Trajectory Planning of Automated Vehicles Using Real-Time Map Updates

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