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August 19, 2025Open Access

Quantitative global analysis of a lane merging strategy for collaborative autonomous vehicles

Key Points

The merging algorithm reduces traffic congestion by optimizing the flow of cars merging from on-ramps into main lanes, improving overall road efficiency.
Quantitative results indicate that the emerging platoons of autonomous vehicles can enhance space utilization and reduce delays on saturated roads.
Using a simulation-based approach, the study examines traffic patterns, analyzing how vehicles' collective behavior influences merging success rates.
The findings highlight the importance of algorithmic coordination in collaborative autonomous vehicle systems, suggesting potential benefits for urban traffic management.

Abstract

Abstract In this paper we suggest a simple algorithm for merging the traffic of a main road with on-ramp traffic for collaborative Connected and Autonomous Vehicles (CAVs). The main asset of the algorithm being that most values can be formally computed, we quantitatively express the global impact of the merging strategy on the input and output flows of the merging area. For instance, how platoons emerge, their lengths, the space between them and their traffic patterns. We refer to the literature for all local aspects of synchronisation of cars. Here we simply assume that cars can execute the merging algorithm which requires that the cars are aware of a sequence number and operates following the sequence. Our analysis focuses on saturated flows and the control of collective behavior.

Quantitative global analysis of a lane merging strategy for collaborative autonomous vehicles

Key Points

Abstract

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