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arXiv · 2501.08868

Processing and Analyzing Real-World Driving Data: Insights on Trips, Scenarios, and Human Driving Behaviors

Jihun Han·Dominik Karbowski·Ayman Moawad·Namdoo Kim·Aymeric Rousseau·Shihong Fan·Jason Hoon Lee·Jinho Ha
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Abstract

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.

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Jihun Han, Dominik Karbowski, Ayman Moawad, Namdoo Kim, Aymeric Rousseau, Shihong Fan, Jason Hoon Lee, Jinho Ha. 2025-01-15. Processing and Analyzing Real-World Driving Data: Insights on Trips, Scenarios, and Human Driving Behaviors. https://arxiv.org/abs/2501.08868

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