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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2402.05009 |
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| _version_ | 1866915082465705984 |
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| author | Zhou, Hang Ma, Ke Li, Xiaopeng |
| author_facet | Zhou, Hang Ma, Ke Li, Xiaopeng |
| contents | This paper presents a comprehensive review of trajectory data of Advanced Driver Assistance System equipped-vehicle, with the aim of precisely model of Autonomous Vehicles (AVs) behavior. This study emphasizes the importance of trajectory data in the development of AV models, especially in car-following scenarios. We introduce and evaluate several datasets: the OpenACC Dataset, the Connected & Autonomous Transportation Systems Laboratory Open Dataset, the Vanderbilt ACC Dataset, the Central Ohio Dataset, and the Waymo Open Dataset. Each dataset offers unique insights into AV behaviors, yet they share common challenges in terms of data availability, processing, and standardization. After a series of data cleaning, outlier removal and statistical analysis, this paper transforms datasets of varied formats into a uniform standard, thereby improving their applicability for modeling AV car-following behavior. Key contributions of this study include: 1. the transformation of all datasets into a unified standard format, enhancing their utility for broad research applications; 2. a comparative analysis of these datasets, highlighting their distinct characteristics and implications for car-following model development; 3. the provision of guidelines for future data collection projects, along with the open-source release of all processed data and code for use by the research community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_05009 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Review on Trajectory Datasets on Advanced Driver Assistance System Zhou, Hang Ma, Ke Li, Xiaopeng Applications This paper presents a comprehensive review of trajectory data of Advanced Driver Assistance System equipped-vehicle, with the aim of precisely model of Autonomous Vehicles (AVs) behavior. This study emphasizes the importance of trajectory data in the development of AV models, especially in car-following scenarios. We introduce and evaluate several datasets: the OpenACC Dataset, the Connected & Autonomous Transportation Systems Laboratory Open Dataset, the Vanderbilt ACC Dataset, the Central Ohio Dataset, and the Waymo Open Dataset. Each dataset offers unique insights into AV behaviors, yet they share common challenges in terms of data availability, processing, and standardization. After a series of data cleaning, outlier removal and statistical analysis, this paper transforms datasets of varied formats into a uniform standard, thereby improving their applicability for modeling AV car-following behavior. Key contributions of this study include: 1. the transformation of all datasets into a unified standard format, enhancing their utility for broad research applications; 2. a comparative analysis of these datasets, highlighting their distinct characteristics and implications for car-following model development; 3. the provision of guidelines for future data collection projects, along with the open-source release of all processed data and code for use by the research community. |
| title | A Review on Trajectory Datasets on Advanced Driver Assistance System |
| topic | Applications |
| url | https://arxiv.org/abs/2402.05009 |