CoT-VLM4Tar: Chain-of-Thought Guided Vision-Language Models for Traffic Anomaly Resolution

• URL: http://arxiv.org/abs/2503.01632v1
• Date: Mon, 03 Mar 2025 15:07:25 GMT
• Title: CoT-VLM4Tar: Chain-of-Thought Guided Vision-Language Models for Traffic Anomaly Resolution
• Authors: Tianchi Ren, Haibo Hu, Jiacheng Zuo, Xinhong Chen, Jianping Wang, Chun Jason Xue, Jen-Ming Wu, Nan Guan,
• Abstract summary: CoT-VLM4Tar: (Chain of Thought Visual-Language Model for Traffic Anomaly Resolution)<n>This paper introduces a new chain-of-thought to guide the VLM in analyzing, reasoning, and generating solutions for traffic anomalies with greater reasonable and effective solution.<n>Our results demonstrate the effectiveness of VLM in the resolution of real-time traffic anomalies, providing a proof-of-concept for its integration into autonomous traffic management systems.
• Score: 14.703196966156288
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the acceleration of urbanization, modern urban traffic systems are becoming increasingly complex, leading to frequent traffic anomalies. These anomalies encompass not only common traffic jams but also more challenging issues such as phantom traffic jams, intersection deadlocks, and accident liability analysis, which severely impact traffic flow, vehicular safety, and overall transportation efficiency. Currently, existing solutions primarily rely on manual intervention by traffic police or artificial intelligence-based detection systems. However, these methods often suffer from response delays and inconsistent management due to inadequate resources, while AI detection systems, despite enhancing efficiency to some extent, still struggle to handle complex traffic anomalies in a real-time and precise manner. To address these issues, we propose CoT-VLM4Tar: (Chain of Thought Visual-Language Model for Traffic Anomaly Resolution), this innovative approach introduces a new chain-of-thought to guide the VLM in analyzing, reasoning, and generating solutions for traffic anomalies with greater reasonable and effective solution, and to evaluate the performance and effectiveness of our method, we developed a closed-loop testing framework based on the CARLA simulator. Furthermore, to ensure seamless integration of the solutions generated by the VLM with the CARLA simulator, we implement an itegration module that converts these solutions into executable commands. Our results demonstrate the effectiveness of VLM in the resolution of real-time traffic anomalies, providing a proof-of-concept for its integration into autonomous traffic management systems.

Related papers

• Meta-Federated Learning: A Novel Approach for Real-Time Traffic Flow Management [0.0]
  This paper introduces a novel approach by combining Federated Learning (FL) and Meta-Learning (ML) to create a decentralized, scalable, and adaptive traffic management system. We implement our model across a simulated network of smart traffic devices, demonstrating that Meta-Federated Learning significantly outperforms traditional models in terms of prediction accuracy and response time. Our approach shows remarkable adaptability to sudden changes in traffic patterns, suggesting a scalable solution for real-time traffic management in smart cities.
  arXiv  Detail & Related papers  (2025-01-28T07:24:24Z)
• Decentralized Semantic Traffic Control in AVs Using RL and DQN for Dynamic Roadblocks [9.485363025495225]
  We present a novel semantic traffic control system that entrusts semantic encoding responsibilities to the vehicles themselves. This system processes driving decisions obtained from a Reinforcement Learning (RL) agent, streamlining the decision-making process.
  arXiv  Detail & Related papers  (2024-06-26T20:12:48Z)
• IncidentResponseGPT: Generating Traffic Incident Response Plans with Generative Artificial Intelligence [2.1711205684359247]
  IncidentResponseGPT is a novel system that applies generative artificial intelligence (AI) to traffic incident response. It generates incident response plans adapted to specific area, aiming to expedite decision-making for traffic management authorities.
  arXiv  Detail & Related papers  (2024-04-29T09:45:46Z)
• Agent-Agnostic Centralized Training for Decentralized Multi-Agent Cooperative Driving [17.659812774579756]
  We propose an asymmetric actor-critic model that learns decentralized cooperative driving policies for autonomous vehicles. By employing attention neural networks with masking, our approach efficiently manages real-world traffic dynamics and partial observability.
  arXiv  Detail & Related papers  (2024-03-18T16:13:02Z)
• LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban Environments [3.7788636451616697]
  This work introduces an innovative approach that integrates Large Language Models into traffic signal control systems. A hybrid framework that augments LLMs with a suite of perception and decision-making tools is proposed. The findings from our simulations attest to the system's adeptness in adjusting to a multiplicity of traffic environments.
  arXiv  Detail & Related papers  (2024-03-13T08:41:55Z)
• A Holistic Framework Towards Vision-based Traffic Signal Control with Microscopic Simulation [53.39174966020085]
  Traffic signal control (TSC) is crucial for reducing traffic congestion that leads to smoother traffic flow, reduced idling time, and mitigated CO2 emissions. In this study, we explore the computer vision approach for TSC that modulates on-road traffic flows through visual observation. We introduce a holistic traffic simulation framework called TrafficDojo towards vision-based TSC and its benchmarking.
  arXiv  Detail & Related papers  (2024-03-11T16:42:29Z)
• TOP-Former: A Multi-Agent Transformer Approach for the Team Orienteering Problem [47.40841984849682]
  Route planning for a fleet of vehicles is an important task in applications such as package delivery, surveillance, or transportation. We introduce TOP-Former, a multi-agent route planning neural network designed to efficiently and accurately solve the Team Orienteering Problem.
  arXiv  Detail & Related papers  (2023-11-30T16:10:35Z)
• AI-aided Traffic Control Scheme for M2M Communications in the Internet of Vehicles [61.21359293642559]
  The dynamics of traffic and the heterogeneous requirements of different IoV applications are not considered in most existing studies. We consider a hybrid traffic control scheme and use proximal policy optimization (PPO) method to tackle it.
  arXiv  Detail & Related papers  (2022-03-05T10:54:05Z)
• Road Network Guided Fine-Grained Urban Traffic Flow Inference [108.64631590347352]
  Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem. We propose a novel Road-Aware Traffic Flow Magnifier (RATFM) that exploits the prior knowledge of road networks. Our method can generate high-quality fine-grained traffic flow maps.
  arXiv  Detail & Related papers  (2021-09-29T07:51:49Z)
• A Deep Reinforcement Learning Approach for Traffic Signal Control Optimization [14.455497228170646]
  Inefficient traffic signal control methods may cause numerous problems, such as traffic congestion and waste of energy. This paper first proposes a multi-agent deep deterministic policy gradient (MADDPG) method by extending the actor-critic policy gradient algorithms.
  arXiv  Detail & Related papers  (2021-07-13T14:11:04Z)
• End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning [63.56464608571663]
  Navigating through intersections is one of the main challenging tasks for an autonomous vehicle. In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided. We propose a multi-agent system using a continuous, model-free Deep Reinforcement Learning algorithm used to train a neural network for predicting both the acceleration and the steering angle at each time step.
  arXiv  Detail & Related papers  (2021-04-28T07:54:40Z)
• Multi-Modal Fusion Transformer for End-to-End Autonomous Driving [59.60483620730437]
  We propose TransFuser, a novel Multi-Modal Fusion Transformer, to integrate image and LiDAR representations using attention. Our approach achieves state-of-the-art driving performance while reducing collisions by 76% compared to geometry-based fusion.
  arXiv  Detail & Related papers  (2021-04-19T11:48:13Z)
• Multi-intersection Traffic Optimisation: A Benchmark Dataset and a Strong Baseline [85.9210953301628]
  Control of traffic signals is fundamental and critical to alleviate traffic congestion in urban areas. Because of the high complexity of modelling the problem, experimental settings of current works are often inconsistent. We propose a novel and strong baseline model based on deep reinforcement learning with the encoder-decoder structure.
  arXiv  Detail & Related papers  (2021-01-24T03:55:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.