# Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding

arXiv: https://arxiv.org/abs/2508.17205 · HTML: https://mlsanity.com/paper/2508.17205/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Chain-of-thought reasoning for driving, one of 17 directions in LLM reasoning.
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 24 Aug 2025.

## Placed in

- LLM reasoning / [Chain-of-thought reasoning for driving](https://mlsanity.com/lr/chain-of-thought-reasoning-for-driving/)
- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)
- Mixture-of-experts / [MoE in reinforcement learning and robotics](https://mlsanity.com/moe/moe-in-reinforcement-learning-and-robotics/)

## Builds on (most cited first, 12 of 12)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [ClimateBert: A Pretrained Language Model for Climate-Related Text](https://mlsanity.com/paper/2110.12010/) (2021)
- [ClimateGPT: Towards AI Synthesizing Interdisciplinary Research on Climate Change](https://mlsanity.com/paper/2401.09646/) (2024)
- [NuPlanQA: A Large-Scale Dataset and Benchmark for Multi-View Driving Scene Understanding in Multi-Modal Large Language Models](https://mlsanity.com/paper/2503.12772/) (2025)
- [Prompt Tuning for Generative Multimodal Pretrained Models](https://mlsanity.com/paper/2208.02532/) (2022)
- [KD-VLP: Improving End-to-End Vision-and-Language Pretraining with Object Knowledge Distillation](https://mlsanity.com/paper/2109.10504/) (2021)
- [Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectives](https://mlsanity.com/paper/2402.02968/) (2024)
- [When language and vision meet road safety: leveraging multimodal large language models for video-based traffic accident analysis](https://mlsanity.com/paper/2501.10604/) (2025)
- [Deep Learning based Computer Vision Methods for Complex Traffic Environments Perception: A Review](https://mlsanity.com/paper/2211.05120/) (2022)
- [Co-supervised learning paradigm with conditional generative adversarial networks for sample-efficient classification](https://mlsanity.com/paper/2212.13589/) (2022)
- [Modeling Cloud Reflectance Fields using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2002.07579/) (2020)
- [Traffic congestion anomaly detection and prediction using deep learning](https://mlsanity.com/paper/2006.13215/) (2020)

## Built on by (most cited first, 3 of 3)

- [Multi-view Phase-aware Pedestrian-Vehicle Incident Reasoning Framework with Vision-Language Models](https://mlsanity.com/paper/2511.14120/) (2025)
- [PrismAgent: Illuminating Harm in Memes via a Zero-Shot Interpretable Multi-Agent Framework](https://mlsanity.com/paper/2605.02940/) (2026)
- [EG-ARSA: An Expert-Grounded Open Model for Visual Road Safety Auditing in Low-Resource Settings](https://mlsanity.com/paper/2608.23563/) (2026)

## Abstract (arXiv)

This paper introduces a multi-agent framework for comprehensive highway scene understanding, designed around a mixture-of-experts strategy. In this framework, a large generic vision-language model (VLM), such as GPT-4o, is contextualized with domain knowledge to generates task-specific chain-of-thought (CoT) prompts. These fine-grained prompts are then used to guide a smaller, efficient VLM (e.g., Qwen2.5-VL-7B) in reasoning over short videos, along with complementary modalities as applicable. The framework simultaneously addresses multiple critical perception tasks, including weather classification, pavement wetness assessment, and traffic congestion detection, achieving robust multi-task reasoning while balancing accuracy and computational efficiency. To support empirical validation, we curated three specialized datasets aligned with these tasks. Notably, the pavement wetness dataset is multimodal, combining video streams with road weather sensor data, highlighting the benefits of multimodal reasoning. Experimental results demonstrate consistently strong performance across diverse traffic and environmental conditions. From a deployment perspective, the framework can be readily integrated with existing traffic camera systems and strategically applied to high-risk rural locations, such as sharp curves, flood-prone lowlands, or icy bridges. By continuously monitoring the targeted sites, the system enhances situational awareness and delivers timely alerts, even in resource-constrained environments.