Smartphone-based GPS-integrated Location Prediction Model for OBD-II-Equipped Land Vehicle Localization
| dc.contributor.author | Chai Nee Ping | |
| dc.date.accessioned | 2026-10-15T02:05:59Z | |
| dc.date.issued | 2018 | |
| dc.description | Vehicle localization is important to track the movement of a particular land vehicle especially company vehicle. Global Positioning System (GPS) is commonly utilized in current vehicle localization applications. However, GPS has the issues of Non-Line-of-Sight (NLOS). Tremendous of research works had been carried out to remedy the shortcoming. Most of the research works deployed off-the-shelf Inertial Measurement Unit (IMU) to support GPS positioning when GPS signal lost. However, the IMU that can provide high accuracy positioning is usually expensive and the size is bulky. These make it not user-friendly and not applicable for civilian users. Thus, the objective of the study is to develop affordable smartphone-based GPS-integrated location prediction model for OBD-II-equipped vehicle localization to track the real-time location of vehicle. The focus of this study is the development of location prediction model which can perform integration of GPS data, OBD-II data and the vehicle’s heading direction to present the location of vehicle with high continuity and reasonable accuracy. To validate the findings, experimental works were carried out for 5 different scenarios, involving straight road, right-angle-turning, non-right-angle-turning, roundabout and U-turn. The results are presented in the graphical form to show its continuity and in the form of Root Mean Square Error (RMSE) to show its accuracy. The accuracy shows that the GPS-integrated location prediction model is within an accuracy range of 5.2 to 15.8 meters while the cost is affordable and the size is small. | |
| dc.identifier.uri | https://scholarhub.unimas.my/handle/123456789/3080 | |
| dc.language.iso | English | |
| dc.publisher | UNIVERSITI MALAYSIA SARAWAK | |
| dc.relation.ispartofseries | Faculty of Engineering | |
| dc.subject | Smartphone-based, GPS; OBD-II, Vehicle’s Heading Direction, Location Prediction Model | |
| dc.title | Smartphone-based GPS-integrated Location Prediction Model for OBD-II-Equipped Land Vehicle Localization | |
| dc.type | PhD |