Building a Lane Merge Coordination for Connected Vehicles Using Deep Reinforcement Learning

This article presents a data-driven framework for trajectory recommendation in automated and cooperative driving. The considered cooperative driving maneuver is lane-merge coordination, and while the trajectory recommendation can only be communicated to the connected vehicles, in computation of those recommendations both connected and unconnected vehicles are taken into account. The data-driven framework is implemented centrally, comprising of two main components of a traffic orchestrator (TO) and data fusion (DF). The TO predicts the safest trajectories for connected vehicles involved in the lane-merge maneuver. The DF incorporates camera detected vehicles in order to map all vehicles, including connected and unconnected. To this end, the recommendations are built using various state-of-the-art machine learning (ML) techniques, including deep reinforcement learning and dueling deep Q-network. Our evaluations are conducted using the real-system deployed in the test track, with a mix of connected and unconnected vehicles. The results demonstrate the precision of predicted trajectories, and the percentage of successful lane merge achieved deploying different ML techniques.

Automated summary

This study introduces a data-driven framework for trajectory recommendation in automated and cooperative driving, focusing on lane-merge coordination. The framework is implemented centrally, with components including a traffic orchestrator and data fusion. Evaluations demonstrate the percentage of successful lane merges achieved and the precision of predicted trajectories.