A Survey on Localization for Autonomous Vehicles
Debasis Kumar, Naveed Muhammad
Abstract
Research on autonomous vehicles is making significant advances in recent years. To operate an autonomous vehicle safely and effectively, precise localization is essential. This study aims to present state of the art in localization to scientists new to the area. It presents and summarizes works from the field of localization, and suggests a classification for the works. Approaches to localization are mainly divided into three categories: conventional localization, machine-learning-based localization, and vehicle-to-everything (V2X) localization. Conventional localization primarily depends on high-definition (HD) maps or on certain marks, such as landmarks and road marks. Machine-learning-based localization approaches include using neural networks, end-to-end approaches, as well as reinforcement learning for performing, or improving, localization. And lastly, V2X localization methods localize a vehicle by communicating with other vehicles (V2V) or infrastructures (V2I). This study not only presents a bigger picture of the area of localization in autonomous driving, but also presents the potentials and drawbacks of different localization methods. At the end of the review, some research areas that are open for future research are also highlighted.