Decentralized Cooperative Merging of Platoons of Connected and Automated Vehicles at Highway On-Ramps

Kumaravel, Sharmila Devi; Malikopoulos, Andreas A.; Ayyagari, Ramakalyan

doi:10.23919/acc50511.2021.9483390

Cited by 14 publications

(5 citation statements)

References 21 publications

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“…Ref. [25] transformed the complex 2D multi-platoon cooperation problem into a 1D platoon following control problem and derived an analytical solution to the optimal control. Ref.…”

Section: Related Workmentioning

confidence: 99%

Research on Reinforcement-Learning-Based Truck Platooning Control Strategies in Highway On-Ramp Regions

Chen,

Zhou,

Duan

et al. 2023

WEVJ

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With the development of autonomous driving technology, truck platooning control has become a reality. Truck platooning can improve road capacity by maintaining a minor headway. Platooning systems can significantly reduce fuel consumption and emissions, especially for trucks. In this study, we designed a Platoon-MAPPO algorithm to implement truck platooning control based on multi-agent reinforcement learning for a platooning facing an on-ramp scenario on highway. A centralized training, decentralized execution algorithm was used in this paper. Each truck only computes its actions, avoiding the data computation delay problem caused by centralized computation. Each truck considers the truck status in front of and behind itself, maximizing the overall gain of the platooning and improving the global operational efficiency. In terms of performance evaluation, we used the traditional rule-based platooning following model as a benchmark. To ensure fairness, the model used the same network structure and traffic scenario as our proposed model. The simulation results show that the algorithm proposed in this paper has good performance and improves the overall efficiency of the platoon while guaranteeing traffic safety. The average energy consumption decreased by 14.8%, and the road occupancy rate decreased by 43.3%.

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“…Ref. [25] transformed the complex 2D multi-platoon cooperation problem into a 1D platoon following control problem and derived an analytical solution to the optimal control. Ref.…”

Section: Related Workmentioning

confidence: 99%

Research on Reinforcement-Learning-Based Truck Platooning Control Strategies in Highway On-Ramp Regions

Chen,

Zhou,

Duan

et al. 2023

WEVJ

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“…Defining a safe passing sequence used by the CAVs to travel through the merging zone is one concept that can be used to tackle the merging scenario [12]. In [13], the authors proposed a grouping-based cooperative driving strategy for onramp merging on highway.…”

Section: Related Work and Objectivesmentioning

confidence: 99%

Altruistic Coordination Strategy for On-Ramp Merging on Highway of a Formation of Cooperative Automated Vehicles

Saidi,

Adouane,

Talj

2023

2023 27th International Conference on Methods and Models in Automation and Robotics (MMAR)

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It is getting increasingly recognized that, to get full advantage from Automated Vehicles (AVs), a number of situations involving multiple AVs will compulsory require the coordination of their relative activities and movements. Under the Multi-Vehicle Systems (MVS) paradigm, instead of considering individually each AV, it is proposed to create with several AVs a group that evolves under a certain coordination strategy. In this paper, it is proposed to utilize Cooperative Automated Vehicles (CAVs) synchronization ability to tackle one challenging scenario: on-ramp merging on highway. The main contribution of this paper is an overall collaborative approach, called Altruistic Formation Reconfiguration Strategy (AFRS), based on a multi-criteria optimization, to guarantee the safety and the energetic efficiency of CAVs, performing on-ramp merging on highway. Under the AFRS, it is proposed the extension of the Constrained Optimal Reconfiguration Matrix (CORM) [1] in order to overcome the CORM limitations, while guarantying both the CAV's non-collision requirement and the smooth collaborative navigation of the fleet. Several simulations are performed to evaluate the safety and reliability of the proposed approach.

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“…The majority of existing research efforts have considered coordination and control of CAVs to improve fuel efficiency and alleviate traffic congestion. Some research efforts applied the framework at different traffic scenarios such as signal-free intersections [3]- [6], merging roadways [7]- [9], and corridors [10], [11]. More recent efforts have focused on developing learning techniques for coordinating CAVs [12]- [15].…”

Section: Introductionmentioning

confidence: 99%

Combined Optimal Routing and Coordination of Connected and Automated Vehicles

Bang,

Chalaki,

Malikopoulos

2022

Preprint

Self Cite

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In this letter, we consider a transportation network with a 100% penetration rate of connected and automated vehicles (CAVs), and present an optimal routing approach which takes into account the efficiency achieved in the network by coordinating the CAVs at specific traffic scenarios, e.g., intersections, merging roadways, roundabouts, etc. To derive the optimal route of a travel request, we use the information of the CAVs that have already received a routing solution. This enables each CAV to consider the traffic conditions on the roads. Given the trajectories of CAVs resulting by the routing solutions, the solution of any new travel request determines the optimal travel time at each traffic scenario while satisfying all state, control, and safety constraints. We validate the performance of our framework through numerical simulations. To the best of our knowledge, this is the first attempt to consider the coordination of CAVs in a routing problem.

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Decentralized Cooperative Merging of Platoons of Connected and Automated Vehicles at Highway On-Ramps

Cited by 14 publications

References 21 publications

Research on Reinforcement-Learning-Based Truck Platooning Control Strategies in Highway On-Ramp Regions

Research on Reinforcement-Learning-Based Truck Platooning Control Strategies in Highway On-Ramp Regions

Altruistic Coordination Strategy for On-Ramp Merging on Highway of a Formation of Cooperative Automated Vehicles

Combined Optimal Routing and Coordination of Connected and Automated Vehicles

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