# A Generic Framework for Enhancing Autonomous Driving Accuracy through Multimodal Data Fusion
## 2 Background and Motivation
### Overview about Sensors
#### 2.1.2 Introduction to Data Fusion  
Sensory data fusion (SDF) is the act of combining various data sources to provide information that is more consistent, reliable, and relevant than any single data source. Data fusion improves the accuracy of raw sensory input gathered from many internal and external sensors, hence enhancing the AVs' conceptual awareness. Sensor data often exhibit  
Figure 3: Different types of external sensors. multimodal characteristics. Multimodal data are difficult to process, and hence they must be fused and converted into a unique data format that simplifies further processing. Due to the complexity prevailing in data fusion, advanced mathematical models are used for transformations and calculations. Figure 4 illustrates the process of data fusion. There are different modules, performing different activities. The first phase is the data acquisition stage, where different formats of heterogeneous data are collected from various sensors. The second phase is the data preprocessing stage, where various discrepancies existing in the data are identified and treated further using robust data-cleaning models. This task is followed by the feature extraction process, where the mandatory features are extracted from the data to minimize the fusion process. The next stage is data fusion. There are two types of fusion: (i) early fusion, where data are fused with partial preprocessed data, and (ii) high-level fusion, where strongly preprocessed data are used for fusion. The next phase is the situation refinement task, where the context of the perceived environment data is created for the AVs using the enhanced fused data. The information related to sensors, data objects, and conceptual awareness is represented and stored using advanced data structures, and appropriate models are used to process the data. The main advantage of data fusion is that it combines data from a variety of sensors and sources to create something more intelligent, decisive, reasonable, and precise. The data from each sensor may not make much sense on their own. Computing the (N) independent observations offers a statistical benefit of fusion; the data should be integrated as efficiently as feasible. Making extremely low-power sensors that do not require battery replacements during their lifetime is a crucial requirement in IoT, and this has increased demand for energy-efficient sensors. High-precision sensors are well-recognized for using a lot of energy. A collection of low-accuracy sensors with low power consumption can be used to solve this challenge. Data fusion enables the generation of exceptionally precise data. Another important advantage of data fusion is that it assists in the concealing of critical data or semantics that are responsible for the fused results. Based on the mathematical approaches used, data fusion strategies can be divided into three categories:  
Figure 4: Overall architecture of data fusion. Artificial intelligence (AI)-based approaches such as classical machine learning, fuzzy logic, artificial neural networks (ANN), and genetic assessment. * Probability-based methods such as Bayesian analysis, statistics, and recursive operators. * Evidence-based data fusion strategies based on theory  
Among fusing different data formats, fusing image and audio data is more difficult. Normally, image data fusion is performed at three levels: (i) pixel level fusion, which provides extensive image detail information that cannot be obtained at any other level; (ii) feature level, which is the intermediate level and not only stores but also compresses information; and (iii) decision-level fusion, which is the highest level and most sophisticated, with a reduced demand for picture registration. Recursive approaches, multiplicative algorithms, principal component analysis, high PostFilter, the Brovey transform image fusion technique, the color rotated technique, and discrete wavelet transformation are some of the most often used image data fusion techniques.