Neuro-Symbolic Reasoning for Autonomous Driving
From Scene Graphs to Answers: Selective Neuro-Symbolic Reasoning for Autonomous Driving
This approach shifts LLMs from universal reasoning engines to targeted semantic reasoners, handling deterministic computations exactly and efficiently. The largest gains occurred on counting questions, showing how selective reasoning improves both accuracy and inference efficiency. Developers working on autonomous systems can implement this hybrid approach to optimize performance on specific query types.
Researchers developed a query-adaptive neuro-symbolic reasoning framework for autonomous-driving question answering.
The system uses a hierarchical Spatiotemporal Scene Graph (STSG) to separate object identities from frame-specific states.
Evaluated on 5,916 NuScenes-QA questions, the framework achieved 80.63% accuracy with GPT-5.
4-mini, improving over LLM-only by 5.48 points.
With DeepSeek-V4-Flash, the improvement reached 6.64 points.
From Scene Graphs to Answers: Selective Neuro-Symbolic Reasoning for Autonomous Driving
Autonomous-driving question answering requires reasoning over structured scene information, yet existing vision-language approaches largely delegate heterogeneous reasoning operations to a single neural inference process. We argue that this uniform strategy overlooks a fundamental distinction: some queries admit exact symbolic solutions, while others require semantic interpretation. We introduce a query-adaptive neuro-symbolic reasoning framework that explicitly allocates computation according to the nature of the query. At its core is a hierarchical Spatiotemporal Scene Graph (STSG) that separates persistent object identities from frame-specific states and represents spatial relations and temporal transitions as explicit directed structures. Given a query, a symbolic executor first attempts to resolve it through exact graph operations; only when symbolic execution abstains is an LLM invoked for semantic reasoning. For these unresolved queries, query-conditioned graph retrieval and evidence filtering preserve relation direction, temporal locality, and object semantics, providing the LLM with compact and verified task-relevant evidence. This design shifts the role of the LLM from a universal reasoning engine to a targeted semantic reasoner, while allowing deterministic computation to be handled exactly and efficiently. We evaluate the framework on 5,916 NuScenes-QA questions across all ten scenes of nuScenes v1.0-mini under an oracle-perception setting. The complete system achieves 80.63 percent overall accuracy with GPT-5.4-mini, improving over the corresponding LLM-only configuration by 5.48 percentage points; with DeepSeek-V4-Flash, the improvement reaches 6.64 points. The largest gains occur on counting questions, with improvements of 10.20 and 12.61 points, respectively. These results show that selective reasoning improves both accuracy and inference efficiency.
This report is categorized under AITOP's "Papers" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include Neuro-Symbolic Reasoning, Spatiotemporal Scene Graph, GPT-5.4-mini, DeepSeek-V4-Flash.
Primary source verification: published directly by arXiv: DeepSeek with first-party authority.