Autonomous vehicles wheel wander: Structural impact on flexible pavements

Georgouli, Konstantina; Plati, Christina; Loizos, Andreas

doi:10.1016/j.jtte.2021.04.002

Cited by 12 publications

(5 citation statements)

References 17 publications

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“…This study considered a typical flexible pavement structure consisting of an HMA layer and a base layer placed on a subgrade layer. The thickness of the HMA and base layers could significantly impact the pavement fatigue performance [19,32]. Therefore, to account for this, the present study proposed different thicknesses for the HMA layer (i.e., 10 cm, 15 cm, and 20 cm) and the base layer (i.e., 20 cm and 40 cm).…”

Section: Flexible Pavement Cross-section Combinationsmentioning

confidence: 94%

“…In recent years, the implementation of automated driving technology into trucks has garnered significant attention due to its potential to provide a range of benefits to freight transportation, including reduced operational costs, improved fuel efficiency, and enhanced road safety [1][2][3][4][5][6][7]. However, it is essential to consider the potential differences between automated trucks (ATs) and human-driven trucks (HDTs), which could impact pavement performance differently [1,2,[8][9][10][11][12][13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

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Automated Trucks’ Impact on Pavement Fatigue Damage

Yeganeh,

Vandoren,

Pirdavani

2024

Applied Sciences

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The automated truck’s steering system can potentially control its lateral movement (i.e., wander mode) within the lane. The controlled wander mode of automated trucks could affect the transverse loading distribution of the wheels and consequently influence pavement fatigue damage in the long term. This study examines the effects of potential wander modes on pavement fatigue damage, considering the effects of lane width, market penetration rate, flexible pavement layers’ thickness, and stiffness of the materials. This study uses a finite element model to calculate the flexible pavement response. The mechanistic–empirical method is used to compute the total fatigue damage index for a specific design period, incorporating the wander mode effect. Comparing the fatigue damage indices indicates that automated trucks could either reduce the damage index value from −1.41% to −7.05% (i.e., mitigator scenario) or increase it from +11.6% to +278.57% (i.e., aggravator scenario), depending on their deployment scenarios. Moreover, the findings show that using a uniform-wander mode instead of a zero-wander mode or increasing the thickness and stiffness of the pavement layers could effectively reduce the adverse effect of automated trucks on fatigue damage and reduce the damage indices from −0.06% to −42.95%. However, their impact is considerably influenced by market penetration rate and lane width.

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Section: Flexible Pavement Cross-section Combinationsmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Automated Trucks’ Impact on Pavement Fatigue Damage

Yeganeh,

Vandoren,

Pirdavani

2024

Applied Sciences

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“…Konstantina Georgouli et al presented and discussed currently published relevant research and findings on the quantification of the wheel wander, both on the negative and on the positive aspects, and provided potential insightful future areas of enquiry to help mold and shape future research for this emerging field [17]. This study focused on all the effects of zero lateral wheel wander of autonomous vehicles on the accumulated damage on new and existing pavements.…”

Section: Road Roughness Classification Of Autonomous Vehiclesmentioning

confidence: 99%

The Braking-Pressure and Driving-Direction Determination System (BDDS) Using Road Roughness and Passenger Conditions of Surrounding Vehicles

Jeong

Son

Lee

et al. 2022

Sensors

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A fully autonomous vehicle must ensure not only fully autonomous driving but also the safety and comfort of its passengers. However, the self-driving technology that is currently completed focuses only on perfect driving and does not guarantee the safety and comfort of passengers. This paper proposes a braking-pressure and driving-direction determination system (BDDS), which computes the brake pressure and steering angle optimized for passenger safety by utilizing more diverse information than existing autonomous vehicles. The BDDS proposed in this paper consists of two modules. The road roughness classification module (RRCM) classifies the roughness of the road by using the pressure data applied to the suspension and the K-NN algorithm and computes the optimal brake pressure. The passenger recognition and sharing module (PRSM) identifies the current occupant status of the vehicle by using a body pressure sensor and CNN, shares the information with surrounding vehicles, and computes the optimal steering angle using passenger information and road information. As a result of the simulations described in this paper, the parameters of AI models were optimized. In addition, the RRCS was about 7% more accurate than the K-means clustering algorithm, and PRS was about 9% more accurate than the existing seat recognition system.

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“…Considering the vast investment in pavement construction and maintenance, Georgouli et al [8] also researched the impact of the AVs lateral wheel wander on both new flexible pavement design and the damage accumulation within the existing flexible pavements. They found that the AVs zero lateral wheel wander has a negative structural impact on flexible pavements compared to non-autonomous vehicles that follow a normal lateral wheel wander distribution.…”

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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Autonomous vehicles wheel wander: Structural impact on flexible pavements

Cited by 12 publications

References 17 publications

Automated Trucks’ Impact on Pavement Fatigue Damage

Automated Trucks’ Impact on Pavement Fatigue Damage

The Braking-Pressure and Driving-Direction Determination System (BDDS) Using Road Roughness and Passenger Conditions of Surrounding Vehicles

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

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