Optimization of Lateral Position of Autonomous Trucks

Gungor, Osman Erman; She, Ruifeng; Al-Qadi, Imad L.; Ouyang, Yanfeng

doi:10.36501/0197-9191/20-009

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…Based on the International Energy Agency database (IEA, 2022), freight vehicles alone consumed 17 million barrels of oil per day, and this demand is expected to increase by 2.5 times by 2050 [2]. To address this growing concern, several innovative solutions have been proposed, including the use of widebase tires [3], aerodynamic truck designs [4], and optimization of truck routes [5].…”

Section: Introductionmentioning

confidence: 99%

Advancing infrastructure resilience: machine learning-based prediction of bridges’ rating factors under autonomous truck platoons

Elshazli,

Hussein,

Bhat

et al. 2024

J Infrastruct Preserv Resil

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The operational characteristics of freight shipment will significantly change after the implementation of Autonomous and Connected Trucks (ACT). This change will have a significant impact on freight mobility, transportation safety, and the sustainability of infrastructure. Truck platooning is an emerging truck configuration that is expected to become operational in the future due to the rapid advancements in connected vehicle technology and autonomous driving assistance. The platooning configuration enables trucks to be connected with themselves and the surrounding infrastructure. This arrangement has shown to be a promising solution to improve the vehicles’ fuel efficiency, reduce carbon dioxide emission, reduce traffic congestion, and improve transportation service. However, platooning may accelerate the damage accumulation of pavement and bridge structures due to the formation of multiple load axles within each platoon since those structures were not designed for such loads. According to AASHTO, bridges are designed based on a notional live load model comprised of one or two trucks per lane in conjunction with or separate from an applied uniform load (AASHTO, LRFD 2022). This damage, if accumulated, its repair would require billions of dollars from the government and would impede the movement of both people and goods. The potential damage to infrastructure may arise due to various factors such as the number of trucks in a platoon, gap spacing between trucks, and the type of trucks. This research work includes a thorough parametric study with 295,200 computer simulations using SAP 2000. The goal was to evaluate the effect of different truck platooning configurations on the load rating of existing bridges. The obtained results served as the dataset for training various machine learning models, including Random Tree, Random Forest, Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), K-Nearest Neighbor (KNN), and Extreme Gradient Boosting (XGBoost). Results showed that Random Forest model performed the best, with the lowest prediction errors. The proposed machine learning model has shown its effectiveness in identifying optimal platooning configurations for bridge structures within the scope of the study. Graphical Abstract

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Section: Introductionmentioning

confidence: 99%

Advancing infrastructure resilience: machine learning-based prediction of bridges’ rating factors under autonomous truck platoons

Elshazli,

Hussein,

Bhat

et al. 2024

J Infrastruct Preserv Resil

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“…Gungor et al [6] highlighted that autonomous and connected trucks (truck platoons) during travel can increase pavement damage because of channelized load application. The factors contributing to damage are constant lateral truck position and reduced resting time between two consecutive loadings.…”

Section: Introductionmentioning

confidence: 99%

Wander Effect on Pavement Performance for Application in Connected and Autonomous Vehicles

Pais,

Pereira,

Thives

2023

Infrastructures

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Connected and Autonomous Vehicles (CAV) will change how road engineers design road pavements because they can position themselves within a traffic lane, keeping their position in the lane more precisely than human-driven vehicles. These vehicles will have lower lateral wandering, which can induce more damage to pavements, such as cracking and permanent deformation, than the conventional vehicles, with consequences for the infrastructures due to the increased cracking and reduced safety due to the rutting. Thus, it is essential to assess the wander effect on pavement performance to define policies for its implementation on CAV. This paper studies the impact of the lateral wander of the traffic on pavement performance, considering its fatigue and permanent deformation resistance. This impact can be used to define limits for the wander to minimize distresses on the pavement. The results of this study allow us to conclude that for a pavement with a 10 cm asphalt layer, the wander effect is more significant for fatigue life. A pavement life increase of 20% was observed for a wander of 0.2 m, while for 0.6 m, the fatigue life can increase up to 48%. For the permanent deformation, a pavement life increase of 2% for a wander of 0.2 m was observed, but for 0.6 m, the pavement life can be increased up to 34%.

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Optimization of Lateral Position of Autonomous Trucks

Cited by 2 publications

References 21 publications

Advancing infrastructure resilience: machine learning-based prediction of bridges’ rating factors under autonomous truck platoons

Advancing infrastructure resilience: machine learning-based prediction of bridges’ rating factors under autonomous truck platoons

Wander Effect on Pavement Performance for Application in Connected and Autonomous Vehicles

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