Source 1primary paper2023IEEE Transactions on Intelligent Transportation Systems
Toolkit/CoTV
CoTV
Also known as: Cooperative control for traffic light signals and connected autonomous vehicles, multi-agent Deep Reinforcement Learning (DRL) system
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
CoTV is a multi-agent deep reinforcement learning system that cooperatively controls traffic light signals and connected autonomous vehicles in mixed-autonomy urban traffic scenarios. It was reported as a computational control method and evaluated in SUMO simulation.
Usefulness & Problems
Why this is useful
CoTV is useful for coordinated control of infrastructure and vehicles in urban traffic settings where both traffic signals and connected autonomous vehicles can be actuated. The reported system balances reductions in travel time, fuel consumption, and emissions in simulation.
Problem solved
CoTV addresses the control problem of jointly optimizing traffic light signals and connected autonomous vehicles in realistic mixed-autonomy urban scenarios. It also targets scalability in complex urban settings by limiting cooperation to one nearest connected autonomous vehicle on each incoming road.
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete method used to build, optimize, or evolve an engineered system.
Techniques
Computational DesignTarget processes
No target processes tagged yet.
Input: Light
Implementation Constraints
The method is implemented as a multi-agent deep reinforcement learning system for cooperative control of traffic light signals and connected autonomous vehicles. Practical details such as model architecture, training procedure, software stack, and deployment requirements are not specified in the supplied evidence.
The available evidence is limited to a single 2023 publication and simulation-based validation in SUMO. No experimental deployment, biological relevance, or independent replication is provided in the supplied evidence.
Validation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Ranked Claims
The paper demonstrates the effectiveness of CoTV in SUMO simulation under various grid maps and realistic urban scenarios with mixed-autonomy traffic.
We describe the system design of CoTV and demonstrate its effectiveness in a simulation study using SUMO under various grid maps and realistic urban scenarios with mixed-autonomy traffic.
The paper demonstrates the effectiveness of CoTV in SUMO simulation under various grid maps and realistic urban scenarios with mixed-autonomy traffic.
We describe the system design of CoTV and demonstrate its effectiveness in a simulation study using SUMO under various grid maps and realistic urban scenarios with mixed-autonomy traffic.
The paper demonstrates the effectiveness of CoTV in SUMO simulation under various grid maps and realistic urban scenarios with mixed-autonomy traffic.
We describe the system design of CoTV and demonstrate its effectiveness in a simulation study using SUMO under various grid maps and realistic urban scenarios with mixed-autonomy traffic.
The paper demonstrates the effectiveness of CoTV in SUMO simulation under various grid maps and realistic urban scenarios with mixed-autonomy traffic.
We describe the system design of CoTV and demonstrate its effectiveness in a simulation study using SUMO under various grid maps and realistic urban scenarios with mixed-autonomy traffic.
The paper demonstrates the effectiveness of CoTV in SUMO simulation under various grid maps and realistic urban scenarios with mixed-autonomy traffic.
We describe the system design of CoTV and demonstrate its effectiveness in a simulation study using SUMO under various grid maps and realistic urban scenarios with mixed-autonomy traffic.
The paper demonstrates the effectiveness of CoTV in SUMO simulation under various grid maps and realistic urban scenarios with mixed-autonomy traffic.
We describe the system design of CoTV and demonstrate its effectiveness in a simulation study using SUMO under various grid maps and realistic urban scenarios with mixed-autonomy traffic.
The paper demonstrates the effectiveness of CoTV in SUMO simulation under various grid maps and realistic urban scenarios with mixed-autonomy traffic.
We describe the system design of CoTV and demonstrate its effectiveness in a simulation study using SUMO under various grid maps and realistic urban scenarios with mixed-autonomy traffic.
CoTV can balance reduction of travel time, fuel, and emissions.
Therefore, our CoTV can well balance the reduction of travel time, fuel, and emissions.
CoTV can balance reduction of travel time, fuel, and emissions.
Therefore, our CoTV can well balance the reduction of travel time, fuel, and emissions.
CoTV can balance reduction of travel time, fuel, and emissions.
Therefore, our CoTV can well balance the reduction of travel time, fuel, and emissions.
CoTV can balance reduction of travel time, fuel, and emissions.
Therefore, our CoTV can well balance the reduction of travel time, fuel, and emissions.
CoTV can balance reduction of travel time, fuel, and emissions.
Therefore, our CoTV can well balance the reduction of travel time, fuel, and emissions.
CoTV can balance reduction of travel time, fuel, and emissions.
Therefore, our CoTV can well balance the reduction of travel time, fuel, and emissions.
CoTV can balance reduction of travel time, fuel, and emissions.
Therefore, our CoTV can well balance the reduction of travel time, fuel, and emissions.
CoTV is scalable to complex urban scenarios by cooperating with only one nearest connected autonomous vehicle on each incoming road.
CoTV is also scalable to complex urban scenarios by cooperating with only one CAV that is nearest to the traffic light controller on each incoming road.
CoTV is scalable to complex urban scenarios by cooperating with only one nearest connected autonomous vehicle on each incoming road.
CoTV is also scalable to complex urban scenarios by cooperating with only one CAV that is nearest to the traffic light controller on each incoming road.
CoTV is scalable to complex urban scenarios by cooperating with only one nearest connected autonomous vehicle on each incoming road.
CoTV is also scalable to complex urban scenarios by cooperating with only one CAV that is nearest to the traffic light controller on each incoming road.
CoTV is scalable to complex urban scenarios by cooperating with only one nearest connected autonomous vehicle on each incoming road.
CoTV is also scalable to complex urban scenarios by cooperating with only one CAV that is nearest to the traffic light controller on each incoming road.
CoTV is scalable to complex urban scenarios by cooperating with only one nearest connected autonomous vehicle on each incoming road.
CoTV is also scalable to complex urban scenarios by cooperating with only one CAV that is nearest to the traffic light controller on each incoming road.
CoTV is scalable to complex urban scenarios by cooperating with only one nearest connected autonomous vehicle on each incoming road.
CoTV is also scalable to complex urban scenarios by cooperating with only one CAV that is nearest to the traffic light controller on each incoming road.
CoTV is scalable to complex urban scenarios by cooperating with only one nearest connected autonomous vehicle on each incoming road.
CoTV is also scalable to complex urban scenarios by cooperating with only one CAV that is nearest to the traffic light controller on each incoming road.
CoTV is a multi-agent deep reinforcement learning system that cooperatively controls both traffic light signals and connected autonomous vehicles.
this paper presents a multi-agent Deep Reinforcement Learning (DRL) system called CoTV, which Cooperatively controls both Traffic light signals and Connected Autonomous Vehicles (CAV)
CoTV is a multi-agent deep reinforcement learning system that cooperatively controls both traffic light signals and connected autonomous vehicles.
this paper presents a multi-agent Deep Reinforcement Learning (DRL) system called CoTV, which Cooperatively controls both Traffic light signals and Connected Autonomous Vehicles (CAV)
CoTV is a multi-agent deep reinforcement learning system that cooperatively controls both traffic light signals and connected autonomous vehicles.
this paper presents a multi-agent Deep Reinforcement Learning (DRL) system called CoTV, which Cooperatively controls both Traffic light signals and Connected Autonomous Vehicles (CAV)
CoTV is a multi-agent deep reinforcement learning system that cooperatively controls both traffic light signals and connected autonomous vehicles.
this paper presents a multi-agent Deep Reinforcement Learning (DRL) system called CoTV, which Cooperatively controls both Traffic light signals and Connected Autonomous Vehicles (CAV)
CoTV is a multi-agent deep reinforcement learning system that cooperatively controls both traffic light signals and connected autonomous vehicles.
this paper presents a multi-agent Deep Reinforcement Learning (DRL) system called CoTV, which Cooperatively controls both Traffic light signals and Connected Autonomous Vehicles (CAV)
CoTV is a multi-agent deep reinforcement learning system that cooperatively controls both traffic light signals and connected autonomous vehicles.
this paper presents a multi-agent Deep Reinforcement Learning (DRL) system called CoTV, which Cooperatively controls both Traffic light signals and Connected Autonomous Vehicles (CAV)
CoTV is a multi-agent deep reinforcement learning system that cooperatively controls both traffic light signals and connected autonomous vehicles.
this paper presents a multi-agent Deep Reinforcement Learning (DRL) system called CoTV, which Cooperatively controls both Traffic light signals and Connected Autonomous Vehicles (CAV)
Using only the nearest connected autonomous vehicle avoids costly coordination with all possible connected autonomous vehicles and leads to stable convergence of CoTV training in a large-scale multi-agent scenario.
This avoids costly coordination between traffic light controllers and all possible CAVs, thus leading to the stable convergence of training CoTV under the large-scale multi-agent scenario.
Using only the nearest connected autonomous vehicle avoids costly coordination with all possible connected autonomous vehicles and leads to stable convergence of CoTV training in a large-scale multi-agent scenario.
This avoids costly coordination between traffic light controllers and all possible CAVs, thus leading to the stable convergence of training CoTV under the large-scale multi-agent scenario.
Using only the nearest connected autonomous vehicle avoids costly coordination with all possible connected autonomous vehicles and leads to stable convergence of CoTV training in a large-scale multi-agent scenario.
This avoids costly coordination between traffic light controllers and all possible CAVs, thus leading to the stable convergence of training CoTV under the large-scale multi-agent scenario.
Using only the nearest connected autonomous vehicle avoids costly coordination with all possible connected autonomous vehicles and leads to stable convergence of CoTV training in a large-scale multi-agent scenario.
This avoids costly coordination between traffic light controllers and all possible CAVs, thus leading to the stable convergence of training CoTV under the large-scale multi-agent scenario.
Using only the nearest connected autonomous vehicle avoids costly coordination with all possible connected autonomous vehicles and leads to stable convergence of CoTV training in a large-scale multi-agent scenario.
This avoids costly coordination between traffic light controllers and all possible CAVs, thus leading to the stable convergence of training CoTV under the large-scale multi-agent scenario.
Using only the nearest connected autonomous vehicle avoids costly coordination with all possible connected autonomous vehicles and leads to stable convergence of CoTV training in a large-scale multi-agent scenario.
This avoids costly coordination between traffic light controllers and all possible CAVs, thus leading to the stable convergence of training CoTV under the large-scale multi-agent scenario.
Using only the nearest connected autonomous vehicle avoids costly coordination with all possible connected autonomous vehicles and leads to stable convergence of CoTV training in a large-scale multi-agent scenario.
This avoids costly coordination between traffic light controllers and all possible CAVs, thus leading to the stable convergence of training CoTV under the large-scale multi-agent scenario.
Approval Evidence
1 source5 linked approval claimsfirst-pass slug cotv
this paper presents a multi-agent Deep Reinforcement Learning (DRL) system called CoTV, which Cooperatively controls both Traffic light signals and Connected Autonomous Vehicles (CAV)
Source:
effectivenesssupports
The paper demonstrates the effectiveness of CoTV in SUMO simulation under various grid maps and realistic urban scenarios with mixed-autonomy traffic.
We describe the system design of CoTV and demonstrate its effectiveness in a simulation study using SUMO under various grid maps and realistic urban scenarios with mixed-autonomy traffic.
Source:
performancesupports
CoTV can balance reduction of travel time, fuel, and emissions.
Therefore, our CoTV can well balance the reduction of travel time, fuel, and emissions.
Source:
scalabilitysupports
CoTV is scalable to complex urban scenarios by cooperating with only one nearest connected autonomous vehicle on each incoming road.
CoTV is also scalable to complex urban scenarios by cooperating with only one CAV that is nearest to the traffic light controller on each incoming road.
Source:
system descriptionsupports
CoTV is a multi-agent deep reinforcement learning system that cooperatively controls both traffic light signals and connected autonomous vehicles.
this paper presents a multi-agent Deep Reinforcement Learning (DRL) system called CoTV, which Cooperatively controls both Traffic light signals and Connected Autonomous Vehicles (CAV)
Source:
training stabilitysupports
Using only the nearest connected autonomous vehicle avoids costly coordination with all possible connected autonomous vehicles and leads to stable convergence of CoTV training in a large-scale multi-agent scenario.
This avoids costly coordination between traffic light controllers and all possible CAVs, thus leading to the stable convergence of training CoTV under the large-scale multi-agent scenario.
Source:
Comparisons
Source-backed strengths
The source paper reports effectiveness in SUMO simulation across various grid maps and realistic urban scenarios with mixed-autonomy traffic. It also reports simultaneous balancing of travel time, fuel, and emissions, and claims scalability through a localized cooperation strategy.
Source:
Therefore, our CoTV can well balance the reduction of travel time, fuel, and emissions.
Ranked Citations
- 1.