From Prompts to Pavement Through Time: Temporal Grounding in Agentic Scene-to-Plan Reasoning

Problem
This preprint addresses a significant gap in the literature regarding the temporal grounding of scene interpretation and planning in Autonomous Vehicles (AVs). Previous approaches utilizing ensembles of Large Language Models (LLMs) and Large Multimodal Models (LMMs) have largely overlooked the role of time, which can lead to inconsistencies in reasoning about continuous actions. This oversight poses risks to both the safety and interpretability of AV systems. The authors aim to investigate whether incorporating temporal conditioning in inter-agent communication can enhance coherence in reasoning without compromising semantic or logical consistency.

Method
The authors propose three planner architectures that progressively integrate temporal conditioning into their design. These architectures are evaluated on curated subsets of the BDD-X dataset, which is specifically tailored for AV planning tasks. The evaluation metrics include semantic, syntactic, and logical measures to assess the performance of the planners. The training compute details are not disclosed, but the focus is on the architectural modifications that allow for temporal integration. The study emphasizes qualitative analysis alongside standard NLP-based correctness metrics to provide a comprehensive view of the impact of temporal grounding.

Results
The results indicate that while the introduction of temporal conditioning alters the reasoning style of the planners, it does not yield statistically significant improvements in standard correctness metrics when compared to baseline models. However, qualitative assessments reveal notable advancements in specific areas: predictive hazard reasoning is enhanced, corrective behaviors are more stable, and strategic divergence is observed in the Sentinel architecture. These findings suggest that while temporal grounding may not improve traditional performance metrics, it contributes to more nuanced and contextually aware reasoning in AV planning.

Limitations
The authors acknowledge that the lack of statistically significant improvements in standard metrics may limit the perceived effectiveness of their approach. Additionally, the qualitative nature of some findings may introduce subjectivity in interpretation. The study is also constrained by the specific dataset used (BDD-X), which may not fully represent the complexities of real-world driving scenarios. Furthermore, the temporal conditioning methods explored may not generalize across all types of AV planning tasks, and the architectures have not been tested against a broader range of benchmarks.

Why it matters
This work is significant as it establishes the first empirical benchmark for temporal scene-to-plan reasoning in the context of AVs, highlighting the importance of temporal factors in high-level planning tasks. The findings suggest that while traditional metrics may not capture the benefits of temporal grounding, qualitative improvements in reasoning can enhance the safety and interpretability of AV systems. This research opens avenues for future work to explore more robust temporal integration methods and encourages the development of evaluation frameworks that account for the complexities of time in autonomous decision-making.

Authors: Ahmed Y. Gado, Omar Y. Goba, Alaa Hassanein, Catherine M. Elias, Ahmed Hussein
Source: arXiv:2605.19824
URL: https://arxiv.org/abs/2605.19824v1