# Compression of GNSS Data with the Aim of Speeding up Communication to Autonomous Vehicles
## 1 Introduction  
Autonomous vehicles [1, 2] are able to control a full car system in collaboration with human interaction. Sometimes, the vehicle's control and computer system can also take full control when the driver cannot handle them, as in falling asleep behind the steering wheel, experiencing an emergency medical situation, or undergoing a vehicle emergency due to a flat tire or a mechanical problem. One of the challenges today is gaining public trust in the concept of autonomous driving [3]. Today, technology giants and automakers have been working toward full automation with the goal of selling cars that can drive safely and efficiently with an emphasis on reducing GNSS errors [4]. In our former paper, we explained the correlation between compression and errors [5]. Ideally, essential information passes in a fraction of a second and without delays and losses. Unfortunately, navigation software is often slow in delivering initial results, or, when there are no satellite signals, the navigation software works well because it receives wrong data. The compression and decompression methods presented in this investigation endeavor to improve this situation. We review in this paper some relevant aspects of the suggested system--autonomous vehicles, Data Compression, GNSS devices and the NMEA Standard [6]. We deal with several compression methods, some of them known; however, we made an effort to produce a new (hybrid) method that is based on known methods and algorithms. During our research, we noticed that adjacent frames of GNSS data are frequently very similar; accordingly, we looked for a compression method that efficiently works with differences to prepare the data for an entropy encoder like Huffman coding. We analyzed several compression methods, eliminating ones like JPEG2000 [7] because, not making use of the comparison of frames, they were unsuitable for our method as a preprocessing step. The aim of this work is not to correct errors but to suggest a time-saving method to transmit information with no less reliability than raw information transmission. Ifthe original information is invalid, our algorithm will filter out the errors before the compression process begins, as can be seen in Figure 14 in Appendix A. Many kinds of data like video, audio, images, and many other information files are usually compressed. Therefore, it is very important to have good and fast compression and decompression methods for many applications in autonomous vehicles. The method presented below is very similar to the well-known H.264. It is a relatively simple standard that plays an important role in many consumer electronics applications, including VCDs, DVDs, video phones, portable media players, video conferencing, video recording, e-learning, etc. It is mainly employed when there is a need to find solutions of relatively high quality, low transmission rate and good compression ratios [8; 9]. H.264 takes advantage of the frequent similarity of adjacent frames. GNSS systems usually produce information that also has this attribute of similarity between adjacent blocks; thus, we have adapted the concept of H.264. During this research, an algorithm that is a cornerstone and foundation in computer science, Huffman coding, was used as part of the compression process. Using this algorithm, we have achieved particularly high durability and survivability compared to other existing methods. In this study we investigate and show different methods for compressing GNSS data in vehicles to a good level of about 12% with the critical attribute of error correction. The rest of this paper is organized as follows: Section 2 gives a background of autonomous vehicles, data compression, GNSS, and the NMEA standard. Section 3 reviews literature and related works on GNSS data compression. Section 4 explains how and why we employ H.264-like compression. Section 6 describes the testbed for our experiments. Section 7 presents the results while Section 8 concludes and discusses the implications of this research and future investigations.