Lane-Level Navigation: A Local Drive Guide Sitting by the Roadside

In this research, a lane-level navigation system is designed to enhance vehicle mobility at signalized intersections. A deep learning-based lane-level long-term speed prediction (LLSP) predictor was developed to forecast traffic conditions for the upcoming planning horizon. Additionally, a lane-level navigation with speed guidance (LNSG) planner was introduced to determine the optimal lane-level route and the recommended travel speed for the ego vehicle. The performance of the proposed system was assessed using a software-in-the-loop simulation platform, considering various scenarios such as different traffic demands, vehicle arrival times at the control area, and planning resolutions. The evaluation results demonstrate that the proposed navigation system effectively improves the mobility of the ego vehicle by providing optimal lane and speed recommendations. Compared to the lane-keeping strategy, the system can reduce travel time by up to 30.2% in various traffic conditions.