Robust Global-Local Behavior Arbitration via Continuous Command Fusion Under LiDAR Errors
For modular autonomous driving systems, this work provides a practical command-level robustness evaluation under realistic sensing impairments, though it is incremental as it combines existing controllers with a learned fusion policy.
This paper presents a ROS2-native arbitration module that fuses global and local controllers using a PPO-trained policy to produce continuous drive commands, achieving robust performance under LiDAR errors. In close-proximity passing scenarios, the method maintains safe operation under noise, delay, dropout, and false outliers, with reported success/failure rates and runtime.
Modular autonomous driving systems must coordinate global progress objectives with local safety-driven reactions under imperfect sensing and strict real-time constraints. This paper presents a ROS2-native arbitration module that continuously fuses the outputs of two unchanged and interpretable controllers: a global reference-tracking controller based on Pure Pursuit and a reactive LiDAR-based Gap Follow controller. At each control step, both controllers propose Ackermann commands, and a PPO-trained policy predicts a continuous gate from a compact feature observation to produce a single fused drive command, augmented with practical safety checks. For comparison under identical ROS topic inputs and control rate, we implement a lightweight sampling-based predictive baseline. Robustness is evaluated using a ROS2 impairment protocol that injects LiDAR noise, delay, and dropout, and additionally sweeps forward-cone false short-range outliers. In a repeatable close-proximity passing scenario, we report safe success and failure rates together with per-step end-to-end controller runtime as sensing stress increases. The study is intended as a command-level robustness evaluation in a modular ROS2 setting, not as a replacement for planning-level interaction reasoning.