Computer vision-based model for detecting turning lane features on Florida's public roadways
- URL: http://arxiv.org/abs/2406.08822v1
- Date: Thu, 13 Jun 2024 05:28:53 GMT
- Title: Computer vision-based model for detecting turning lane features on Florida's public roadways
- Authors: Richard Boadu Antwi, Samuel Takyi, Kimollo Michael, Alican Karaer, Eren Erman Ozguven, Ren Moses, Maxim A. Dulebenets, Thobias Sando,
- Abstract summary: This study detects roadway features on Florida's public roads from high-resolution aerial images using AI.
The extracted roadway geometry data can be integrated with crash and traffic data to provide valuable insights to policymakers and roadway users.
- Score: 2.5849315636929475
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Efficient and current roadway geometry data collection is critical to transportation agencies in road planning, maintenance, design, and rehabilitation. Data collection methods are divided into land-based and aerial-based. Land-based methods for extensive highway networks are tedious, costly, pose safety risks. Therefore, there is the need for efficient, safe, and economical data acquisition methodologies. The rise of computer vision and object detection technologies have made automated extraction of roadway geometry features feasible. This study detects roadway features on Florida's public roads from high-resolution aerial images using AI. The developed model achieved an average accuracy of 80.4 percent when compared with ground truth data. The extracted roadway geometry data can be integrated with crash and traffic data to provide valuable insights to policymakers and roadway users.
Related papers
- MSight: An Edge-Cloud Infrastructure-based Perception System for
Connected Automated Vehicles [58.461077944514564]
This paper presents MSight, a cutting-edge roadside perception system specifically designed for automated vehicles.
MSight offers real-time vehicle detection, localization, tracking, and short-term trajectory prediction.
Evaluations underscore the system's capability to uphold lane-level accuracy with minimal latency.
arXiv Detail & Related papers (2023-10-08T21:32:30Z) - RSRD: A Road Surface Reconstruction Dataset and Benchmark for Safe and
Comfortable Autonomous Driving [67.09546127265034]
Road surface reconstruction helps to enhance the analysis and prediction of vehicle responses for motion planning and control systems.
We introduce the Road Surface Reconstruction dataset, a real-world, high-resolution, and high-precision dataset collected with a specialized platform in diverse driving conditions.
It covers common road types containing approximately 16,000 pairs of stereo images, original point clouds, and ground-truth depth/disparity maps.
arXiv Detail & Related papers (2023-10-03T17:59:32Z) - Automatic Extraction of Relevant Road Infrastructure using Connected
vehicle data and Deep Learning Model [4.235459779667272]
We propose a novel approach that leverages connected vehicle data and cutting-edge deep learning techniques.
By employing geohashing to segment vehicle trajectories and then generating image representations of road segments, we utilize the YOLOv5 algorithm for accurate classification of both straight road segments and intersections.
Experimental results demonstrate an impressive overall classification accuracy of 95%, with straight roads achieving a remarkable 97% F1 score and intersections reaching a 90% F1 score.
arXiv Detail & Related papers (2023-08-10T15:57:47Z) - RoadScan: A Novel and Robust Transfer Learning Framework for Autonomous
Pothole Detection in Roads [0.0]
This research paper presents a novel approach to pothole detection using Deep Learning and Image Processing techniques.
The system aims to address the critical issue of potholes on roads, which pose significant risks to road users.
arXiv Detail & Related papers (2023-08-07T10:47:08Z) - Road Damages Detection and Classification with YOLOv7 [0.0]
This work proposes to collect and label road damage data using Google Street View and use YOLOv7 (You Only Look Once version 7)
The proposed approaches are applied to the Crowdsensing-based Road Damage Detection Challenge (CRDDC2022), IEEE BigData 2022.
arXiv Detail & Related papers (2022-10-31T18:55:58Z) - Vision-Cloud Data Fusion for ADAS: A Lane Change Prediction Case Study [38.65843674620544]
We introduce a novel vision-cloud data fusion methodology, integrating camera image and Digital Twin information from the cloud to help intelligent vehicles make better decisions.
A case study on lane change prediction is conducted to show the effectiveness of the proposed data fusion methodology.
arXiv Detail & Related papers (2021-12-07T23:42:21Z) - An Experimental Urban Case Study with Various Data Sources and a Model
for Traffic Estimation [65.28133251370055]
We organize an experimental campaign with video measurement in an area within the urban network of Zurich, Switzerland.
We focus on capturing the traffic state in terms of traffic flow and travel times by ensuring measurements from established thermal cameras.
We propose a simple yet efficient Multiple Linear Regression (MLR) model to estimate travel times with fusion of various data sources.
arXiv Detail & Related papers (2021-08-02T08:13:57Z) - Convolutional Recurrent Network for Road Boundary Extraction [99.55522995570063]
We tackle the problem of drivable road boundary extraction from LiDAR and camera imagery.
We design a structured model where a fully convolutional network obtains deep features encoding the location and direction of road boundaries.
We showcase the effectiveness of our method on a large North American city where we obtain perfect topology of road boundaries 99.3% of the time.
arXiv Detail & Related papers (2020-12-21T18:59:12Z) - Deep traffic light detection by overlaying synthetic context on
arbitrary natural images [49.592798832978296]
We propose a method to generate artificial traffic-related training data for deep traffic light detectors.
This data is generated using basic non-realistic computer graphics to blend fake traffic scenes on top of arbitrary image backgrounds.
It also tackles the intrinsic data imbalance problem in traffic light datasets, caused mainly by the low amount of samples of the yellow state.
arXiv Detail & Related papers (2020-11-07T19:57:22Z) - Urban Sensing based on Mobile Phone Data: Approaches, Applications and
Challenges [67.71975391801257]
Much concern in mobile data analysis is related to human beings and their behaviours.
This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data.
arXiv Detail & Related papers (2020-08-29T15:14:03Z)
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.