# Implementation of Open-ended Commands in Autonomous Driving: An LLM-driven Multi-planner Scheduling Framework

> This paper proposes an instruction implementation framework based on large language models (LLMs), which converts passengers' natural language commands into executable vehicle control signals by scheduling multiple MPC motion planners, achieving effective decoupling between semantic reasoning and vehicle control.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-09T09:32:21.000Z
- 最近活动: 2026-04-10T01:46:25.553Z
- 热度: 116.8
- 关键词: 自动驾驶, 大语言模型, 人机交互, 运动规划, MPC, 自然语言理解, 多规划器调度, 智能交通
- 页面链接: https://www.zingnex.cn/en/forum/thread/llm-6ee2492c
- Canonical: https://www.zingnex.cn/forum/thread/llm-6ee2492c
- Markdown 来源: floors_fallback

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## [Introduction] LLM-driven Multi-planner Scheduling Framework: A New Solution for Open-ended Command Implementation in Autonomous Driving

This paper proposes an instruction implementation framework based on large language models (LLMs), which converts passengers' natural language commands into executable vehicle control signals by scheduling multiple MPC motion planners, achieving effective decoupling between semantic reasoning and vehicle control. This framework addresses the limitations of traditional autonomous driving systems in handling open-ended commands, improving task completion rate, safety, and decision interpretability.

## Background: Challenges in Autonomous Driving Human-Machine Interaction and Limitations of Traditional Methods

### New Challenges in Human-Machine Interaction
In the development of autonomous driving technology, existing HMI research mostly focuses on driver interaction, ignoring passengers' open-ended control needs (such as commands like "drive slower to enjoy the scenery"). The accuracy and interpretability of converting these commands into control signals are key challenges.
### Limitations of Traditional Methods
Traditional layered architectures rely on predefined command mappings and lack flexibility in open-ended language processing; the tightly coupled design where high-level semantics are directly mapped to low-level control leads to difficulties in handling complex commands and decision black-box issues, affecting safety verification.

## Core Method: Three-layer Architecture Design with Centralized Scheduling

The framework adopts a centralized scheduling design to achieve decoupling between semantic reasoning and control:
1. **LLM Semantic Parsing Layer**: Deeply understands the intent, constraints, and priorities of commands (e.g., parsing the trade-off between the dual goals of "arrive as soon as possible but without jolting");
2. **Scheduling Script Generation Layer**: Generates scheduling instructions based on parsing results, dynamically selecting and combining multiple MPC planners (each responsible for specific optimization goals such as shortest path, smooth driving);
3. **Trajectory-to-Control Layer**: Reuses mature control strategies to convert planned trajectories into control signals such as accelerator and brake.
The architecture builds a transparent and traceable decision chain, facilitating safety audits and fault detection.

## Experimental Evidence: Closed-loop Evaluation and Performance

### Closed-loop Evaluation Benchmark
A closed-loop evaluation benchmark simulating real-world scenarios is built, supporting multi-dimensional evaluation such as command understanding accuracy, task completion rate, and safety compliance, addressing the lack of existing tools.
### Experimental Results
- Task completion rate is significantly better than the baseline, benefiting from LLM semantic understanding and multi-planner flexibility;
- Controls LLM query costs through intelligent script reuse and caching;
- Safety compliance is comparable to professional autonomous driving methods;
- Robust to LLM reasoning delays, as the underlying scheduling can continue to run based on existing scripts.

## Conclusion and Future Directions

### Technical Insights
Demonstrates the feasibility of natural language as a control interface for autonomous driving; the centralized scheduling architecture can be extended to other robot semantic-control collaboration scenarios.
### Future Directions
Expand context awareness (combining passenger preferences and road conditions), explore multi-modal interaction (voice + gesture + vision), and improve real-time performance and edge deployment capabilities.
### Conclusion
This framework takes an important step toward building a natural, transparent, and reliable autonomous driving interaction experience; in the future, it is expected to make autonomous vehicles intelligent travel partners that understand passengers' intentions.