Towards Detection of Road Weather Conditions using Large-Scale Vehicle Fleets

Mercelis, Siegfried; Watelet, Sylvain; Casteels, Wim; Bogaerts, Toon; Bergh, Joris Van den; Reyniers, Maarten; Hellinckx, Peter

doi:10.1109/vtc2020-spring48590.2020.9128484

Cited by 11 publications

(10 citation statements)

References 17 publications

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“…These measurements are validated by comparing the observations by our mobile sensors to meteorological forecasts. 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 ].…”

Section: Materials and Methodssupporting

confidence: 84%

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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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Section: Materials and Methodssupporting

confidence: 84%

“…Local measurements are gathered around the city of Antwerp in Belgium from a fleet of postal vans [ 22 ]. The vehicles are equipped with various external sensors.…”

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

Self Cite

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“…Vehicle-based observations of air temperature from built-in sensors are known to be consistent with reliable observations obtained from roadside weather information stations (RWISs), provided the external air-temperature sensor is located away from the engine block (Mercelis et al, 2020). We therefore use RWIS data provided by Highways England (2018) to provide a comparison with similar point observations for different weather conditions.…”

Section: Roadside Weather Information Station Observationsmentioning

confidence: 80%

“…Vehicle-based observations can also be obtained through several non-crowdsourcing methods. For example, the data can be obtained directly from vehicle manufacturers through connected vehicle initiatives (e.g., Mahoney and O'Sullivan, 2013), from built-in sensors of vehicle fleets via the controller area network (CAN) (e.g., Mercelis et al, 2020), or through externally mounted sensors (e.g., Anderson et al, 2012). In this paper, vehicle-based observations of air temperature are obtained from built-in vehicle sensors through on-board diagnostic (OBD) dongles.…”

Section: Introductionmentioning

confidence: 99%

Exploring the characteristics of a vehicle-based temperature dataset for convection-permitting numerical weather prediction

Bell,

Dance,

Waller

2021

Preprint

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Crowdsourced vehicle-based observations have the potential to improve forecast skill in convection-permitting numerical weather prediction (NWP). The aim of this paper is to explore the characteristics of vehicle-based observations of air temperature. We describe a novel lowprecision vehicle-based observation dataset obtained from a Met Office proof-of-concept trial. In this trial, observations of air temperature were obtained from built-in vehicle air-temperature sensors, broadcast to an application on the participant's smartphone and uploaded, with relevant metadata, to the Met Office servers. We discuss the instrument and representation uncertainties associated with vehicle-based observations and present a new quality-control procedure. It is shown that, for some observations, location metadata may be inaccurate due to unsuitable smartphone application settings. The characteristics of the data that passed quality-control are examined through comparison with United Kingdom variable-resolution model data, roadside weather information station observations, and Met Office integrated data archive system observations. Our results show that the uncertainty associated with vehicle-based observationminus-model comparisons is likely to be weather-dependent and possibly vehicle-dependent. Despite the low precision of the data, vehicle-based observations of air temperature could be a useful source of spatially-dense and temporally-frequent observations for NWP.

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“…Vehicle‐based observations can also be obtained through several non‐crowdsourcing methods. For example, the data can be obtained directly from vehicle manufacturers through connected vehicle initiatives (Mahoney III & O'Sullivan, 2013), from built‐in sensors of vehicle fleets via the controller area network (Mercelis et al, 2020), or through externally mounted sensors (Anderson et al, 2012). In this paper, vehicle‐based observations of air temperature are obtained from built‐in vehicle sensors through on‐board diagnostic (OBD) dongles.…”

Section: Introductionmentioning

confidence: 99%

Exploring the characteristics of a vehicle‐based temperature dataset for kilometre‐scale data assimilation

Bell

Dance

Waller

2022

Meteorological Applications

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Towards Detection of Road Weather Conditions using Large-Scale Vehicle Fleets

Cited by 11 publications

References 17 publications

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

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

Exploring the characteristics of a vehicle-based temperature dataset for convection-permitting numerical weather prediction

Exploring the characteristics of a vehicle‐based temperature dataset for kilometre‐scale data assimilation

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