Automated On-Ramp Merging System for Congested Traffic Situations

Milanés, Vicente; Godoy, Jorge; Villagrá, Jorge; Perez, Joshué · 2010 · OpenAlex-citations

DOI: 10.1109/tits.2010.2096812

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Summary

This paper addresses the challenge of traffic congestion caused by on-ramp merging in urban environments, where human drivers often struggle to coordinate acceleration and deceleration with main-road traffic. The authors propose an automated merging system designed to allow vehicles from minor roads to enter major roads fluidly while minimizing disruption to existing traffic flow. The system aims to reduce delays and pollution by optimizing the longitudinal control of the merging vehicle and the trailing vehicle on the main road. The methodology relies on the AUTOPIA program’s cooperative driving architecture, which incorporates a Local Control Station (LCS) to manage vehicle-to-infrastructure (V2I) communication. The LCS detects merging vehicles and identifies the optimal gap between two main-road vehicles (leading and trailing) for the merge. A decision algorithm computes target reference distances for the merging and trailing vehicles, ensuring a smooth transition to a predefined spacing at the merging point. To execute these references, a fuzzy logic controller manages the throttle and brake pedals of the vehicles. This controller uses speed error and distance error as inputs to determine actuator actions, handling the nonlinear dynamics of gasoline-propelled vehicles at low speeds. The system was validated using three production vehicles: two gasoline-powered Citroën C3s and one electric Citroën Berlingo van, tested at the Center for Automation and Robotics (CAR) driving circuit. The gasoline vehicles were equipped with analog cards for throttle control and an electrohydraulic braking system, while the electric vehicle used a differential Hall effect sensor for speed control. The LCS utilized wireless access points to establish V2I communication and coordinate the maneuver. Experimental results demonstrated that the system successfully activated the merging protocol, causing the trailing vehicle to reduce speed slightly to accommodate the merging vehicle. The merging vehicle reached the merging point with the desired spacing of 12 meters, after which all three vehicles maintained automated cruise control. The maneuver was executed without stopping the trailing vehicle, thereby limiting the impact on downstream traffic. The significance of this work lies in its demonstration of a practical, V2I-based automated merging solution using mass-produced vehicles in real-world conditions. By employing fuzzy logic to handle the imprecise data and nonlinear dynamics inherent in low-speed traffic, the system achieves smooth merging that mitigates congestion. The findings suggest that cooperative automated maneuvers can effectively improve traffic flow and reduce the negative effects of ramp entrances on urban transportation networks.

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• traffic density