An Enhanced Graph Representation for Machine Learning Based Automatic Intersection Management
Author:
Affiliation:
1. Robert Bosch GmbH, Corporate Research,Renningen,Germany,D-71272
2. Institute of Measurement, Control and Microtechnology, Ulm University,Ulm,Germany,D-89081
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9921415/9921739/09922515.pdf?arnumber=9922515
Reference22 articles.
1. End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning
2. A Survey of Deep RL and IL for Autonomous Driving Policy Learning
3. DCL-AIM: Decentralized coordination learning of autonomous intersection management for connected and automated vehicles
4. Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic Environments
5. Kinematic and dynamic vehicle models for autonomous driving control design
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1. CAI2M2: A Centralized Autonomous Inclusive Intersection Management Mechanism for Heterogeneous Connected Vehicles;IEEE Open Journal of Vehicular Technology;2024
2. Graph-Based Autonomous Driving with Traffic-Rule-Enhanced Curriculum Learning;2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC);2023-09-24
3. Learned Fourier Bases for Deep Set Feature Extractors in Automotive Reinforcement Learning;2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC);2023-09-24
4. Automatic Intersection Management in Mixed Traffic Using Reinforcement Learning and Graph Neural Networks;2023 IEEE Intelligent Vehicles Symposium (IV);2023-06-04
5. Integration of Reinforcement Learning Based Behavior Planning With Sampling Based Motion Planning for Automated Driving;2023 IEEE Intelligent Vehicles Symposium (IV);2023-06-04
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