1464 results about "Speed limit" patented technology

The invention provides an intelligent traffic decision-making method based on multi-source sensor data, and the method comprises the steps: obtaining vehicle sensor data and road information, and obtaining a standardized traffic flow feature vector through data synchronization, fusion, feature extraction and dimension reduction processing; inputting the standardized traffic flow feature vector into a traffic flow prediction model, and predicting the traffic flow through a vehicle dynamics model constraint state transition matrix; according to the predicted traffic flow and the real-time road condition information, the road congestion state is judged, and a real-time congestion map is generated; in combination with the real-time congestion map, the road network speed limit rule and the starting and ending point position information, an optimal path is planned, and path smoothing processing is performed through a driving behavior mode; and constructing a vehicle intelligent decision-making system, integrating traffic flow prediction, congestion detection and a path planning model, and outputting a vehicle driving strategy according to real-time vehicle sensor data.

An improved system and method is disclosed of accessing mapping data for a geographical area comprising speed limit data for a first road segment, accessing second mapping data providing speed limit data for a plurality of corresponding road segments from a second source, determining an overlap amount between the candidate road segments and the first road segment, using overlap data to identify a first subset of the candidate road segments, identifying a first speed limit that has a highest likelihood of being accurate from speed limits respectively associated with road segments in the second subset of the candidate road segments, transmitting the first speed limit in association with a map comprising the first geographical area to a vehicle device.

The invention discloses an expressway emergency management and control method and system, and the method comprises the steps: building a multi-dimensional heterogeneous data fusion model through a time-space diagram neural network according to the traffic flow, meteorological data and accident reports collected by roadside sensing equipment, and carrying out the dynamic prediction of the risk level of a road segment, and obtaining a real-time risk level map of a whole road network; based on the real-time risk level map, outputting a dynamic shunting scheme containing variable lane marks and a speed limiting strategy; according to the dynamic shunting scheme, generating a vehicle trajectory optimization strategy; and outputting an anti-interference optimization control strategy based on a vehicle trajectory optimization strategy in combination with sudden obstacle data polled by the unmanned aerial vehicle in real time. According to the embodiment of the invention, rapid and accurate path adjustment and cooperative control can be carried out, and the emergency management level of the expressway is improved.

The invention relates to a cloud side-end integrated collaborative digital-intelligent traffic collaborative management and control method, system and device and a medium, and the method comprises the steps: dynamically adjusting the main line speed limit and ramp control rate through driving main line variable speed limit and traffic induction collaborative management and control based on a hierarchical control architecture, so as to enable the performance of a regional road network to be optimal; a collaborative optimization framework is constructed and utilized under a hierarchical control framework, an optimal control scheme of a single ramp is expanded to each partition of a road network in a random environment, and an optimal joint behavior strategy and a behavior selection strategy of each agent are determined, so that adaptive collaborative control of each agent is realized; and a traffic flow optimal equalization strategy of the whole road network is obtained. Therefore, by integrating a main line variable speed limit and traffic induction collaborative management and control technology, a main line and multi-ramp collaborative optimization framework and intelligent agent configuration, optimization of regional road network performance, optimal balance of overall road network traffic flow and improvement of traffic management and control accuracy and response speed are achieved.

The invention discloses a container wharf hazardous chemical transport vehicle intelligent tracking and positioning system comprising a data acquisition module used for acquiring vehicle positioning, driving behaviors and environment state data; the data fusion and time synchronization module is used for performing time alignment on the multi-source data; the behavior modeling and trajectory prediction module is used for extracting driving behavior characteristics and predicting a driving trajectory; the risk assessment and anomaly detection module is used for calculating an anomaly score and predicting a driving risk; the security strategy optimization module is used for constructing a zero-sum game security control model and optimizing speed limit, path and early warning strategies; the remote control and intelligent intervention module is used for executing remote speed regulation, emergency braking, path optimization and alarm pushing in the intelligent dispatching system; and the cloud monitoring and data analysis module is used for storing a track, an environment, a driving score and a safety strategy and predicting a long-term risk trend. According to the invention, based on intelligent game and multi-modal fusion, accurate tracking and safety management and control of the hazardous chemical substance transport vehicle are realized.

The invention relates to the technical field of intelligent traffic systems, and discloses a hard shoulder driving and variable speed limit cooperative control method and system.The method comprises the steps that traffic data are collected, and the traffic data comprise traffic information and speed limit values; according to the hard shoulder lane-borrowing driving motivation, the lane-borrowing safety condition and the emergency or rescue vehicle requirement of the networked autonomous vehicle, whether the networked autonomous vehicle can run through the hard shoulder lane or not is judged; inputting the collected traffic data into a pre-constructed DDPG algorithm to determine a state, an action space and a reward function of the DDPG algorithm, and outputting an optimal speed limit value; and the optimal speed limit value of each lane of the control road section is generated through a DDPG algorithm, and the network-connected automatic driving vehicle adjusts the vehicle speed according to the optimal speed limit value. The method can be used for highway entrance ramp sections with serious congestion, the average travel time and carbon dioxide emission of the road network can be reduced, and a new method is provided for efficient and low-carbon targets of smart highways in China.

In various examples, systems and methods of the present disclosure correspond to speed limit fusion for automotive systems and applications. Systems and methods are disclosed that fuse speed limit data from both one or more map sources (e.g., nav-maps, HD maps, SD maps, etc.) and a perception system, and that employ rules to help resolve conflicts in speed limits from the perception system and the map(s). For example, if a conflict occurs, the rules may indicate whether to use the speed limit data from the perception data, the speed limit data from the map(s), and / or whether to use another type of speed limit data, such as cached speed limit data.

The invention discloses a train control method and a train control system based on credible interval entry check. The train control method comprises the following steps: starting a backup command; the temporary speed limit server starts a sending module used for sending authorization to a train self-protection system; performing interval credible entry check; determining the existence condition of the train in the interval; the temporary speed limit server updates a train existence condition list in the interval; the train obtains a front driving permission; the train runs according to the running permission. According to the train control method and the train control system based on the credible interval entry check, the axle counting equipment is arranged at the interval entrance, software of the temporary speed limit server only needs to adjust individual modules, and the software of the temporary speed limit server can be only used for recording the train condition in the interval and is not used for authorized calculation, so that the train control method and the train control system are convenient to use. The deployment cost of the temporary speed limit server is reduced, the transportation efficiency under the condition of a CTCS-2 system fault is improved, and the toughness of the CTCS-2 system is improved.

The invention provides an expressway variable speed limit control method, system and device and a medium, and belongs to the technical field of intelligent traffic management. The method comprises the following steps: collecting traffic flow data, road condition data and weather data in real time; preprocessing the collected data, and predicting a traffic jam trend and a safety risk index through a machine learning algorithm; determining weight factors of traffic flow, safety risk and congestion prediction through an analytic hierarchy process; dividing a road section speed range by using a clustering algorithm, and calculating a road section unit speed; constructing a target function which aims at maximizing the unit speed of the road section and minimizing the safety risk and the congestion time; and based on the target function, solving an optimal speed limit value by adopting a particle swarm optimization algorithm, and dynamically issuing speed limit information in different lanes. The highway traffic big data is analyzed in real time, the speed limiting strategy is intelligently formulated, the speed limiting strategy is published to the driver in real time through multiple channels, and the passing efficiency and safety of the highway are effectively improved.

The invention provides an accident liability intelligent determination method and system fused with video tracking, and the method comprises the steps: S1, carrying out the alignment evidence collection of a video, an IMU, a GPS, and an OBD, and forming an evidence chain through framing Hash, chain Hash, and a timestamp; s2, completing detection segmentation on the shared trunk multi-branch network; s3, in combination with appearance re-identification and a motion model, realizing cross-frame association, and outputting track, speed and shielding recovery; s4, mapping the trajectory to BEV according to depth and calibration, and constructing a space-time scene graph by fusing lanes, signals, speed limit and the like; s5, extracting events such as lane changing, line merging and signal rushing, and positioning a conflict point and a way giving relation; s6, generating error factors according to a rule base and priority judgment; s7, quantifying the responsibility proportion according to the weight, the collision participation time and the reaction time; and S8, generating a report evidence of the key frame and the rule list. The system is composed of an acquisition evidence obtaining module, a video understanding tracking module, a space-time reconstruction module, an event extraction module, a rule reasoning module, a responsibility quantification module and a report evidence storage module.

An example operation may include one or more of receiving sensor data from a hardware sensor of a vehicle, where the sensor data comprises values of a route sensed as the vehicle is travelling on the route, identifying a speed limit of the route, generating a sequence of predicted speed limit offset values for the vehicle at future locations on the route based on execution of a machine learning model on the received sensor data and the identified speed limit of the route, determining a range estimation of the vehicle based on a current amount of charge of a rechargeable battery of the vehicle and the generated sequence of predicted speed limit offset values for the vehicle at future locations on the route, and displaying the range estimation on a user interface within the vehicle.

The invention discloses an expressway dynamic speed limit method based on multi-source data, and belongs to the technical field of expressway speed limit, and the method comprises the steps: obtaining accident data, constructing an accident risk degree prediction model, and generating an accident risk degree trend chart; acquiring real-time traffic flow data, constructing a traffic flow prediction model, and generating a traffic flow tendency chart; obtaining meteorological data, and generating a local meteorological state distribution diagram; performing space alignment and data fusion on the traffic flow tendency chart and the local meteorological state distribution diagram to generate a road section comprehensive risk index; performing time alignment on the accident risk degree trend chart and the comprehensive risk degree index of the road section to generate a real-time risk degree score of the current road section; smoothly adjusting the speed limit value by adopting a linear interpolation algorithm on the basis of the real-time risk degree score in combination with a preset speed limit adjustment rule; and comparing the current vehicle speed with the adjusted speed limit value, and early warning over-speed. According to the invention, accurate speed limit control based on multi-dimensional data is realized, and the road safety is effectively improved.

The invention relates to a road section risk early warning method based on thunder-vision fusion perception, and the method comprises the following steps: S1, obtaining the high-precision space-time position information of a vehicle in a tunnel through the multi-source information fusion of a millimeter-wave radar and machine vision; s2, mapping the vehicle position under the global coordinate system to a coordinate system expanded along a road center line, and representing the vehicle position as a longitudinal projection position and a transverse offset along the road direction; s3, under the coordinate system, taking anti-collision time as a core index, combining lane judgment and relative speed, constructing a dynamic longitudinal risk grading model, and performing linkage triggering with a warning system; s4, constructing a regional risk integral model; and S5, speed limiting information is issued to all driving-in vehicles in real time through the variable speed limiting screen, and the rear-end collision risk is actively prevented and controlled. According to the method, the sudden risk response time can be shortened, the speed limit control response precision can be improved, the rear-end collision rate of the diversion and convergence areas can be reduced, false alarm interference can be reduced, and the tunnel driving safety and the information guiding efficiency can be improved.

A speed limit value adjustment method includes obtaining an accelerator pedal opening and / or an accelerator pedal opening change rate of a vehicle running in a first mode, where a speed limit value of the vehicle in the first mode is a first speed limit value; and adjusting the speed limit value of the vehicle to a second speed limit value when it is detected that the accelerator pedal opening and / or the accelerator pedal opening change rate meet / meets a preset condition. The second speed limit value is greater than the first speed limit value.

The invention relates to an unmanned surface vessel dynamic obstacle avoidance control method based on real-time nonlinear model predictive control, and the method comprises the steps: constructing a dynamic model, a static obstacle model and a dynamic obstacle model of an unmanned surface vessel; based on the dynamic model, the static obstacle model and the dynamic obstacle model, a multi-objective optimization model is constructed, and the multi-objective optimization model takes trajectory tracking cost, obstacle avoidance cost and control smoothing cost as objective functions and takes a motion range, speed limitation, control input limitation and a collision avoidance rule as constraint conditions; and obtaining the state of the unmanned surface vessel and the obstacle state in real time, inputting a multi-target optimization problem for solving, obtaining control input, and completing dynamic obstacle avoidance control of the unmanned surface vessel based on the control input. According to the invention, the adaptability and safety of the unmanned surface vessel in a complex navigation environment can be improved.

The invention provides a driving control method, device and equipment in severe weather and a storage medium, and the method comprises the steps: calling traction brake level critical point data and running speed critical point data of a target train based on the current severe weather type; and based on the traction braking level critical point data and the running speed critical point data, the traction braking level and the speed limit value of the target train are updated respectively. According to the running control method, device and equipment in the severe weather and the storage medium, the train can automatically adapt to the severe weather for targeted adjustment, so that the probability that the train idles and slips in the severe weather is reduced, dangerous point protection failure caused by unreasonable speed limit value setting is avoided, and the safety of the train is improved. And the operation efficiency of the line can be furthest improved.

The invention provides an elastic traffic control method considering road accumulated water distribution. The method comprises the steps of collecting road surface point cloud data and obtaining a road three-dimensional model; utilizing computational fluid mechanics simulation software to construct a road ponding simulation model under the rainfall condition; according to the simulation result, calculating the critical water skiing speed corresponding to the accumulated water depth, and comparing the critical water skiing speed with the lane speed limit to judge the water skiing risk; the sections with the water skiing risk are divided into risk sections; reducing the maximum limit driving speed of the risk section; dividing a high-risk section by comparing the maximum speed limit with a preset threshold value, and performing bypassing adjustment; by comparing the proportion of the risk section with a preset safety threshold value, a channel sealing measure is taken; and real-time weather and road conditions are continuously monitored and timely adjusted. By means of the method, the lane speed limit and the traffic flow direction are dynamically adjusted, tackle and traffic accidents caused by accumulated water on the road surface are effectively prevented, the water skiing risk of vehicles under the rainfall condition is reduced, and the road traffic safety is ensured.

The invention discloses a group train tracking operation optimization method considering communication delay. The method comprises the following steps: establishing a heavy haul railway train group of a'navigation-front train following 'topological structure; a multi-target optimization model is constructed according to the characteristic constraint, the speed limitation and the safety distance constraint of the vehicle; generating a random communication delay in the model, calculating a time step number corresponding to the delay, and making a control decision on the following train based on state information of a previous time step of the preceding train; and solving the running speed of the following train in the multi-objective optimization model based on a particle swarm algorithm. According to the group train tracking operation optimization method, the problems of uncertainty and dynamic response lag caused by communication delay in the tracking operation process of heavy haul railway group trains are effectively solved, the tracking error between the trains can be remarkably reduced, the speed synchronism is improved, and the tracking operation efficiency is improved. And the distance fluctuation in the acceleration and deceleration stages can be effectively reduced, so that the train distance stability is ensured.

The invention discloses a deep reinforcement learning-based cooperative control method for an expressway confluence area, and the method comprises the steps: building a LiikeSim-Python joint simulation environment, and setting a coil detector in the simulation environment to obtain the traffic flow data of the upstream and downstream of the expressway confluence area; an EM algorithm based on Gaussian mixture distribution is used as a traffic state classifier, traffic flow data of the highway confluence area are used as input, and traffic states of the highway confluence area are divided; designing a state space, an action space and a reward function; taking the state space of the highway confluence area as input, taking the actions of the variable speed-limiting intelligent agent and the ramp metering intelligent agent as output, and constructing a network model of multi-agent sharing experience under time sequence characteristics; an independent experience pool is set for each of the variable speed limit agent and the ramp metering agent, and interaction experience of the agents and the traffic simulation environment is collected with the control period as the frequency; training an agent model by using the sampled samples; and realizing cooperative control of the highway confluence area by using the trained intelligent agent model. According to the invention, the traffic travel delay of the highway confluence area can be reduced.

The invention provides a highland cooperative control method fusing a graph convolutional neural network and reinforcement learning, through controlling urban road traffic signal lamps, an expressway ramp signal machine and a variable speed limit unit, expressway traffic congestion is relieved, the urban traffic passing efficiency is improved, road driving vehicles are controlled by means of the signal lamps and the speed limit unit, and the traffic efficiency is improved. Traffic flow scheduling optimization in a global road network is achieved; the whole structure adopts a distributed cooperative control framework, is controlled by a decision-making agent, and performs autonomous decision-making and adjustment according to real-time traffic conditions, so that the adaptability and robustness of the system are enhanced, and the defects of large data calculation amount, difficulty in processing, control strategy lag and the like caused by traditional centralized control are effectively avoided; the graph convolutional neural network model is constructed to aggregate the feature information of the peripheral decision intelligent agent, and the control weight is dynamically allocated, so that the information sharing capability is enhanced, the suboptimal decision caused by lack of comprehensive perception of the surrounding environment is prevented, and the cooperation efficiency of the whole system is improved.

The invention provides an automatic mode switching method and device of an integrated vehicle-mounted system and a storage medium. The method comprises the following steps: receiving system switching information, and determining a current system and a target system according to the system switching information; according to the speed limiting information before switching and the system switching point position, the movement authorization information of the current system is used for calculating a train operation control curve, and the train speed is monitored; according to the train operation control curve and the train speed, whether the train reaches the system switching point position or not is judged in real time; when the train arrives at the system switching point position, the current system is switched to the target system, and the current system is switched to the background state; and updating the train operation control curve according to the switched speed limit information and the movement authorization information of the target system. The system can be simplified, the cost is reduced, the automatic compatible switching of the system is realized, and the system efficiency and safety are improved.

The invention discloses an elastic speed limiting and vehicle distance cooperative regulation and control method and system in an expressway agglomerate fog scene, particularly relates to the technical field of intelligent traffic control, and is used for solving the problem that a secondary accident risk is induced by neglecting a traffic flow phase change characteristic in an existing dynamic speed limiting method. The method comprises the following steps: collecting traffic density, vehicle spacing and visibility space-time distribution data in real time; performing weighted amplification on the traffic density based on the consistency of the visibility gradient direction and the traffic flow direction to generate corrected traffic density; calculating a phase change margin index representing the flow state stability by combining the corrected traffic density and the optical safety deviation degree; when the phase change margin is lower than a threshold value, dynamically constraining the maximum value of the speed-limiting decreasing amplitude according to the agglomerate fog moving trend, and keeping the positive correlation between the maximum value and the phase change margin; generating a target speed limit value based on the constraint decreasing amplitude value, and calculating a safe vehicle distance instruction by fusing the visibility data; and issuing an instruction to the vehicles in the agglomerate fog dynamic influence area to realize collaborative optimization of traffic system stability and single vehicle safety.

The invention discloses a model predictive control-based train formation distributed cooperative control method, which comprises the following steps of: acquiring a train tracking speed running curve, train speed limiting information and a train running resistance coefficient, and establishing a train formation model; according to a preset model predictive control algorithm, in a set predictive control time domain, according to a train formation model, a control target and constraint conditions, state variables of positions and running speeds of all trains in a train formation and control variables of control forces of all the trains in the train formation are used to predict the train formation according to state variables of the positions and running speeds of all the trains in the train formation. Respectively generating optimization control problems of the leading train and the following train in the train formation; and based on a synchronization theory, solving an optimization control problem of each train to obtain a control force of each train in the formation, and inputting the control force into a train operation control system to realize distributed control of each train in the train formation. The method is based on a model predictive control algorithm, uses a synchronization theory, and reduces the use of computing resources and communication resources in practice.

The invention discloses a rail vehicle braking control method and device and a computer storage medium. The method comprises the steps that whether a rail vehicle meets a first condition or not is judged; if yes, based on the first target adhesion coefficient, a first action is applied to a target free shaft, a first actual adhesion coefficient of the railway vehicle is obtained, and the target free shaft points to a wheel shaft capable of determining the actual running speed of the current railway vehicle; and according to the numerical relationship between the first target adhesion coefficient and the first actual adhesion coefficient, adjusting the braking force of a second action subsequently applied to the target free shaft. According to the method, it can be ensured that the braking process better fits the current track condition through a dynamic adjustment mechanism, so that severe shaft speed fluctuation caused by track condition changes is reduced, the positioning loss risk caused by shaft speed fluctuation is effectively reduced, and then the possibility of entering a speed limiting degradation mode is reduced.

A test method for a variable speed limit (VSL) strategy in a mixed traffic flow considering driver compliance is provided. A road scenario is built, and multiple mixed traffic flows formed by human-driven vehicles (HDVs) and connected and automated vehicles (CAVs) are set, which vary in CAV penetration rate. The VSL strategy is established, and a speed limit of an upstream of the bottleneck area is calculated based on the VSL strategy. A HDV compliance is established, and in combination with the speed limit, the HDV drivers are classified, and a proportion of the HDV drivers in individual types in the road scenario is determined. Based on a traffic flow scenario in which the HDV drivers and CAV drivers are located, indicator data reflecting traffic capacity is calculated. A test system for performing the test method is further provided.

The invention relates to the technical field of road damage detection, in particular to an intelligent road damage recognition method and system based on image analysis, and the method comprises the following steps: obtaining road images, screening out substandard images, extracting local features of the images, executing enhancement processing, converting structural parameters, and matching a type template; and combining the vehicle axle load and the damage index to calculate a structure expansion trend, and generating damage expansion risk information. According to the method, the accuracy of damage type identification is improved by combining geometric parameters such as edge length, curvature range and closed region area value and performing scale conversion based on pixel resolution, and an evaluation mechanism of the influence of vehicle load on the damaged region is established by combining traffic flow and vehicle axle load data. The expansion trend is judged through the traffic frequency and the impact weight, the damage development situation is effectively predicted, the accident frequency, the speed limit information and the traffic obstruction factor are introduced into priority ranking calculation, and the urgency recognition capability of road maintenance is enhanced.

The invention discloses a vehicle remote control method, system and device based on a remote vehicle locking platform. The vehicle remote control method based on the remote vehicle locking platform comprises the steps that a cloud end receives vehicle remote control instructions, and the vehicle remote control instructions comprise a speed limiting instruction, a vehicle locking instruction and an unlocking instruction; verifying whether the remote control function of the vehicle is activated or not; if yes, a verification code is issued to the owner of the vehicle ownership, and after the verification of the owner of the vehicle ownership is passed, a vehicle remote control instruction is sent to the vehicle, so that the vehicle executes an action corresponding to the vehicle remote control instruction; and if the vehicle feedback is not received within the preset time, determining that the remote control of the vehicle fails, and ending the operation, otherwise, updating the vehicle state according to the vehicle feedback. By the adoption of the method and device, remote operation such as vehicle locking function activation, forbidding, speed limiting, vehicle locking and unlocking can be rapidly conducted on the vehicle, the method and device have the advantages of being high in success rate, safe and reliable, and convenience is provided for remote management of the vehicle.

The utility model discloses a safety protection system for a standing driving type piling car, which belongs to the technical field of storage car safety protection and comprises a controller, an interlocking switch, a lifting buffer switch, a descending buffer switch, an OPS switch, a transportation safety switch, a high-position speed limiting switch, an arm protection switch, a standing plate sensing switch and a handle. The lifting height H of the portal frame is judged through the controller, and the driving motor and the pump motor are controlled to work according to the signal states of the handle, the standing plate sensing switch, the OPS switch, the arm protection switch, the transportation safety switch, the high-position speed limiting switch, the lifting buffer switch and the descending buffer switch. According to the utility model, the associated control of the running speed and the lifting and stacking height of the standing type stacking vehicle can be realized.

The invention belongs to the technical field of roads and bridges, and discloses a bridge floor icing traffic safety intelligent monitoring and early warning method, which comprises the following steps: S1, bridge floor time-varying temperature multi-source prediction based on thermal environment change; s2, predicting the time-varying friction coefficient of the frozen bridge floor based on the temperature field; s3, icing driving safety prediction based on the friction coefficient and the line condition; and S4, bridge deck icing traffic safety comprehensive speed-limiting intelligent early warning is carried out. The core innovation of the invention lies in a full-chain intelligent early warning system, through a closed-loop process of multi-source temperature prediction, dynamic friction modeling, driving safety evaluation and intelligent speed limit decision, the early warning precision and practicability are significantly improved, and through a technical path of data driven prediction, accurate safety evaluation and intelligent decision output, the early warning precision and practicability are improved. The technical performance, the economic benefit and the social safety are all broken through, and a good solution is provided for road traffic safety in winter.

The invention relates to a TPA-LSTM-based shunting area wet and slippery road surface vehicle type-based dynamic early warning and speed guiding method. The method comprises the following steps: S1, collecting traffic flow operation time sequence data in a highway shunting area wet and slippery road surface scene; s2, predicting speed limit values of different vehicle types for safe passing on the wet and slippery road surface in the shunting area; s3, formulating early warning information release content; s4, determining an early warning information issuing mode; s5, determining an information issuing position of the highway shunting area, wherein the information issuing position is comprehensively determined according to an existing shunting area ETC portal layout principle in combination with a warning information issuing standard; and S6, early warning information is pushed to the vehicle end, and the ETC door frame receives the optimal passing speed information of the wet and slippery road surface pushed by the traffic management background and issues the information to vehicle-mounted OBU equipment with a voice broadcast function of the vehicle. According to the method, the real-time performance, the dynamic adaptability, the personalized early warning and the driver response effect are remarkably improved, so that the occurrence rate of accidents in the diversion area in a wet and slippery road scene in rainy days is greatly reduced.