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

2025-01-15

AI Summary

Real-world driving data are characterized by massive scale, weak semantic annotation, and analytical intractability. Method: This study establishes a multi-level analytical framework grounded in confidential on-road testing data spanning millions of kilometers. It introduces a novel three-tier coupled analysis paradigm—“trip–scenario–driving behavior”—integrating multi-scale data cleaning and augmentation, event-driven scenario segmentation, parametric behavioral representation, and statistical pattern mining to bridge macro-level mobility characteristics with micro-level decision-making mechanisms. Contribution/Results: The framework quantitatively identifies数十 categories of high-frequency deceleration scenarios and constructs a reusable trip-feature atlas and a driving-behavior fingerprint library. These outcomes support standardized test-scenario design for connected and automated vehicles (CAVs) and human-driver behavioral modeling, providing empirical foundations for regulatory development in intelligent transportation systems.

Technology Category

• Application Domains: Mobility, Driving & Flight
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Security and Privacy: Large-scale security measurements
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

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.

Problem

• Big Data Analysis
• Driving Patterns
• Autonomous Vehicles Regulation

Innovation

• Large-scale Driving Data Analysis
• Trip-based Testing Design
• Autonomous Vehicle Regulation