Using floating car data for more precise road weather forecasts

Hellweg, Meike; Acevedo-Valencia, John-Walter; Paschalidi, Zoi; Nachtigall, Jens; Kratzsch, Thomas; Stiller, Christoph

doi:10.1109/vtc2020-spring48590.2020.9129401

Cited by 6 publications

(4 citation statements)

References 1 publication

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“…The positive mean value for both methods shows a clear trend towards underestimation of the ambient temperature measured by the vehicle’s sensor. A tendency of vehicle sensors to measure higher temperature values when compared to stationary measurements was also found in investigations of other authors, 15,16 a probable explanation for this is the influence of vehicle waste heat on the measurement. Since the investigated vehicle is electric driven, the influence is less strong in this case.…”

Section: Idw Using Local Climate Zonessupporting

confidence: 65%

Kriging and local climate zones – key to higher accuracy in range prediction?

Herzlieb

Sturm

2023

Composites and Advanced Materials

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Range prediction of electric vehicles requires knowledge of many parameters, including information about the vehicle environment. As an alternative to onboard measurement, external data sources (secondary data) can be used. The presented methods are suitable for estimating climatic data for vehicle application including urban areas and areas of complex topography. Compared to an inverse distance weighting approach for ambient temperature values, Kriging yields better overall accuracy especially for areas of complex topography, improving accuracy in urban regions, however, remains a challenge. An approach to improve accuracy in urban areas by considering local climate zones in the selection of secondary data sources produces mixed results.

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Section: Idw Using Local Climate Zonessupporting

confidence: 65%

Kriging and local climate zones – key to higher accuracy in range prediction?

Herzlieb

Sturm

2023

Composites and Advanced Materials

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“…This is a continuation of our previous research [ 22 , 43 ]. Given the large amounts of generated data, artificial intelligence and machine learning are perfectly suited to improve the sensor accuracy [ 15 ]. The accuracy of data-driven approaches rises with the amount of data available, outperforming statistical methods.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…Accurate forecasts of dangerous road conditions allow the distribution of real-time warnings to nearby vehicles to alert drivers or to inform road management authorities, who can take preventative measures such as salting roads or measures such as snow removal after an event. The Finnish Meteorological Institute recently introduced a system to distribute road weather data amongst vehicles [ 14 ], and in [ 15 ], the potential of floating car data was also addressed. The potential of vehicles-based observations is continuously increasing [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Leveraging Artificial Intelligence and Fleet Sensor Data towards a Higher Resolution Road Weather Model

Bogaerts

Watelet

Bruyne³

et al. 2022

Sensors

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Road weather conditions such as ice, snow, or heavy rain can have a significant impact on driver safety. In this paper, we present an approach to continuously monitor the road conditions in real time by equipping a fleet of vehicles with sensors. Based on the observed conditions, a physical road weather model is used to forecast the conditions for the following hours. This can be used to deliver timely warnings to drivers about potentially dangerous road conditions. To optimally process the large data volumes, we show how artificial intelligence is used to (1) calibrate the sensor measurements and (2) to retrieve relevant weather information from camera images. The output of the road weather model is compared to forecasts at road weather station locations to validate the approach.

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“…Although the PeWeMos system [ 6 ] argues that it may monitor the very fine details and weather changes within the one area and provide very fine weather information and changes in even a sufficient amount of time, the interpretation of the sensed weather data and the cooperation of the buses, bus stops, and passengers for weather monitoring are not addressed. Hellweg et al [ 7 ] uses floating car data for road weather forecasts, which aims to increase the resolution of the weather observation network and the forecast model. The preliminary results show that bias corrections and quality control of the raw signals are key issues to enable safe autonomous driving.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Weather Monitoring and Prediction Using City Buses and Machine Learning

Huang

Chen²,

Wen

2020

Sensors

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Accurate weather data are important for planning our day-to-day activities. In order to monitor and predict weather information, a two-phase weather management system is proposed, which combines information processing, bus mobility, sensors, and deep learning technologies to provide real-time weather monitoring in buses and stations and achieve weather forecasts through predictive models. Based on the sensing measurements from buses, this work incorporates the strengths of local information processing and moving buses for increasing the measurement coverage and supplying new sensing data. In Phase I, given the weather sensing data, the long short-term memory (LSTM) model and the multilayer perceptron (MLP) model are trained and verified using the data of temperature, humidity, and air pressure of the test environment. In Phase II, the trained learning model is applied to predict the time series of weather information. In order to assess the system performance, we compare the predicted weather data with the actual sensing measurements from the Environment Protection Administration (EPA) and Central Weather Bureau (CWB) of Taichung observation station to evaluate the prediction accuracy. The results show that the proposed system has reliable performance at weather monitoring and a good forecast for one-day weather prediction via the trained models.

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Using floating car data for more precise road weather forecasts

Cited by 6 publications

References 1 publication

Kriging and local climate zones – key to higher accuracy in range prediction?

Kriging and local climate zones – key to higher accuracy in range prediction?

Leveraging Artificial Intelligence and Fleet Sensor Data towards a Higher Resolution Road Weather Model

Real-Time Weather Monitoring and Prediction Using City Buses and Machine Learning

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