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

Mohamed Elgouhary, Amr S. El-Wakeel

Abstract

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.

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

Abstract

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.

Paper Structure

This paper contains 32 sections, 12 equations, 3 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Contributions
  3. Related Work
  4. Geometric tracking for Ackermann platforms
  5. Reactive LiDAR-based control and collision avoidance
  6. Hybrid architectures and arbitration between behaviors
  7. Predictive control and sampling-based approximations
  8. Learning-based decision layers and learning-augmented control
  9. Robustness to sensing imperfections and error-injection evaluation
  10. System Overview
  11. ROS2 Implementation and Topic Interfaces
  12. Low-Level Controllers
  13. Pure Pursuit (global reference tracking)
  14. Gap Follow (local reactive collision avoidance control)
  15. Learning-Based Arbitration Module
  16. ...and 17 more sections

Figures (3)

  • Figure 1: System overview. Pure Pursuit (PP) provides global reference tracking, while Gap Follow (GF) provides local LiDAR-based reactive collision avoidance. A PPO-trained arbiter outputs a continuous gate $\alpha\in[0,1]$ that fuses their candidate Ackermann commands into a single drive command. A safety monitor can override the fused command when clearance or data-health checks fail.
  • Figure 2: Qualitative interaction sequence in simulation. The ego vehicle transitions from global reference tracking to a local free-space maneuver to pass the leading vehicle, then returns to reference tracking.
  • Figure 3: Robustness under base LiDAR impairments (noise, 200 ms delay, 0.3 dropout) with front-cone outliers swept over $p_{\text{out}}\in\{0,0.2,0.4\}$.