Ride comfort assessment for automated vehicles utilizing a road surface model and Monte Carlo simulations

Genser, Alexander; Spielhofer, Roland; Nitsche, Philippe; Kouvelas, Anastasios

doi:10.1111/mice.12787

Cited by 9 publications

(4 citation statements)

References 35 publications

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“…An example of a study that has been carried out is using Monte Carlo simulation and road surface models to assess the level of driving comfort in automatic vehicles. The results are compared with the classification based on IRI empirical data using the threshold method and the ISO 2631 standard driving comfort guidelines [94]. The sixth clusters are in light blue, consisting of 5 keywords with the most common keywords as follows: decision trees, learning algorithms, machine learning, mean square error, and neural networks.…”

Section: Figure 6 Network Visualizationmentioning

confidence: 99%

A Systematic Literature Network Analysis: Research Mapping of International Roughness Index

Lendra¹,

Wibowo²,

Hatmoko³

2023

I2M

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The International Roughness Index (IRI) is a widely used measure of pavement roughness that has important implications for vehicle safety, ride quality, and road maintenance. Over the years, many research studies have been conducted on IRI, but the literature is dispersed and lacks an overall research mapping. To address this issue, a Systematic Literature Network Analysis (SLNA) method to review the topic of IRI from scientific publications from 2000-2021 to obtain state-of-the-art mapping, trending topics, and future work projection. The results show a significant increase in scientific document publications. Network visualization contains 189 keywords divided into six clusters. The biggest cluster is focuses on measuring road surface conditions to obtain the IRI value as part of monitoring road surface conditions using a mechanical method and vibration response. The keywords featured on the word cloud are pavements, surface roughness, road and street, pavement performance, asphalt pavement, and concrete pavement. Top trend topics are predictive analytics, decision trees, machine learning, and roughness prediction. The keywords machine learning and learning algorithms are up-to-date topics and closely related to forecasting and the international roughness index. The IRI prediction model is still feasible for further research by using a machine learning prediction model.

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Section: Figure 6 Network Visualizationmentioning

confidence: 99%

A Systematic Literature Network Analysis: Research Mapping of International Roughness Index

Lendra¹,

Wibowo²,

Hatmoko³

2023

I2M

View full text Add to dashboard Cite

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“…The other development is the pursuit of increasing the level of driving automation to develop autonomous vehicles. Thus, enhancing vehicles' ability to evaluate road profiles has recently been a focus of a tremendous amount of research [45,54,[187][188][189]. Equipping vehicles with the ability to evaluate road roughness is essential to facilitate effective road-preview-based suspension control systems in future vehicles.…”

Section: Growing Demand For Roughness Data Measurement By Different I...mentioning

confidence: 99%

Industry- and Academic-Based Trends in Pavement Roughness Inspection Technologies over the Past Five Decades: A Critical Review

Fares

Zayed

2023

Remote Sensing

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Roughness is widely used as a primary measure of pavement condition. It is also the key indicator of the riding quality and serviceability of roads. The high demand for roughness data has bolstered the evolution of roughness measurement techniques. This study systematically investigated the various trends in pavement roughness measurement techniques within the industry and research community in the past five decades. In this study, the Scopus and TRID databases were utilized. In industry, it was revealed that laser inertial profilers prevailed over response-type methods that were popular until the 1990s. Three-dimensional triangulation is increasingly used in the automated systems developed and used by major vendors in the USA, Canada, and Australia. Among the research community, a boom of research focusing on roughness measurement has been evident in the past few years. The increasing interest in exploring new measurement methods has been fueled by crowdsourcing, the effort to develop cheaper techniques, and the growing demand for collecting roughness data by new industries. The use of crowdsourcing tools, unmanned aerial vehicles (UAVs), and synthetic aperture radar (SAR) images is expected to receive increasing attention from the research community. However, the use of 3D systems is likely to continue gaining momentum in the industry.

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“…For a pavement, the amount of raw road profile data is too large and increases the difficulties for quantification. Instead of road profiles, pavement roughness evaluated by the international roughness index (IRI) is used to represent the fundamental property of pavements (Genser et al., 2021). The IRI is a macro‐parameter and is related to vertical comfort.…”

Section: Dp‐based Speed Planningmentioning

confidence: 99%

A hierarchical framework for improving ride comfort of autonomous vehicles via deep reinforcement learning with external knowledge

Chen

Zhao

et al. 2022

Computer aided Civil Eng

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Ride comfort plays an important role in determining the public acceptance of autonomous vehicles (AVs). Many factors, such as road profile, driving speed, and suspension system, influence the ride comfort of AVs. This study proposes a hierarchical framework for improving ride comfort by integrating speed planning and suspension control in a vehicle-to-everything environment. Based on safe, comfortable, and efficient speed planning via dynamic programming, a deep reinforcement learning-based suspension control is proposed to adapt to the changing pavement conditions. Specifically, a deep deterministic policy gradient with external knowledge (EK-DDPG) algorithm is designed for the efficient selfadaptation of suspension control strategies. The external knowledge of action selection and value estimation from other AVs are combined into the loss functions of the DDPG algorithm. In numerical experiments, real-world pavements detected in 11 districts of Shanghai, China, are applied to verify the proposed method. Experimental results demonstrate that the EK-DDPG-based suspension control improves ride comfort on untrained rough pavements by 27.95% and 3.32%, compared to a model predictive control (MPC) baseline and a DDPG baseline, respectively. Meanwhile, the EK-DDPG-based suspension control improves computational efficiency by 22.97%, compared to the MPC baseline, and performs at the same level as the DDPD baseline. This study provides a generalized and computationally efficient approach for improving the ride comfort of AVs.

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Ride comfort assessment for automated vehicles utilizing a road surface model and Monte Carlo simulations

Cited by 9 publications

References 35 publications

A Systematic Literature Network Analysis: Research Mapping of International Roughness Index

A Systematic Literature Network Analysis: Research Mapping of International Roughness Index

Industry- and Academic-Based Trends in Pavement Roughness Inspection Technologies over the Past Five Decades: A Critical Review

A hierarchical framework for improving ride comfort of autonomous vehicles via deep reinforcement learning with external knowledge

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