A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles
In this survey, we systematically summarize the current literature on studies that apply reinforcement learning (RL) to the motion planning and control of autonomous vehicles. Many existing contributions can be attributed to the pipeline approach, which consists of many hand-crafted modules, each with a functionality selected for the ease of human interpretation. However, this approach does not automatically guarantee maximal performance due to the lack of a system-level optimization. Therefore, this paper also presents a growing trend of work that falls into the end-to-end approach, which typically offers better performance and smaller system scales. However, their performance also suffers from the lack of expert data and generalization issues. Finally, the remaining challenges applying deep RL algorithms on autonomous driving are summarized, and future research directions are also presented to tackle these challenges.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous DrivingAutonomous VehiclesDeep Reinforcement LearningMotion PlanningReinforcement Learning (RL)Similar Papers 제목 키워드 기반
Motion Planning in Dynamic Environments: A Survey from Classical to Modern Methods
Motion planning in dynamic environments requires robots to continuously adapt their paths in response to environmental changes for safe and uninterrupted navigation. While many surveys have reviewed planning in static se…
Reinforcement LearningMotion PlanningSurvey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles
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…
Autonomous DrivingAutonomous VehiclesBIG-bench Machine LearningDecision Making+6A Survey of Deep Learning Techniques for Autonomous Driving
The last decade witnessed increasingly rapid progress in self-driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence. The objective of this paper is to survey th…
Autonomous DrivingDeep LearningDeep Reinforcement LearningReinforcement Learning+1Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills using a Quadrupedal Robot
We address the problem of enabling quadrupedal robots to perform precise shooting skills in the real world using reinforcement learning. Developing algorithms to enable a legged robot to shoot a soccer ball to a given ta…
Deep Reinforcement LearningFrictionHierarchical Reinforcement LearningMotion Planning+3Planning for Novelty: Width-Based Algorithms for Common Problems in Control, Planning and Reinforcement Learning
Width-based algorithms search for solutions through a general definition of state novelty. These algorithms have been shown to result in state-of-the-art performance in classical planning, and have been successfully appl…