Academic

DRIV-EX: Counterfactual Explanations for Driving LLMs

arXiv:2603.00696v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as reasoning engines in autonomous driving, yet their decision-making remains opaque. We propose to study their decision process through counterfactual explanations, which identify the minimal semantic changes to a scene description required to alter a driving plan. We introduce DRIV-EX, a method that leverages gradient-based optimization on continuous embeddings to identify the input shifts required to flip the model's decision. Crucially, to avoid the incoherent text typical of unconstrained continuous optimization, DRIV-EX uses these optimized embeddings solely as a semantic guide: they are used to bias a controlled decoding process that re-generates the original scene description. This approach effectively steers the generation toward the counterfactual target while guaranteeing the linguistic fluency, domain validity, and proximity to the original input, essential for interpretabi

A
Amaia Cardiel, Eloi Zablocki, Elias Ramzi, Eric Gaussier · · 1 min read · 3 views
#cs.CL

arXiv:2603.00696v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as reasoning engines in autonomous driving, yet their decision-making remains opaque. We propose to study their decision process through counterfactual explanations, which identify the minimal semantic changes to a scene description required to alter a driving plan. We introduce DRIV-EX, a method that leverages gradient-based optimization on continuous embeddings to identify the input shifts required to flip the model's decision. Crucially, to avoid the incoherent text typical of unconstrained continuous optimization, DRIV-EX uses these optimized embeddings solely as a semantic guide: they are used to bias a controlled decoding process that re-generates the original scene description. This approach effectively steers the generation toward the counterfactual target while guaranteeing the linguistic fluency, domain validity, and proximity to the original input, essential for interpretability. Evaluated using the LC-LLM planner on a textual transcription of the highD dataset, DRIV-EX generates valid, fluent counterfactuals more reliably than existing baselines. It successfully exposes latent biases and provides concrete insights to improve the robustness of LLM-based driving agents.

Executive Summary

The article proposes DRIV-EX, a method for counterfactual explanations of driving large language models (LLMs) that leverages gradient-based optimization to identify input shifts required to alter the model's decision. DRIV-EX uses optimized embeddings as a semantic guide to bias a controlled decoding process, generating valid, fluent counterfactuals that expose latent biases and improve the robustness of LLM-based driving agents. The method is evaluated using the LC-LLM planner on the highD dataset, outperforming existing baselines in generating reliable counterfactual explanations.

Key Points

  • ▸ DRIV-EX proposes a novel method for counterfactual explanations of driving LLMs.
  • ▸ The method leverages gradient-based optimization to identify input shifts required to alter the model's decision.
  • ▸ DRIV-EX uses optimized embeddings as a semantic guide to bias a controlled decoding process.

Merits

Improved Interpretability

DRIV-EX provides a more transparent understanding of the decision-making process of driving LLMs, enabling the identification of latent biases and improvements to the robustness of LLM-based driving agents.

Enhanced Robustness

By exposing latent biases and generating valid counterfactuals, DRIV-EX contributes to the development of more robust LLM-based driving agents.

Demerits

Computational Complexity

DRIV-EX's reliance on gradient-based optimization and controlled decoding process may introduce computational complexity, potentially limiting its scalability and practical application.

Limited Domain Applicability

The method's performance is evaluated on a specific dataset and planner, raising questions about its generalizability and applicability to other domains or scenarios.

Expert Commentary

While DRIV-EX represents a significant advancement in the development of counterfactual explanations for driving LLMs, its practical application and scalability remain to be fully explored. The method's reliance on gradient-based optimization and controlled decoding process introduces computational complexity, which may limit its use in resource-constrained environments. Nevertheless, the article's findings are a crucial contribution to the ongoing efforts to develop explainable AI systems, and its implications for autonomous driving and responsible AI development are significant. As such, the article is a valuable addition to the literature on AI explainability and its applications.

Recommendations

  • ✓ Future research should focus on addressing the computational complexity of DRIV-EX and exploring its generalizability to other domains and scenarios.
  • ✓ Developers and policymakers should prioritize the incorporation of explainability and transparency in the design and deployment of AI systems, particularly in high-stakes decision-making scenarios like autonomous driving.

Sources

Related Articles