A data-driven generative model for GPS sensors for autonomous driving

Autonomous driving (AD) is envisioned to have a significant impact on people's life regarding safety and comfort. Positioning is one of the key challenges in realizing AD, where global navigation systems (GNSS) is traditionally used as an important source of information. The area of GNSS are we...

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Bibliographic Details
Published in:2018 IEEE ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS) pp. 1 - 5
Main Authors: Karlsson, Erik, Mohammadiha, Nasser
Format: Conference Proceeding
Language:English
Published: New York, NY, USA ACM 28.05.2018
Series:ACM Conferences
Subjects:
Automobiles
Autonomous driving
Computational modeling
Computing methodologies > Modeling and simulation > Model development and analysis > Model verification and validation
Computing methodologies > Modeling and simulation > Model development and analysis > Modeling methodologies
Data models
Global navigation satellite system
Global Positioning System
GPS modeling
Mathematical model
Sensors
Time series modeling
time series modeling
GPS modeling
autonomous driving
ISBN:1450357393, 9781450357395
Online Access:Get full text
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Summary:Autonomous driving (AD) is envisioned to have a significant impact on people's life regarding safety and comfort. Positioning is one of the key challenges in realizing AD, where global navigation systems (GNSS) is traditionally used as an important source of information. The area of GNSS are well explored and the different sources of error are deeply investigated. However the existing modeling methods often have very comprehensive requirements for the training data where all affecting conditions such as ephemeris data should be well known. The main goal of this paper is to develop a solution to model GPS error that only requires information which is available in the vehicle without having access to detailed information about the conditions. We propose a statistical generative model using autoregression and Gaussian mixture models and develop a learning algorithm to estimate the parameters using the data collected in real traffic. The proposed model is evaluated by comparing the produced artificial data with the validation data collected at different traffic conditions and the results indicate that the model is successfully mimicking the sensor behavior.
ISBN:1450357393
9781450357395
DOI:10.1145/3194085.3194089