Road classification for two-wheeled vehicles

This publication presents a three-part road classification system that utilises the vehicle's onboard signals of two-wheeled vehicles. First, a curve estimator was developed to identify and classify road curves. In addition, the curve estimator continuously classifies the road curviness. Second, the road slope was evaluated to determine the hilliness of a given road. Third, a modular road profile estimator has been developed to classify the road profile according to ISO 8608, which utilises the vehicle's transfer functions. The road profile estimator continuously classifies the driven road. The proposed methods for the classification of curviness, hilliness, and road roughness have been validated with measurements. The road classification system enables the collection of vehicle-independent field data of two-wheeled vehicles. The road properties are part of the customer usage profiles which are essential to define vehicle design targets

Response-based methods to measure road surface irregularity: a state-of-the-art review

"jats:sec" "jats:title"Purpose"/jats:title" "jats:p"With the development of smart technologies, Internet of Things and inexpensive onboard sensors, many response-based methods to evaluate road surface conditions have emerged in the recent decade. Various techniques and systems have been developed to measure road profiles and detect road anomalies for multiple purposes such as expedient maintenance of pavements and adaptive control of vehicle dynamics to improve ride comfort and ride handling. A holistic review of studies into modern response-based techniques for road pavement applications is found to be lacking. Herein, the focus of this article is threefold: to provide an overview of the state-of-the-art response-based methods, to highlight key differences between methods and thereby to propose key focus areas for future research."/jats:p" "/jats:sec" "jats:sec" "jats:title"Methods"/jats:title" "jats:p"Available articles regarding response-based methods to measure road surface condition were collected mainly from “Scopus” database and partially from “Google Scholar”. The search period is limited to the recent 15 years. Among the 130 reviewed documents, 37% are for road profile reconstruction, 39% for pothole detection and the remaining 24% for roughness index estimation."/jats:p" "/jats:sec" "jats:sec" "jats:title"Results"/jats:title" "jats:p"The results show that machine-learning techniques/data-driven methods have been used intensively with promising results but the disadvantages on data dependence have limited its application in some instances as compared to analytical/data processing methods. Recent algorithms to reconstruct/estimate road profiles are based mainly on passive suspension and quarter-vehicle-model, utilise fewer key parameters, being independent on speed variation and less computation for real-time/online applications. On the other hand, algorithms for pothole detection and road roughness index estimation are increasingly focusing on GPS accuracy, data aggregation and crowdsourcing platform for large-scale application. However, a novel and comprehensive system that is comparable to existing International Roughness Index and conventional Pavement Management System is still lacking."/jats:p" "/jats:sec Document type: Articl

Pavement Performance Evaluation Using Connected Vehicles

Roads deteriorate at different rates from weathering and use. Hence, transportation agencies must assess the ride quality of a facility regularly to determine its maintenance needs. Existing models to characterize ride quality produce the International Roughness Index (IRI), the prevailing summary of roughness. Nearly all state agencies use Inertial Profilers to produce the IRI. Such heavily instrumented vehicles require trained personnel for their operation and data interpretation. Resource constraints prevent the scaling of these existing methods beyond 4% of the network. This dissertation developed an alternative method to characterize ride quality that uses regular passenger vehicles. Smartphones or connected vehicles provide the onboard sensor data needed to enable the new technique. The new method provides a single index summary of ride quality for all paved and unpaved roads. The new index is directly proportional to the IRI. A new transform integrates sensor data streams from connected vehicles to produce a linear energy density representation of roughness. The ensemble average of indices from different speed ranges converges to a repeatable characterization of roughness. The currently used IRI is undefined at speeds other than 80 km/h. This constraint mischaracterizes roughness experienced at other speeds. The newly proposed transform integrates the average roughness indices from all speed ranges to produce a speed-independent characterization of ride quality. This property avoids spatial wavelength bias, which is a critical deficiency of the IRI. The new method leverages the emergence of connected vehicles to provide continuous characterizations of ride quality for the entire roadway network. This dissertation derived precision bounds of deterioration forecasting for models that could utilize the new index. The results demonstrated continuous performance improvements with additional vehicle participation. With practical traversal volumes, the achievable precision of forecast is within a few days. This work also quantified capabilities of the new transform to localize roadway anomalies that could pose travel hazards. The methods included derivations of the best sensor settings to achieve the desired performances. Several case studies validated the findings. These new techniques have the potential to save agencies millions of dollars annually by enabling predictive maintenance practices for all roadways, worldwide.Mountain Plains Consortium (MPC

Road Roughness Estimation Using Machine Learning

Road roughness is a very important road condition for the infrastructure, as the roughness affects both the safety and ride comfort of passengers. The roads deteriorate over time which means the road roughness must be continuously monitored in order to have an accurate understand of the condition of the road infrastructure. In this paper, we propose a machine learning pipeline for road roughness prediction using the vertical acceleration of the car and the car speed. We compared well-known supervised machine learning models such as linear regression, naive Bayes, k-nearest neighbor, random forest, support vector machine, and the multi-layer perceptron neural network. The models are trained on an optimally selected set of features computed in the temporal and statistical domain. The results demonstrate that machine learning methods can accurately predict road roughness, using the recordings of the cost approachable in-vehicle sensors installed in conventional passenger cars. Our findings demonstrate that the technology is well suited to meet future pavement condition monitoring, by enabling continuous monitoring of a wide road network

Variability of gravel pavement roughness: an analysis of the impact on vehicle dynamic response and driving comfort

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

Ride and handling assessment of vehicles using four-post rig testing and simulation

The tuning of production road car suspension parameters in the development stage of a vehicle can be a lengthy and expensive procedure and commonly relies on the subjective judgements of test drivers to assess various aspects of ride and handling. The work in this thesis aims to create a testing and tuning technique using four-post rig testing and vehicle simulation to significantly reduce the amount of physical and subjective testing required within the development stage. A four-post rig testing technique is developed using modal sine sweep inputs to acquire the response of the vehicle in the heave, pitch, roll and warp modes of excitation. An analysis and parameter estimation method is developed based on four-post data and a 7 degree-of-freedom model, with four-post test data used to validate the parameter estimation and vehicle model simultaneously, obtaining satisfactory results in all but the roll mode of excitation. The BS 6841 [1] discomfort acceleration weightings are applied to the modal responses, with road input PSDs representative of standardised roads and driving cycles used to produce a comfort index value for a tested vehicle or setup. A novel performance index is created to estimate grip loss due to static and dynamic tyre properties for each axle, which allows the prediction of road input effects on the total grip and balance of the vehicle, as well as a driver requirement of steering input. MATLAB code is constructed for the parameter estimation procedure and for three general user interfaces to assist with the testing, tuning and benchmarking procedure. An objective-subjective validation exercise is carried out using a single vehicle with four different component setups which are tested on the four-post rig to determine comfort and performance index values, as well as recording the subjective assessments of three test drivers on two drive routes in the UK and Germany. The results show fair to good correlation for comfort measures but generally poor correlation to the performance index, mostly because of large variations between the drivers’ subjective assessment criteria

Advanced Modeling and Design Methodology for Pavements using Plasticity-Based Shakedown Theory

Pavement design is a process intended to find the most economical combination of layer thickness and material type for the pavement, taking into account the properties of the subgrade soil and the traffic to be carried during the service life of the road. The currently prevalent methods of pavement analysis and design, however, are more or less empirical in U.S., which possess the shortcoming that the important type of pavement distress of rutting related to the accumulation of plastic or permanent deformations cannot be effectively considered. This project proposes an exploratory study on the application of the plasticity theory-based shakedown concept to the analysis and design of pavements under repeated loading, with a more realistic incorporation of the roughness impact of the top pavement layer on the dynamic amplification of vehicle loading as well as on the elastic stress responses in the underlying subsoils. Numerical results from the newly developed vehicle-road coupling model show that the total vehicle load amplification factor ranges from 0.88 to 1.16 under different roughness levels and traveling speeds. This indicates the necessity and importance of incorporating the factors of roughness/vehicle speed in the pavement response analysis. Extensive parametric analyses for the shakedown limit show that increases in the pavement cohesion strength and internal friction angle and in the pavement thickness have a positive influence on the calculated shakedown limit value. The analysis results also indicate that there generally exists an optimal Young’s modulus ratio between the pavement and subsoil, for which a maximum shakedown load of the pavement system will be reached. The outcomes of this project on one hand add contributions to the development of a more rational theoretical framework for the pavement design/analysis. On the other hand, the shakedown design approach can prevent the flexible pavement from excessive rutting failure, and hence is of great practical value for prediction/design of the vehicle load, traveling speed, and layer thickness that is required to warrant shakedown state of the pavements (i.e., no excessive rutting) in the long run

Performance and Safety Enhancement Strategies in Vehicle Dynamics and Ground Contact

Recent trends in vehicle engineering are testament to the great efforts that scientists and industries have made to seek solutions to enhance both the performance and safety of vehicular systems. This Special Issue aims to contribute to the study of modern vehicle dynamics, attracting recent experimental and in-simulation advances that are the basis for current technological growth and future mobility. The area involves research, studies, and projects derived from vehicle dynamics that aim to enhance vehicle performance in terms of handling, comfort, and adherence, and to examine safety optimization in the emerging contexts of smart, connected, and autonomous driving.This Special Issue focuses on new findings in the following topics:(1) Experimental and modelling activities that aim to investigate interaction phenomena from the macroscale, analyzing vehicle data, to the microscale, accounting for local contact mechanics; (2) Control strategies focused on vehicle performance enhancement, in terms of handling/grip, comfort and safety for passengers, motorsports, and future mobility scenarios; (3) Innovative technologies to improve the safety and performance of the vehicle and its subsystems; (4) Identification of vehicle and tire/wheel model parameters and status with innovative methodologies and algorithms; (5) Implementation of real-time software, logics, and models in onboard architectures and driving simulators; (6) Studies and analyses oriented toward the correlation among the factors affecting vehicle performance and safety; (7) Application use cases in road and off-road vehicles, e-bikes, motorcycles, buses, trucks, etc

Fatigue performance of existing bridges under dynamic loads from winds and vehicles

During the life cycle of bridges, varied amplitude of stress ranges on structural details are induced by the random traffic and wind loads. The progressive deteriorated road surface conditions might accelerate the fatigue damage accumulations. Micro-cracks in structural details might be initiated. An effective structural modeling scheme and a reasonable fatigue damage accumulation rule are essential for stress range acquisitions and fatigue life estimation. The present research targets at the development of a fatigue life and reliability prediction methodology for existing steel bridges under real wind and traffic environment with the capability of including multiple random parameters and variables in bridges’ life cycle. Firstly, the dynamic system is further investigated to acquire more accuracy stresses for fatigue life estimations for short and long span bridges. For short span bridges, the random effects of vehicle speed and road roughness condition are included in the limit state function, and fatigue reliability of the structural details is attained. For long-span bridges, a multiple scale modeling and simulation scheme based on the EOMM method is presented to obtain the stress range history of structural details, while the calculation cost and accuracy are saved. Secondly, a progressive fatigue reliability assessment approach based on a nonlinear continuous fatigue damage model is presented. At each block of stress cycles, types and numbers of passing vehicles are recorded to calculate the road surface’s progressive deterioration and nonlinear cumulative fatigue damage, and the random road profiles are generated. Thirdly, this study discussed the fatigue design of short and long span bridges based on the dynamic analysis on the vehicle-bridge or vehicle-bridge-wind system. For short span bridges, a reliability-based dynamic amplification factor on revised equivalent stress ranges (DAFS) is proposed. For long span bridges, a comprehensive framework for fatigue reliability analysis under combined dynamic loads from vehicles and winds is presented. The superposed dynamic stress ranges cannot be ignored for fatigue reliability assessment of long-span bridges, although the stresses from either the vehicle loads or wind loads may not be able to induce serious fatigue issues alone