mlsanityData to 7 Oct 2026

AI-Based Framework for Understanding Car Following Behaviors of Drivers in A Naturalistic Driving Environment

arXiv 2301.09315 ↗First posted 23 Jan 2023cs.CVcited by 3

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Abstract (arXiv)

The most common type of accident on the road is a rear-end crash. These crashes have a significant negative impact on traffic flow and are frequently fatal. To gain a more practical understanding of these scenarios, it is necessary to accurately model car following behaviors that result in rear-end crashes. Numerous studies have been carried out to model drivers' car-following behaviors; however, the majority of these studies have relied on simulated data, which may not accurately represent real-world incidents. Furthermore, most studies are restricted to modeling the ego vehicle's acceleration, which is insufficient to explain the behavior of the ego vehicle. As a result, the current study attempts to address these issues by developing an artificial intelligence framework for extracting features relevant to understanding driver behavior in a naturalistic environment. Furthermore, the study modeled the acceleration of both the ego vehicle and the leading vehicle using extracted information from NDS videos. According to the study's findings, young people are more likely to be aggressive drivers than elderly people. In addition, when modeling the ego vehicle's acceleration, it was discovered that the relative velocity between the ego vehicle and the leading vehicle was more important than the distance between the two vehicles.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 27 of its 49 references that are in the map. First posted on arXiv 23 Jan 2023.

Builds on

27 of 49 references.

References · 27

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15 more
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Cited by year

Citing papers per year: 2023: 2; 2024: 0; 2025: 1.

3 papers.

  1. 2025Integrating Travel Behavior Forecasting and Generative Modeling for Predicting Future Urban Mobility and Spatial Transformationscited by 5
  2. 2023Real-Time Helmet Violation Detection Using YOLOv5 and Ensemble Learningcited by 1
  3. 2023SigSegment: A Signal-Based Segmentation Algorithm for Identifying Anomalous Driving Behaviours in Naturalistic Driving Videoscited by 0