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

📰 ArXiv cs.AI

Robust arbitration module for autonomous driving systems fuses global and local controllers under LiDAR errors

advanced Published 31 Mar 2026
Action Steps
  1. Develop a global reference-tracking controller based on Pure Pursuit
  2. Implement a reactive LiDAR-based Gap Follow controller
  3. Design a continuous command fusion module to arbitrate between the two controllers
  4. Test and evaluate the arbitration module under various LiDAR error scenarios
Who Needs to Know This

This research benefits autonomous driving system developers and engineers, particularly those working on modular systems that require coordination between global progress objectives and local safety-driven reactions

Key Insight

💡 Continuous command fusion can effectively arbitrate between global and local controllers in autonomous driving systems under imperfect sensing conditions

Share This
💡 Autonomous driving systems: robust arbitration module fuses global & local controllers under LiDAR errors

Key Takeaways

Robust arbitration module for autonomous driving systems fuses global and local controllers under LiDAR errors

Full Article

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

Abstract:
arXiv:2603.27273v1 Announce Type: cross 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 cont
Read full paper → ← Back to Reads

Related Videos

Agentic AI System Design- Complete Roadmap
Agentic AI System Design- Complete Roadmap
Aishwarya Srinivasan
How To Build Your Own RAG AI System - Better Results Than Claude
How To Build Your Own RAG AI System - Better Results Than Claude
Web Dev Simplified
Build AI Agents in 2 Minutes using Microsoft Foundry
Build AI Agents in 2 Minutes using Microsoft Foundry
Rajeev Kanth | BEPEC
Evaluating Agentic AI Skills (using OpenHands)
Evaluating Agentic AI Skills (using OpenHands)
Rajistics - data science, AI, and machine learning
Dynamic Workflows using Openhands SDK
Dynamic Workflows using Openhands SDK
Rajistics - data science, AI, and machine learning
I built a custom Hermes plugin. #HermesAgent #Claudecode #openaicodex #openclaw #nousresearch
I built a custom Hermes plugin. #HermesAgent #Claudecode #openaicodex #openclaw #nousresearch
Tech Friend AJ