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Urban Intersection Classification: A Comparative Analysis.

Augusto Luis Ballardini1, Álvaro Hernández Saz1, Sandra Carrasco Limeros1

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Detecting urban intersections is vital for autonomous vehicles and driver assistance systems. Camera field of view significantly impacts performance, more than temporal integration or dataset quality, according to this study.

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CNNGANRNNintelligent transportation systemsintersection classificationscene understandingself driving

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Area of Science:

  • Computer Vision
  • Robotics
  • Autonomous Systems

Background:

  • Urban intersections are critical safety zones for autonomous vehicles and Advanced Driver Assistance Systems (ADAS), accounting for a significant portion of road fatalities.
  • Accurate perception of intersection geometry is essential for safe navigation in complex urban environments.
  • Current research faces challenges due to the scarcity of relevant datasets and the need for robust detection methods.

Purpose of the Study:

  • To investigate and evaluate Deep Neural Network (DNN) based methods for detecting and classifying urban intersections from onboard vehicle cameras.
  • To assess the impact of different methodologies, including single-frame and temporal integration techniques, on intersection classification performance.
  • To analyze the influence of camera field of view, dataset characteristics, and data augmentation on system generalizability.

Main Methods:

  • Evaluation of state-of-the-art Deep Neural Network (DNN) approaches for intersection detection and classification.
  • Comparison of single-frame analysis versus temporal integration schemes.
  • Creation of a new dataset using Generative Adversarial Network (GAN) based data augmentation to enhance real-world data and improve model generalizability.
  • Analysis of performance based on camera field of view and data quality.

Main Results:

  • System performance is predominantly influenced by the camera's field of view, outweighing other factors like temporal integration or dataset characteristics.
  • Generative Adversarial Network (GAN) augmentation demonstrated potential in increasing generalizability, though data quality remains a key consideration.
  • Despite limitations in existing datasets, extensive experiments provided insights into individual system performances.

Conclusions:

  • The field of view of front-facing cameras is a primary determinant of success in urban intersection detection and classification for autonomous systems.
  • Further research and development of specialized intersection datasets are necessary to advance the capabilities of autonomous driving systems.
  • Data augmentation techniques, such as GANs, offer a promising avenue for improving model robustness and generalizability in the face of data scarcity.