Fluent coordination of autonomous vehicles at intersections

Makarem, Laleh; Gillet, Denis

doi:10.1109/icsmc.2012.6378130

Cited by 54 publications

(30 citation statements)

References 9 publications

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“…Interestingly, a great deal of the literature has focused on Phase 4, and several studies have considered cooperative strategies for intersection scenarios, though generally abstracting from the wireless communication and signal processing aspects. Decentralized approaches based on predictive control and reachability analysis [30], [31] and on navigation function controllers [32] have been considered. A somewhat different approach is taken in [33], where the dynamics are abstracted away and a number of protocols for distributed decision making are presented and compared.…”

Section: A Control Theorymentioning

confidence: 99%

Challenges for cooperative ITS: Improving road safety through the integration of wireless communications, control, and positioning

Wymeersch

Campos

Falcone

et al. 2015

2015 International Conference on Computing, Networking and Communications (ICNC)

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For intelligent transportation systems (ITS) to achieve situational awareness beyond their sensing horizon and to harness coordination capabilities, some form of cooperation will be required. Such cooperation is enabled by vehicle-to-vehicle and vehicle-to-infrastructure wireless communication. The integration between the communication, signal processing, and control sub-systems is non-trivial and requires a co-design, which in turn requires collaboration between these three disciplines. This paper presents a possible evolution of these three disciplines within the context of ITS, as well as several challenges and opportunities.

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Section: A Control Theorymentioning

confidence: 99%

Challenges for cooperative ITS: Improving road safety through the integration of wireless communications, control, and positioning

Wymeersch

Campos

Falcone

et al. 2015

2015 International Conference on Computing, Networking and Communications (ICNC)

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“…One of the scenarios considered to test our model was an intersection as shown in [12,13,14,15]. Therefore, an algorithm has been developed which focuses on allowing equal traffic flow from each side of the entries.…”

Section: Acceleration (Deceleration) Modelmentioning

confidence: 99%

Acceleration (Deceleration) Model Supporting Time Delays to Refresh Data

Carrillo-González

Lizárraga²,

Barbosa-Santillán³

2018

PROMET

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This paper proposes a mathematical model to regulate the acceleration (deceleration) applied by self-driving vehicles in car-following situations. A virtual environment is designed to test the model in different circumstances: (1) the followers decelerate in time if the leader decelerates, considering a time delay of up to 5 s to refresh data (vehicles position coordinates) required by the model, (2) with the intention of optimizing space, the vehicles are grouped in platoons, where 3 s of time delay (to update data) is supported if the vehicles have a centre-to-centre spacing of 20 m and a time delay of 1 s is supported at a spacing of 6 m (considering a maximum speed of 20 m/s in both cases), and (3) an algorithm is presented to manage the vehicles’ priority at a traffic intersection, where the model regulates the vehicles’ acceleration (deceleration) and a balance in the number of vehicles passing from each side is achieved.

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“…Here, each agent follows a potential field leading the swarm to a configuration where the minimum distance between the swarm members is identical. The same idea of artificial potential functions was also used for studying behaviorbased control as in, e.g., ; Donald et al (2000); Reif et al (1999)], as well as air traffic and road traffic control [Fankhauser et al (2011); Makarem et al (2012)].…”

Section: Related Researchmentioning

confidence: 99%

Distributed control of compact formations for multi-robot swarms

Campos

Dimarogonas

Seuret

et al. 2017

IMA Journal of Mathematical Control and Information

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This paper proposes a distributed algorithm for the compact deployment of robots, using both distanceand angular-based arguments in the controllers' design. Our objective is to achieve a configuration maximizing the coverage of the environment while increasing the graph's connectivity. First, we provide: (i) a dispersion protocol guaranteeing connectivity maintenance; and (ii) a compactness controller with static and variable control gains that minimizes the inter-agent angles. Second, we present a sequential, multi-stage strategy and analyse its stability. Finally, we validate our theoretical results with simulations, where a group of robots are deployed to carry out sensing or communication tasks.

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Fluent coordination of autonomous vehicles at intersections

Cited by 54 publications

References 9 publications

Challenges for cooperative ITS: Improving road safety through the integration of wireless communications, control, and positioning

Challenges for cooperative ITS: Improving road safety through the integration of wireless communications, control, and positioning

Acceleration (Deceleration) Model Supporting Time Delays to Refresh Data

Distributed control of compact formations for multi-robot swarms

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