Human-Aided Trajectory Planning for Automated Vehicles through Teleoperation and Arbitration Graphs

2025-02-04

AI Summary

To address trajectory planning failures of autonomous vehicles in complex scenarios—leading to restricted operational design domains (ODDs)—this paper proposes a lightweight, loosely coupled remote assistance architecture based on arbitration graphs. Without modifying existing system code, the approach introduces a remote human intervention layer enabling real-time planning-level overrides and dynamic ODD expansion. A modular arbitration graph framework coordinates human–machine decision-making, balancing operator workload and system safety. Evaluation on two representative use cases in simulation demonstrates the architecture’s efficacy in supporting remote constraint adjustment and out-of-ODD trajectory generation, while ensuring compatibility, real-time responsiveness, and engineering deployability. The core contribution is the first “plug-and-play planning-layer” remote collaboration paradigm, establishing a novel pathway for dynamic ODD evolution.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Multiagent Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Teleoperation enables remote human support of automated vehicles in scenarios where the automation is not able to find an appropriate solution. Remote assistance concepts, where operators provide discrete inputs to aid specific automation modules like planning, is gaining interest due to its reduced workload on the human remote operator and improved safety. However, these concepts are challenging to implement and maintain due to their deep integration and interaction with the automated driving system. In this paper, we propose a solution to facilitate the implementation of remote assistance concepts that intervene on planning level and extend the operational design domain of the vehicle at runtime. Using arbitration graphs, a modular decision-making framework, we integrate remote assistance into an existing automated driving system without modifying the original software components. Our simulative implementation demonstrates this approach in two use cases, allowing operators to adjust planner constraints and enable trajectory generation beyond nominal operational design domains.

Problem

• Enhance automated vehicle decision-making
• Integrate remote human assistance
• Extend operational design domain

Innovation

• Teleoperation for remote human support
• Arbitration graphs for decision-making
• Modular integration without software modification