Szántó M, Hidalgo C, González L, Pérez J, Asua E, Vajta L. 2023. Trajectory Planning of Automated Vehicles Using Real-Time Map Updates. IEEE Access. 2023. Vol. 11. 2023


IEEE Access 2023. Vol. 11. 2023

Szántó M, Hidalgo C, González L, Pérez J, Asua E, and Vajta L.

The development of connected and automated vehicles (CAVs) presents a great opportunity to extend the current range of vehicle vision, by gathering information outside of its sensors. Two main sources could be aggregated for this extended perception; vehicles making use of vehicle-to-vehicle communication (V2V), and infrastructure using vehicle-to-infrastructure communication (V2I). In this paper, we focus on the infrastructure side and make the case for low-latency obstacle mapping using V2I communication. A map management framework is proposed, which allows vehicles to broadcast and subscribe to traffic information-related messages using the Message Queuing Telemetry Transport (MQTT) protocol. This framework makes use of our novel candidate/employed map (C/EM) model for the real-time updating of obstacles broadcast by individual vehicles. This solution has been implemented and tested using a scenario that contains real and simulated CAVs tasked with doing lane change and braking maneuvers. As a result, the simulated vehicle can optimize its trajectory planning based on information which could not be observed by its sensor suite but is instead received from the presented map-management module, while remaining capable of performing the maneuvers in an automated manner.

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