A Survey of Decision-Making and Planning Methods for Self-Driving Vehicles

Jun Hu ¹ YueFeng Wang ¹ Shuai Cheng ^1* JingHan Xu ² Ningjia Wang ³ BingJie Fu ³ ZuoTao Ning ¹ JingYao Li ¹ HuaLin Chen ³ ChaoLu Feng ⁴ Yin Zhang ¹

• ¹ Neusoft Reach Automotive Technology Ltd, Shenyang, Liaoning Province, China
• ² Liaoning University of Technology, Jinzhou, Liaoning Province, China
• ³ School of Computer Science and Engineering, Northeastern University, Shenyang, China
• ⁴ Key Laboratory of Medical Cell Biology, Ministry of Education, China Medical University, Shenyang, Liaoning Province, China

Autonomous driving technology has garnered significant attention due to its potential to revolutionize transportation through advanced robotic systems. Despite optimistic projections for commercial deployment, the development of sophisticated autonomous driving systems remains largely experimental, with the effectiveness of neuroroboticsbased decision-making and planning algorithms being crucial for success. This paper delivers a comprehensive review of decision-making and planning algorithms in autonomous driving, covering both knowledge-driven and data-driven approaches. For knowledge-driven methods, this paper explores independent decision-making systems, including rule based, state transition based, game-theory based methods and independent planing systems including search based, sampling based, and optimization based methods. For data-driven methods, it provides a detailed analysis of machine learning paradigms such as imitation learning, reinforcement learning, and inverse reinforcement learning. Furthermore, the paper discusses hybrid models that amalgamate the strengths of both data-driven and knowledge-driven approaches, offering insights into their implementation and challenges. By evaluating experimental platforms, this paper guides the selection of appropriate testing and validation strategies. Through comparative analysis, this paper elucidates the advantages and disadvantages of each method, facilitating the design of more robust autonomous driving systems. Finally, this paper addresses current challenges and offers a perspective on future developments in this rapidly evolving field.