A Lane Merge Coordination Model for a V2X Scenario
This addresses lane merging challenges for autonomous vehicles in V2X scenarios, but it appears incremental as it builds on existing algorithms.
The paper tackles lane merge coordination for connected vehicles using a centralized system with a Traffic Orchestrator, applying machine learning to predict successful merges and determine safe acceleration and heading parameters, with results showing performance evaluation and parameter selection to avoid over-fitting.
Cooperative driving using connectivity services has been a promising avenue for autonomous vehicles, with the low latency and further reliability support provided by 5th Generation Mobile Network (5G). In this paper, we present an application for lane merge coordination based on a centralised system, for connected cars. This application delivers trajectory recommendations to the connected vehicles on the road. The application comprises of a Traffic Orchestrator as the main component. We apply machine learning and data analysis to predict whether a connected vehicle can successfully complete the cooperative manoeuvre of a lane merge. Furthermore, the acceleration and heading parameters that are necessary for the completion of a safe merge are elaborated. The results demonstrate the performance of several existing algorithms and how their main parameters were selected to avoid over-fitting.