Cooperative Merging Strategy Between Connected Autonomous Vehicles in Mixed Traffic

Andreotti, Eleonora; Selpi; Aramrattana, Maytheewat · 2022 · Crossref

DOI: 10.1109/ojits.2022.3179125

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Summary

This paper addresses the challenge of safe and efficient on-ramp merging in mixed traffic environments, where connected autonomous vehicles (CAVs) coexist with non-connected autonomous vehicles and manually driven vehicles. On-ramp merging is a primary cause of highway congestion, and while previous cooperative strategies often assumed homogeneous CAV traffic or single-lane roads, this study focuses on realistic multi-lane scenarios with varying penetration rates of autonomous technology. The authors propose a novel cooperative merging strategy designed to reduce the risk of cut-in maneuvers by dynamically coordinating the speed and lane choices of a pair of CAVs approaching a merge point. The study employs traffic simulations using the Simulation of Urban Mobility (SUMO) platform, modeling a 2-km section of a highway in Gothenburg, Sweden, including a three-lane main road and a 300-meter on-ramp. The simulation incorporates real traffic flow data and uses the Krauss car-following and LC2013 lane-changing models to represent vehicle behavior. The proposed strategy activates when two CAVs enter a communication zone within a specific time window. It calculates optimal accelerations or decelerations for both vehicles to ensure a minimum safe space gap at the merge point. Crucially, the strategy allows for dynamic adjustments, including lane changes by the main-road vehicle to create space, and adapts to interactions with surrounding non-connected vehicles. The safety performance is evaluated using the Cut-In Risk Indicator (CRI) across scenarios with CAV penetration rates ranging from 20% to 100% in 10% increments. The results demonstrate that the proposed cooperative strategy significantly improves safety compared to non-connected autonomous vehicles. On average, the strategy achieves a 35% reduction in cut-in risk maneuvers. Analysis of the probability density functions of the CRI reveals that this risk reduction is not uniform across all indicator values; rather, it depends on the penetration rate of autonomous vehicles and the severity of the merging maneuver. The strategy effectively handles mixed traffic by dynamically splitting speed adjustments between vehicles when necessary to accommodate unforeseen events, such as cut-ins by non-connected vehicles, thereby maintaining safety without requiring all surrounding vehicles to be autonomous. The significance of this work lies in its contribution to the development of cooperative driving strategies for the transitional period of mixed traffic. By demonstrating that CAVs can reduce merging risks through coordinated speed and lane adjustments, even in the presence of non-connected vehicles, the study supports the integration of V2V technologies into existing infrastructure. The findings suggest that cooperative strategies can enhance highway capacity and safety without relying on centralized control or homogeneous vehicle fleets, offering a practical approach for near-future traffic systems.

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