Identification of critical ride comfort sections by use of a validated vehicle model and Monte Carlo simulations
| Autor: | Alexander Genser, Philippe Nitsche, Anastasios Kouvelas |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
050210 logistics & transportation
Automated Vehicle Operation Motion Planning Navigation Simulation and Modeling Human Factors in Intelligent Transportation Systems Computer science 05 social sciences Monte Carlo method Process (computing) Vehicle dynamics Identification (information) Road surface 0502 economics and business 0501 psychology and cognitive sciences Motion planning 050107 human factors Simulation |
| Zdroj: | 2019 IEEE Intelligent Transportation Systems Conference (ITSC) ITSC |
| ISSN: | 5386-7025 |
| DOI: | 10.3929/ethz-b-000353359 |
| Popis: | A coherent way to enhance the user acceptance of autonomous vehicles (AV) is to ensure maximum ride comfort along the driven route. This paper proposes a sub-microscopic simulation framework that can be utilized to assess the ride comfort based on data from vehicle dynamics. In a future connected vehicle environment, this work can be used to enable an optimized route and motion planning, by avoiding sections with poor ride comfort and/or adapting the driving style and behavior. The developed methodology proposes a process chain for producing accurate and representative comfort estimates, by utilizing a road surface model, a non-linear model optimization, and Monte Carlo simulations. A case study with three real road sites demonstrates the effective tuning of the framework with real data and achieves high-resolution comfort results. The simulation investigations of the developed framework provide results and insights that justify the importance of enhancing available data sources with ride comfort data. 2019 IEEE Intelligent Transportation Systems Conference (ITSC) ISBN:978-1-5386-7024-8 ISBN:978-1-5386-7023-1 ISBN:978-1-5386-7025-5 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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