-control instruction is generated to act on a drive-by-wire chassis, so that the rescue vehicle can smoothly deal with hidden or developmental risks in advance, and the driving safety and stability are improved.

The invention discloses a vehicle driving right transfer method based on abnormal driving behaviors and electronic equipment, and the method comprises the steps: collecting driver state data, vehicle driving state data and road environment data, and carrying out the multi-source data fusion; abnormal driving behavior detection is performed according to the multi-source data, and the driving risk is evaluated; a rule engine decision-making model based on the situation is used for deciding countermeasures to be taken, and the countermeasures comprise early warning type actions, auxiliary control type actions, forced take-over type actions and mutual switching among the actions based on an advanced auxiliary driving system or an automatic driving system; after the vehicle is forced to take over and runs for n minutes, whether the current vehicle is suitable for the driver to take over or not is judged according to driver state data collected in real time and the current comprehensive driving risk score, and whether driving right transfer is conducted or not is determined according to the judgment result. The accuracy of abnormal driving behavior detection is improved, the vehicle driving right is intelligently transferred, and the driving safety is improved.

The invention discloses an automatic driving safety operation system integrating environmental perception and decision reasoning. The system generates and dynamically updates a security risk map covering an operation area by fusing real-time environment perception, historical operation data and traffic management information. On the basis, the collaborative safety decision-making module further carries out behavior modeling and intention prediction on other traffic participants, and in combination with map risks and traffic instructions, a driving strategy is actively adjusted under a dynamic game framework. According to the invention, the safety control is improved from passive response to an active mode of behavior pre-judgment and game dominance, and the safety and reliability of the operating vehicle in a complex environment and the cooperative capability of the operating vehicle and traffic management are obviously enhanced.

The invention belongs to the technical field of image data processing, and discloses a driver fatigue state early warning method and system based on face recognition tracking, and the method comprises the steps: extracting a face region through semantic segmentation, splitting a single-channel gray-scale map, calculating the local complexity and noise degree of a pixel point, obtaining a window adjustment coefficient, and carrying out the adjustment of the window adjustment coefficient; the size of the adaptive window is determined, different pixel region features can be adapted in a targeted manner, and the processing deviation caused by a fixed window is effectively reduced; an illumination component is subsequently extracted based on an adaptive window, and the image is enhanced through a Retinex algorithm, so that the face image quality under complex illumination can be improved, and the detail definition is improved; and finally, the fatigue degree is detected in combination with a PERCLOS algorithm, so that the facial fatigue characteristics of the driver can be more accurately captured, and effective recognition and early warning of the fatigue state are realized. According to the invention, continuous and stable monitoring can be realized in the driving process, reliable guarantee is provided for driving safety of a driver, and the risk of accidents caused by fatigue driving is reduced.

The invention provides a vehicle-road cooperation method and system, and the method comprises the steps: S1, obtaining original road side data and original vehicle end data which are reported by a road side and a vehicle respectively, and carrying out the standardization processing; and S2, carrying out sensing fusion on the standardized roadside data and the vehicle end data to obtain fused sensing data, carrying out sensing calculation by using the sensing data, and meanwhile, re-engraving the original vehicle end data, the original roadside data, the fused sensing data and a sensing calculation result. And S3, acquiring duplicated data in real time, performing quasi-real-time scene calculation in combination with the signal lamp, the roadside equipment and the vehicle end equipment, and acquiring and duplicating a quasi-real-time scene calculation result. And S4, issuing the quasi-real-time scene calculation result to the vehicle end or the roadside, and sending an early warning signal to the vehicle end or controlling roadside equipment. According to the invention, accurate replication and dynamic early warning of the vehicle-road data are realized, the traffic efficiency and the driving safety are improved, and the reliability and expansibility requirements of a large-scale vehicle-road collaborative network are met.

The invention belongs to the field of artificial intelligence and intelligent traffic systems, particularly relates to a path control system combining multi-modal perception and dynamic trajectory prediction, and aims to solve the problems of insufficient perception fusion, low trajectory prediction precision and control response lag in a complex dynamic environment. The system comprises a multi-modal perception fusion unit, a dynamic interaction modeling unit, a space-time coupling prediction unit, a risk field construction unit and an adaptive path generation unit. Multi-source sensor data are fused through confidence coefficient weighting, an interaction weight matrix under an attention mechanism is constructed, a future trajectory is predicted in combination with individual dynamics and a social force model, a four-dimensional space-time risk field is generated, and a minimum risk path is solved based on an improved fast marching algorithm. The system realizes sensing-prediction-control closed-loop cooperation, the end-to-end delay is less than 250 milliseconds, and the driving safety and comfort in a complex traffic scene are improved.

The invention provides a safety key scene enhancement generation method fusing traffic rule hard constraints and a dynamic feasible region, and relates to the technical field of automatic driving safety tests.The method comprises the steps that a multi-strategy initial data set is constructed, pre-training an optimal feasible value network of the automatic driving vehicle through offline reinforcement learning by utilizing the multi-strategy initial data set, and generating a dynamic feasible region; a traffic rule is represented in a formalized mode through linear sequential logic, and a rule hard constraint and feasibility guide dual-action mask mechanism is constructed; and controlling the confrontation background vehicle to generate a compliance confrontation scene by maximizing a rule-feasible region dual-guided confrontation objective function, feeding back the generated effective key scene to an initial data set, and realizing data-driven feasible region dynamic updating. According to the method, by combining rule formalization constraint and feasible region reachability analysis, the scene is ensured to be compliant and close to a system capability boundary.

The invention discloses a driver fatigue state real-time identification system and method based on multi-modal deep learning, and relates to the technical field of fatigue driving detection. Firstly, feature extraction is performed on brain wave shapes and eye movement coordinates, and respective weights are calculated by using an attention mechanism, so that dynamic distribution of different modal features is realized. And then, in-vehicle illumination data is introduced to establish a credibility mapping function so as to carry out adaptive correction on an eye movement weight, thereby effectively reducing interference of a complex illumination environment on an identification result. And carrying out weighted splicing on the corrected multi-modal features, mapping the multi-modal features into a brain-eye collaborative fatigue value, and carrying out judgment in combination with the duration, so as to finally realize stable and reliable early warning control. The method has the advantages of high fusion precision, high environmental adaptability and low false alarm rate while ensuring the real-time performance, and the driving safety guarantee capability can be remarkably improved.

The invention discloses a sudden accident alarm system for a highway, and belongs to the technical field of highway operation management. According to the system, a seven-in-one intelligent terminal integrated with a laser radar sensor, an emergency alarm button, an induction lamp, broadcasting equipment, a two-dimensional code label, a self-luminous plate and a reflective coating film is arranged along the line. A laser radar periodically scans a front 8-meter range, a primary event label is generated in combination with an emergency alarm button signal, and an accident is found in time; calling adjacent terminal data for transverse comparison, tracking a time sequence change trend, and comprehensively judging a risk level; the colors and flicker frequencies of the induction lamps are activated according to the grades, broadcast voice is triggered, and night identification is enhanced through a self-luminous plate and a reflective coating film. The system realizes on-site discovery, on-site response and on-site warning, the response time is remarkably shortened, the problems of lagging discovery and insufficient warning of a traditional monitoring means are solved, and the emergency efficiency and the driving safety are improved.

The invention relates to an automatic driving safety behavior decision-making method with interactive risk consciousness, which belongs to the technical field of automatic driving behavior decision-making, and comprises the following steps: S1, extracting the state of a vehicle and driving environment information based on sensor data; s2, defining a state space and an action space of deep reinforcement learning; s3, adopting a D3QN reinforcement learning algorithm to construct a decision framework and integrating a self-attention mechanism to capture multi-vehicle interaction features; s4, establishing a driving risk potential field model considering multi-vehicle interaction according to the multi-vehicle interaction characteristics and the motion state and the relative position of the environment vehicle; s5, constructing a comprehensive reward function based on the driving risk potential field; s6, performing network training by adopting an experience playback mechanism; and S7, deploying a risk action correction module at the output end of the value network.

The invention discloses an automatic driving safety-efficiency decision-making method based on risk soft constraint, and belongs to the technical field of automatic driving decision-making. The problems that a traditional automatic driving decision-making method is poor in extreme scene adaptability and lagged in risk response in the prior art are solved. According to the method, an automatic driving safety-efficiency decision-making framework is constructed, the environment state of an environment perception layer is input into a multi-expert strategy layer, three experts are adopted to set the multi-expert strategy layer, and candidate car-following actions are output through the multi-expert strategy layer; embedding the risk adjustment mechanism into a loss function of each expert in a multi-expert strategy layer to obtain an Actor network loss function of each expert; on the basis of the output candidate car-following actions, taking a basic reward function as a basic behavior constraint of all experts; and generating an automatic driving safety-efficiency decision. The method effectively improves the interpretability of the automatic driving decision-making method and the robustness of the car-following strategy, and can be applied to automatic driving decision-making.

The invention provides an abnormal early warning method and system based on driving behaviors, and is applied to the field of data processing. According to the method, multi-level judgment of vehicle driving behaviors is realized by combining individual driving habits of drivers, road types and traffic control information, firstly, real-time driving parameters are detected based on driving identity information pre-recorded by a central controller and are compared with the individual habits, and only behaviors obviously deviating from the habits enter subsequent analysis; the driving behavior data is generated through the prediction model in combination with the road type and the traffic control information, whether the behavior is reasonable or not is further judged, the early warning pertinence is ensured, and finally the abnormal driving behavior is compared by using the group driving behavior baseline, so that the individual difference dynamic correction is realized. Therefore, on the basis of fully considering individual differences of drivers, the false alarm rate is effectively reduced, meanwhile, timely early warning of abnormal behaviors is ensured, and the driving safety and the early warning practicability are improved.

The invention relates to an intelligent vehicle taillight system, a control method thereof and a vehicle, the intelligent vehicle taillight system comprises a data acquisition module used for acquiring vehicle dynamic operation parameters and external environment information, the vehicle dynamic operation parameters comprise at least one of brake pedal stroke, steering angle, vehicle speed and automatic driving state data, and the external environment information is used for acquiring the external environment information; the external environment information comprises at least one of a rear vehicle distance and a pedestrian position; the intelligent control module is connected with the data acquisition module and is used for generating current working condition information of the vehicle based on the vehicle dynamic operation parameters and the external environment information; and the tail lamp is connected with the intelligent control module and performs corresponding display and projection operation according to the current working condition information. According to the invention, the driving safety can be improved.

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.