Temporal-Aware Reasoning in Autonomous Driving Scenario Planning: A Study on Temporal Grounding from Prompt to Road Surface

This paper focuses on the problem of temporal-aware reasoning in autonomous driving scenario planning and explores the impact of temporal conditions on multi-agent reasoning. Through experiments comparing three planner architectures (Baseline, Moderate Temporal Integration, and Deep Temporal Integration Sentinel), it reveals the limitations of prompt-based temporal grounding and establishes the first empirical benchmark for temporal scenario-to-planning reasoning. Key findings include: Introducing temporal information through pure prompt engineering did not significantly improve quantitative metrics, but the deep temporal integration architecture showed advantages such as predictive hazard reasoning and stable corrective behavior in qualitative analysis.

The decision-making ability of autonomous driving systems in complex traffic environments is a focus of attention in academia and industry. In recent years, the rise of Large Language Models (LLMs) and Large Multimodal Models (LMMs) has provided new technical paths for scenario understanding and high-level planning. However, existing research generally has a key flaw: treating time as a secondary attribute rather than a core dimension of reasoning. This lack of temporal grounding leads to logical inconsistencies when models handle continuous actions, affecting both safety and the interpretability of the system. For example, when a model needs to understand the dynamic process of "the vehicle ahead is decelerating", reasoning without temporal awareness often only gives static, isolated judgments, and cannot establish a complete cognitive chain of how actions evolve over time.

The core hypothesis of this study is: Introducing temporal conditioning into multi-agent communication can improve the coherence of reasoning without compromising semantic or logical consistency. To verify this hypothesis, the research team designed three planner architectures, representing different degrees of temporal integration: 1. Baseline Architecture: No explicit temporal modeling; 2. Moderate Temporal Integration Architecture: Introduces timestamp information in inter-agent communication; 3. Deep Temporal Integration Architecture (Sentinel): A fully temporal-aware design that treats time as a first-class citizen in reasoning.

The research team chose the BDD-X dataset as the experimental basis. BDD-X (Berkeley DeepDrive eXplanation) is a widely used dataset in autonomous driving research, containing rich driving scenario videos and corresponding behavior descriptions. Researchers carefully selected a subset from this dataset to ensure the experimental data is representative and covers various driving scenarios. The evaluation metrics cover three dimensions: Semantic metrics (assessing the content rationality and scenario relevance of generated plans), Syntactic metrics (testing the normativeness and consistency of output structures), and Logical metrics (verifying the rigor of reasoning chains and the correctness of causal relationships).

Quantitative Results: Temporal conditions change the reasoning style, but do not bring statistically significant improvements in standard NLP correctness metrics, indicating that introducing temporal information through pure prompt engineering cannot directly translate into higher task completion accuracy. Qualitative Analysis: The Sentinel architecture (deep temporal integration) shows three key advantages: Predictive hazard reasoning (predicting risk evolution trajectories and planning avoidance strategies in advance), Stable corrective behavior (smooth and coherent decision adjustments when the environment changes, avoiding decision jitter), and Strategic divergence (identifying multiple future paths and demonstrating contingency thinking).

Research Significance: For the first time, it systematically quantifies the capability boundary of prompt-based temporal grounding, pointing out the limitations of prompt engineering, the importance of architecture design, and the need to optimize evaluation metrics (standard NLP metrics cannot fully capture the quality of temporal reasoning). Limitations: The scale of experimental data is relatively limited and concentrated on the BDD-X dataset; the Sentinel architecture has high computational overhead, which may affect the feasibility of real-time deployment.

Future work can be deepened in the following aspects: Temporal modeling at the architecture level (exploring the integration of temporal awareness mechanisms at the model architecture level), Multimodal temporal fusion (combining multi-source sensor data such as vision and LiDAR to build robust temporal representations), Real-time performance optimization (reducing computational complexity to meet the real-time requirements of in-vehicle systems), and Standardized evaluation system (establishing an evaluation benchmark specifically for temporal scenario planning).

Temporal awareness is a core challenge in autonomous driving scenario planning. Through rigorous experimental design, this study quantifies the effect boundary of prompt-based temporal grounding for the first time, and finds that although temporal conditions can change the reasoning style, it is difficult to achieve substantial performance improvement through simple prompt engineering. The predictive reasoning and stable decision-making capabilities shown by the Sentinel architecture in qualitative analysis provide important insights for future architecture innovation. Truly realizing temporal awareness at the model level is a key topic that needs continuous exploration in this field.