Trajectory Planning of Automated Vehicles Using Real-Time Map Updates
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2023
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Abstract
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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Publisher Copyright: © 2013 IEEE.
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Szántó , M , Hidalgo , C , González , L , Rastelli , J P , Asua , E & Vajta , L 2023 , ' Trajectory Planning of Automated Vehicles Using Real-Time Map Updates ' , IEEE Access , vol. 11 , pp. 67468-67481 . https://doi.org/10.1109/ACCESS.2023.3291350