# A Generic Framework for Enhancing Autonomous Driving Accuracy through Multimodal Data Fusion
## 1 Introduction  
Autonomous cars have the ability to cut traffic accidents significantly. This hypothesis is based on the fact that autonomous cars reduce the influence of a human component on the likelihood of a traffic collision. Autonomous vehicles are being tested in real-life traffic circumstances all around the world [1]. According to [1], if AVs become accessible in 2025, the market in the United States of America might reach 8 million in ten years. The widespread deployment of autonomous vehicles is intended to minimize collisions, relieve traffic congestion, increase fuel efficiency, reduce parking demands, and provide mobility to individuals who are unable to drive [2]. The same survey predicted that by 2040 about 33 million autonomous vehicles will be on the road. This just means that people will haveto become acclimated to self-driving automobiles. This is supported by facts and data, yet further testing is needed to ensure everyone's safety. In order to avoid accidents caused by the AVs, the decisions taken by the AVs to overcome roadside events must be instant and accurate [3]. For accurate decision-making, the environmental data perceived by the AVs must be clear and accurate. Perception systems must be exact in order to provide a precise comprehension of the world. They must be able to work in adverse conditions and even when particular sensors are broken or defective. In order to collect environmental data, as well as data relating to autonomous vehicle characteristics, sensor systems must be functional. However, data received from a variety of devices, including sensors, thermal cameras, radars, and so on, have heterogeneous multimodal features, making it difficult to achieve the correct perception. In order to enhance the situation awareness of the AV, there has to be more emphasis on the data preprocessing tasks of the AVs. Data preprocessing involves data cleaning and multimodal fusion. The upcoming paragraphs highlight the relationship between situation awareness and data preprocessing. According to [4], situation awareness (SA) is defined as \"the perception of the elements in the environment within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future\". SA bridges the gap between what is known about the environment and what is occurring in it, and what could happen in the future. To establish the situation awareness of their perceived environment, AVs collect the environmental data using external and internal sensors, namely LiDAR, radar, and ultrasonic, followed by stereo and thermal cameras. The vehicular information collected can exist in different formats, such as textual, image, video, and audio, and also exhibit many discrepancies. The discrepancies exhibited by the vehicular data might be in the form of irrelevant data, missing data, outliers, and duplicate data. In addition, images may contain missing data in the form of blurred images and audio data that contains noisy irrelevant data. Hence, preprocessing the sensory data is essential to improve the accuracy. Since data cleaning has been well studied, this proposal focuses on the second task of data preprocessing, namely data fusion. The sensory data exhibit multimodal characteristics, hence an effective data fusion model is mandatory to convert the heterogeneous data into a unique format and also to enhance their accuracy for further effective processing. Most of the studies fail to propose a generic multimodal fusion methodology to handle the diversity existing among different datasets. The relevant research literature also does not clearly explain key operations such as feature selection and dimensionality reduction of multimodal data, the mechanisms for 2D to 3D multimodal data transformation and storage, and the methodology for converting multimodal data to a single unique data format. Further very few contributions have been carried out to fuse multispectral environment data collected from sensors and satellites.