Vehicles as sensors: high-accuracy rainfall maps from windshield wiper measurements

TL;DR

This paper demonstrates how connected vehicle windshield wiper data can be integrated with radar to produce high-accuracy, real-time rainfall maps, improving flood prediction and stormwater management.

Contribution

It introduces a Bayesian filtering framework that combines vehicle wiper data with radar measurements to enhance rainfall estimation accuracy.

Findings

Wiper measurements outperform traditional gauges and radar in predicting rainfall presence.

The method captures rainfall events missed by conventional sensors.

Enhanced rainfall maps can improve flood warnings and stormwater system operation.

Abstract

Connected vehicles are poised to transform the field of environmental sensing by enabling acquisition of scientific data at unprecedented scales. Drawing on a real-world dataset collected from almost 70 connected vehicles, this study generates improved rainfall estimates by combining weather radar with windshield wiper observations. Existing methods for measuring precipitation are subject to spatial and temporal uncertainties that compromise high-precision applications like flash flood forecasting. Windshield wiper measurements from connected vehicles correct these uncertainties by providing precise information about the timing and location of rainfall. Using co-located vehicle dashboard camera footage, we find that wiper measurements are a stronger predictor of binary rainfall state than traditional stationary gages or radar-based measurements. We introduce a Bayesian filtering framework that generates improved rainfall estimates by updating radar rainfall fields with windshield wiper observations. We find that the resulting rainfall field estimate captures rainfall events that would otherwise be missed by conventional measurements. We discuss how these enhanced rainfall maps can be used to improve flood warnings and facilitate real-time operation of stormwater infrastructure.