Mathfi-Transport
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A new AI-powered service helping your decision experts, or your AI-transport platform or automated engine diagnosis platform (as examples), make binary and multi-class decisions by performing binary or multi-class predictions and classifications for example or sanity checking their existing predictions/classifications an decisions:
1. Predict the battery failure in electric cars in next 2 hours: Butterfly AI can help predict whether battery of an electric car will fail in next 2 hours or not. In battery electric cars, it is crucial to have a predictive view of the performance high-voltage batteries as they face significant load fluctuations due to environmental parameters such as humidity, temperature, driving conditions and driver behaviour. Having measured and recorded the key data features of thousands of past journeys of the electric cars as indicators of the performance of their batteries including time (s), velocity (km/h), elevation (m), battery voltage (V) at max speed, battery voltage (V) at max acceleration, battery voltage (V) at max outside car temperature, battery voltage (V) at max air conditioning usage, battery voltage (V) at max heating usage, battery current (A) at max speed, battery current (A) at max acceleration, battery current (A) at max outside car temperature, battery current (A) at max air conditioning usage, battery current (A) at max heating usage, battery temperature (°C) at max speed, battery temperature (°C) at max acceleration, battery temperature (°C) at max outside car temperature, battery temperature (°C) at max air conditioning usage, battery temperature (°C) at max heating usage, max battery temperature (°C), throttle (%), motor torque (Nm), longitudinal acceleration (m/s^2), regenerative braking signal, SoC (%), displayed SoC (%), min. SoC (%), max SoC (%), heating power CAN (kW), heating power LIN (W), requested heating power (W), AirCon power (kW), heater signal, heater voltage (V), heater current (A), ambient temperature (°C), ambient temperature sensor (°C), coolant temperature heater core (°C), requested coolant temperature (°C), coolant temperature inlet (°C), coolant volume flow +500 (l/h), heat exchanger temperature (°C), cabin temperature sensor (°C), temperature coolant heater inlet (°C), temperature coolant heater outlet (°C), temperature heat exchanger outlet (°C), temperature defrost lateral left (°C), temperature defrost lateral right (°C), temperature defrost central (°C), temperature defrost central left (°C), temperature defrost central right (°C), temperature foot wheel driver (°C), temperature foot wheel co-driver (°C), temperature feet vent co-driver (°C), temperature feet vent Driver (°C), temperature head co-driver (°C), temperature head driver (°C), temperature vent right (°C), temperature vent central right (°C), temperature vent central left (°C), temperature vent right (°C) and having verified, recorded and labelled whether the battery has failed 2 hours after the point of measurements of the parameters (described above) during those past journey, for current ongoing electric car journeys, predict whether the battery will fail in next 2 hours (and present the probability and certainty of prediction) provided that the data features (presented above) are available during the time of prediction.
2. Predict the multi-class spatio-temporal traffic volume based on the historical traffic volume and other features in neighbouring locations across major highways, within next 15 minutes into future for all the road sensor locations: Imagine the traffic volume is measured every 15 minutes at N sensor locations along M major highways. Knowing the data features including the historical sequence of traffic volume, as of three levels/classes low (less than 0.33), medium (between 0.33 and 0.66) and high (more than 0.66), sensed during the 10 most recent sample points, week day, hours of day, road direction, number of lanes ,and name of the road, predict the traffic volume in neighbouring locations. within next 15 minutes for all the road sensor locations.
3. Predict whether an aircraft or car engine part is going to fail within certain period of time: Having the sensory and IoT data features such as performance of brakes, tyre pressure, overall fuel performance, performance of part A of engine, performance of part B of engine, overall speed performance, gear performance, and performance of part C and history of many other failed part C of other engines and many other relevant data features and knowing whether or not within certain time period, those part Cs failed or not (the target of prediction), for a target engine predict whether part C will fail within that period of time.
4. Predict if a driver will involve in an accident and file an insurance claim: Butterfly AI can help auto insurance companies assess the risk level associated with each driver (e.g. a new applicant). Knowing the key data features of thousands of past customers’ profile and journeys including number of past insurance claims, the number of times they have been in 500 m radius of an accident when it happened while driving during past year, the top three most frequent travel routes, number of accident hot spots within the top three routes, age, gender, number of years the driver has held a driving incense, past speeding fines, number of license penalty points, highest speed limits across driver’s most regular commute routes, travel regularly in early mornings (yes or no), travel regularly late night (yes or no), the weather status across the regular routes during the usual times of commute and during those past recorded journeys (rainy, cloudy, windy, lightening, storm, extreme cold and etc.), the lighting status across the top travel routes, the make of the car, the number of similar car models involved in accidents in recent months across the driver’s regular travel routes, history of driving under influence of alcohol, the known distracting factors that led to past accidents across driver’s regular travel routes (for example a direct blinding sunlight on the front screen during specific time of the day in parts of the regular routes or sudden black ice during the cold season on the road across those regular travel routes, etc.), MOT health score, millage on the car, number of breakdowns in past months, smoker, eyesight score, heart disease history, stroke history, high blood sugar, high blood pressure, diabetes, medication taken that may make driver drowsy or lose concentration, number of drivers with similar age, license, travel pattern and health status to the target driver who have been involved in accidents across target driver’s top travel routes, and having verified, labelled and recorded whether those past target drivers during those recorded journeys were ultimately involved in any accident or not (the target and label of the prediction), predict whether a new insurance applicant or existing customer (driver) will involve in an accident or not (and present the probability and certainty) provided that the data features (describe above) are available for that insurance applicant.
5. Predict whether a traffic route is going to go above the allowed CO2 emissions levels in next couple of hours to reroute the traffic by showing route signals to drivers to choose other alternative routes to avoid that: Having the historic sensory and IoT data features in a similar time of day including the number of cars or trucks on that road, their average speed, congestion on other surrounding roads, wind speed, temperature, rain, the samples of air in terms of level of CO2 emissions, PM2.5, PM10, NO, NO2, NOx, NH3, CO, SO2, O3, Benzene, Toluene, Xylene, AQI and knowing whether under those conditions the CO2 emissions passed the allowed limit (the target of prediction), predict, as of now, for this route the CO2 emissions will pass the allowed limit with the next couple of hours if we take no actions.
Or any other binary or multi-class transport classification or prediction use cases.
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