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Farm Vehicle Following Distance Estimation Using Deep Learning and Monocular Camera Images.

Saeed Arabi1, Anuj Sharma1, Michelle Reyes2

  • 1Department of Civil, Construction, and Environmental Engineering, Iowa State University, Ames, IA 50011, USA.

Sensors (Basel, Switzerland)
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Summary

This study estimates following distance using only monocular camera images and deep learning. It compares three methods, finding artificial neural networks most effective for improving road safety.

Keywords:
deep learningdistance estimationdriving behaviorsafety

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Area of Science:

  • Computer Vision
  • Automotive Safety
  • Machine Learning

Background:

  • Rear-end collisions remain a significant safety concern.
  • Accurate estimation of following distance is crucial for advanced driver-assistance systems (ADAS).
  • Existing methods often rely on complex sensor suites.

Purpose of the Study:

  • To develop and evaluate a vision-only method for estimating the distance to a following vehicle.
  • To compare the performance of linear regression, pinhole model, and artificial neural network (ANN) for this task.
  • To provide a foundation for understanding driver behavior and reducing traffic accidents.

Main Methods:

  • Instrumented vehicles with real-time kinematic (RTK) GPS for ground truth.
  • Recorded video footage of following vehicles (sedan and pickup truck).
  • Applied a deep-learning framework for vehicle detection and compared three distance estimation models (linear regression, pinhole, ANN).

Main Results:

  • The artificial neural network (ANN) demonstrated superior performance in following distance estimation compared to linear regression and the pinhole model.
  • RTK GPS data served as accurate ground truth for validating the estimation methods.
  • The deep learning approach successfully utilized monocular vision data for distance calculation.

Conclusions:

  • Vision-based distance estimation using deep learning is a viable approach for automotive safety.
  • ANNs offer a promising solution for accurate following distance estimation from low-resolution monocular cameras.
  • This research contributes to developing technologies for reducing rear-end collisions and understanding driver behavior.