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X-Blocks: Linguistic Building Blocks of Natural Language Explanations for Automated Vehicles

arXiv:2602.13248v1 Announce Type: new Abstract: Natural language explanations play a critical role in establishing trust and acceptance of automated vehicles (AVs), yet existing approaches lack systematic frameworks for analysing how humans linguistically construct driving rationales across diverse scenarios. This paper introduces X-Blocks (eXplanation Blocks), a hierarchical analytical framework that identifies the linguistic building blocks of natural language explanations for AVs at three levels: context, syntax, and lexicon. At the context level, we propose RACE (Reasoning-Aligned Classification of Explanations), a multi-LLM ensemble framework that combines Chain-of-Thought reasoning with self-consistency mechanisms to robustly classify explanations into 32 scenario-aware categories. Applied to human-authored explanations from the Berkeley DeepDrive-X dataset, RACE achieves 91.45 percent accuracy and a Cohens kappa of 0.91 against cases with human annotator agreement, indicating

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Ashkan Y. Zadeh, Xiaomeng Li, Andry Rakotonirainy, Ronald Schroeter, Sebastien Glaser, Zishuo Zhu · · 1 min read · 12 views
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arXiv:2602.13248v1 Announce Type: new Abstract: Natural language explanations play a critical role in establishing trust and acceptance of automated vehicles (AVs), yet existing approaches lack systematic frameworks for analysing how humans linguistically construct driving rationales across diverse scenarios. This paper introduces X-Blocks (eXplanation Blocks), a hierarchical analytical framework that identifies the linguistic building blocks of natural language explanations for AVs at three levels: context, syntax, and lexicon. At the context level, we propose RACE (Reasoning-Aligned Classification of Explanations), a multi-LLM ensemble framework that combines Chain-of-Thought reasoning with self-consistency mechanisms to robustly classify explanations into 32 scenario-aware categories. Applied to human-authored explanations from the Berkeley DeepDrive-X dataset, RACE achieves 91.45 percent accuracy and a Cohens kappa of 0.91 against cases with human annotator agreement, indicating near-human reliability for context classification. At the lexical level, log-odds analysis with informative Dirichlet priors reveals context-specific vocabulary patterns that distinguish driving scenarios. At the syntactic level, dependency parsing and template extraction show that explanations draw from a limited repertoire of reusable grammar families, with systematic variation in predicate types and causal constructions across contexts. The X-Blocks framework is dataset-agnostic and task-independent, offering broad applicability to other automated driving datasets and safety-critical domains. Overall, our findings provide evidence-based linguistic design principles for generating scenario-aware explanations that support transparency, user trust, and cognitive accessibility in automated driving systems.

Executive Summary

The article 'X-Blocks: Linguistic Building Blocks of Natural Language Explanations for Automated Vehicles' introduces a hierarchical framework, X-Blocks, for analyzing natural language explanations in automated vehicles (AVs). The framework operates at three levels: context, syntax, and lexicon. At the context level, the RACE (Reasoning-Aligned Classification of Explanations) framework classifies explanations into 32 scenario-aware categories with high accuracy. At the lexical level, log-odds analysis identifies context-specific vocabulary patterns, while at the syntactic level, dependency parsing reveals reusable grammar families. The study demonstrates the framework's applicability across diverse scenarios and its potential to enhance transparency and user trust in AV systems.

Key Points

  • ▸ Introduction of the X-Blocks framework for analyzing natural language explanations in AVs.
  • ▸ RACE framework achieves high accuracy in classifying explanations into scenario-aware categories.
  • ▸ Lexical and syntactic analyses reveal context-specific vocabulary and grammar patterns.
  • ▸ Framework is dataset-agnostic and task-independent, applicable to other safety-critical domains.

Merits

Comprehensive Framework

The X-Blocks framework provides a systematic and hierarchical approach to analyzing natural language explanations, addressing a gap in existing methodologies.

High Accuracy

The RACE framework demonstrates near-human reliability in classifying explanations, indicating robust performance.

Broad Applicability

The dataset-agnostic and task-independent nature of the framework enhances its utility across various domains beyond automated driving.

Demerits

Limited Dataset

The study relies on the Berkeley DeepDrive-X dataset, which may limit the generalizability of findings to other datasets or scenarios.

Complexity

The hierarchical and multi-level nature of the framework may pose challenges in implementation and practical application.

Human Annotation Bias

The reliance on human annotators for validation may introduce biases, despite the high agreement rates.

Expert Commentary

The article presents a significant advancement in the field of natural language explanations for automated vehicles. The X-Blocks framework offers a rigorous and systematic approach to analyzing explanations at multiple levels, providing valuable insights into the linguistic construction of driving rationales. The high accuracy of the RACE framework is particularly noteworthy, as it demonstrates the potential for automated systems to achieve near-human reliability in classifying explanations. The study's findings on context-specific vocabulary and grammar patterns contribute to a deeper understanding of how humans linguistically construct explanations, which is crucial for enhancing transparency and user trust. However, the reliance on a single dataset and the complexity of the framework pose challenges that need to be addressed in future research. The broad applicability of the framework to other safety-critical domains underscores its potential impact beyond automated driving. Overall, the study provides a robust foundation for developing scenario-aware explanations that support transparency, user trust, and cognitive accessibility in automated driving systems.

Recommendations

  • ✓ Future research should validate the X-Blocks framework across multiple datasets to enhance its generalizability.
  • ✓ Efforts should be made to simplify the framework's implementation to facilitate broader adoption in practical applications.

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