DARTS: A Drone-Based AI-Powered Real-Time Traffic Incident Detection System

• URL: http://arxiv.org/abs/2510.26004v1
• Date: Wed, 29 Oct 2025 22:32:16 GMT
• Title: DARTS: A Drone-Based AI-Powered Real-Time Traffic Incident Detection System
• Authors: Bai Li, Achilleas Kourtellis, Rong Cao, Joseph Post, Brian Porter, Yu Zhang,
• Abstract summary: DARTS is a drone-based, AI-powered real-time traffic incident detection system.<n>The system achieved 99% detection accuracy on a self-collected dataset.<n>In a field test, DARTS detected and verified a rear-end collision 12 minutes earlier than the local transportation management center.
• Score: 4.230713219853656
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rapid and reliable incident detection is critical for reducing crash-related fatalities, injuries, and congestion. However, conventional methods, such as closed-circuit television, dashcam footage, and sensor-based detection, separate detection from verification, suffer from limited flexibility, and require dense infrastructure or high penetration rates, restricting adaptability and scalability to shifting incident hotspots. To overcome these challenges, we developed DARTS, a drone-based, AI-powered real-time traffic incident detection system. DARTS integrates drones' high mobility and aerial perspective for adaptive surveillance, thermal imaging for better low-visibility performance and privacy protection, and a lightweight deep learning framework for real-time vehicle trajectory extraction and incident detection. The system achieved 99% detection accuracy on a self-collected dataset and supports simultaneous online visual verification, severity assessment, and incident-induced congestion propagation monitoring via a web-based interface. In a field test on Interstate 75 in Florida, DARTS detected and verified a rear-end collision 12 minutes earlier than the local transportation management center and monitored incident-induced congestion propagation, suggesting potential to support faster emergency response and enable proactive traffic control to reduce congestion and secondary crash risk. Crucially, DARTS's flexible deployment architecture reduces dependence on frequent physical patrols, indicating potential scalability and cost-effectiveness for use in remote areas and resource-constrained settings. This study presents a promising step toward a more flexible and integrated real-time traffic incident detection system, with significant implications for the operational efficiency and responsiveness of modern transportation management.

Related papers

• Surveillance Video-Based Traffic Accident Detection Using Transformer Architecture [2.621034368312571]
  Traffic accidents represent a leading cause of mortality globally, with incidence rates due to increasing population, urbanization and motorization.<n>Traditional computer methods for accident detection struggle with limited understanding and poor cross-domain generalization.<n>We propose an accident detection model based on a transformer architecture using pre-extracted spatial video features.
  arXiv  Detail & Related papers  (2025-12-12T07:57:36Z)
• T2I-Based Physical-World Appearance Attack against Traffic Sign Recognition Systems in Autonomous Driving [40.067678927952336]
  Traffic Sign Recognition (TSR) systems play a critical role in Autonomous Driving (AD) systems.<n>Recent research has exposed their vulnerability to physical-world adversarial appearance attacks.<n>We present DiffSign, a novel T2I-based appearance attack framework.
  arXiv  Detail & Related papers  (2025-11-17T04:29:55Z)
• UAV-Based Intelligent Traffic Surveillance System: Real-Time Vehicle Detection, Classification, Tracking, and Behavioral Analysis [2.6341839987582345]
  This paper introduces an advanced unmanned aerial vehicle (UAV)-based traffic surveillance system.<n>It is capable of accurate vehicle detection, classification, tracking, and behavioral analysis in real-world, unconstrained urban environments.
  arXiv  Detail & Related papers  (2025-09-04T19:19:25Z)
• Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance [12.513296074529727]
  This paper proposes the Real-time Edge-based Autonomous Co-pilot Trajectory planner (REACT) for autonomous driving.<n>REACT is a V2X-integrated trajectory optimization framework for AD based on a fine-tuned lightweight Vision-Language Model (VLM)<n> evaluated on the DeepAccident benchmark, REACT achieves state-of-the-art performance, a 77% collision rate reduction, a 48.2% Video Panoptic Quality (VPQ), and a 0.57-second inference latency on the Jetson AGX Orin.
  arXiv  Detail & Related papers  (2025-08-01T20:16:04Z)
• Automating Traffic Monitoring with SHM Sensor Networks via Vision-Supervised Deep Learning [0.18148496440453665]
  Bridges, as critical components of civil infrastructure, are increasingly affected by deterioration.<n>Recent advances in deep learning have enabled progress toward continuous, automated monitoring.<n>We propose a fully automated deep-learning pipeline for continuous traffic monitoring using structural health monitoring (SHM) sensor networks.
  arXiv  Detail & Related papers  (2025-06-23T18:27:14Z)
• Explainable Machine Learning for Cyberattack Identification from Traffic Flows [5.834276858232939]
  We simulate cyberattacks in a semi-realistic environment, using a traffic network to analyze disruption patterns.<n>We develop a deep learning-based anomaly detection system, demonstrating that Longest Stop Duration and Total Jam Distance are key indicators of compromised signals.<n>This work enhances AI-driven traffic security, improving both detection accuracy and trustworthiness in smart transportation systems.
  arXiv  Detail & Related papers  (2025-05-02T17:34:14Z)
• Multi-Source Urban Traffic Flow Forecasting with Drone and Loop Detector Data [61.9426776237409]
  Drone-captured data can create an accurate multi-sensor mobility observatory for large-scale urban networks.<n>A simple yet effective graph-based model HiMSNet is proposed to integrate multiple data modalities and learn-temporal correlations.
  arXiv  Detail & Related papers  (2025-01-07T03:23:28Z)
• Convolutional Neural Network Design and Evaluation for Real-Time Multivariate Time Series Fault Detection in Spacecraft Attitude Sensors [41.94295877935867]
  This paper presents a novel approach to detecting stuck values within the Accelerometer and Inertial Measurement Unit of a drone-like spacecraft. A multi-channel Convolutional Neural Network (CNN) is used to perform multi-target classification and independently detect faults in the sensors. An integration methodology is proposed to enable the network to effectively detect anomalies and trigger recovery actions at the system level.
  arXiv  Detail & Related papers  (2024-10-11T09:36:38Z)
• CCDSReFormer: Traffic Flow Prediction with a Criss-Crossed Dual-Stream Enhanced Rectified Transformer Model [32.45713037210818]
  We introduce Criss-Crossed Dual-Stream Enhanced Rectified Transformer model (CCDSReFormer) It includes three innovative modules: Enhanced Rectified Spatial Self-attention (ReSSA), Enhanced Rectified Delay Aware Self-attention (ReDASA) and Enhanced Rectified Temporal Self-attention (ReTSA) These modules aim to lower computational needs via sparse attention, focus on local information for better traffic dynamics understanding, and merge spatial and temporal insights through a unique learning method.
  arXiv  Detail & Related papers  (2024-03-26T14:43:57Z)
• Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics [77.34726150561087]
  This work aims to carry out a study on the current scenario of camera and radar-based perception for ADAS and autonomous vehicles. Concepts and characteristics related to both sensors, as well as to their fusion, are presented. We give an overview of the Deep Learning-based detection and segmentation tasks, and the main datasets, metrics, challenges, and open questions in vehicle perception.
  arXiv  Detail & Related papers  (2023-03-08T00:48:32Z)
• Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach [76.45949280328838]
  We propose a Laplacian enhanced low-rank tensor (LETC) framework featuring both lowrankness and multi-temporal correlations for large-scale traffic speed kriging. We then design an efficient solution algorithm via several effective numeric techniques to scale up the proposed model to network-wide kriging.
  arXiv  Detail & Related papers  (2022-10-21T07:25:57Z)
• Training-free Monocular 3D Event Detection System for Traffic Surveillance [93.65240041833319]
  Existing event detection systems are mostly learning-based and have achieved convincing performance when a large amount of training data is available. In real-world scenarios, collecting sufficient labeled training data is expensive and sometimes impossible. We propose a training-free monocular 3D event detection system for traffic surveillance.
  arXiv  Detail & Related papers  (2020-02-01T04:42:57Z)

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.