Evaluation of lane-merging approaches for connected vehicles

Santos, Tiago C. dos; Bruno, Diego Renan; Osório, Fernando Santos; Wolf, Denis F.

doi:10.1109/ivs.2019.8813802

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…Conversely, 30 m is found to be comfortable and easier to merge into the platoon. Authors of [ 12 ] produced an optimal speed profile during platoon merging on highway on-ramp to reduce the energy consumption of battery electric vehicles (BEVs), while [ 13 ] evaluated three different interactions protocols of two platoons facing lane-merging to optimize the total time to complete the maneuver, the string platoon length, and the average speed of the platoon. Two platoons of five vehicles were used in the simulation environment SUMO.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Cooperative Platoon Merging Control Based on Reinforcement Learning

Irshayyid

Chen

2023

Sensors

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The time that a vehicle merges in a lane reduction can significantly affect passengers’ safety, comfort, and energy consumption, which can, in turn, affect the global adoption of autonomous electric vehicles. In this regard, this paper analyzes how connected and automated vehicles should cooperatively drive to reduce energy consumption and improve traffic flow. Specifically, a model-free deep reinforcement learning approach is used to find the optimal driving behavior in the scenario in which two platoons are merging into one. Several metrics are analyzed, including the time of the merge, energy consumption, and jerk, etc. Numerical simulation results show that the proposed framework can reduce the energy consumed by up to 76.7%, and the average jerk can be decreased by up to 50%, all by only changing the cooperative merge behavior. The present findings are essential since reducing the jerk can decrease the longitudinal acceleration oscillations, enhance comfort and drivability, and improve the general acceptance of autonomous vehicle platooning as a new technology.

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Section: Introductionmentioning

confidence: 99%

Comparative Study of Cooperative Platoon Merging Control Based on Reinforcement Learning

Irshayyid

Chen

2023

Sensors

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“…Automated vehicles have been a popular research topic over the last decades [1]. Platooning of connected and automated vehicles is regarded as a possible solution to future transportation, with improved throughput and safety [2][3][4]. Adaptive cruise control (ACC) system was one of the first technologies to achieve platooning to some extent, and cooperative adaptive cruise control (CACC) is its extension.…”

Section: Introductionmentioning

confidence: 99%

A Cooperative Protocol for Vehicle Merging Using Bi-dimensional Artificial Potential Fields

Liu

et al. 2022

Robot Intelligence Technology and Applications 6

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In recent years, platooning solutions like cooperative adaptive cruise control (CACC) have been deeply studied. It is common in such platooning literature to assume that the vehicles drive on the same lane (longitudinal platooning). At the same time, lateral control during merging maneuvers is commonly addressed as a path planning problem, in which the ego vehicle changes the lane during merging without necessarily cooperating with its neighboring vehicles (i.e. without considering gap closing). The primary objective of this article is to develop a control strategy which involves both longitudinal and lateral vehicle dynamics, where the vehicles merge and form a platoon in a cooperative way without a priori path planning. Appropriately designed bi-dimensional artificial potential fields are used to achieve this goal and the proposed protocol is verified through simulations with CarSim.

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Large-Scale Vehicle Platooning: Advances and Challenges in Scheduling and Planning Techniques

Hou¹,

Chen²,

Huang³

et al. 2023

Engineering

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Evaluation of lane-merging approaches for connected vehicles

Cited by 4 publications

References 10 publications

Comparative Study of Cooperative Platoon Merging Control Based on Reinforcement Learning

Comparative Study of Cooperative Platoon Merging Control Based on Reinforcement Learning

A Cooperative Protocol for Vehicle Merging Using Bi-dimensional Artificial Potential Fields

Large-Scale Vehicle Platooning: Advances and Challenges in Scheduling and Planning Techniques

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