4.6 Article

Multiagent Manuvering with the Use of Reinforcement Learning

Related references

Note: Only part of the references are listed.
Article Engineering, Civil

Game-Theoretic Modeling of Multi-Vehicle Interactions at Uncontrolled Intersections

Nan Li et al.

Summary: In this paper, we propose a game-theoretic framework for simulating the interactive behavior between autonomous and human-driven vehicles in traffic. The model exhibits reasonable traffic behavior and capability in resolving conflicts, and has manageable computational complexity compared to traditional models.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

Article Engineering, Civil

Cooperative Decision Making of Connected Automated Vehicles at Multi-Lane Merging Zone: A Coalitional Game Approach

Peng Hang et al.

Summary: A cooperative decision-making framework using a coalitional game approach is designed for addressing safety and efficiency issues in multi-lane merging zones for connected automated vehicles (CAVs). By establishing a motion prediction module, designing cost functions and constraints, considering different driving characteristics, and defining coalition models, the proposed approach shows reasonable decisions and performance adaptability for CAVs at complex traffic conditions, ensuring safety and efficiency while accommodating individual vehicle objectives.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

Article Engineering, Civil

Deep Reinforcement Learning for Autonomous Driving: A Survey

B. Ravi Kiran et al.

Summary: This paper summarizes deep reinforcement learning algorithms, provides a taxonomy of automated driving tasks, discusses key computational challenges in real world deployment of autonomous driving agents, and explores adjacent domains as well as the role of simulators in training agents.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

Article Engineering, Civil

Social Coordination and Altruism in Autonomous Driving

Behrad Toghi et al.

Summary: Despite limitations in cooperating and coordinating with human-driven vehicles, autonomous vehicles can work together with humans through altruistic decision-making and multi-agent reinforcement learning, leading to improved traffic flow and safety.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

Proceedings Paper Computer Science, Artificial Intelligence

Context-Aware Scene Prediction Network (CASPNet)

Maximilian Schaefer et al.

Summary: Predicting the future motion of surrounding road users is crucial and challenging for autonomous driving and driver-assistance systems. This study proposes a method based on CNN and RNN to jointly learn and predict the motion of all road users in a scene. Evaluation on the nuScenes dataset shows that the proposed method achieves state-of-the-art results in prediction benchmark.

2022 IEEE 25TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) (2022)

Proceedings Paper Computer Science, Artificial Intelligence

Autonomous Driving Strategies at Intersections: Scenarios, State-of-the-Art, and Future Outlooks

Lianzhen Wei et al.

Summary: This paper provides a brief overview of state-of-the-art autonomous driving strategies at intersections, including common intersection scenarios, simulation platforms, and datasets. By reviewing previous studies, characteristics of existing autonomous driving strategies have been summarized and classified into categories. Finally, problems with current autonomous driving strategies are identified, and valuable research outlooks are proposed.

2021 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC) (2021)

Article Engineering, Civil

Multi-Agent Deep Reinforcement Learning for Large-Scale Traffic Signal Control

Tianshu Chu et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2020)

Article Multidisciplinary Sciences

Mastering Atari, Go, chess and shogi by planning with a learned model

Julian Schrittwieser et al.

NATURE (2020)

Proceedings Paper Computer Science, Artificial Intelligence

Multi-Agent Connected Autonomous Driving using Deep Reinforcement Learning

Praveen Palanisamy

2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) (2020)

Article Computer Science, Information Systems

A Conflict Decision Model Based on Game Theory for Intelligent Vehicles at Urban Unsignalized Intersections

Xuemei Chen et al.

IEEE ACCESS (2020)

Article Engineering, Civil

Cooperative Driving at Unsignalized Intersections Using Tree Search

Huile Xu et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2020)

Proceedings Paper Computer Science, Artificial Intelligence

Cooperative Schedule-Driven Intersection Control with Connected and Autonomous Vehicles

Hsu-Chieh Hu et al.

2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) (2019)

Article Engineering, Civil

Cooperative Intersection Control Based on Virtual Platooning

Alejandro Ivan Morales Medina et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2018)

Proceedings Paper Automation & Control Systems

Distributed Model Predictive Control for Intersection Automation Using a Parallelized Optimization Approach

Alexander Katriniok et al.

IFAC PAPERSONLINE (2017)

Article Engineering, Electrical & Electronic

Cooperative vehicle-actuator system: a sequence-based framework of cooperative intersections management

Jia Wu et al.

IET INTELLIGENT TRANSPORT SYSTEMS (2014)