Existing reasoning segmentation frameworks typically employ multimodal large language models (MLLMs) with broad world knowledge to extract semantics in a human-like manner and generate corresponding s...
arXiv:2511.20022v3 Announce Type: replace-cross Abstract: Recent advancements in multimodal large language models (MLLMs) have shown strong understanding of driving scenes, drawing interest in their...
Recent multimodal large language models (MLLMs) have shown strong potential for autonomous driving scene understanding, yet existing methods still face a fundamental trade-off between temporal reasoning and spatial precision. Models that rely on single-frame or low-resolution inputs often miss small, distant, or partially occluded hazards, while language-centric driving models frequently provide limited grounded evidence for their explanations.
arXiv:2606. 24759v1 Announce Type: cross Abstract: Recent multimodal large language models (MLLMs) have shown strong potential for autonomous driving scene understanding, yet existing methods still face a fundamental trade-off between temporal reasoning and spatial precision.
arXiv:2606. 15749v1 Announce Type: cross Abstract: Traffic scene understanding requires models to reason beyond object recognition, including lane topology, multi-view geometry, temporal evolution, and signal-phase semantics.
arXiv:2603. 06576v2 Announce Type: replace-cross Abstract: The integration of Large Language Models (LLMs) into autonomous driving has attracted growing interest for their strong reasoning and semantic understanding abilities, which are essential for handling complex decision-making and long-tail scenarios.
STRIDE (SpatioTemporal Reasoning In Driving Edge Cases) is a new benchmark designed to evaluate vision-language models on complex, safety-critical driving scenarios. It includes six task families—spatial perception, spatial understanding, temporal memory, temporal extrapolation, trajectory prediction, and scene-context awareness—using multiple-choice questions and free-form visual question answering that require detailed scene explanations and driving decision justifications. The benchmark focuses on unusual object behaviors and complex traffic interactions, and results show that even the best model, GPT‑6‑Astra, scores only 43.4% on average, highlighting significant gaps in spatiotemporal reasoning.
arXiv:2603. 28251v3 Announce Type: replace-cross Abstract: Drivers' visual attention provides critical cues for anticipating latent hazards and directly shapes decision-making and control maneuvers, where its absence can compromise traffic safety.
DriveMind is a multimodal large language model (MLLM) framework for autonomous driving that maintains multiple hypotheses about a traffic scene and updates their weights as new frames arrive, inspired by particle filtering. By propagating each hypothesis through a vision-language model, scoring against current observations, and resampling to preserve diversity, DriveMind improves driving performance on the Bench2Drive benchmark, raising the Driving Score from 68.50 to 81.37 and the Success Rate from 41.36% to 54.09%. The approach yields the largest gains in ambiguous scenarios such as unsignalized intersections and oncoming-traffic interactions, and also enhances fine‑grained interactive driving tasks like merging, emergency braking, and give‑way.
arXiv:2606. 03564v2 Announce Type: replace-cross Abstract: Reasoning segmentation aims to segment target objects described by complex language through joint visual-textual reasoning.
MV-STRIDE is a Multi‑View hierarchical Spatial Reasoning dataset that models dependencies among perception, scene understanding, and contextual reasoning to support 3D spatial cognition. It introduces a QA generation pipeline that enforces cross‑view constraints, producing multi‑level reasoning tasks with chain‑of‑thought supervision. Experiments show that training on MV‑STRIDE yields state‑of‑the‑art performance on multi‑view spatial benchmarks, enabling MLLMs to reason robustly across diverse viewpoints.
arXiv:2607. 03591v1 Announce Type: cross Abstract: Recent studies on multimodal traffic accident understanding have mainly relied on infrastructure-camera footage, satellite imagery, or structured crash records.