Technological Measures of Forefront Road Identification for Vehicle Comfort and Safety Improvement

Žuraulis, Vidas; Surblys, Vytenis; Šabanovič, Eldar

doi:10.3846/transport.2019.10372

Cited by 14 publications

(11 citation statements)

References 42 publications

(52 reference statements)

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“…The most pronounced fractures in the elevated PSD are observed for the third pavement surface PS-3 state (Figure 6): a peak for 10 km/h at 5-6 Hz, a peak for 30 km/h at 15-18 Hz, a peak for 50 km/h at 23-30 Hz, and a peak for 70 km/h at 33-42 Hz. Driving at lower speeds (10-30 km/h) through short wavelength roughness (PS-3) formed on the gravel pavement (macro-texture level) demonstrates that vibration frequency caused by waves approaches the natural frequency of vehicle USM (11)(12)(13)(14)(15). This order vibration tends to increase the variability in the normal load of the wheel described by the DLC [87,88].…”

Section: Vehicle Response To the Pavement Surface Statementioning

confidence: 99%

“…The excitation of the road pavement to the vehicle driven at a speed of 10 km/h corresponds to an increase in pavement deterioration level (Figure 8a). Although there is no significant rise in pavement excitation for the SM and USM dynamics at 10 km/h (at 1-2 Hz and 10-14 Hz, respectively), the undesired excitation occurs at a human body sensitive frequency range of 4-8 Hz (ISO 2631-1) on PS-3 and PS- Driving at lower speeds (10-30 km/h) through short wavelength roughness (PS-3) formed on the gravel pavement (macro-texture level) demonstrates that vibration frequency caused by waves approaches the natural frequency of vehicle USM (11)(12)(13)(14)(15). This order vibration tends to increase the variability in the normal load of the wheel described by the DLC [87,88].…”

Section: Vehicle Response To the Pavement Surface Statementioning

confidence: 99%

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Variability of Gravel Pavement Roughness: An Analysis of the Impact on Vehicle Dynamic Response and Driving Comfort

et al. 2021

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Gravel pavement has lower construction costs but poorer performance than asphalt surfaces on roads. It also emits dust and deforms under the impact of vehicle loads and ambient air factors; the resulting ripples and ruts constantly deepen, and therefore increase vehicle vibrations and fuel consumption, and reduce safe driving speed and comfort. In this study, existing pavement quality evaluation indexes are analysed, and a methodology for adapting them for roads with gravel pavement is proposed. We report the measured wave depth and length of gravel pavement profile using the straightedge method on a 160 m long road section at three stages of road utilization. The measured pavement elevation was processed according to ISO 8608, and the frequency response of a vehicle was investigated using simulations in MATLAB/Simulink. The international roughness index (IRI) analysis showed that a speed of 30–45 km/h instead of 80 km/h provided the objective results of the IRI calculation on the flexible pavement due to the decreasing velocity of a vehicle’s unsprung mass on a more deteriorated road pavement state. The influence of the corrugation phenomenon of gravel pavement was explored, identifying specific driving safety and comfort cases. Finally, an increase in the dynamic load coefficient (DLC) at a low speed of 30 km/h on the most deteriorated pavement and a high speed of 90 km/h on the middle-quality pavement demonstrated the demand for timely gravel pavement maintenance and the complicated prediction of a safe driving speed for drivers. The main relevant objectives of this study are the adaptation of a road roughness indicator to gravel pavement, including the evaluation of vehicle dynamic responses at different speeds and pavement deterioration states.

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Section: Vehicle Response To the Pavement Surface Statementioning

confidence: 99%

Section: Vehicle Response To the Pavement Surface Statementioning

confidence: 99%

Variability of Gravel Pavement Roughness: An Analysis of the Impact on Vehicle Dynamic Response and Driving Comfort

et al. 2021

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“…5): peak for 10 km/h at 5-6 Hz, a peak for 30 km/h at 15-18 Hz, a peak for 50 km/h at 23-30 Hz, a peak for 70 km/h at 33-42 Hz. Driving at lower speeds (10-30 km/h) through short wavelength roughness (PS-3) formed on the gravel pavement (macro-texture level) demonstrates that vibration frequency caused by waves approaches the natural frequency of vehicle USM (11)(12)(13)(14)(15). This order vibration tends to increase the variability in the normal load of the wheel described by the DLC [77,78].…”

Section: The Iri Approach To the Gravel Pavementmentioning

confidence: 99%

“…Moreover, many road accidents occur on gravel roads with relatively high traffic volume [7]. Finally, safety, stability and comfort systems including suspension development must also be adapted to operate efficiently under an imperfect pavement having different roughness levels [8,9,10,11,12,13,14].…”

Section: Introductionmentioning

confidence: 99%

Variability of Gravel Pavement Roughness: Analysis of the Impact on Vehicle Response and Driving Comfort

Žuraulis¹,

Sivilevičius²,

Šabanovič³

et al. 2021

Preprint

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The gravel road pavement has a lower construction cost but poorer performance than the asphalt surface. It also emits dust and deforms under the impact of vehicle loads and ambient air factors. The resulting ripples and ruts are constantly deepening, increasing vehicle vibrations and fuel consumption, reducing safe driving speed and comfort. In this article, existing pavement quality evaluation indexes are analysed, and a methodology for their adaptation for roads with gravel pavement is proposed. This article reports the measured wave depth and length of the gravel pavement profile by the straightedge method of a 160 m long road section in three road exploitation stages. The measured pavement elevation was processed according to ISO 8608, and vehicle frequency response has been investigated using simulations in MATLAB/Simulink. The applied International Roughness Index (IRI) analysis showed that a speed of 30-45 km/h instead of 80 km/h provides the objective results of IRI calculation on the flexible pavement due to a decreasing velocity of vehicle's unsprung mass on a more deteriorated road pavement state. The influence of the corrugation phenomenon of gravel pavement has been explored, identifying specific driving safety and comfort cases. Finally, an increase in the Dynamic Load Coefficient (DLC) at a low speed of 30 km/h on the most deteriorated pavement and a high speed of 90 km/h on the middle-quality pavement demonstrates the demand for timely gravel pavement maintenance and the complicated prediction of a safe driving speed for drivers.

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“…Road friction estimation is a useful tool for various aspects in driving safety alerting a driver about the road-surface conditions, modifying vehicle active safety systems thresholds or reporting information to a vehicle or road infrastructure network. The applications of such technologies have been already introduced for patenting and near future implementation in new vehicle production [1].…”

Section: Introductionmentioning

confidence: 99%

Identification of Road-Surface Type Using Deep Neural Networks for Friction Coefficient Estimation

Šabanovič

Žuraulis

Prentkovskis

et al. 2020

Sensors

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Nowadays, vehicles have advanced driver-assistance systems which help to improve vehicle safety and save the lives of drivers, passengers and pedestrians. Identification of the road-surface type and condition in real time using a video image sensor, can increase the effectiveness of such systems significantly, especially when adapting it for braking and stability-related solutions. This paper contributes to the development of the new efficient engineering solution aimed at improving vehicle dynamics control via the anti-lock braking system (ABS) by estimating friction coefficient using video data. The experimental research on three different road surface types in dry and wet conditions has been carried out and braking performance was established with a car mathematical model (MM). Testing of a deep neural networks (DNN)-based road-surface and conditions classification algorithm revealed that this is the most promising approach for this task. The research has shown that the proposed solution increases the performance of ABS with a rule-based control strategy.

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Technological Measures of Forefront Road Identification for Vehicle Comfort and Safety Improvement

Cited by 14 publications

References 42 publications

Variability of Gravel Pavement Roughness: An Analysis of the Impact on Vehicle Dynamic Response and Driving Comfort

Variability of Gravel Pavement Roughness: An Analysis of the Impact on Vehicle Dynamic Response and Driving Comfort

Variability of Gravel Pavement Roughness: Analysis of the Impact on Vehicle Response and Driving Comfort

Identification of Road-Surface Type Using Deep Neural Networks for Friction Coefficient Estimation

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