Saim, Muhammad (Ohio State University), Ozguner, Umit (Ohio State Univ.)

Entropy Based Metric to Assess the Accuracy of PNT Information

Scheduled for presentation during the Regular Session "Position, Navigation, and Timing Security in Highly Automated Vehicles" (TuBT3), Tuesday, August 30, 2022, 16:20−16:40, Ballroom

10th IFAC International Symposium on Advances in Automotive Control, August 28-31, 2022, Columbus, Ohio, USA

This information is tentative and subject to change. Compiled on April 19, 2024

Abstract

Entropy measures uncertainty present within the data. Highly Automated Vehicles (HAVs) can navigate safely and efficiently if location information of occluded dynamic objects is available. It is assumed that dynamic objects have GPS receivers, and location information can be acquired through a fast communication link. However, GPS info can be easily modified or suffers from high error because the transmission link is not secure or due to Non Line of Sight (NLOS) between transmitter and receiver. To solve this problem, an entropy metric is introduced to ascertain the value of the supplied information and reject information with a high amount of error present within the data. This work focuses on pedestrians as dynamic objects and uses Finite State Machine (FSM) based hierarchical control to navigate HAVs. It is shown that the entropy metric can improve the efficiency of the control of HAVs.