Platoon Forming Algorithms for Intelligent Street Intersections (1901.04583v2)

Abstract: We study intersection access control for autonomous vehicles. Platoon forming algorithms, which aim to organize individual vehicles in platoons, are very promising. To create those platoons, we slow down vehicles before the actual arrival at the intersection in such a way that each vehicle can traverse the intersection at high speed. This increases the capacity of the intersection significantly, offering huge potential savings with respect to travel time compared to nowadays traffic. We propose several new platoon forming algorithms and provide an approximate mean delay analysis for our algorithms. A comparison between the current day practice at intersections (through a case study in SUMO) and our proposed algorithms is provided. Simulation results for fairness are obtained as well, showing that platoon forming algorithms with a low mean delay sometimes are relatively unfair, indicating a potential need for balancing mean delay and fairness.

• The paper presents novel algorithms that form vehicle platoons to optimize intersection throughput for autonomous vehicles.
• It leverages queueing theory and polling models to significantly reduce delays compared to traditional traffic light controls.
• Simulations show that both exhaustive and gated approaches enhance traffic flow, while balancing delay reduction with fairness trade-offs.

Platoon Forming Algorithms for Intelligent Street Intersections

Introduction

The paper "Platoon Forming Algorithms for Intelligent Street Intersections" (1901.04583) addresses the need for advanced traffic management strategies, specifically for autonomous vehicles at urban intersections. Traditional methods using static or dynamic traffic lights often result in inefficiencies and congestion, especially as traffic volume increases. This paper proposes the use of Platoon Forming Algorithms (PFAs) to organize vehicles into platoons, facilitating efficient and high-speed traversal of intersections.

Proposed Algorithms

The paper introduces new platoon forming algorithms grounded in queueing theory and polling models. The goal is to optimize intersection capacity and reduce mean travel delays:

• Exhaustive Platoon Forming Algorithm: Vehicles are grouped into platoons where each platoon waits for any potentially joining vehicle within a fixed time interval. This approach minimizes mean delays but can compromise fairness, as some vehicles might experience longer waits if continuously postponed.
• Gated Platoon Forming Algorithm: Instead of allowing constant additions to a platoon, a 'gate' is placed once a platoon is formed, and subsequent vehicles wait for the next batch. This technique strikes a balance between delay and fairness.

The algorithms operate by adjusting vehicle speeds upon entering a designated control area, ensuring they cross intersections at optimal speeds, which both maximizes throughput and minimizes idling and acceleration times.

Simulation and Comparison

Through simulations in the SUMO traffic modeling environment, the effectiveness of PFAs is benchmarked against traditional traffic light control systems. Results demonstrate that PFAs significantly enhance throughput and reduce vehicle delays compared to both fixed and adaptive traffic light strategies. The exhaustive PFA achieves the most substantial reductions in delay, although it might show lesser fairness due to the skewed prioritization of batch formation.

For scenarios with balanced traffic loads, PFAs greatly surpassed traditional controls in efficiency, maintaining reduced delays even under asymmetric load distributions. This affirms the potential of PFAs to manage intersections in future urban settings where self-driving vehicles dominate.

Implications and Future Directions

The findings highlight the substantial efficiency benefits of PFAs, particularly for autonomous vehicles, by leveraging coordinated speed control and strategic platoon scheduling:

• Practical Implications: Effective implementation of PFAs can revolutionize urban traffic management, especially as vehicle autonomy becomes widespread, promising reduced congestion, lower emissions, and improved travel times.
• Theoretical Implications: The connection between PFAs and polling models advances the theoretical understanding of traffic dynamics, providing a robust framework for performance analysis of intersection algorithms.

Future research could explore further optimizations balancing delay and fairness, potentially incorporating adaptive parameters or hybrid approaches combining different polling strategies. Additionally, extending the current model to networked intersections and addressing integration with semi-autonomous traffic remains a promising avenue for continued investigation.

Conclusion

This paper presents Platoon Forming Algorithms as a promising solution for intelligent intersection management in environments dominated by autonomous vehicles. By utilizing methodologies from queueing theory, specifically polling models, PFAs offer a robust framework for maximizing intersection efficiency while balancing fairness and minimizing delays. These advances pave the way for more sustainable and efficient urban mobility systems.