Image-based classification of GPS noise level using convolutional neural networks for accurate distance estimation

Abstract

Accurate route prediction and distance calculation is an integral part of processing GPS data, particularly in the ride-sharing industry. One common approach has been to map match GPS data to estimate driving traces under noise and sparsity conditions. However, map-matched traces have proven to be at most as good as the underlying map data. Incorrect or missing map data can lead to large, improbable deviations, even when the geometry of the underlying raw GPS data is within tolerance of the actual route. Ideally, we want to take advantage of both the map-matched route and the GPS data to minimize the distance error. Therefore, we propose a method to classify the noise level (or trustworthiness) of small sub-sections of the input data on any given route to conditionally select between using the raw GPS data and the map-matched route as the best estimate of the driving path. For the classifier, each section is treated as an image matrix and is fed through a convolutional neural network trained only on a large amount of synthetic data. The result is a classifier that achieves human-level performance and can be used in a real-time system to reduce distance errors between the predicted and ground-truth traces of actual ride data.