OpenNovelty.org

ClarifyVC: Clarifying Ambiguous Commands in Vehicle Control with a Hybrid Data Augmentation Pipeline

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Interactive Control SystemsClarification-First DialogueAmbiguity ResolutionHybrid Data AugmentationFunction-Calling Language ModelsHuman Validation and Robustness
Abstract:

Natural language interfaces for vehicle control must contend with vague commands, evolving dialogue context, and strict protocol constraints. We introduce ClarifyVC, a unified framework that integrates a hybrid data-augmentation pipeline (ClarifyVC-Data), reference models trained on the data (ClarifyVC-Models) and a evaluation protocol (ClarifyVC-Eval). The agent-orchestrated pipeline generates diverse, ambiguity-rich dialogues from real-world seeded queries under schema and safety constraints, while the evaluation protocol systematically probes single-turn parsing, conservative clarification under extreme fuzziness, and multi-turn grounding. Fine-tuning on ClarifyVC-Data yields consistent gains—up to 15% higher parsing accuracy, 20% stronger ambiguity resolution, and 98% protocol compliance—across realistic in-cabin scenarios, with human-in-the-loop assessments confirming high realism, coherence, and applicability. ClarifyVC thus advances beyond simulation-only datasets by tightly coupling real-world grounding with scalable generation and standardized evaluation, and provides a generalizable pipeline for broader interactive control domains.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

ClarifyVC introduces a unified framework for handling ambiguous natural language commands in vehicle control, combining data augmentation, reference models, and evaluation protocols. The paper sits within the Multimodal Grounding and Visual Context Integration leaf, which contains only four papers total. This represents a relatively sparse research direction within the broader taxonomy of 41 papers across the field. The sibling papers in this leaf focus on vision-language-action integration and spatial grounding, suggesting ClarifyVC occupies a niche addressing dialogue-based ambiguity resolution rather than pure multimodal alignment.

The taxonomy reveals neighboring research directions that contextualize ClarifyVC's position. Adjacent leaves include Speech-Based Command Execution (four papers on voice-to-action mapping) and Semantic Rule Formalization (two papers on logical extraction). The broader Natural Language Command Interpretation branch encompasses these three leaves, while parallel branches address Trajectory Generation, HMI Design, and System Engineering. ClarifyVC bridges multimodal grounding with interactive clarification strategies, connecting to HMI work on uncertainty communication while remaining distinct from pure trajectory generation or retrieval tasks. The taxonomy's scope notes confirm ClarifyVC's focus on command interpretation with visual context, excluding pure interface design or trajectory output.

Among 30 candidates examined across three contributions, none were identified as clearly refuting ClarifyVC's claims. The ClarifyVC Framework contribution examined 10 candidates with zero refutable matches, as did ClarifyVC-Data/Models and ClarifyVC-Eval. This suggests that within the limited search scope, the specific combination of agent-orchestrated data generation, ambiguity-focused evaluation, and vehicle control domain appears underexplored. However, the analysis explicitly notes this is based on top-K semantic search plus citation expansion, not exhaustive coverage. The absence of refutable candidates may reflect either genuine novelty or limitations in search scope and candidate selection.

Based on the limited literature search, ClarifyVC appears to occupy a relatively novel position combining dialogue-based clarification with multimodal vehicle control. The sparse population of its taxonomy leaf and lack of refutable candidates among 30 examined papers suggest the specific integration of data augmentation, evaluation protocols, and ambiguity handling is underrepresented in prior work. However, this assessment is constrained by the search methodology and does not preclude relevant work outside the examined candidate set.

Taxonomy

Core-task Taxonomy Papers
41
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Clarifying ambiguous natural language commands in vehicle control systems. The field encompasses a diverse set of research directions organized into six main branches. Natural Language Command Interpretation and Grounding focuses on parsing and understanding user intent, often integrating multimodal cues from vision and language to resolve ambiguities in commands. Trajectory and Motion Generation from Language translates interpreted commands into executable vehicle behaviors, bridging symbolic understanding with continuous control. Retrieval and Matching with Natural Language addresses how systems identify relevant objects or locations mentioned in commands, while Human-Machine Interface Design and Interaction explores how users communicate with vehicles and how systems can effectively present information or request clarification. System Engineering and Quality Assurance tackles the practical challenges of deploying robust language-enabled systems, and Foundational and Cross-Domain Frameworks provide general architectures and methods applicable across different vehicle types and scenarios. Representative works like GPT-4 Multimodal Grounding[1] and Vision-Language-Action Models[9] illustrate the integration of visual context with linguistic input, while LLVM-drone[3] and Speech-Guided Drone[8] demonstrate applications in aerial vehicle control. A particularly active line of work centers on multimodal grounding, where systems must align linguistic references with visual scenes to resolve spatial and object ambiguities. This contrasts with purely symbolic approaches that rely on formal semantic representations, as seen in Semantic Role Formalization[2] and Ontology Customisation Management[5]. ClarifyVC[0] sits within the multimodal grounding cluster, emphasizing visual context integration to disambiguate commands in driving scenarios, closely aligned with Vision-Language-Action Models[9] and Grounding Linguistic Commands[11]. Compared to GPT-4 Multimodal Grounding[1], which leverages large-scale foundation models, ClarifyVC[0] appears more specialized for vehicle control contexts. Meanwhile, interface-focused studies like Uncertainty on Display[4] and HMI Negotiation Methods[17] explore complementary questions about how to communicate system uncertainty or negotiate ambiguous commands with users, highlighting ongoing tensions between fully autonomous interpretation and interactive clarification strategies.

Claimed Contributions

ClarifyVC Framework

The authors introduce ClarifyVC, a comprehensive framework that combines data generation, model training, and evaluation components to handle ambiguous natural language commands in vehicle control. The framework provides an integrated solution for building safe and deployable language interfaces in interactive control systems.

10 retrieved papers
ClarifyVC-Data and ClarifyVC-Models

The authors develop a dataset constructed from over 20,000 authentic in-vehicle commands, augmented through a hybrid pipeline with controlled ambiguity injection and adversarial perturbations. They also provide reference models trained on this data that demonstrate improvements in parsing accuracy, ambiguity resolution, and protocol compliance.

10 retrieved papers
ClarifyVC-Eval evaluation protocol with Dataset Quality Score

The authors propose a comprehensive evaluation protocol that systematically assesses single-turn parsing, ambiguity clarification, and multi-turn dialogue grounding. They also introduce a Dataset Quality Score metric to validate benchmark realism and quality, addressing gaps in conventional single-turn accuracy evaluation.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

ClarifyVC Framework

The authors introduce ClarifyVC, a comprehensive framework that combines data generation, model training, and evaluation components to handle ambiguous natural language commands in vehicle control. The framework provides an integrated solution for building safe and deployable language interfaces in interactive control systems.

Contribution

ClarifyVC-Data and ClarifyVC-Models

The authors develop a dataset constructed from over 20,000 authentic in-vehicle commands, augmented through a hybrid pipeline with controlled ambiguity injection and adversarial perturbations. They also provide reference models trained on this data that demonstrate improvements in parsing accuracy, ambiguity resolution, and protocol compliance.

Contribution

ClarifyVC-Eval evaluation protocol with Dataset Quality Score

The authors propose a comprehensive evaluation protocol that systematically assesses single-turn parsing, ambiguity clarification, and multi-turn dialogue grounding. They also introduce a Dataset Quality Score metric to validate benchmark realism and quality, addressing gaps in conventional single-turn accuracy evaluation.

Table of Contents