Platooning Trajectory Optimization for Connected Automated Vehicles in Coordinated Arterials

Guerra, Agustin; Elefteriadou, Lily

doi:10.1177/03611981221112099

Cited by 4 publications

(3 citation statements)

References 44 publications

(82 reference statements)

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“…Minimized travel time and fuel consumption: AI-powered systems significantly reduce vehicle travel time by avoiding congested routes and selecting paths with optimal traffic conditions. This saves drivers time and reduces fuel consumption, leading to cost savings and, importantly, decreased emissions, contributing to environmental sustainability [84]. Environmental impact: One of the key benefits of AI-driven route optimization is its positive impact on the environment.…”

Section: Ai-innovations: Transforming Diverse Industriesmentioning

confidence: 99%

AI-Powered Innovation in Digital Transformation: Key Pillars and Industry Impact

Aldoseri,

Al-Khalifa,

Hamouda

2024

Sustainability

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Digital transformation systems generate a substantial volume of data, creating opportunities for potential innovation, particularly those driven by artificial intelligence. This study focuses on the intricate relationship between artificial intelligence and innovation as foundational elements in the digital transformation framework for sustained growth and operational excellence. This study provides a holistic perspective on the cultivation and pillars of AI-powered innovation, highlighting their pivotal role in revolutionizing industries, including healthcare, education, finance, manufacturing, transportation, and agriculture. The work emphasizes the key pillars essential for fostering AI-powered innovation, including monitoring performance measurement to use the power of the present, continuous learning and innovation, data analytics and insights, predictive analytics, and innovative product development. This study investigates how these pillars serve as the foundation for groundbreaking advancements, driving efficiency, enhancing decision-making processes, and fostering creativity within organizations. This study explores the significance of continuous learning, interdisciplinary collaboration, and industry partnerships in nurturing a thriving AI-powered innovation ecosystem. By understanding and harnessing these fundamental elements, businesses can navigate the complexities of the digital age, fostering innovation that not only optimizes processes but also enhances the overall human experience, ushering in a new era of technological excellence and societal progress.

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Section: Ai-innovations: Transforming Diverse Industriesmentioning

confidence: 99%

AI-Powered Innovation in Digital Transformation: Key Pillars and Industry Impact

Aldoseri,

Al-Khalifa,

Hamouda

2024

Sustainability

View full text Add to dashboard Cite

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“…Storani et al (2022) proposed a hybrid traffic flow model using signal transition to connect signalized arterial [15]. Guerra et al (2022) compared this with a pre-timed coordinated signal control scheme, they proposed an algorithm that enabled a smooth transition to the traffic system target scheme [16]. Moradi et al (2023) used information of connected vehicles to estimate the saturation flow rate during the transition period [17].…”

Section: Introductionmentioning

confidence: 99%

OAS Deep Q-Learning-Based Fast and Smooth Control Method for Traffic Signal Transition in Urban Arterial Tidal Lanes

Dong,

Xie,

et al. 2024

Sensors

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To address traffic flow fluctuations caused by changes in traffic signal control schemes on tidal lanes and maintain smooth traffic operations, this paper proposes a method for controlling traffic signal transitions on tidal lanes. Firstly, the proposed method includes designing an intersection overlap phase scheme based on the traffic flow conflict matrix in the tidal lane scenario and a fast and smooth transition method for key intersections based on the flow ratio. The aim of the control is to equalize average queue lengths and minimize average vehicle delays for different flow directions at the intersection. This study also analyses various tidal lane scenarios based on the different opening states of the tidal lanes at related intersections. The transitions of phase offsets are emphasized after a comprehensive analysis of transition time and smoothing characteristics. In addition, this paper proposes a coordinated method for tidal lanes to optimize the phase offset at arterial intersections for smooth and rapid transitions. The method uses Deep Q-Learning, a reinforcement learning algorithm for optimal action selection (OSA), to develop an adaptive traffic signal transition control and enhance its efficiency. Finally, a simulation experiment using a traffic control interface is presented to validate the proposed approach. This study shows that this method leads to smoother and faster traffic signal transitions across different tidal lane scenarios compared to the conventional method. Implementing this solution can benefit intersection groups by reducing traffic delays, improving traffic efficiency, and decreasing air pollution caused by congestion.

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“…To simplify the problem, the works usually developed the specific coordination rules for multiple AVs or multiple intersections. Guerra and Elefteriadou [ 30 ] optimized the trajectories of AVs along the arterial under the assumption of fully automated. The trajectories are adjusted to form a platoon at saturated headway to ensure that vehicles reach the downstream intersection within the green light.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Planning for Multiple Autonomous Vehicles at Short‐Distance Tandem Signalized Intersections Based on Rule‐Free Framework

Lin,

Hu,

Wang

2024

Advanced Intelligent Systems

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High‐level autonomous vehicles (AVs) have more possibilities for improving traffic efficiency. The improvement of traffic efficiency for mixed flow at near‐saturated short‐distance tandem signalized intersections (STSI) needs attention. Most of the existing studies design a generalized control rule for AVs, ignoring the heterogeneity among different AVs. Herein, a multivehicle trajectory planning framework based on a multiagent reinforcement learning (MRL) algorithm is designed to heuristically explore the optimal traffic efficiency of mixed flow at STSI. The core algorithm of this framework is improved from the classical MRL algorithm multi‐agent proximal policy optimization based on the idea of the virtual group instead of designing control rules. The trajectories planned by the framework show outstanding performance in improving throughputs and reducing emissions at the global system level, comparing natural driving, classic adaptive cruise control model and cooperative adaptive cruise control model. The framework can be used to explore optimal traffic efficiency for mixed flow and better heterogeneous rules for high‐level AVs.

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Platooning Trajectory Optimization for Connected Automated Vehicles in Coordinated Arterials

Cited by 4 publications

References 44 publications

AI-Powered Innovation in Digital Transformation: Key Pillars and Industry Impact

AI-Powered Innovation in Digital Transformation: Key Pillars and Industry Impact

OAS Deep Q-Learning-Based Fast and Smooth Control Method for Traffic Signal Transition in Urban Arterial Tidal Lanes

Trajectory Planning for Multiple Autonomous Vehicles at Short‐Distance Tandem Signalized Intersections Based on Rule‐Free Framework

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