Recent Advances in Planning for Autonomous Driving and Mining Trucks: A Survey

Autonomous driving technology, which enhances driving safety, comfort, and reduces energy consumption, has become a prominent research focus in recent years. Path planning is a crucial component of autonomous driving technology, focusing on the optimal route for a vehicle to travel from one point to another. As a category of autonomous driving technology, the modular methods are highly favored for their interpretability and stability. However, there remains a lack of comprehensive reviews that encapsulate the most recent advancements in modular methods. Consequently, in this paper, a detailed survey of modular methods is conducted, with a particular emphasis on those pertinent to autonomous driving within the mining area. We further explore the advantages and limitations of various approaches and present commonly used vehicle models. Additionally, we provide a comprehensive overview of the current research challenges and future directions in autonomous driving technologies, with a specific focus on applications within mining area.

I. Introduction

Over the past four decades, autonomous driving technology has garnered significant attention from both academia and industry [1]–[3]. In academia field, various competitions and research projects have driven the continuous development of autonomous driving technology [4]. In industry field, autonomous driving technology has been deployed in a variety of applications, such as Navigate on Autopilot (NOA) [5] [6], open-pit mines [7], and ports [8]. With substantial governmental support, the development of autonomous driving technology has demonstrated promising momentum. Autonomous driving test sites [9], established in various locations have provided platforms for the further verification and enhancement of the technology. In addition, many companies are conducting public road tests to evaluate the performance and safety of autonomous driving technology under real-world road conditions [10] [11].