Identification of the Impact of GNSS Positioning on the Evaluation of Informative Speed Adaptation

Abstract

Autonomous vehicles (AVs) are self-driving vehicles that operate and perform tasks under their own power. They may possess features such as the capacity to sense environment, collect information, and manage communications with other vehicles. Many autonomous vehicles in development use a combination of cameras, various kinds of sensors, GPS, GNSS, radar, and LiDAR, with an on-board computer. These technologies work together to map the vehicle’s position and its proximity to everything around it. To estimate AV positioning, GNSS data are used. However, the quality of raw GNSS observables is affected by a number of factors that originate from satellites, signal propagation, and receivers. The prevailing speed limit is generally obtained by a real-time map matching process that requires positioning data based on a GNSS and a digital map with up to date speed limit information. This paper focuses on the identification of the impact of GNSS positioning error data on the evaluation of informative speed adaptation. It introduces a new methodology for increasing the accuracy and reliability of positioning information, which is based on a position error model. Applying the sensitivity analysis method to informative speed adaptation yields interesting results which show that the performance of informative speed adaptation is positively affected by minimizing positioning error.