Infrastructure Sensor-enabled Vehicle Data Generation using Multi-Sensor Fusion for Proactive Safety Applications at Work Zone

• URL: http://arxiv.org/abs/2509.25452v1
• Date: Mon, 29 Sep 2025 19:58:27 GMT
• Title: Infrastructure Sensor-enabled Vehicle Data Generation using Multi-Sensor Fusion for Proactive Safety Applications at Work Zone
• Authors: Suhala Rabab Saba, Sakib Khan, Minhaj Uddin Ahmad, Jiahe Cao, Mizanur Rahman, Li Zhao, Nathan Huynh, Eren Erman Ozguven,
• Abstract summary: Infrastructure-based sensing and real-time trajectory generation show promise for improving safety in high-risk roadway segments such as work zones.<n>This study tackles these barriers by integrating roadside camera and LiDAR sensors into a cosimulation environment.<n>In simulation, the fusion algorithm reduced longitudinal error by up to 70 percent compared to individual sensors.
• Score: 8.125809724901366
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Infrastructure-based sensing and real-time trajectory generation show promise for improving safety in high-risk roadway segments such as work zones, yet practical deployments are hindered by perspective distortion, complex geometry, occlusions, and costs. This study tackles these barriers by integrating roadside camera and LiDAR sensors into a cosimulation environment to develop a scalable, cost-effective vehicle detection and localization framework, and employing a Kalman Filter-based late fusion strategy to enhance trajectory consistency and accuracy. In simulation, the fusion algorithm reduced longitudinal error by up to 70 percent compared to individual sensors while preserving lateral accuracy within 1 to 3 meters. Field validation in an active work zone, using LiDAR, a radar-camera rig, and RTK-GPS as ground truth, demonstrated that the fused trajectories closely match real vehicle paths, even when single-sensor data are intermittent or degraded. These results confirm that KF based sensor fusion can reliably compensate for individual sensor limitations, providing precise and robust vehicle tracking capabilities. Our approach thus offers a practical pathway to deploy infrastructure-enabled multi-sensor systems for proactive safety measures in complex traffic environments.

Related papers

• Robust sensor fusion against on-vehicle sensor staleness [5.473556120706307]
  Temporal misalignment between sensor modalities leads to inconsistent object state estimates.<n>We present a novel and model-agnostic approach to address this problem.<n>Our approach reaches consistently good performance across both synchronized and stale conditions.
  arXiv  Detail & Related papers  (2025-06-06T06:18:54Z)
• Distance Estimation in Outdoor Driving Environments Using Phase-only Correlation Method with Event Cameras [5.690128924544198]
  We present a method for distance estimation using a monocular event camera and a roadside LED bar.<n>The proposed approach achieves over 90% success rate with less than 0.5-meter error for distances ranging from 20 to 60 meters.<n>Future work includes extending this method to full position estimation by leveraging infrastructure such as smart poles equipped with LEDs.
  arXiv  Detail & Related papers  (2025-05-23T07:44:33Z)
• Harnessing Smartphone Sensors for Enhanced Road Safety: A Comprehensive Dataset and Review [7.867406170788454]
  This study introduces a comprehensive dataset derived from smartphone sensors. These sensors capture parameters such as acceleration force, gravitation, rotation rate, magnetic field strength, and vehicle speed. The dataset is designed to enhance road safety, infrastructure maintenance, traffic management, and urban planning.
  arXiv  Detail & Related papers  (2024-11-11T19:15:29Z)
• An Efficient Approach to Generate Safe Drivable Space by LiDAR-Camera-HDmap Fusion [13.451123257796972]
  We propose an accurate and robust perception module for Autonomous Vehicles (AVs) for drivable space extraction. Our work introduces a robust easy-to-generalize perception module that leverages LiDAR, camera, and HD map data fusion. Our approach is tested on a real dataset and its reliability is verified during the daily (including harsh snowy weather) operation of our autonomous shuttle, WATonoBus.
  arXiv  Detail & Related papers  (2024-10-29T17:54:02Z)
• AGSENet: A Robust Road Ponding Detection Method for Proactive Traffic Safety [30.305692955291033]
  Road ponding poses a serious threat to road safety by causing vehicles to lose control and leading to accidents ranging from minor fender benders to severe collisions. Existing technologies struggle to accurately identify road ponding due to complex road textures and variable ponding coloration influenced by reflection characteristics. We propose a novel approach called Self-Attention-based Global Saliency-Enhanced Network (AGSENet) for proactive road ponding detection and traffic safety improvement.
  arXiv  Detail & Related papers  (2024-10-22T13:21:36Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systems [56.838297900091426]
  Smoke and dust affect the performance of any mobile robotic platform due to their reliance on onboard perception systems. This paper proposes a novel modular computation filtration pipeline based on intensity and spatial information.
  arXiv  Detail & Related papers  (2023-08-14T16:48:57Z)
• Automated Automotive Radar Calibration With Intelligent Vehicles [73.15674960230625]
  We present an approach for automated and geo-referenced calibration of automotive radar sensors. Our method does not require external modifications of a vehicle and instead uses the location data obtained from automated vehicles. Our evaluation on data from a real testing site shows that our method can correctly calibrate infrastructure sensors in an automated manner.
  arXiv  Detail & Related papers  (2023-06-23T07:01:10Z)
• 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)
• Complex-valued Convolutional Neural Networks for Enhanced Radar Signal Denoising and Interference Mitigation [73.0103413636673]
  We propose the use of Complex-Valued Convolutional Neural Networks (CVCNNs) to address the issue of mutual interference between radar sensors. CVCNNs increase data efficiency, speeds up network training and substantially improves the conservation of phase information during interference removal.
  arXiv  Detail & Related papers  (2021-04-29T10:06:29Z)
• LIBRE: The Multiple 3D LiDAR Dataset [54.25307983677663]
  We present LIBRE: LiDAR Benchmarking and Reference, a first-of-its-kind dataset featuring 10 different LiDAR sensors. LIBRE will contribute to the research community to provide a means for a fair comparison of currently available LiDARs. It will also facilitate the improvement of existing self-driving vehicles and robotics-related software.
  arXiv  Detail & Related papers  (2020-03-13T06:17:39Z)

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.