# A Systematic Review of Perception System and Simulators for Autonomous Vehicles Research
## 4 Fusion Algorithms
### Fusion Methods in Perception Systems  
There are numerous works related to the sensors mentioned in this review that detect vehicles, pedestrians, lanes, signs, and so on. In [31], the data supplied by a LiDAR and a stereo camera are fused to improve a vehicle's detection. The method has two stages: (1) Hypothesis generation, where a vehicle candidate is obtained, combining Haar features from depth maps with the AdaBoost classifier; and (2) hypothesis verification, where a shape estimation of the candidates is calculated using the LiDAR information and a support vector machine. The proposed fusion approach achieves a lower false alarm rate in urban environments. A method that fuses the MMW RADAR with camera information is presented in [32]. The work presents a collaborative fusion approach to achieve an optimal balance between vehicle detection accuracy and computational efficiency. The MMW radar first detects the potential vehicle and provides a region of interest. The vision processing module employs symmetry detection and active contour detection to identify the vehicle inside the region of interest provided by the MMW radar. The experimental results show a 92.36% detection rate and 0% false alarm rate using a real-world dataset. In [33], the advantages of the fusion of LiDAR and RADAR sensors are used in order to provide permanent precise spatial and dynamical data. The work presents a real-time algorithm, which enables an autonomous car to comfortably follow other cars at various speeds, while maintaining a safe distance. In [34], an unscented Kalman filter and Joint Probabilistic Data Association is used to fuse the data from a 2D LiDAR and camera information to detect vehicles. The presented approach achieves an increase in safety for vehicle detection in single-lane carriage-ways, where casualties are higher than for other road classes. The increase in precision and robustness obtained with the fusion methods when redundant information is available is especially relevant when the systems can produce injuries or death to people. Pedestrian detection is a common area to use the fusion algorithms, as demonstrated by numerous works found on this topic. In [35], the distance data and the reflexivity of a 3D LiDAR are fused through a set of 50 features to detect pedestrians. The features are composed by three subsets: (1) Shape features obtained in XYZ projections of pedestrian cloud points, (2) Hu invariants moments from XYZ projections and, (3) statistical features from the reflexivity data. The data fused was used to train an SVM. The pedestrian detection method obtained a higher classification rate when it was compared with similar algorithms. In [36], a set of machine-learning algorithms (MLA) are tested with different fusion schemes with the proposal of determining the best performance in pedestrian detection. The experimental results provide a false positive rate, AUC (Area Under Curve ROC is a metric to evaluate the performance of two classifiers), and provide an accuracy of 96.67%. An RGB camera and a LiDAR are fused forpedestrian detection. LiDAR is used to evaluate the value of depth perception for pedestrian detection. In the work, the detectors were trained on both input modalities (from KITTI database) and various fusion strategies. The best performance was obtained by a late re-scoring strategy that was designed to be sensitive to geometric context. The advance in GPUs has allowed algorithm execution times that would have been unthinkable a few years ago (i.e., CNN); there are numerous projects that use convolutional neural networks (CNN) to obtain high grades of data abstraction. In [37], multispectral information is used from a thermal camera and an RGB camera (TRGB channels) and a multi-layer fused CNN to detect pedestrians under adverse illumination conditions. The algorithm development on the basis of a multiple-layer fusion technique can significantly reduce the detection miss rate. In [38], the distance data with reflectance information from 3D LiDAR is fused by means of a CNN.