Abstract
Academic research in the field of autonomous vehicles has reached high popularity in recent years related to several topics as sensor technologies, V2X communications, safety, security, decision making, control, and even legal and standardization rules. Besides classic control design approaches, Artificial Intelligence and Machine Learning methods are present in almost all of these fields. Another part of research focuses on different layers of Motion Planning, such as strategic decisions, trajectory planning, and control. A wide range of techniques in Machine Learning itself have been developed, and this article describes one of these fields, Deep Reinforcement Learning (DRL). The paper provides insight into the hierarchical motion planning problem and describes the basics of DRL. The main elements of designing such a system are the modeling of the environment, the modeling abstractions, the description of the state and the perception models, the appropriate rewarding, and the realization of the underlying neural network. The paper describes vehicle models, simulation possibilities and computational requirements. Strategic decisions on different layers and the observation models, e.g., continuous and discrete state representations, grid-based, and camera-based solutions are presented. The paper surveys the state-of-art solutions systematized by the different tasks and levels of autonomous driving, such as car-following, lane-keeping, trajectory following, merging, or driving in dense traffic. Finally, open questions and future challenges are discussed.
Highlights
M OTION planning for autonomous vehicle functions is a vast and long-researched area using a wide variety of approaches such as different optimization techniques, modern control methods, artificial intelligence, and machine learning
As an area of Artificial Intelligence and Machine Learning, Reinforcement learning (RL) deals with the problem of a learning agent placed in an environment to achieve a goal
SCENARIO-BASED CLASSIFICATION OF THE APPROACHES. Though this survey focuses on Deep Reinforcement Learning based motion planning research, it is essential to mention that some papers try to solve some subtasks of automated driving through classic reinforcement techniques
Summary
M OTION planning for autonomous vehicle functions is a vast and long-researched area using a wide variety of approaches such as different optimization techniques, modern control methods, artificial intelligence, and machine learning. This article presents the achievements of the field from recent years focused on Deep Reinforcement Learning (DRL) approach. DRL combines the classic reinforcement learning with deep neural networks, and gained popularity after the breakthrough article from Deepmind [1], [2]. In the number of research papers about autonomous vehicles and the DRL has Manuscript received January 28, 2020; revised July 2, 2020; accepted September 8, 2020. Been increased in the last few years (see Fig. 1.), and because of the complexity of the different motion planning problems, it is a convenient choice to evaluate the applicability of DRL for these problems
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