通过仲裁图实现远程人工干预，扩展自动驾驶车辆的运行范围。
Human-Aided Trajectory Planning for Automated Vehicles through Teleoperation and Arbitration Graphs
- 用仲裁图框架将远程协助模块化集成到现有系统中
- 实测可让操作员调整规划约束，突破原有限制范围
- 适合需动态扩展功能的自动驾驶系统开发团队
远程操作可在自动化系统无法找到合适解的场景下提供人类支持。当前基于离散输入辅助规划等模块的远程协助概念因对人机负荷低且提升安全性而受到关注，但其深度集成与自动驾驶系统的交互使其难以实现与维护。本文提出一种新方案，通过仲裁图这一模块化决策框架，在不修改原有软件组件的前提下，将远程协助引入现有自动驾驶系统，实现规划层干预，并在运行时扩展车辆的运行设计域。仿真测试验证了该方法在两个用例中的有效性，使操作员能够调整规划约束，实现超出原始运行设计域的轨迹生成。
原文摘要 · Abstract (English)
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.
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