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This paper explores the application of modeling and simulation techniques to evaluate the performance of multi-articulated vehicle combinations in real-world scenarios within the context of the ZEFES (Zero-emission flexible vehicle platforms with modular powertrains serving the long-haul freight eco-system) project. ZEFES aims to address transport sector emissions, by pioneering zero-emission vehicle technologies. Assessing the capabilities of these vehicles in challenging real-world conditions, such as roundabouts and sharp turns is necessary, since long vehicle combinations (up to 33.5 m) require large road width for maneuvering. Rollover tendencies in highway entry and exits are also considered. This assessment aides in determining the right vehicle in the right duty for cross border operation. An automated workflow identifies critical road sections using GPS data and constructs digital road models using the OpenCRG framework. GPS data is interpolated for feature extraction, focusing on analyzing heading angle changes. To perform swept path analysis, the available road-width is extracted using computer vision and known road design standards. Realistic vehicle reference trajectories are generated, and driver behavior is modelled to simulate maneuvering, considering the unique characteristics of multi-articulated vehicles, based on empirical methods.

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Performance Assessment of Multi-articulated Flexible Vehicle Platforms in Realistic Road Infrastructure Models

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • Nikhil Muthakana,
  • Dixon Devasia,
  • Siddharth Ajaykumar,
  • Karel Kural

摘要

This paper explores the application of modeling and simulation techniques to evaluate the performance of multi-articulated vehicle combinations in real-world scenarios within the context of the ZEFES (Zero-emission flexible vehicle platforms with modular powertrains serving the long-haul freight eco-system) project. ZEFES aims to address transport sector emissions, by pioneering zero-emission vehicle technologies. Assessing the capabilities of these vehicles in challenging real-world conditions, such as roundabouts and sharp turns is necessary, since long vehicle combinations (up to 33.5 m) require large road width for maneuvering. Rollover tendencies in highway entry and exits are also considered. This assessment aides in determining the right vehicle in the right duty for cross border operation. An automated workflow identifies critical road sections using GPS data and constructs digital road models using the OpenCRG framework. GPS data is interpolated for feature extraction, focusing on analyzing heading angle changes. To perform swept path analysis, the available road-width is extracted using computer vision and known road design standards. Realistic vehicle reference trajectories are generated, and driver behavior is modelled to simulate maneuvering, considering the unique characteristics of multi-articulated vehicles, based on empirical methods.