Connected cooperative control for multiple-lane automated vehicle flocking on highway scenarios

Caruntu, Constantin F.; Ferariu, Lavinia; Pascal, Carlos; Cleju, Nicolae; Comsa, Ciprian R.

doi:10.1109/icstcc.2019.8885496

Cited by 10 publications

(6 citation statements)

References 8 publications

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“…To further increase traffic flow, ref. [4] extends the idea of single-lane platoons to multi-lane platoons [10][11][12]. Vehicles should consider all available lanes on a given road sector when creating a group of vehicles and travel with small distances between them at high speeds.…”

Section: Traffic Optimizationmentioning

confidence: 99%

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Architectural Framework to Enhance Image-Based Vehicle Positioning for Advanced Functionalities

Beti,

Herghelegiu,

Caruntu

2024

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The growing number of vehicles on the roads has resulted in several challenges, including increased accident rates, fuel consumption, pollution, travel time, and driving stress. However, recent advancements in intelligent vehicle technologies, such as sensors and communication networks, have the potential to revolutionize road traffic and address these challenges. In particular, the concept of platooning for autonomous vehicles, where they travel in groups at high speeds with minimal distances between them, has been proposed to enhance the efficiency of road traffic. To achieve this, it is essential to determine the precise position of vehicles relative to each other. Global positioning system (GPS) devices have an intended positioning error that might increase due to various conditions, e.g., the number of available satellites, nearby buildings, trees, driving into tunnels, etc., making it difficult to compute the exact relative position between two vehicles. To address this challenge, this paper proposes a new architectural framework to improve positioning accuracy using images captured by onboard cameras. It presents a novel algorithm and performance results for vehicle positioning based on GPS and video data. This approach is decentralized, meaning that each vehicle has its own camera and computing unit and communicates with nearby vehicles.

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Section: Traffic Optimizationmentioning

confidence: 99%

“…To increase the efficiency of road traffic, the idea of grouping autonomous vehicles into platoons through information exchange was proposed in [4]. Vehicles should consider all available lanes on a given road sector when forming a group and travel at high speeds with minimal safety distances between them.…”

Section: Introductionmentioning

confidence: 99%

Architectural Framework to Enhance Image-Based Vehicle Positioning for Advanced Functionalities

Beti,

Herghelegiu,

Caruntu

2024

Information

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“…Their model could handle different events, such as splitting, merging, overtaking, and obstacle avoidance. Several researchers (16)(17)(18)(19)(20) used ideas from swarm robotics for developing multiple-lane platooning strategies that control each vehicle in the group 21) developed a model that manages platoons in two dimensions at signalized intersections. Xu et al (22) proposed a method for multi-lane vehicle formation control that is combined with a method to calculate conflict-free passing sequences at unsignalized intersections.…”

Section: Cav Platoons Across Multiple Lanesmentioning

confidence: 99%

“…Their model could handle different events, such as splitting, merging, overtaking, and obstacle avoidance. Several researchers ( 16 – 20 ) used ideas from swarm robotics for developing multiple-lane platooning strategies that control each vehicle in the group longitudinally and laterally. Hao et al ( 21 ) developed a model that manages platoons in two dimensions at signalized intersections.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Modeling Vehicle Flocking in Lane-Free Automated Traffic

Rostami-Shahrbabaki

Weikl

Niels

et al. 2023

Transportation Research Record: Journal of the Transportation R

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In automated lane-free traffic, vehicles can choose any arbitrary lateral location. This enables vehicle flocking where, compared to platooning, the grouping of vehicles is possible with smaller space gaps, not only longitudinally but also laterally. Vehicle flocking can fulfill several purposes, such as increasing the road capacity, saving energy by reducing the aerodynamic drag force, and dampening shockwaves. Within this paper, we develop a control framework for modeling vehicle flocks in automated lane-free traffic. The proposed control algorithm considers two types of agents: [Formula: see text] -agents representing potential flock mates and a [Formula: see text] -agent representing the virtual leader with collective objectives (e.g., slowing down in the case of traffic congestion ahead). Our algorithm is based on energy functions for flock centering and collision avoidance, a consensus algorithm for velocity matching, and navigational feedback exerted by the virtual leader. The virtual leader’s path, which should be followed by the flock, is defined in an upper-level controller. In addition, a feedback algorithm for dynamic road boundary control is implemented. We simulate the proposed approach with very promising results. We show that vehicle flocks are efficiently formed within a few seconds, speeds are successfully aligned, and vehicle arrangements stay stable under different scenarios. In addition, the lateral and longitudinal flock extension changes with different energy functions and changing road boundaries, and vehicle flocks follow the trajectory of the virtual leader. Most importantly, vehicle flocks stay stable in the case of perturbations and the induced shock is dampened efficiently because of slight changes in the vehicles’ lateral locations.

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“…Rajamani [14] uses the bicycle model to model the lateral dynamics of AGVs. While Caruntu et al [15] show that the longitudinal dynamics can be modeled by a second-order model. These models describe the position and orientation of the vehicle relative to a coordinate system.…”

Section: Related Workmentioning

confidence: 99%

Integrated Design Methodology of Automated Guided Vehicles Based on Swarm Robotics

et al. 2021

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In recent years, collaborative robots have become one of the main drivers of Industry 4.0. Compared to industrial robots, automated guided vehicles (AGVs) are more productive, flexible, versatile, and safer. They are used in the smart factory to transport goods. Today, many producers and developers of industrial robots have entered the AGV sector. However, they face several challenges in designing AGV systems, such as the complexity and discontinuity of the design process, as well as the difficulty of defining a decentralized system decision. In this paper, we propose a new integrated design methodology based on swarm robotics to address the challenges of functional, physical, and software integration. This methodology includes two phases: a top-down phase from requirements specification to functional and structural modeling using the systems modeling language (SysML); with a bottom-up phase for model integration and implementation in the robot operating system (ROS). A case study of an automated guided vehicle (AGV) system was chosen to validate our design methodology and illustrate its contributions to the efficient design of AGVs. The novelty of this proposed methodology is the combination of SysML and ROS to address traceability management between the different design levels of AGV systems, in order to achieve functional, physical and software integration.

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Connected cooperative control for multiple-lane automated vehicle flocking on highway scenarios

Cited by 10 publications

References 8 publications

Architectural Framework to Enhance Image-Based Vehicle Positioning for Advanced Functionalities

Architectural Framework to Enhance Image-Based Vehicle Positioning for Advanced Functionalities

Modeling Vehicle Flocking in Lane-Free Automated Traffic

Integrated Design Methodology of Automated Guided Vehicles Based on Swarm Robotics

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