A hybrid liar/radar-based deep learning and vehicle recognition engine for autonomous vehicle precrash control

Authors

  • Bassant Mohamed Elbagoury Humboldt University in Berlin Unter den Linden, 6, Berlin, Germany, 10099, Germany
  • Rytis Maskeliunas Kaunas University of Technology K. Donelaičio str., 73, Kaunas, Lithuania, 44249, Lithuania https://orcid.org/0000-0002-2809-2213
  • Abdel Badeeh Mohamed M. Salem University of Economics – Varna Research Institute of the University of Economics – Varna Knyaz Boris I blvd., 77, Varna, Bulgaria, 9002, Bulgaria

DOI:

https://doi.org/10.15587/1729-4061.2018.141298

Keywords:

Deep Learning, LiDAR sensor Dataset, KD Tree Algorithms, Point Cloud, Vehicle Recognition Module

Abstract

PreCrash problem of Intelligent Control of autonomous vehicles robot is a very complex problem, especially vehicle pre-crash scenarios and at points of intersections in real-time environments.

The goal of this research is to develop a new artificial intelligent adaptive controller for autonomous vehicle Pre-Crash system along with vehicle recognition module and tested in MATLAB including some detailed modules. Following tasks were set: finding Objects in sensor Data (LiDAR. RADAR), Speed and Steering control, vehicle Recognition using convolution neural network and Alexnet.

In this research paper, we implemented a real-time image/Lidar processing. At the beginning, we presented a real-time system which is composed of comprehensive modules, these modules are 3d object detection, object clustering and search, ground removal, deep learning using convolutional neural networks. Starting with nearest vehicle  module our target is to find the nearest ahead car and consider it as our primary obstacle.

This paper presents an Adaptive cruise pre-crash system and vehicle recognition. The Adaptive cruise pre-crash system module depends on Deep Learning and LiDAR sensor data, which meant to control the driver reckless behavior on the road by adjusting the vehicle speed to maintain a safe distance from objects ahead (such as cars, humans, bicycle or whatever the object) when the driver tries to raise speed. At the very moment the vehicle recognition module, detects and recognizes the vehicles surrounding to the car.

Author Biographies

Bassant Mohamed Elbagoury, Humboldt University in Berlin Unter den Linden, 6, Berlin, Germany, 10099

PhD

Department of Artificial Intelligence and Robotics

Rytis Maskeliunas, Kaunas University of Technology K. Donelaičio str., 73, Kaunas, Lithuania, 44249

Professor

Department of Multimedia Engineering

Faculty of Informatics

Abdel Badeeh Mohamed M. Salem, University of Economics – Varna Research Institute of the University of Economics – Varna Knyaz Boris I blvd., 77, Varna, Bulgaria, 9002

PhD, Professor

References

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Published

2018-09-12

How to Cite

Elbagoury, B. M., Maskeliunas, R., & Salem, A. B. M. M. (2018). A hybrid liar/radar-based deep learning and vehicle recognition engine for autonomous vehicle precrash control. Eastern-European Journal of Enterprise Technologies, 5(9 (95), 6–17. https://doi.org/10.15587/1729-4061.2018.141298

Issue

Section

Information and controlling system