# Robust Global-Local Behavior Arbitration via Continuous Command Fusion Under LiDAR Errors

arXiv: https://arxiv.org/abs/2603.27273 · HTML: https://mlsanity.com/paper/2603.27273/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 28 Mar 2026.

## Builds on (most cited first, 9 of 9)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Mixture of Experts (MoE): A Big Data Perspective](https://mlsanity.com/paper/2501.16352/) (2025)
- [Lateral control for autonomous vehicles: A comparative evaluation](https://mlsanity.com/paper/2311.07987/) (2023)
- [Assessing the Robustness of LiDAR, Radar and Depth Cameras Against Ill-Reflecting Surfaces in Autonomous Vehicles: An Experimental Study](https://mlsanity.com/paper/2309.10504/) (2023)
- [Hierarchical Control for Head-to-Head Autonomous Racing](https://mlsanity.com/paper/2202.12861/) (2022)
- [Testing the Fault-Tolerance of Multi-Sensor Fusion Perception in Autonomous Driving Systems](https://mlsanity.com/paper/2504.13420/) (2025)
- [Advancing Autonomous Racing: A Comprehensive Survey of the RoboRacer (F1TENTH) Platform](https://mlsanity.com/paper/2506.15899/) (2025)
- [Factor Graph-Based Planning as Inference for Autonomous Vehicle Racing](https://mlsanity.com/paper/2203.03224/) (2022)

## Abstract (arXiv)

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.