How Prediction Accuracy can Affect the Decision-Making Process in Pavement Management System

Hosseini, Seyed Amirhossein; Smadi, Omar

doi:10.31224/osf.io/t28ue

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…Early detection of cracks can prevent potential damage and failure (12). Building a fast, robust, and cost-effective image processing algorithm for detecting the pavement surface cracks is one of the most critical priorities for building a robust pavement management system (13)(14)(15)(16)(17).…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive Pixels Segmentation Algorithm for Pavement Crack Detection

Safaei¹,

Smadi²,

Safaei³

et al. 2021

Preprint

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Cracks considerably reduce the life span of pavement surfaces. Currently, there is a need for the development of robust automated distress evaluation systems that comprise a low-cost crack detection method for performing fast and cost-effective roadway health monitoring practices. Most of the current methods are costly and have labor-intensive learning processes, so they are not suitable for small local-level projects with limited resources or are only usable for specific pavement types.This paper proposes a new method that uses an improved version of the weighted neighborhood pixels segmentation algorithm to detect cracks in 2-D pavement images. This method uses the Gaussian cumulative density function as the adaptive threshold to overcome the drawback of fixed thresholds in noisy environments. The proposed algorithm was tested on 300 images containing a wide range of noise representative of different noise conditions. This method proved to be time and cost-efficient as it took less than 3.15 seconds per 320 × 480 pixels image for a Xeon (R) 3.70 GHz CPU processor to determine the detection results. This makes the model a perfect choice for county-level pavement maintenance projects requiring cost-effective pavement crack detection systems. The validation results were promising for the detection of low to severe-level cracks (Accuracy = 97.3%, Precision = 79.21%, Recall= 89.18% and F1 score = 83.9%).

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Section: Introductionmentioning

confidence: 99%

A Novel Adaptive Pixels Segmentation Algorithm for Pavement Crack Detection

Safaei¹,

Smadi²,

Safaei³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…This generated trajectory is (i) used to conduct eco-driving experiments, and (ii) leveraged as a look-ahead preview by MPC-based eco-heating strategy. Note that, other approaches have also been developed for short-and longterm predictions of vehicle speed using V2X [20,[27][28][29][30][31][32][33][34] which can be integrated with our eco-heating strategy.…”

Section: Introductionmentioning

confidence: 99%

Experimental Validation of Eco-Driving and Eco-Heating Strategies for Connected and Automated HEVs

Amini

Wang

et al. 2021

Preprint

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This paper presents experimental results that validate eco-driving and eco-heating strategies developed for connected and automated vehicles (CAVs). By exploiting vehicle-to-infrastructure (V2I) communications, traffic signal timing, and queue length estimations, optimized and smoothed speed profiles for the ego-vehicle are generated to reduce energy consumption. Next, the planned eco-trajectories are incorporated into a real-time predictive optimization framework that coordinates the cabin thermal load (in cold weather) with the speed preview, i.e., eco-heating. To enable eco-heating, the engine coolant (as the only heat source for cabin heating) and the cabin air are leveraged as two thermal energy storages. Our eco-heating strategy stores thermal energy in the engine coolant and cabin air while the vehicle is driving at high speeds, and releases the stored energy slowly during the vehicle stops for cabin heating without forcing the engine to idle to provide the heating source. To test and validate these solutions, a power-split hybrid electric vehicle (HEV) has been instrumented for cabin thermal management, allowing to regulate heating, ventilation, and air conditioning (HVAC) system inputs (cabin temperature setpoint and blower flow rate) in real-time. Experiments were conducted to demonstrate the energy-saving benefits of eco-driving and eco-heating strategies over realworld city driving cycles at different cold ambient temperatures. The data confirmed average fuel savings of 14.5% and 4.7% achieved by eco-driving and eco-heating, respectively, offering a combined energy saving of more than 19% when comparing to the baseline vehicle driven by a human driver with a constant-heating strategy.

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Applications of Deep Learning in Intelligent Transportation Systems

Haghighat

Ravichandra-Mouli

Chakraborty

et al. 2020

J. Big Data Anal. Transp.

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In recent years, Intelligent Transportation Systems (ITS) have seen efficient and faster development by implementing deep learning techniques in problem domains which were previously addressed using analytical or statistical solutions and also in some areas that were untouched. These improvements have facilitated traffic management and traffic planning, increased safety and security in transit roads, decreased costs of maintenance, optimized public transportation and ride-sharing company's performance, and advanced driver-less vehicle development to a new stage. This papers primary objective was to provide a review and comprehensive insight into the applications of deep learning models on intelligent transportation systems accompanied by presenting the progress of ITS research due to deep learning. First, different techniques of deep learning and their state-of-the-art are discussed, followed by an in-depth analysis and explanation of the current applications of these techniques in transportation systems. This enumeration of deep learning on ITS highlights its significance in the domain. The applications are furthermore categorized based on the gap they are trying to address. Finally, different embedded systems for deployment of these techniques are investigated and their advantages and weaknesses over each other are discussed. Based on this systematic review, credible benefits of deep learning models on ITS are demonstrated and directions for future research are discussed.

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How Prediction Accuracy can Affect the Decision-Making Process in Pavement Management System

Cited by 7 publications

References 11 publications

A Novel Adaptive Pixels Segmentation Algorithm for Pavement Crack Detection

A Novel Adaptive Pixels Segmentation Algorithm for Pavement Crack Detection

Experimental Validation of Eco-Driving and Eco-Heating Strategies for Connected and Automated HEVs

Applications of Deep Learning in Intelligent Transportation Systems

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