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arxiv: 2606.12236 · v2 · pith:B45M37ATnew · submitted 2026-06-10 · 💻 cs.RO · cs.CV

DrivingAgent: Design and Scheduling Agents for Autonomous Driving Systems

Zhongyu Xia , Wenhao Chen , Yongtao Wang , Ming-Hsuan Yang This is my paper

Pith reviewed 2026-06-27 10:03 UTC · model grok-4.3

classification 💻 cs.RO cs.CV
keywords autonomous drivingagent frameworklarge language modelsreinforcement learningsystem designreal-time schedulingnuScenes benchmarkBench2Drive benchmark
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The pith

DrivingAgent automates module design with LLMs and real-time scheduling with RL-trained agents for autonomous driving.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents DrivingAgent as a framework that separates autonomous driving system creation from its runtime operation. In the design phase the agent interprets architectures, writes code, and validates modules through super-network training. In the scheduling phase a lightweight LLM trained by reinforcement learning uses structured memory to assign modules under real-time limits. The authors report that the resulting systems reach a better speed-accuracy balance than prior approaches on the nuScenes and Bench2Drive benchmarks.

Core claim

DrivingAgent automates module development by interpreting system architecture, generating code, and validating modules via super-network training; it then employs a lightweight LLM trained with reinforcement learning to dynamically orchestrate system modules in real time, supported by a structured memory that integrates long-term storage with timestamped short-term context.

What carries the argument

DrivingAgent framework that splits design (LLM code generation plus super-network validation) from scheduling (RL-trained lightweight LLM with structured memory).

If this is right

  • Module integration no longer requires manual coding for each new foundation model.
  • Real-time constraints can be met by dynamic rather than static module assignment.
  • The same agent structure can be reused across different vehicle platforms without re-engineering the scheduler.
  • Validation through super-network training reduces the need for separate per-module training runs.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the RL scheduler generalizes, similar lightweight agents could coordinate perception-planning-control stacks in other real-time robotic domains.
  • The separation of design and scheduling phases suggests that verification effort can be concentrated on the scheduler rather than every generated module.
  • Continuous operation implies the memory structure must handle long-duration drives without unbounded growth in stored context.

Load-bearing premise

The generated modules and the RL scheduling policy remain safe and correct when the vehicle encounters distribution shifts or edge cases not seen during training.

What would settle it

A controlled test on a closed track or simulator where the DrivingAgent-controlled vehicle exhibits a higher rate of safety violations or missed deadlines than a hand-tuned baseline under identical distribution-shift conditions.

Figures

Figures reproduced from arXiv: 2606.12236 by Ming-Hsuan Yang, Wenhao Chen, Yongtao Wang, Zhongyu Xia.

Figure 1
Figure 1. Figure 1: Compared with existing autonomous driving paradigms, DrivingAgent automatically [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: DrivingAgent overview. DrivingAgent separates offline adapter construction from online scheduling through a structured Tool Profile. (a) The Design Agent discovers planner capability gaps, generates and verifies adapter code, trains candidate modules, and validates them before deployment. (b) The Agent-Tool Profile serves as the design–runtime contract, encoding attachment points, tensor/cache interfaces, … view at source ↗
Figure 3
Figure 3. Figure 3: Speed–accuracy trade-off on nuScenes and Bench2Drive. Each method is plotted by planning quality versus theoretical compute FPS on an Nvidia A100 GPU. DrivingAgent (ours, ⋆) is evaluated under three budgets: Require 2/5/8,FPS. The scheduler adapts agent-tool invocation without retraining the base planner, achieving a favorable trade-off across both benchmarks. 4.2 Implementation Details We use the Design A… view at source ↗
Figure 4
Figure 4. Figure 4: Ablation on the Scheduling Agent. The experimental results demonstrate the speed–accuracy improvements achieved by GRPO fine-tuning, the proposed reward design, and the addition of an FPS calculator as an agent tool. Ablation on the Scheduling Agent. As shown in [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: –8 shows the specific neural network architectures of several designed modules listed in [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: IntentGuidedAgentGate. Architecture of an automatically designed white-box planner￾side gating adapter attached at pre_planning. The module summarizes previous ego in￾tent from prev_plan_queries, scores each agent query against that intent, and rewrites plan_queries_refined through intent-conditioned agent gating. 12 [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: IntentConditionedAgentGate. Architecture of an automatically designed white-box planner-side adapter attached at pre_planning. The module selects an intent source from trajectory-derived and cached planner context, projects the resulting intent into multi-head query features, attends over planner-side agent tokens, and rewrites plan_queries_refined with directionality-aware intent-conditioned agent modulat… view at source ↗
Figure 8
Figure 8. Figure 8: IntentCreditRouter. Architecture of an automatically designed white-box planning router attached at pre_planning. The module computes per-agent planning credit from previous planner context, selects the highest-credit agent subset, rewrites only those selected tokens with a context￾conditioned non-identity update, and reconstructs plan_queries_refined for the host planner. 13 [PITH_FULL_IMAGE:figures/full… view at source ↗
read the original abstract

Many autonomous driving systems are increasingly incorporating foundation models to improve generalization and handle long-tail scenarios. However, this trend introduces two key challenges: (i) the manual and labor-intensive process of designing and integrating new models, and (ii) the lack of intelligent, dynamic scheduling mechanisms to meet strict real-time constraints. While Large Language Model (LLM)-based agents offer a promising avenue for automation, existing frameworks are ill-suited for autonomous driving. Specifically, they fail to distinguish between the fundamentally different requirements of system design and real-time scheduling, treat modules as opaque black boxes, and are not designed for continuous operation. To address these limitations, we propose DrivingAgent, a novel agent framework tailored to the dual challenges of autonomous driving system design and scheduling. In the design phase, DrivingAgent automates module development by interpreting system architecture, generating code, and validating modules via super-network training. In the scheduling phase, it employs a lightweight LLM trained with reinforcement learning to dynamically orchestrate system modules in real time, supported by a structured memory that integrates long-term storage with timestamped short-term context. Experimental results demonstrate that DrivingAgent achieves a superior speed--accuracy trade-off on both the nuScenes and Bench2Drive benchmarks.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The manuscript proposes DrivingAgent, an LLM-based agent framework for autonomous driving systems. In the design phase it automates module creation via architecture interpretation, code generation, and super-network validation; in the scheduling phase a lightweight RL-trained LLM dynamically orchestrates modules using structured long-term/short-term memory. The central claim is that the resulting system achieves a superior speed-accuracy trade-off on the nuScenes and Bench2Drive benchmarks.

Significance. If the reported benchmark results can be substantiated with full experimental protocols, the framework would address two practical bottlenecks in foundation-model-based autonomous driving: labor-intensive module integration and real-time scheduling under latency constraints. This could reduce development time while preserving or improving the accuracy-latency frontier.

major comments (1)
  1. [Abstract] Abstract: the claim that DrivingAgent 'achieves a superior speed--accuracy trade-off on both the nuScenes and Bench2Drive benchmarks' is presented without any metrics, baselines, ablation studies, statistical tests, or description of how the RL scheduler's latency and per-module accuracy are jointly evaluated. This absence makes the central empirical claim impossible to assess.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their detailed review and the opportunity to clarify the presentation of our empirical claims. We address the major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that DrivingAgent 'achieves a superior speed--accuracy trade-off on both the nuScenes and Bench2Drive benchmarks' is presented without any metrics, baselines, ablation studies, statistical tests, or description of how the RL scheduler's latency and per-module accuracy are jointly evaluated. This absence makes the central empirical claim impossible to assess.

    Authors: We agree that the abstract, as currently written, states the central claim at a high level without quantitative support or evaluation details, which limits immediate assessment from the abstract alone. The full manuscript (Experiments section) contains the requested elements: specific metrics and baselines on both benchmarks, ablation studies isolating the RL scheduler, statistical significance reporting, and a description of the joint latency-accuracy evaluation protocol for the scheduler. To address the concern directly, we will revise the abstract to incorporate key quantitative results and a concise statement of the evaluation approach while remaining within length constraints. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper describes a framework for LLM-based module design and RL-based scheduling in autonomous driving, with central claims resting on experimental results reported on the nuScenes and Bench2Drive benchmarks. No mathematical derivations, equations, fitted parameters presented as predictions, or self-citation chains appear in the provided text. The architecture is presented as a novel combination of existing techniques without any load-bearing step that reduces to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are identifiable from the abstract alone.

pith-pipeline@v0.9.1-grok · 5751 in / 1014 out tokens · 29334 ms · 2026-06-27T10:03:32.215084+00:00 · methodology

discussion (0)

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