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Inferring the Driver's Lane Change Intention through LiDAR-Based Environment Analysis Using Convolutional Neural

Alberto Díaz-Álvarez1, Miguel Clavijo2, Felipe Jiménez2

  • 1Department of Artificial Intelligence, Escuela Técnica Superior de Ingeniería de Sistemas Informáticos, Universidad Politécnica de Madrid, 28031 Madrid, Spain.

Sensors (Basel, Switzerland)
|January 14, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a modified Convolutional Neural Network (CNN) to predict driver lane changes by analyzing spatial and non-spatial data. The model accurately anticipates lane change intentions, enhancing driver assistance systems and autonomous driving.

Keywords:
ADASConvolutional Neural NetworksIntelligent Transportation Systemsautonomous drivingdriver’s behaviourlane change

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Automotive Engineering

Background:

  • Predicting driver behavior is crucial for developing advanced driver assistance systems (ADAS) and autonomous driving technologies.
  • Understanding the surrounding environment and vehicle dynamics is essential for tactical driving maneuvers like lane changes.

Purpose of the Study:

  • To develop and evaluate a Convolutional Neural Network (CNN) model for predicting driver lane change intentions.
  • To adapt a CNN architecture for integrating both spatial and non-spatial driving data.
  • To explore the application of lane change prediction in ADAS and autonomous vehicle decision-making.

Main Methods:

  • A modified CNN architecture was designed to process spatial (surrounding environment) and non-spatial (relative speed, etc.) input variables.
  • The CNN model was trained and tested within a driving simulation environment.
  • Performance was evaluated by comparing the model's accuracy against random guessing and its ability to differentiate driving profiles.

Main Results:

  • The proposed CNN model demonstrated a higher accuracy in predicting lane changes than random chance.
  • The model successfully captured subtle behavioral differences among various driving profiles.
  • The integrated approach effectively utilized both environmental and vehicle kinematic data.

Conclusions:

  • CNNs are effective tools for modeling and predicting driver lane change behavior.
  • Anticipating lane changes can significantly enhance the safety and human-like performance of ADAS and autonomous vehicles.
  • The developed model provides a valuable data source for improving vehicle decision-making algorithms.