Characterization of Road Condition with Data Mining Based on Measured Kinematic Vehicle Parameters

Masino, Johannes; Thumm, Jakob; Levasseur, Guillaume; Frey, Michael; Gauterin, Frank; Mikut, Ralf; Reischl, Markus

doi:10.1155/2018/8647607

Cited by 5 publications

(2 citation statements)

References 15 publications

(16 reference statements)

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“…In road engineering and construction, the classification of a good road is by its shape, width, thickness, and the materials it is made of [ 49 ]. Materials of the road are usually found on a railway crossing, cobbled road, pothole, damaged road, speed bump, manhole covers, and a road in good condition [ 50 ]. Roads are a barrier to animal movement as they contribute to roadkill during crossing attempts or behavioral avoidance.…”

Section: Rqn1: What Are the Negative Factors That Lead To The Occurre...mentioning

confidence: 99%

Intelligent Systems Using Sensors and/or Machine Learning to Mitigate Wildlife–Vehicle Collisions: A Review, Challenges, and New Perspectives

Nandutu

Atemkeng

Okouma

2022

Sensors

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Worldwide, the persistent trend of human and animal life losses, as well as damage to properties due to wildlife–vehicle collisions (WVCs) remains a significant source of concerns for a broad range of stakeholders. To mitigate their occurrences and impact, many approaches are being adopted, with varying successes. Because of their increased versatility and increasing efficiency, Artificial Intelligence-based methods have been experiencing a significant level of adoption. The present work extensively reviews the literature on intelligent systems incorporating sensor technologies and/or machine learning methods to mitigate WVCs. Included in our review is an investigation of key factors contributing to human–wildlife conflicts, as well as a discussion of dominant state-of-the-art datasets used in the mitigation of WVCs. Our study combines a systematic review with bibliometric analysis. We find that most animal detection systems (excluding autonomous vehicles) are relying neither on state-of-the-art datasets nor on recent breakthrough machine learning approaches. We, therefore, argue that the use of the latest datasets and machine learning techniques will minimize false detection and improve model performance. In addition, the present work covers a comprehensive list of associated challenges ranging from failure to detect hotspot areas to limitations in training datasets. Future research directions identified include the design and development of algorithms for real-time animal detection systems. The latter provides a rationale for the applicability of our proposed solutions, for which we designed a continuous product development lifecycle to determine their feasibility.

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Section: Rqn1: What Are the Negative Factors That Lead To The Occurre...mentioning

confidence: 99%

Intelligent Systems Using Sensors and/or Machine Learning to Mitigate Wildlife–Vehicle Collisions: A Review, Challenges, and New Perspectives

Nandutu

Atemkeng

Okouma

2022

Sensors

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show abstract

“…Automated pavement detections using cameras [4], lasers, and ultrasonic sensors [5] are widely used as replacements to manual work, which significantly improves the efficiency and lowers the cost [6]. Among them, the camera is the priority choice in pavement detection because of not only its low cost and intuitive data but also its lightweight and detachable features that satisfy the requirements of multiple-vehicle detection and rough-set data collection [7]. erefore, pavement condition recognition based on video image has become a central issue [8].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Pavement Distress Image Stitching Based on Fine-Grained Feature Matching

Weng

Liu

et al. 2020

Journal of Advanced Transportation

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Camera-based pavement distress detection plays an important role in pavement maintenance. Duplicate collections for the same distress and multiple overlaps of defects are both practical problems that greatly affect the detection results. In this paper, we propose a fine-grained feature-matching and image-stitching method for pavement distress detection to eliminate duplications and visually demonstrates local pavement distress. The original images are processed through a hierarchical structure, including rough data filtering, feature matching, and image stitching. The original data are firstly filtered based on the global position system (GPS) information, which can avoid full-dataset comparison and improve the calculating efficiency. A scale-invariant feature transform is introduced for feature matching based on the extracted key regions using spectral saliency mapping and bounding boxes. Two parameters: the mean Euclidean distance (MEuD) and the matching rate (MCR) are constructed to identify the duplication between two images. A support vector machine is then applied to determine the threshold of MEuD and MCR. This paper further discusses the correlation between the sampling frequency and the number of detection vehicles. The method provided can effectively solve the problem of duplications in pavement distress detection and enhances the feasibility of multivehicle pavement distress detection based on images.

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Low-cost infrared-based pavement roughness data acquisition for low volume roads

Kheirati

Golroo

2020

Automation in Construction

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Characterization of Road Condition with Data Mining Based on Measured Kinematic Vehicle Parameters

Cited by 5 publications

References 15 publications

Intelligent Systems Using Sensors and/or Machine Learning to Mitigate Wildlife–Vehicle Collisions: A Review, Challenges, and New Perspectives

Intelligent Systems Using Sensors and/or Machine Learning to Mitigate Wildlife–Vehicle Collisions: A Review, Challenges, and New Perspectives

Dynamic Pavement Distress Image Stitching Based on Fine-Grained Feature Matching

Low-cost infrared-based pavement roughness data acquisition for low volume roads

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