# OBU for Accurate Navigation through Sensor Fusion in the Framework of the EMERGE Project
## 1 Introduction  
Urban projections until 2050 [1] show an increased concentration of population in urban areas. This scenario presents challenges in terms of mobility and traffic that aim to efficiently and sustainably manage resources to increase the quality of life. There are many approaches behind the concept of smart mobility [2; 3; 4]:  
* the use of technologies that provide helpful information (IoT, sensors, network);
* the creation of structures that include smart roads and monitoring platforms;
* the reduction of the ecological footprint (energy saving, bike lanes, etc.). However, most solutions rely on efficient and accurate navigation processes. The development of advanced driver assistance systems (ADAS) for connected and automated mobility makes the accuracy [5] of the navigation process a key issue. Sensors involved in the process of perception and comprehension of the environment, as well as fusion algorithms and decision-makers, have played a significant and evolving role in recent years. Nowadays, many resources provide helpful information to determine position, orientation, time or velocity. Nevertheless, some scenarios and phenomena directly affect the performance of sensor technologies. Thus, it is essential to develop architectures that use a multi-sensor and redundant approach to mitigate the adverse effects caused by the temporary under-performance of one or several sensors. Recently, proposed architectural solutions have concentrated on sensor fusion [6; 7] rather than implementing the entire On Board Unit (OBU) system [8; 9; 10; 11; 12]. This work addresses the fusion process as the core element of the OBU, dedicating a complete architectural layer. The elements used as sensors in our implementation are the inertial measurement unit (IMU) and the global navigation satellite system (GNSS) receiver, complemented by a GNSS augmentation system. The primary obstacle in enabling AV navigation lies in establishing a consistently accurate and dependable navigation solution across all landscapes. GNSS stands out as the most prevalent source of navigation solutions due to its enduring stability in offering long-term solutions [13]. However, GNSS encounters limitations in providing accurate or any navigation solution in certain circumstances, notably within urban settings that include tunnels, underground parking areas, and high-rise buildings [14; 15; 16]. Satellite navigation in the urban environment is subject to some objectively critical issues, mainly related to the following items. * Satellite visibility: due to the presence of urban canyons, the number of visible satellites, crucial for obtaining an accurate position, is limited. * Multipath and non-line-of-sight reception: the typicality and singularity of the urban context, in which the vehicles carry out their service, produces a non-trivial multipath error that seriously compromises the performance of the GNSS signal. * Other kinds of interference that can have a significant impact in an urban context. These criticalities result in considerable variability in the reception of the GNSS signal, ranging from optimal conditions to complete absence. On the other hand, inertial navigation systems (INS) possess several advantages. They operate continuously, exhibit low short-term noise, and resist jamming and interference. However, INS accuracy degrades over time due to integration errors, and maintaining effective sole-means navigation is costly. While GNSS ensures high long-term accuracy, its output rate is lower, and signal obstruction leads to discontinuous navigation. By integrating INS and GNSS, their advantages complement each other. GNSS measurements prevent inertial solution drift, while INS smoothes GNSS output and bridges signal outages. This integration results in a continuous, high-bandwidth navigation solution with long and short-term accuracy [17].