DL-AMP and DBTO: An Automatic Merge Planning and Trajectory Optimization and Its Application in Autonomous Driving

TL;DR

This paper introduces DL-AMP and DBTO, a novel automatic merging framework for autonomous vehicles that enhances merge opportunity detection, planning, control, and driving comfort.

Contribution

It proposes a decoupled dual-layer approach combining merge planning and trajectory optimization, improving autonomous merging performance.

Findings

Enhanced merge opportunity detection accuracy

Improved lateral and longitudinal merge control

Increased driving comfort during merging

Abstract

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.