Building a Lane Merge Coordination for Connected Vehicles Using Deep Reinforcement Learning

Nassef, Omar; Sequeira, Luis; Salam, Elias; Mahmoodi, Toktam

doi:10.1109/jiot.2020.3017931

Cited by 27 publications

(11 citation statements)

References 30 publications

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“…To ensure lateral safety, either (14) or ( 15) must be satisfied. Therefore, we impose the lateral safety constraint using minimum function as…”

Section: Coordination Of Connected and Automated Vehiclesmentioning

confidence: 99%

Combined Optimal Routing and Coordination of Connected and Automated Vehicles

Bang,

Chalaki,

Malikopoulos

2022

Preprint

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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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“…To ensure lateral safety, either (14) or ( 15) must be satisfied. Therefore, we impose the lateral safety constraint using minimum function as…”

Section: Coordination Of Connected and Automated Vehiclesmentioning

confidence: 99%

Combined Optimal Routing and Coordination of Connected and Automated Vehicles

Bang,

Chalaki,

Malikopoulos

2022

Preprint

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show abstract

“…El retardo es un parámetro crítico para ciertas aplicaciones y servicios en tiempo real (por ejemplo en aplicaciones de robótica en la nube, la telecirugía [16] o incluso los vehículos conectados [17,18]); es también un factor de importancia en el diseño de las redes [19,20] y en ocasiones es utilizado como una medida del rendimiento de una red. Puede definirse como el tiempo que necesita un dato para viajar a través de la red desde un nodo a otro.…”

Section: Retardounclassified

El impacto del buffer en la calidad de servicio

Sequeira

2021

Preprint

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“…From the content of the strategies, the cooperative merging strategies (both lane merging and on-ramp merging) proposed by most of the previous work often ask the vehicles involved to accelerate or decelerate or ask the merging vehicle to wait until it is safe to merge. We are only aware of a few papers (e.g., [26] and their previous work) that explicitly took into account the possibility of asking the vehicle on the destination lane to change lane as part of the cooperative merging strategies. Note that the merging targeted in [26] and their previous work was the lane merging into an adjacent lane on the same road, not on-ramp merging.…”

Section: Introductionmentioning

confidence: 99%