3139 results about "Weather condition" patented technology

A system and method of calculating an estimated time of arrival for a traveler is provided, along with a notification of this estimated time that is sent to a third party. The traveler is equipped with a navigation unit having a global positioning sensor and a wireless communications device. The navigation unit communicates with a service center equipped with a server capable of generating a preferred navigational route for the traveler. The service center also includes a traffic database for maintaining current traffic information. Once the traveler has selected a predetermined time and frequency of notifications based upon a selected criterion, the navigation unit transmits the traveler's position and velocity to the service center. The service center then calculates the estimated time of arrival based upon the traveler's position, velocity, distance to be traveled and traffic and weather conditions. Once this time is calculated, the service center sends a notification to the third party.

Dynamic geofences may be generated and updated based on locations of related assets. For example, locations of a group of related assets, including a primary asset and one or more secondary assets, may be determined. A geofence associated with the primary asset may then be analyzed to identify any geofences of secondary assets that overlap. This process may be repeated for each overlapping geofence to identify additional overlap and expand or move a combined dynamic geofence. Environmental aspects such as time of day, weather conditions, and risk levels may be considered for dynamic geofence adjustments. The combined geofence and asset locations may be displayed on a user interface.

The invention relates to a low-altitude logistics unmanned aerial vehicle path planning method, which comprises the following steps of 1, discretizing an urban low-altitude region into computable grid units, a dynamic-static obstacle classification modeling technology is combined to endow a static obstacle with a basic risk degree, a height attenuation factor is introduced to quantify the influence of a high-altitude obstacle on a low-altitude path, and a collision risk of a dynamic obstacle is dynamically evaluated through a time dimension risk degree prediction model; 2, based on an improved genetic algorithm, comprehensively considering flight time, height change, risk degree and dynamic risk cost through a multi-objective optimization objective function, and generating a globally optimal task allocation scheme; 3, generating a local optimal three-dimensional path considering meteorological conditions and dynamic obstacle influence by adopting an improved particle swarm algorithm; and 4, performing path planning to feed back actual flight time and energy consumption to task allocation for optimization.

The invention relates to the technical field of environment perception, and discloses an optimization method and device for a multi-climate self-adaptive environment perception cooling fan system, and the method comprises the steps: collecting multi-point temperature, humidity and air pressure data, and carrying out the filtering processing, and obtaining an environment state vector; executing mathematical modeling of the multi-degree-of-freedom cooling system according to the environment state vector to obtain a linearized state space model; performing temperature gradient estimation and humidity compensation based on the linearized state space model to obtain an adaptive control law; inputting the adaptive control law into a model-free adaptive prediction controller to obtain a rolling optimization control sequence; weight self-adaptive adjustment and heat dissipation efficiency hierarchical prediction control are carried out on the rolling optimization control sequence based on climate conditions, fan rotating speed control and wind direction adjustment execution signals are generated, the problem that a traditional heat dissipation system is not sensitive to space temperature distribution and humidity changes is solved, and the heat dissipation control precision under different humidity conditions is improved.

The invention provides an aircraft digital twinning situation deduction and risk assessment method in a complex meteorological environment, and belongs to the technical field of data processing, the aircraft digital twinning situation deduction and risk assessment method in the complex meteorological environment comprises the following steps: collecting and processing weather data covering parameters such as wind direction and wind speed; obtaining aircraft performance parameters and building a database; and inputting and analyzing the flight plan. By establishing a three-dimensional deduction model including dynamics, weather influence and environment models, aircraft situations under different weathers are deduced, and flight safety, performance and task risks are evaluated. The method has the advantages that weather factors are comprehensively and accurately analyzed, and influences are quantified through data acquisition and processing and a meteorological model; the system is suitable for various aircrafts, can integrate navigation and control systems, and realizes remote monitoring management; risk assessment is scientific and reliable, multiple factors are integrated, powerful support is provided for flight decision making, and aircraft safety and task success rate in complex weather are effectively improved.

Described are systems and methods for adjusting a takeoff platform for a vertical takeoff and landing (VTOL) aerial vehicle. The described systems and methods allow a VTOL aerial vehicle to be oriented prior to takeoff to reduce the effects that a VTOL aerial vehicle may experience due to weather conditions at takeoff. In view of the aerodynamic characteristics of a VTOL aerial vehicle and certain weather conditions, a heading, pitch, and / or roll of the VTOL can be provided to counteract the effects the VTOL may experience from the weather conditions at takeoff.

The invention relates to a multi-scene self-adaptive high-speed obstacle early warning method and device, equipment and a medium. The method comprises the following steps: firstly, acquiring multi-source data of traffic sensing equipment, and carrying out space-time calibration and semantic analysis on the data to obtain a vehicle track feature vector set; thirdly, calculating real-time traffic flow parameters of the gridding area according to the feature vector set, and further generating a traffic feature database; secondly, modeling is carried out on the traffic characteristic database according to time periods and weather, and theoretical speed baseline parameters of all scenes are obtained; and finally, constructing a multi-dimensional feature matrix according to the theoretical speed baseline parameters, inputting the multi-dimensional feature matrix into the classification model to calculate an abnormal probability value, and if it is detected that a continuous abnormal probability value exceeds a dynamic threshold value in a preset time window, generating an early warning signal. By adopting the method, real-time monitoring and early warning of obstacles in multiple scenes of the expressway can be realized, and more effective guarantee is provided for driving safety of the expressway.

The invention discloses a rainy day target detection method based on paired image data generation and knowledge distillation, and belongs to the technical field of automatic driving target detection. The method comprises the following steps: acquiring a real image of a road scene and a semantic segmentation mask; inputting the semantic segmentation mask and the weather condition text information into a trained improved diffusion model to obtain a road scene generation image under a rainy day condition; introducing a CoT module and an ODConv module into the target detection model for optimization, and then improving the target detection model by adopting a dynamic weighted aggregation loss function to obtain an optimized target detection model; performing knowledge distillation to obtain a rainy day target detection enhancement model; and performing target detection by using the rainy day target detection enhancement model. The method provided by the invention is excellent in performance in a rainy day scene, has good generalization ability, can provide higher-precision target detection in various complex scenes, and effectively improves the detection accuracy and stability.

The invention relates to the technical field of intelligent control, in particular to an intelligent control method and system for heat supply parameters of a thermal power plant, and the method comprises the following steps: capturing temperature sensor data of multiple key parts of the thermal power plant, combining operation time with external weather condition data, carrying out data integration, analyzing the current operation state, and obtaining heat balance basic data. According to the method, comprehensive analysis is carried out in combination with external weather conditions, dynamic adjustment of heat supply parameters is achieved, the reaction speed is increased, the fuel ratio and load are optimized, the energy utilization efficiency is improved, long-term weather prediction data are integrated, heat supply demand changes are predicted and adapted, energy waste is reduced, and the energy utilization efficiency is improved. Real-time load adjustment and fuel efficiency calculation enable the thermal power plant to accurately match the actual heat demand, the combustion efficiency is optimized, and by predicting maintenance points and potential faults, the equipment operation reliability is improved, the maintenance plan is updated in time, the production safety is ensured, the operation flexibility and economic benefits are improved, and the environmental pollution risk is reduced.

The invention discloses a method and a system suitable for identifying bird echoes by a Chinese S-band dual-polarization weather radar. The method comprises the following steps: preprocessing observation parameter characteristics of Chinese S-band dual-polarization weather radar base data; establishing a membership function and a weight parameter of each parameter of the weather radar to form a fuzzy logic model; and through comprehensive judgment of statistical bird flock membership function aggregation values, accurate distinguishing of bird flock and rainfall data is realized. Based on four observation parameters and three derivations of the dual-polarization radar, probability density distribution function characteristics of bird flock and rainfall data are analyzed, a fuzzy logic algorithm process and weight parameters suitable for S-band dual-polarization radar bird flock recognition in China are constructed, the accuracy of bird flock and rainfall echo distinguishing is improved, and the probability density distribution function characteristics of the bird flock and rainfall data are analyzed. Through individual verification under different weather conditions such as bird separation, rainfall and clear sky, the effectiveness of the method is verified, and a new technical path is provided for future weather radar biological echo recognition and ecological monitoring.

The invention provides an aircraft digital twinning situation deduction and risk assessment method in a complex meteorological environment, and belongs to the technical field of data processing, the aircraft digital twinning situation deduction and risk assessment method in the complex meteorological environment comprises the following steps: collecting and processing weather data covering parameters such as wind direction and wind speed; obtaining aircraft performance parameters and building a database; and inputting and analyzing the flight plan. By establishing a three-dimensional deduction model including dynamics, weather influence and environment models, aircraft situations under different weathers are deduced, and flight safety, performance and task risks are evaluated. The method has the advantages that weather factors are comprehensively and accurately analyzed, and influences are quantified through data acquisition and processing and a meteorological model; the system is suitable for various aircrafts, can integrate navigation and control systems, and realizes remote monitoring management; risk assessment is scientific and reliable, multiple factors are integrated, powerful support is provided for flight decision making, and aircraft safety and task success rate in complex weather are effectively improved.

The invention discloses an unmanned aerial vehicle three-dimensional path planning method based on an improved artificial travel mouse optimization algorithm, belongs to the technical field of unmanned aerial vehicle path planning methods, and aims at enabling an unmanned aerial vehicle to autonomously navigate, avoid obstacles, find an optimal path and improve task execution efficiency and safety in a complex environment. A constraint condition and a target function of unmanned aerial vehicle three-dimensional path planning are mapped into a search space and a fitness function of an optimization algorithm, an optimal flight path is searched by adopting an artificial travel mouse optimization algorithm, and optimization calculation is performed on the path in combination with unmanned aerial vehicle performance constraints, topographic features and meteorological conditions; aiming at a three-dimensional path planning problem of an unmanned aerial vehicle under a complex condition, an artificial travel mouse algorithm is adaptively improved, and algorithm parameters and structures are respectively adjusted according to mathematical characteristics of a problem model in the aspects of initialization, exploration and development; the flight path is adjusted based on environment change and self state information sensed by a sensor in real time, and the safety and task requirements are met.

The invention discloses an unmanned aerial vehicle patrol route optimization method fusing weather conditions and power consumption prediction, and the method comprises the steps: (1), according to the historical flight data of a patrol unmanned aerial vehicle, combining a random forest and a linear regression method; establishing an unmanned aerial vehicle power consumption prediction model considering the coupling influence of environmental factors and task factors, and drawing a time-power-power curved surface; (2) inputting a patrol object digital map, a required patrol point set, a selectable patrol point set, unmanned aerial vehicle performance data, a route starting point and a route ending point, and obtaining an initial flight route without considering real-time weather conditions; and (3) optimizing the initial flight route in combination with the real-time weather condition and the time-power-electric quantity curve, and when the predicted consumed electric quantity is greater than the acceptable consumed electric quantity, sequentially giving up the selectable patrol points according to the importance sequence and re-drawing the route until the predicted consumed electric quantity is less than or equal to the acceptable consumed electric quantity. The method has a remarkable optimization effect on precise patrol task execution of the unmanned aerial vehicle.

The invention discloses a tree lodging detection method and system combined with reference object image analysis. The method comprises the steps that based on an initial image shot by a low-altitude camera, key point positions are determined through a tree and reference object key point detection model, two straight lines are formed through fitting, and the current weather condition is judged. And if the weather is not the extreme weather, evaluating the state of the tree through the two fitting straight lines, and outputting the image and position information of tree inclination or lodging. If the weather is extreme weather, further judging whether the tree is submerged by accumulated water or not. If not, monitoring the change of the included angle between the fitting straight lines to detect whether the tree locates or not; if so, outputting the lodging position and direction. And if the tree is submerged by the accumulated water, calculating the height of the submerged part by adopting a reference object and accumulated water relation model, updating the key points of the tree, finally determining the state of the tree by comparing the fitting straight line, and outputting a corresponding evaluation result. By implementing the method provided by the invention, the precision and reliability of tree lodging monitoring can be improved.

The invention provides a multifunctional controller for a new energy automobile and the new energy automobile, and relates to the technical field of new energy automobile control. The multifunctional controller for the new energy automobile comprises a data collection module, a data processing module, a prediction module, a storage system, a communication system, a user interface, a software system, a battery management system, an auxiliary driving system, a safety system, an emotion recognition module, a power supply system and a continuous learning system. Through the prediction model and time sequence analysis, the charging and discharging strategy of the battery is optimized, the service life of the battery is prolonged, the energy efficiency is improved, a power system is optimized according to real-time data and a prediction result, the performance and efficiency of the vehicle are improved, and thermal management of the vehicle is optimized according to weather forecast and real-time traffic information. The optimal performance can be kept under different weather conditions, and the system can automatically adjust the internal environment, such as temperature and music playing, of the vehicle by analyzing the emotional state of the driver so as to adapt to the emotion of the driver.

The invention discloses a dynamic public transport scheduling optimization system based on artificial intelligence, and the system comprises a mobile application program which is used for collecting the feedback of passengers, and displaying the real-time bus information, predicted arrival time, route suggestions and ticket price information to the passengers; the background management module is used for monitoring the state of the optimization system, adjusting a scheduling strategy and managing for feedback, and regularly maintaining and updating the optimization system; the data collection module is used for collecting vehicle GPS data, traffic flow monitoring data, weather conditions, special event information and historical and real-time data fed back in real time on social media; and the multi-modal traffic data fusion processing module is used for fusing the historical and real-time data collected by the data collection module and preprocessing the historical and real-time data. The invention also discloses a dynamic public transport scheduling optimization method based on artificial intelligence. According to the invention, the operation efficiency and the passenger satisfaction degree of public transportation are improved.

The invention discloses an unmanned aerial vehicle full-spectrum autonomous inspection system based on visual perception, and belongs to the technical field of unmanned aerial vehicle inspection, and in the unmanned aerial vehicle full-spectrum autonomous inspection system, a self-adaptive environment module can sense changes of the surrounding environment, weather conditions, obstacles, topographic relief and the like in real time and quickly adapt to the changes. And the unmanned aerial vehicle can keep stable flight and accurate data acquisition in a complex and changeable inspection environment. When strong wind or sudden weather change occurs, the self-adaptive environment module can automatically adjust the flight attitude and speed of the unmanned aerial vehicle, safety is ensured, and task continuity is maintained. Besides, by continuously learning environment characteristics, the module can predict potential risks and plan an obstacle avoidance path in advance, so that the autonomy and reliability of the system are improved. The self-adaptive capability remarkably improves the inspection efficiency and safety of the unmanned aerial vehicle, and reduces the risk of task failure or equipment damage caused by environmental factors.

A termite detection and mitigation system can gather data describing conditions at a location, including the location's elevation, soil type, topography, weather conditions, and season. The termite detection and mitigation system can additionally obtain sensor data describing environmental conditions within a structure (e.g., a home or other building) at the location. Sensor data may be obtained using a termite sensing device placed within the structure. The termite detection and mitigation system can develop models that characterize how likely it is that termites are present within a structure based on its sensor data and associated location data. Using the models, the termite detection and mitigation system can alert users when there is a likelihood that termites are present. The termite detection and mitigation system can also deploy mitigation responses, such as poisons or sealants, to mitigate the termite intrusion.

The invention discloses a ship management method and system based on digital twinborn scene construction. The method comprises the following steps: acquiring multi-source scene data of a target water area in real time, and constructing a corresponding ship management digital twin model; performing integrity evaluation on the constructed digital twinborn model, and performing defect compensation according to an evaluation result to obtain an optimized digital twinborn model; further collecting ship density and weather condition data of each sub-region of the target water area, constructing a network transmission delay prediction model, analyzing related data based on the model, and generating a network delay coefficient distribution diagram of the target water area; and according to the delay distribution diagram, determining the real-time updating lag amount of each subunit of the optimization model, and correcting the ship position information in the digital twin model. According to the method, the real-time performance and accuracy of the digital twin model can be remarkably improved, and the ship operation management efficiency and safety in a complex water area environment are enhanced.

The invention discloses a civil aviation wind shear and visibility early warning method, which comprises the following steps of: acquiring an echo signal by using a high-precision and high-temporal-spatial-resolution coherent laser radar, and simultaneously acquiring civil aviation meteorological conventional observation data; calculating wind field distribution through a wind field inversion algorithm, and calculating visibility distribution through a visibility inversion algorithm; positioning a wind shear region by using a wind shear recognition algorithm, analyzing a visibility space-time evolution trend by combining a space-time deduction algorithm, and generating a visibility profile; inputting the wind shear identification data, the visibility profile data and the meteorological routine observation data into a machine learning model for modeling training; generating a wind shear and visibility early warning scheme over the airport based on the trained prediction model; and generating a wind shear alarm signal and visibility early warning information based on the early warning scheme, and outputting the signals to a civil aviation meteorological early warning system. According to the invention, high-precision wind shear and visibility combined early warning can be realized, and the early warning accuracy under complex weather conditions is improved.

The invention provides a meteorological-power collaborative prediction method and device, and belongs to the field of new energy power systems. The method provided by the invention comprises the steps of determining condition parameters of a prediction task and a prediction site, wherein the condition parameters at least comprise multi-source meteorological observation data and new energy station operation data; according to the multi-source meteorological observation data and a pre-trained meteorological prediction large model, generating a future time sequence meteorological prediction result of the prediction site in a future time period; according to the time scale of the prediction task and historical weather conditions, adaptively matching a corresponding parameter set of the pre-trained power prediction large model; and inputting the future time sequence weather prediction result into the large power prediction model to generate a new energy power prediction result of the new energy station. According to the meteorological-power collaborative prediction method and device provided by the invention, the technical problems that the collaboration of meteorological prediction and power prediction in a new energy power system is insufficient, the precision is limited, and a complex scene is difficult to adapt can be solved.

The invention discloses an offshore wind turbine blade online monitoring method based on voiceprint recognition, which belongs to the field of wind turbine blade acoustic signal analysis, and comprises the following steps: acquiring wind turbine voiceprint data, weather condition data and wind turbine generating capacity data, and constructing a signal processing model; the collected voiceprint signals are subjected to denoising, amplification and feature extraction through a signal amplifier, a filter and a feature extraction unit, a deep learning model is constructed, voiceprint signal parameters, weather data and wind turbine generating capacity are imported into the deep learning model to evaluate future wind turbine blade cracks, a crack evaluation model is constructed, and the wind turbine blade cracks are evaluated. And importing the crack sound source position, the crack depth, the crack length and the estimated damage score into a crack evaluation model to evaluate the crack degree of the fan blade under the future weather condition, grading the fan cracks according to the crack damage score, and giving an alarm. The early warning of the future crack condition of the fan blade is realized by combining the voiceprint signal of the offshore fan blade, the weather condition and the generating capacity.

According to several aspects, a method for path planning for a vehicle includes determining a predicted trajectory of a remote vehicle. The predicted trajectory of the remote vehicle includes a plurality of predicted trajectory nodes. The method further includes determining a plurality of possible trajectories for the vehicle. The plurality of possible trajectories includes a plurality of possible trajectory nodes. The method further includes determining one or more evaluation metrics of each of the plurality of possible trajectory nodes based at least in part on a weather condition in an environment surrounding the vehicle. The method further includes selecting an optimal trajectory for the vehicle from the plurality of possible trajectories based at least in part on the one or more evaluation metrics of each of the plurality of possible trajectory nodes. The method further includes performing a first action based at least in part on the optimal trajectory.

The invention belongs to the field of road vehicle control systems, and relates to a method for predicting the risk of collision between an electric bicycle and a vehicle based on the multi-modal behavior of the electric bicycle, which utilizes physical modeling and takes the influence of the braking distance and the transverse and longitudinal stability of the electric bicycle on the vehicle as a basic risk; automobile risks caused by the environment are further considered, and multi-dimensional environment risk assessment modeling is carried out by integrating four dimensions of dynamic distance-speed, adjacent vehicle types, traffic flow density and weather conditions (precipitation, temperature, wind speed and visibility); the risk of electric bicycle operation behaviors (acceleration change and steering angle and frequency) to the automobile is combined; the complex relation between the environment change and the whole vehicle risk is captured in real time by using the environment sensitivity coefficient, and finally a set of comprehensive, accurate and real-time multi-dimensional comprehensive risk assessment method is provided for the vehicle for the multi-mode riding behavior of the electric bicycle, so that the collision risk in the complex traffic flow environment is reduced.

The invention relates to the technical field of intelligent traffic, and discloses an expressway parking risk dynamic early warning method and system based on spatial-temporal characteristics and an adaptive threshold, and the method comprises the steps: collecting vehicle driving data through a multi-type sensor, obtaining vehicle internal data through 5G communication, and employing a D-S evidence theory for fusion; performing spatio-temporal feature processing on the fused data, constructing a spatio-temporal matrix, and extracting features by using a spatio-temporal convolutional neural network; constructing a parking risk assessment model by combining a gating circulation unit of an attention mechanism based on the extracted features, and outputting a parking risk probability in a period of time in the future; according to road sections, time periods and weather conditions, an early warning threshold value is dynamically adjusted by using a reinforcement learning algorithm, multi-channel early warning is triggered when the threshold value is exceeded, and the system comprises a data acquisition and transmission subsystem, a data processing and analysis subsystem and an early warning decision and release subsystem. The objective of the invention is to realize prediction and timely and effective early warning of the parking risk of the high-speed vehicle.

The invention discloses an unmanned aerial vehicle obstacle avoidance method based on sensing fusion and reinforcement learning, and belongs to the technical field of aviation flight control, and the method comprises the following steps: S1, sensor fusion time-space synchronization; s2, sensing fusion obstacle recognition and positioning; s3, obstacle avoidance route planning based on reinforcement learning; the low-resolution global map and a local occupation grid map with the unmanned aerial vehicle as the center are superposed to serve as multi-channel input, and the current state and the historical action sequence of the unmanned aerial vehicle are combined to be input into an intelligent agent model fused by a trained convolutional neural network and a long-short-term memory network; and an obstacle avoidance decision is output through a reward function of the agent model, and the airborne computer executes the obstacle avoidance decision to control the unmanned aerial vehicle to complete an obstacle avoidance action. Through sensor fusion, the obstacle sensing capability under different weather conditions and different flight environments is enhanced, the sensing distance is also increased, and the flight safety under extreme conditions can be ensured.

The invention discloses a multi-weather degraded image restoration method based on a state space model. The method comprises the following steps: acquiring multi-scene clear images, synthesizing images under rain, snow and fog weather conditions by using an image synthesis technology, and constructing a standard multi-weather data set; preprocessing the data set and then dividing the data set; constructing a multi-weather degradation image restoration model based on the state space model, and performing training optimization on the model; the method comprises the following steps: acquiring an image which is degraded due to weather influence, preprocessing the image, inputting the processed image into a pre-trained multi-weather degraded image restoration model for restoration, and outputting the image as a processed clear image. According to the method, a self-adaptive channel attention mechanism is introduced based on the state space model, the capturing capability of the network on image local features is enhanced, images of various scales can be effectively processed through overlapped block embedding and multi-scale processing strategies, and the image recovery performance is remarkably improved. The method provided by the invention can process image restoration tasks including but not limited to various weather degradation caused by rain, snow and fog, and has a wide application prospect.

The invention discloses a rainfall prediction method and system for the Beijing, Tianjin and Hebei region, and relates to the technical field of rainfall prediction, and the method comprises the steps: obtaining multi-source data, carrying out the preprocessing of the data, extracting the core characteristics of the data, carrying out the dynamic rainfall modeling according to the core characteristics of the data, and simulating a rainfall evolution path; and constructing a rainfall prediction model, performing rainfall intensity prediction based on the rainfall evolution path, performing extreme weather early warning based on a prediction result, and generating a rainfall distribution diagram of a high-risk area. Through fusion of multi-source data, the generative adversarial network and spectral analysis, the spatial resolution of the data and the rainfall evolution prediction capability are improved, the real-time performance and the accuracy of rainfall prediction are improved in combination with dynamic rainfall modeling, a fractional order differential equation and a multiple regression model, and the prediction accuracy is improved. The rainfall prediction challenge under complex weather conditions can be effectively dealt with, and accurate prediction of rainfall intensity and extreme weather events is realized.

The invention discloses a road tunnel lighting system and method based on traffic flow and external environment. Relates to the technical field of highway tunnel lighting. At present, a traditional road tunnel lighting system mode neglects factors such as tunnel external environment change and traffic flow density, so that energy waste and poor lighting effect are caused, especially under day and night alternation, weather change or special traffic conditions; according to the system, information such as traffic flow, environment illumination intensity and meteorological conditions inside and outside a tunnel is collected in real time through multiple sensors, and the illumination brightness inside the tunnel is dynamically adjusted by combining calculation and dimming strategies of a central control unit so as to cope with different traffic and weather conditions; according to the road tunnel lighting system and method, through dimming control, the energy utilization efficiency can be remarkably improved while traffic safety is guaranteed, and high economic benefits and social benefits are achieved.

An end-to-end perception perturbation modeling system for a vehicle includes one or more controllers storing a detection model in memory. The detection model includes a plurality of detection model plots that each indicate a probability value that an object in an environment surrounding the vehicle is detected based on a current weather condition and a distance measured between the vehicle and the object detected in the environment surrounding the vehicle. The one or more controllers execute instructions to receive an input state of the vehicle that is observed during non-inclement weather conditions and indicates one or more vehicle states, the current weather condition, and the distance. The controllers determine a perturbed state of the vehicle observed during the current weather condition.