DL-AMP and DBTO: An Automatic Merge Planning and Trajectory Optimization and Its Application in Autonomous Driving
This addresses the specific challenge of safe and efficient merging for autonomous driving systems, appearing to be an incremental improvement in motion planning methods.
The paper tackles the problem of automatic merging for autonomous vehicles by decoupling motion planning into dual-layer merge planning and descent-based trajectory optimization, resulting in improvements in merge opportunity identification, lateral/longitudinal planning, trajectory postprocessing, and driving comfort.
This paper presents an automatic merging algorithm for autonomous driving vehicles, which decouples the specific motion planning problem into a Dual-Layer Automatic Merge Planning (DL_AMP) and a Descent-Based Trajectory Optimization (DBTO). This work leads to great improvements in finding the best merge opportunity, lateral and longitudinal merge planning and control, trajectory postprocessing and driving comfort.