Processing and Analyzing Real-World Driving Data: Insights on Trips, Scenarios, and Human Driving Behaviors
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910785970634752 |
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| author | Han, Jihun Karbowski, Dominik Moawad, Ayman Kim, Namdoo Rousseau, Aymeric Fan, Shihong Lee, Jason Hoon Ha, Jinho |
| author_facet | Han, Jihun Karbowski, Dominik Moawad, Ayman Kim, Namdoo Rousseau, Aymeric Fan, Shihong Lee, Jason Hoon Ha, Jinho |
| contents | Analyzing large volumes of real-world driving data is essential for providing meaningful and reliable insights into real-world trips, scenarios, and human driving behaviors. To this end, we developed a multi-level data processing approach that adds new information, segments data, and extracts desired parameters. Leveraging a confidential but extensive dataset (over 1 million km), this approach leads to three levels of in-depth analysis: trip, scenario, and driving. The trip-level analysis explains representative properties observed in real-world trips, while the scenario-level analysis focuses on scenario conditions resulting from road events that reduce vehicle speed. The driving-level analysis identifies the cause of driving regimes for specific situations and characterizes typical human driving behaviors. Such analyses can support the design of both trip- and scenario-based tests, the modeling of human drivers, and the establishment of guidelines for connected and automated vehicles. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_08868 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Processing and Analyzing Real-World Driving Data: Insights on Trips, Scenarios, and Human Driving Behaviors Han, Jihun Karbowski, Dominik Moawad, Ayman Kim, Namdoo Rousseau, Aymeric Fan, Shihong Lee, Jason Hoon Ha, Jinho Systems and Control Human-Computer Interaction Analyzing large volumes of real-world driving data is essential for providing meaningful and reliable insights into real-world trips, scenarios, and human driving behaviors. To this end, we developed a multi-level data processing approach that adds new information, segments data, and extracts desired parameters. Leveraging a confidential but extensive dataset (over 1 million km), this approach leads to three levels of in-depth analysis: trip, scenario, and driving. The trip-level analysis explains representative properties observed in real-world trips, while the scenario-level analysis focuses on scenario conditions resulting from road events that reduce vehicle speed. The driving-level analysis identifies the cause of driving regimes for specific situations and characterizes typical human driving behaviors. Such analyses can support the design of both trip- and scenario-based tests, the modeling of human drivers, and the establishment of guidelines for connected and automated vehicles. |
| title | Processing and Analyzing Real-World Driving Data: Insights on Trips, Scenarios, and Human Driving Behaviors |
| topic | Systems and Control Human-Computer Interaction |
| url | https://arxiv.org/abs/2501.08868 |