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1/10th scale autonomous vehicle based on convolutional neural network

International Journal on Smart Sensing and Intelligent Systems
Volume 13 (2020): Issue 1 (January 2020)
By:
Open Access
|Aug 2020

Figures & Tables

Figure 1:

Picture of the individual vehicle hardware components used in the system.
Picture of the individual vehicle hardware components used in the system.

Figure 2:

Complete system circuit diagram.
Complete system circuit diagram.

Figure 3:

CAD design for the laser cut base plate implemented on Autodesk Fusion 360 (left) and the base plate placed on the vehicle chassis (right).
CAD design for the laser cut base plate implemented on Autodesk Fusion 360 (left) and the base plate placed on the vehicle chassis (right).

Figure 4:

CAD design for the tower type case implemented on Autodesk Fusion 360 (left) and the physical PLA material 3D printed case (right).
CAD design for the tower type case implemented on Autodesk Fusion 360 (left) and the physical PLA material 3D printed case (right).

Figure 5:

The overview of the software message queue of the system (vehicle state).
The overview of the software message queue of the system (vehicle state).

Figure 6:

Windows host PC installation steps.
Windows host PC installation steps.

Figure 7:

Software configuration steps for the Raspberry Pi 4 setup.
Software configuration steps for the Raspberry Pi 4 setup.

Figure 8:

Screenshot of the camera code configuration and sample 160 × 120 pixels image from the vehicle on board camera.
Screenshot of the camera code configuration and sample 160 × 120 pixels image from the vehicle on board camera.

Figure 9:

Screen capture of the I2C detected on the Rpi terminal.
Screen capture of the I2C detected on the Rpi terminal.

Figure 10:

The Localhost web interface (screen capture) to control the vehicle.
The Localhost web interface (screen capture) to control the vehicle.

Figure 11:

The complete assembly of the vehicle hardware system.
The complete assembly of the vehicle hardware system.

Figure 12:

The code snippet program of the motor calibration.
The code snippet program of the motor calibration.

Figure 13:

The steering servo and PWM working principle.
The steering servo and PWM working principle.

Figure 14:

Table generated for steering Angle and PWM equivalent values.
Table generated for steering Angle and PWM equivalent values.

Figure 15:

The custom-built indoor track design and its implementation.
The custom-built indoor track design and its implementation.

Figure 16:

A sample training dataset from the track training.
A sample training dataset from the track training.

Figure 17:

Training data model loss graph.
Training data model loss graph.

Figure 18:

The car algorithm flowchart.
The car algorithm flowchart.

Figure 19:

Training and autopilot steering angle histogram graph plot analysis.
Training and autopilot steering angle histogram graph plot analysis.

No.Convolution filtersStridesFC layersParametersLoss
112 × 3 × 3, 18 × 3 × 3, 24 × 3 × 3, 36 × 3 × 33,2,2,1900, 246, 321,100k 0.098543
DOI: https://doi.org/10.21307/ijssis-2020-021 | Journal eISSN: 1178-5608
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Language: English
Page range: 1 - 17
Submitted on: Jul 26, 2020
Published on: Aug 25, 2020
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
Autonomous vehicle,
Convolutional neural network,
Raspberry pi 4,
Ultrasonic sensor,
Camera
Related subjects:
Engineering,
Introductions and overviews,
Engineering, other

© 2020 Avishkar Seth, Alice James, Subhas C. Mukhopadhyay, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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