Adaptive strategies to platoon merging with vehicle engine uncertainty

Jain, Vishrut; Liu, Di; Baldi, Simone

doi:10.1016/j.ifacol.2020.12.2027

Cited by 13 publications

(2 citation statements)

References 30 publications

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“…where h[s] is the time gap, r i [m] is the standstill distance which can be made time-varying to the purpose of gap creation, and S m = 2, ..., m is the set of all following vehicles. This kind of desired distance is called constant time headway, which can meet the requirement of string stability [5,23].…”

Section: Vehicle Merging Modelmentioning

confidence: 99%

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

Liu

et al. 2022

Lecture Notes in Networks and Systems

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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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Section: Vehicle Merging Modelmentioning

confidence: 99%

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

Liu

et al. 2022

Lecture Notes in Networks and Systems

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“…Similarly, researchers in academia have extensively studied semiautonomous assisted driving. As a result, the ADAS, SAE L3, and L4 vehicles, and cooperative automated driving via cooperative adaptive cruise control (CACC) [7] have led to promising perspectives to reduce traffic accidents, as well as reducing greenhouse gas emissions, resilient mobility, and the possibility of performing stress-free non-driving related tasks (NDRT) in automated mode [6,8]. These systems are designed and equipped with sensory systems to understand information about road and driving conditions, assess hazards and driving warnings, and provide audio/visual requests to drivers [9].…”

Section: Introductionmentioning

confidence: 99%

DIPNet: Driver intention prediction for a safe takeover transition in autonomous vehicles

Bonyani

Rahmanian

Jahangard

et al. 2023

IET Intelligent Trans Sys

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Following the successful development of advanced driver assistance systems (ADAS), the current research directions focus on highely automated vehicles aiming at reducing human driving tasks, and extending the operational design domain, while maintaining a higher level of safety. Currently, there are high research demands in academia and industry to predict driver intention and understating driver readiness, e.g. in response to a “take‐over request” when a transition from automated driving mode to human mode is needed. A driver intention prediction system can assess the driver's readiness for a safe takeover transition. In this study, a novel deep neural network framework is developed by adopting and adapting the DenseNet, long short‐term memory, attention, FlowNet2, and RAFT models to anticipate the diver maneuver intention. Using the public “Brain4Cars” dataset, the driver maneuver intention will be predicted up to 4 s in advance, before the commencement of the driver's action. The driver intention prediction is assessed based on 1) in‐cabin 2) out‐cabin (road) and 3) both in‐out cabin video data. Utilizing K‐fold cross‐validation, the performance of the model is evaluated using accuracy, precision, recall, and F1‐score metrics. The experiments show the proposed DIPNet model outperforms the state‐of‐the‐art in the majority of the driving scenarios.

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Constrained distributed consensus control of homogeneous vehicle platoons with bidirectional communication

Gaagai,

Horn

2023

Control Engineering Practice

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Adaptive strategies to platoon merging with vehicle engine uncertainty

Cited by 13 publications

References 30 publications

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

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

DIPNet: Driver intention prediction for a safe takeover transition in autonomous vehicles

Constrained distributed consensus control of homogeneous vehicle platoons with bidirectional communication

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