Reinforcement Learning Aided Sequential Optimization for Unsignalized Intersection Management of Robot Traffic
P Tallapragada - arXiv preprint arXiv:2302.05082, 2023 - arxiv.org
We consider the problem of optimal unsignalized intersection management for continual
streams of randomly arriving robots. This problem involves repeatedly solving different …
streams of randomly arriving robots. This problem involves repeatedly solving different …
Concurrent optimization of safety and traffic flow using deep reinforcement learning for autonomous intersection management
With increasing connectivity and autonomy in traffic eco-systems, Autonomous Intersection
Management (AIM) has attracted strong attention from the research community. AIM helps …
Management (AIM) has attracted strong attention from the research community. AIM helps …
Dynamic graph combinatorial optimization with multi-attention deep reinforcement learning
U Gunarathna, R Borovica-Gajic… - Proceedings of the 30th …, 2022 - dl.acm.org
Graph combinatorial optimization (CO) is a widely studied problem with use-cases stemming
from many fields. Typically, in real-world applications, the features of a graph tend to change …
from many fields. Typically, in real-world applications, the features of a graph tend to change …
The Autonomous Intersection Control Method Based on Reduction in Vehicle Conflict Relationships
M Liu, C Zheng, Y Zhu - Sustainability, 2023 - mdpi.com
Current autonomous intersection control strategies are facing issues, such as lack of
foresight, frequent occurrence of deadlock, and low control system efficiency. To address …
foresight, frequent occurrence of deadlock, and low control system efficiency. To address …
A simulation study on prioritizing connected freight vehicles at intersections for traffic flow optimization (industrial paper)
Due to the importance of road freight, there is a significant cost of delaying freight vehicles
on the road. In this work, we focus on freight vehicle optimization by reducing delays at …
on the road. In this work, we focus on freight vehicle optimization by reducing delays at …
Reinforcement Learning-Based Navigation Approach for a Downscaled Autonomous Vehicle in Simplified Urban Scenarios
This paper presents the implementation of a reinforcement learning based navigation
architecture for autonomous vehicles in urban scenarios. These types of scenarios represent …
architecture for autonomous vehicles in urban scenarios. These types of scenarios represent …
e-SMARTS: a system to simulate intelligent traffic management solutions (demo paper)
U Gunarathna, R Borovica-Gajic… - Proceedings of the 30th …, 2022 - dl.acm.org
Intelligent traffic management solutions that leverage machine learning have gained a lot of
interest in recent years. These techniques, however, cannot be deployed in real-world …
interest in recent years. These techniques, however, cannot be deployed in real-world …
Reasoning Graph-Based Reinforcement Learning to Cooperate Mixed Connected and Autonomous Traffic at Unsignalized Intersections
Cooperation at unsignalized intersections in mixed traffic environments, where Connected
and Autonomous Vehicles (CAVs) and Manually Driving Vehicles (MVs) coexist, holds …
and Autonomous Vehicles (CAVs) and Manually Driving Vehicles (MVs) coexist, holds …