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Real-Time Vehicle Roll Angle Estimation Based on Neural Networks in IoT Low-Cost Devices.

Javier García Guzmán1, Lisardo Prieto González2, Jonatan Pajares Redondo3

  • 1Computer Science and Engineering Department, Institute for Automotive Vehicle Safety (ISVA), Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911 Leganés, Madrid, Spain. jgarciag@inf.uc3m.es.

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Summary

This study developed an IoT system using low-cost sensors and neural networks to accurately estimate vehicle roll angle in real-time. The system successfully meets hard real-time constraints, enhancing road safety for heavy vehicles.

Keywords:
FANNIntel EdisonIoTRaspberry Pi 3 Model Bartificial neural networklow cost devicesreal-time estimationroll anglevehicle dynamics

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Area of Science:

  • Engineering
  • Computer Science
  • Automotive Safety

Background:

  • Vehicle rollovers pose significant risks, necessitating advanced Roll Stability Control (RSC) systems.
  • Accurate vehicle dynamics, particularly roll angle, are crucial for effective RSC design.
  • Low-cost sensors and neural networks show promise for real-time roll angle estimation.

Purpose of the Study:

  • To design and develop an IoT architecture integrating Artificial Neural Networks (ANN) with low-cost hardware for real-time vehicle roll angle estimation.
  • To assess the hard real-time performance and accuracy of the developed IoT architecture under dynamic driving conditions.

Main Methods:

  • An IoT-based architecture was developed, embedding ANNs within low-cost kits (Raspberry Pi 3 Model B, Intel Edison).
  • An experimental setup with a van and dual low-cost kits (Intel Edison with SparkFun 9DoF) was utilized.
  • The system was tested during various maneuvers to evaluate performance and accuracy.

Main Results:

  • The integrated IoT architecture successfully estimated vehicle roll angle with high accuracy, closely approximating real values.
  • Both Intel Edison and Raspberry Pi 3 Model B demonstrated sufficient computational power for real-time roll angle estimation.
  • The system met the defined hard real-time operational constraints for rollover risk assessment.

Conclusions:

  • The developed IoT architecture effectively estimates vehicle roll angle using low-cost sensors and ANNs.
  • The system achieves hard real-time performance, crucial for advanced driver-assistance systems (ADAS) like RSC.
  • This approach offers a viable solution for enhancing vehicle stability and preventing rollovers in real-world scenarios.