# A Systematic Review of Perception System and Simulators for Autonomous Vehicles Research
## 4 Fusion Algorithms
### Fusion Methods in Positioning Systems  
The precise determination of the position of a vehicle is a crucial aspect for navigation tasks in related areas such as autonomous vehicles, intelligent transporting systems and intelligent vehicles. The bibliography reports a huge number of applications where fusion methods are used with sensors belonging to both positioning systems and environment perception systems mentioned in this work.  
Traditionally, the Kalman filter and extended Kalman filter has been one of the most used algorithms to reduce the degree of uncertainty in sets of data supplied by different sources and to increase the accuracy in positioning systems. In [40], a real-time data fusion system for improving car positioning precision in urban environments is proposed. The proposed method uses the data from four-wheel speed sensors and low-cost GNSS with an EKF to fuse the data. The urban scenarios are prone to suffer low precision and transient unavailability of the GNSS signal. The main contribution of this work enables accurate car positioning during short GNSS signal outages. In [41], a fusion framework to obtain the cooperative positioning information from radar sensor data is presented. The framework fuses the information received from radar sensors by means of the KF method. The measurements recorded on a highway and a rural road demonstrate that the fusion of both information sources outperforms the positioning estimation using only the radar sensor  
In [42], a fusion method is presented, which integrates the information supplied by a stereo camera, a 2D LiDAR, and a GPS to obtain precise positioning of a vehicle. The proposed method uses a prelaminar stage, where an outlier-rejection invariant closest point method reduces the matching ambiguities of scan alignment during tasks of motion estimation with the 2D LiDAR. Finally, after the validation process, the information is fused by means of an unscented information filter.  
The particle filter (PF) is a method used to obtain a good estimation of vehicle position in non-linear models. In [43], a robust fusion algorithm is presented, based on a particle filter using the entropy information theory. The fusion method uses a 3D urban area mapped previously (point cloud map) together with a 3D LiDAR with 32 channels, an IMU, and wheel odometry for obtaining the location of a vehicle in urban scenarios. The results have been compared with the offline ground-truth obtained with a RTK-GPS. Another example is the location of a vehicle in urban areas by fusing the data and a PF as shown in [44]. In this case, the data input to the particle filter is composed of GPS, an IMU, a camera, and a digital map. The experimental results have shown that this system reliably localizes the vehicle, even while passing through tunnels, long urban canyons, and under an elevated railroad.  
The selection of the most suitable type of filter for data fusion will depend on the linearity of the model used. For linear models, the KF provides an optimal solution to the fusion problem. For non-linear models, other techniques such as EKF, UKF, or PF are used to perform the linearization of the model. For example, in the field of perception systems, the non-linearity of the data produced by RADAR systems requires the use of EKF, UKF or PF filters. The bibliography reports comparisonsin the performance of this type of filters; the best results in the estimation of non-linear models are produced with UKF [45; 46].  
Fusion techniques based on features extraction that use classification algorithms (e.g., AdaBoost, SVM, etc.) are being overcome by the enormous degree of data abstraction achieved by CNNs. The ability to model any system, however complex it may be, through the insertion of thousands or millions of hidden layers with different kernels, coupled with the computational increase of the new GPUs, CNN will have a promising future as a data fusion technique.