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Evaluating Uncertainty of Measurement While Predicting Location in Smart Vehicles

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Abstract

Smart vehicles, capable of exchanging necessary information with each other and transportation infrastructure, have gained much attraction in automotive research. The data are gathered from location, velocity, acceleration and heading measurements, allowing vehicles to make smart decisions regarding safety and other applications. Specifically, accurate prediction of vehicle location measurement is considered crucial for making prompt decisions in emergency situations. These connected vehicles, equipped with advanced technologies, tend to improve driver safety and mobility radically. Still, most of the current vehicular safety applications rely on sensor measurements and uncertainty associated with them. In this paper, we have calculated the uncertainty of measurement for the deep learning-based long short-term memory model developed to estimate future location for smart vehicles. The prediction is effectively performed by exploiting the data retrieved from the past trajectory of the vehicle. Most of the available models, designed to predict a vehicle's location, do not provide any information about uncertainty in their measurements. This research aims to evaluate the uncertainty of measurement in prediction error and validation loss related to location prediction, enabling the system to make reliable decisions in the context of safety applications.

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Correspondence to Neeru Rathee.

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Pahal, S., Rathee, N. Evaluating Uncertainty of Measurement While Predicting Location in Smart Vehicles. MAPAN 36, 377–388 (2021). https://doi.org/10.1007/s12647-021-00458-w

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